{
"nbformat": 4,
"nbformat_minor": 0,
"metadata": {
"colab": {
"name": "Training_YOLO.ipynb",
"provenance": [],
"collapsed_sections": [],
"include_colab_link": true
},
"kernelspec": {
"name": "python3",
"display_name": "Python 3"
},
"accelerator": "GPU"
},
"cells": [
{
"cell_type": "markdown",
"metadata": {
"id": "view-in-github",
"colab_type": "text"
},
"source": [
"![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\")
"
]
},
{
"cell_type": "code",
"metadata": {
"id": "rSiUGMxBfuoQ",
"outputId": "f14bcb35-5609-45c3-da89-ce165b2ec730",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"import os\n",
"if not os.path.exists('darknet'):\n",
" !git clone https://github.com/AlexeyAB/darknet\n",
" %cd darknet\n",
" !sed -i 's/OPENCV=0/OPENCV=1/' Makefile\n",
" # !!! In case you dont have a GPU, make sure to comment out the below 3 lines !!! #\n",
" !sed -i 's/GPU=0/GPU=1/' Makefile\n",
" !sed -i 's/CUDNN=0/CUDNN=1/' Makefile\n",
" !sed -i 's/CUDNN_HALF=0/CUDNN_HALF=1/' Makefile\n",
" !make\n",
" !pip install -q torch_snippets\n",
" !wget --quiet https://www.dropbox.com/s/agmzwk95v96ihic/open-images-bus-trucks.tar.xz\n",
" !tar -xf open-images-bus-trucks.tar.xz\n",
" !rm open-images-bus-trucks.tar.xz\n",
" !wget --quiet https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v3_optimal/yolov4.weights"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Cloning into 'darknet'...\n",
"remote: Enumerating objects: 3, done.\u001b[K\n",
"remote: Counting objects: 100% (3/3), done.\u001b[K\n",
"remote: Compressing objects: 100% (3/3), done.\u001b[K\n",
"remote: Total 14333 (delta 0), reused 0 (delta 0), pack-reused 14330\u001b[K\n",
"Receiving objects: 100% (14333/14333), 12.89 MiB | 24.58 MiB/s, done.\n",
"Resolving deltas: 100% (9776/9776), done.\n",
"/content/darknet\n",
"mkdir -p ./obj/\n",
"mkdir -p backup\n",
"chmod +x *.sh\n",
"g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/image_opencv.cpp -o obj/image_opencv.o\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_detections_cv_v3(void**, detection*, int, float, char**, image**, int, int)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:926:23:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Krgb\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Krgb\u001b[m\u001b[K[3];\n",
" \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid draw_train_loss(char*, void**, int, float, float, int, int, float, int, char*, float, int, int, double)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:1127:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kthis ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’ clause does not guard... [\u001b[01;35m\u001b[K-Wmisleading-indentation\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kif\u001b[m\u001b[K (iteration_old == 0)\n",
" \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:1130:10:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[K...this statement, but the latter is misleadingly indented as if it were guarded by the ‘\u001b[01m\u001b[Kif\u001b[m\u001b[K’\n",
" \u001b[01;36m\u001b[Kif\u001b[m\u001b[K (iteration_old != 0){\n",
" \u001b[01;36m\u001b[K^~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid cv_draw_object(image, float*, int, int, int*, float*, int*, int, char**)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:1424:14:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbuff\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" char \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K[100];\n",
" \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:1400:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kit_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kit_tb_res\u001b[m\u001b[K = cv::createTrackbar(it_trackbar_name, window_name, &it_trackbar_value, 1000);\n",
" \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:1404:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Klr_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Klr_tb_res\u001b[m\u001b[K = cv::createTrackbar(lr_trackbar_name, window_name, &lr_trackbar_value, 20);\n",
" \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:1408:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcl_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kcl_tb_res\u001b[m\u001b[K = cv::createTrackbar(cl_trackbar_name, window_name, &cl_trackbar_value, classes-1);\n",
" \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/image_opencv.cpp:1411:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbo_tb_res\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kbo_tb_res\u001b[m\u001b[K = cv::createTrackbar(bo_trackbar_name, window_name, boxonly, 1);\n",
" \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/http_stream.cpp -o obj/http_stream.o\n",
"In file included from \u001b[01m\u001b[K./src/http_stream.cpp:580:0\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K./src/httplib.h:129:0:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[K\"INVALID_SOCKET\" redefined\n",
" #define INVALID_SOCKET (-1)\n",
" \n",
"\u001b[01m\u001b[K./src/http_stream.cpp:73:0:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kthis is the location of the previous definition\n",
" #define INVALID_SOCKET -1\n",
" \n",
"\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool JSON_sender::write(const char*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:249:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = _write(client, outputbuf, outlen);\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In member function ‘\u001b[01m\u001b[Kbool MJPG_sender::write(const cv::Mat&)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:507:113:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%zu\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Ksize_t\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" sprintf(head, \"--mjpegstream\\r\\nContent-Type: image/jpeg\\r\\nContent-Length: %zu\\r\\n\\r\\n\", outlen\u001b[01;35m\u001b[K)\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid set_track_id(detection*, int, float, float, float, int, int, int)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:845:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
" for (int i = 0; \u001b[01;35m\u001b[Ki < v.size()\u001b[m\u001b[K; ++i) {\n",
" \u001b[01;35m\u001b[K~~^~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:853:33:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
" for (int old_id = 0; \u001b[01;35m\u001b[Kold_id < old_dets.size()\u001b[m\u001b[K; ++old_id) {\n",
" \u001b[01;35m\u001b[K~~~~~~~^~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:873:31:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
" for (int index = 0; \u001b[01;35m\u001b[Kindex < new_dets_num*old_dets.size()\u001b[m\u001b[K; ++index) {\n",
" \u001b[01;35m\u001b[K~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/http_stream.cpp:908:28:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
" if (\u001b[01;35m\u001b[Kold_dets_dq.size() > deque_size\u001b[m\u001b[K) old_dets_dq.pop_front();\n",
" \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/gemm.c -o obj/gemm.o\n",
"\u001b[01m\u001b[K./src/gemm.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kconvolution_2d\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/gemm.c:2038:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" const int \u001b[01;35m\u001b[Kout_w\u001b[m\u001b[K = (w + 2 * pad - ksize) / stride + 1; // output_width=input_width for stride=1 and pad=1\n",
" \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/gemm.c:2037:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" const int \u001b[01;35m\u001b[Kout_h\u001b[m\u001b[K = (h + 2 * pad - ksize) / stride + 1; // output_height=input_height for stride=1 and pad=1\n",
" \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/utils.c -o obj/utils.o\n",
"\u001b[01m\u001b[K./src/utils.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcustom_hash\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/utils.c:1039:12:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around assignment used as truth value [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
" while (\u001b[01;35m\u001b[Kc\u001b[m\u001b[K = *str++)\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dark_cuda.c -o obj/dark_cuda.o\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcudnn_check_error_extended\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:224:20:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between ‘\u001b[01m\u001b[KcudaError_t {aka enum cudaError}\u001b[m\u001b[K’ and ‘\u001b[01m\u001b[Kenum \u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wenum-compare\u001b[m\u001b[K]\n",
" if (status \u001b[01;35m\u001b[K!=\u001b[m\u001b[K CUDNN_STATUS_SUCCESS)\n",
" \u001b[01;35m\u001b[K^~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpre_allocate_pinned_memory\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:276:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\"pre_allocate: size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB, num_of_blocks = %Iu, block_size = %Iu MB \\n\",\n",
" \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
" \u001b[32m\u001b[Ksize / (1024*1024)\u001b[m\u001b[K, num_of_blocks, pinned_block_size / (1024 * 1024));\n",
" \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K \n",
"\u001b[01m\u001b[K./src/dark_cuda.c:276:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\"pre_allocate: size = %Iu MB, num_of_blocks = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K, block_size = %Iu MB \\n\",\n",
" \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:276:82:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%u\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kunsigned int\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\"pre_allocate: size = %Iu MB, num_of_blocks = %Iu, block_size = \u001b[01;35m\u001b[K%Iu\u001b[m\u001b[K MB \\n\",\n",
" \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%Ilu\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:286:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Allocated \u001b[01;35m\u001b[K%d\u001b[m\u001b[K pinned block \\n\", pinned_block_size);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kcuda_make_array_pinned_preallocated\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:307:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\"\\n Pinned block_id = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, filled = %f %% \\n\", pinned_block_id, filled);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:322:64:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\"Try to allocate new pinned memory, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/dark_cuda.c:328:63:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Klong unsigned int\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\"Try to allocate new pinned BLOCK, size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K MB \\n\", \u001b[32m\u001b[Ksize / (1024 * 1024)\u001b[m\u001b[K);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K \u001b[32m\u001b[K~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/convolutional_layer.c -o obj/convolutional_layer.o\n",
"\u001b[01m\u001b[K./src/convolutional_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_convolutional_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/convolutional_layer.c:1337:32:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kt_intput_size\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" size_t \u001b[01;35m\u001b[Kt_intput_size\u001b[m\u001b[K = binary_transpose_align_input(k, n, state.workspace, &l.t_bit_input, ldb_align, l.bit_align);\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/list.c -o obj/list.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/image.c -o obj/image.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/activations.c -o obj/activations.o\n",
"\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kactivate\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KRELU6\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
" \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"\u001b[01m\u001b[K./src/activations.c:79:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KNORM_CHAN_SOFTMAX_MAXVAL\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"\u001b[01m\u001b[K./src/activations.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kgradient\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KSWISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(a){\n",
" \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"\u001b[01m\u001b[K./src/activations.c:310:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KHARD_MISH\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/im2col.c -o obj/im2col.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/col2im.c -o obj/col2im.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/blas.c -o obj/blas.o\n",
"\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbackward_shortcut_multilayer_cpu\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/blas.c:207:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kout_index\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kout_index\u001b[m\u001b[K = id;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_sim\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/blas.c:597:59:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Error: find_sim(): sim isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:597:67:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Error: find_sim(): sim isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:597:75:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Error: find_sim(): sim isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfind_P_constrastive\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/blas.c:611:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Error: find_P_constrastive(): P isn't found: i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, j = %d, z = %d \\n\", i, j, z);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:611:76:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, z = %d \\n\", i, j, z);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:611:84:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Error: find_P_constrastive(): P isn't found: i = %d, j = %d, z = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, j, z);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive_f\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/blas.c:651:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:651:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[KP_constrastive\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/blas.c:785:79:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 3 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K, l = %d \\n\", i, l);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/blas.c:785:87:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 4 has type ‘\u001b[01m\u001b[Ksize_t {aka long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" fprintf(stderr, \" Error: in P_constrastive must be i != l, while i = %d, l = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K \\n\", i, l);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/crop_layer.c -o obj/crop_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dropout_layer.c -o obj/dropout_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/maxpool_layer.c -o obj/maxpool_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/softmax_layer.c -o obj/softmax_layer.o\n",
"\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_contrastive_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/softmax_layer.c:203:101:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 9 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" fprintf(stderr, \"contrastive %4d x%4d x%4d x emb_size %4d x batch: %4d classes = %4d, step = \u001b[01;35m\u001b[K%4d\u001b[m\u001b[K \\n\", w, h, l.n, l.embedding_size, batch, l.classes, step);\n",
" \u001b[01;35m\u001b[K~~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%4ld\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/softmax_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_contrastive_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/softmax_layer.c:244:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kmax_truth\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kmax_truth\u001b[m\u001b[K = 0;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/softmax_layer.c:423:71:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat ‘\u001b[01m\u001b[K%d\u001b[m\u001b[K’ expects argument of type ‘\u001b[01m\u001b[Kint\u001b[m\u001b[K’, but argument 2 has type ‘\u001b[01m\u001b[Ksize_t {aka const long unsigned int}\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wformat=\u001b[m\u001b[K]\n",
" printf(\" Error: too large number of bboxes: contr_size = \u001b[01;35m\u001b[K%d\u001b[m\u001b[K > max_contr_size = %d \\n\", contr_size, max_contr_size);\n",
" \u001b[01;35m\u001b[K~^\u001b[m\u001b[K\n",
" \u001b[32m\u001b[K%ld\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/data.c -o obj/data.o\n",
"\u001b[01m\u001b[K./src/data.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kload_data_detection\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/data.c:1297:24:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kx\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int k, \u001b[01;35m\u001b[Kx\u001b[m\u001b[K, y;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/data.c:1090:43:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kr_scale\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
" float r1 = 0, r2 = 0, r3 = 0, r4 = 0, \u001b[01;35m\u001b[Kr_scale\u001b[m\u001b[K = 0;\n",
" \u001b[01;35m\u001b[K^~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/matrix.c -o obj/matrix.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/network.c -o obj/network.o\n",
"\u001b[01m\u001b[K./src/network.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_network\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/network.c:615:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet->input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped))\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/network.c:1\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/connected_layer.c -o obj/connected_layer.o\n",
"\u001b[01m\u001b[K./src/connected_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_connected_layer_gpu\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/connected_layer.c:346:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kone\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kone\u001b[m\u001b[K = 1; // alpha[0], beta[0]\n",
" \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/connected_layer.c:344:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kc\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float * \u001b[01;35m\u001b[Kc\u001b[m\u001b[K = l.output_gpu;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/connected_layer.c:343:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kb\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float * \u001b[01;35m\u001b[Kb\u001b[m\u001b[K = l.weights_gpu;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/connected_layer.c:342:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ka\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float * \u001b[01;35m\u001b[Ka\u001b[m\u001b[K = state.input;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/connected_layer.c:341:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kn\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kn\u001b[m\u001b[K = l.outputs;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/connected_layer.c:340:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kk\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kk\u001b[m\u001b[K = l.inputs;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/connected_layer.c:339:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Km\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Km\u001b[m\u001b[K = l.batch;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/cost_layer.c -o obj/cost_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/parser.c -o obj/parser.o\n",
"\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kparse_network_cfg_custom\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/parser.c:1669:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Knet.input_pinned_cpu, size * sizeof(float), cudaHostRegisterMapped)) net.input_pinned_cpu_flag = 1;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/activation_layer.h:4\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/parser.c:6\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/parser.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kget_classes_multipliers\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/parser.c:428:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kargument 1 range [18446744071562067968, 18446744073709551615] exceeds maximum object size 9223372036854775807 [\u001b[01;35m\u001b[K-Walloc-size-larger-than=\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kclasses_multipliers = (float *)calloc(classes_counters, sizeof(float))\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K./src/parser.c:3:0\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/include/stdlib.h:541:14:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kin a call to allocation function ‘\u001b[01m\u001b[Kcalloc\u001b[m\u001b[K’ declared here\n",
" extern void *\u001b[01;36m\u001b[Kcalloc\u001b[m\u001b[K (size_t __nmemb, size_t __size)\n",
" \u001b[01;36m\u001b[K^~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/option_list.c -o obj/option_list.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/darknet.c -o obj/darknet.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/detection_layer.c -o obj/detection_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/captcha.c -o obj/captcha.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/route_layer.c -o obj/route_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/writing.c -o obj/writing.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/box.c -o obj/box.o\n",
"\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kbox_iou_kind\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/box.c:154:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kenumeration value ‘\u001b[01m\u001b[KMSE\u001b[m\u001b[K’ not handled in switch [\u001b[01;35m\u001b[K-Wswitch\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kswitch\u001b[m\u001b[K(iou_kind) {\n",
" \u001b[01;35m\u001b[K^~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/box.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdiounms_sort\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/box.c:898:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbeta_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kbeta_prob\u001b[m\u001b[K = pow(dets[j].prob[k], 2) / sum_prob;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/box.c:897:27:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kalpha_prob\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kalpha_prob\u001b[m\u001b[K = pow(dets[i].prob[k], 2) / sum_prob;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/nightmare.c -o obj/nightmare.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/normalization_layer.c -o obj/normalization_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/avgpool_layer.c -o obj/avgpool_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/coco.c -o obj/coco.o\n",
"\u001b[01m\u001b[K./src/coco.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_coco_recall\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/coco.c:248:11:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kbase\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" char *\u001b[01;35m\u001b[Kbase\u001b[m\u001b[K = \"results/comp4_det_test_\";\n",
" \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/dice.c -o obj/dice.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/yolo.c -o obj/yolo.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/detector.c -o obj/detector.o\n",
"\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kprint_cocos\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/detector.c:478:29:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kformat not a string literal and no format arguments [\u001b[01;35m\u001b[K-Wformat-security\u001b[m\u001b[K]\n",
" fprintf(fp, \u001b[01;35m\u001b[Kbuff\u001b[m\u001b[K);\n",
" \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Keliminate_bdd\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/detector.c:571:21:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kstatement with no effect [\u001b[01;35m\u001b[K-Wunused-value\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kfor\u001b[m\u001b[K (k; buf[k + n] != '\\0'; k++)\n",
" \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/detector.c:692:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd2\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kmkd2\u001b[m\u001b[K = make_directory(buff2, 0777);\n",
" \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/detector.c:690:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kmkd\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kmkd\u001b[m\u001b[K = make_directory(buff, 0777);\n",
" \u001b[01;35m\u001b[K^~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvalidate_detector_map\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/detector.c:1323:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_recall\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kclass_recall\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)(truth_classes_count[i] - tp_for_thresh_per_class[i]));\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/detector.c:1322:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kclass_precision\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kclass_precision\u001b[m\u001b[K = (float)tp_for_thresh_per_class[i] / ((float)tp_for_thresh_per_class[i] + (float)fp_for_thresh_per_class[i]);\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/detector.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdraw_object\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/detector.c:1857:19:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kinv_loss\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kinv_loss\u001b[m\u001b[K = 1.0 / max_val_cmp(0.01, avg_loss);\n",
" \u001b[01;35m\u001b[K^~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/layer.c -o obj/layer.o\n",
"\u001b[01m\u001b[K./src/layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfree_layer_custom\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/layer.c:205:68:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Ksuggest parentheses around ‘\u001b[01m\u001b[K&&\u001b[m\u001b[K’ within ‘\u001b[01m\u001b[K||\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wparentheses\u001b[m\u001b[K]\n",
" if (l.delta_gpu && (l.optimized_memory < 1 || \u001b[01;35m\u001b[Kl.keep_delta_gpu && l.optimized_memory < 3\u001b[m\u001b[K)) cuda_free(l.delta_gpu), l.delta_gpu = NULL;\n",
" \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/compare.c -o obj/compare.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/classifier.c -o obj/classifier.o\n",
"\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Ktrain_classifier\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/classifier.c:146:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kcount\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kcount\u001b[m\u001b[K = 0;\n",
" \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kpredict_classifier\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/classifier.c:855:13:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktime\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" clock_t \u001b[01;35m\u001b[Ktime\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/classifier.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdemo_classifier\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/classifier.c:1287:49:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_result\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" struct timeval tval_before, tval_after, \u001b[01;35m\u001b[Ktval_result\u001b[m\u001b[K;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/classifier.c:1287:37:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Ktval_after\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" struct timeval tval_before, \u001b[01;35m\u001b[Ktval_after\u001b[m\u001b[K, tval_result;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/local_layer.c -o obj/local_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/swag.c -o obj/swag.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/shortcut_layer.c -o obj/shortcut_layer.o\n",
"\u001b[01m\u001b[K./src/shortcut_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_shortcut_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/shortcut_layer.c:55:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kscale\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float \u001b[01;35m\u001b[Kscale\u001b[m\u001b[K = sqrt(2. / l.nweights);\n",
" \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/activation_layer.c -o obj/activation_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn_layer.c -o obj/rnn_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/gru_layer.c -o obj/gru_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn.c -o obj/rnn.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/rnn_vid.c -o obj/rnn_vid.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/crnn_layer.c -o obj/crnn_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/demo.c -o obj/demo.o\n",
"\u001b[01m\u001b[K./src/demo.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kdetect_in_thread\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/demo.c:100:16:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kprediction\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" float *\u001b[01;35m\u001b[Kprediction\u001b[m\u001b[K = network_predict(net, X);\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/demo.c:98:15:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" layer \u001b[01;35m\u001b[Kl\u001b[m\u001b[K = net.layers[net.n - 1];\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/tag.c -o obj/tag.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/cifar.c -o obj/cifar.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/go.c -o obj/go.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/batchnorm_layer.c -o obj/batchnorm_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/art.c -o obj/art.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/region_layer.c -o obj/region_layer.o\n",
"\u001b[01m\u001b[K./src/region_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_region_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/region_layer.c:59:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_h\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kold_h\u001b[m\u001b[K = l->h;\n",
" \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/region_layer.c:58:9:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kunused variable ‘\u001b[01m\u001b[Kold_w\u001b[m\u001b[K’ [\u001b[01;35m\u001b[K-Wunused-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kold_w\u001b[m\u001b[K = l->w;\n",
" \u001b[01;35m\u001b[K^~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/reorg_layer.c -o obj/reorg_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/reorg_old_layer.c -o obj/reorg_old_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/super.c -o obj/super.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/voxel.c -o obj/voxel.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/tree.c -o obj/tree.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/yolo_layer.c -o obj/yolo_layer.o\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_yolo_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:68:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:75:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs*sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_yolo_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:106:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:115:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/activations.h:3\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/layer.h:4\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/yolo_layer.c:1\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kforward_yolo_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/yolo_layer.c:394:25:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kbest_match_t\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
" int \u001b[01;35m\u001b[Kbest_match_t\u001b[m\u001b[K = 0;\n",
" \u001b[01;35m\u001b[K^~~~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/gaussian_yolo_layer.c -o obj/gaussian_yolo_layer.o\n",
"\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kmake_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:71:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.output, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.output_pinned = 1;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:78:38:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess == cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl.delta, batch*l.outputs * sizeof(float), cudaHostRegisterMapped)) l.delta_pinned = 1;\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kresize_gaussian_yolo_layer\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:110:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->output, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"\u001b[01m\u001b[K./src/gaussian_yolo_layer.c:119:42:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kpassing argument 1 of ‘\u001b[01m\u001b[KcudaHostAlloc\u001b[m\u001b[K’ from incompatible pointer type [\u001b[01;35m\u001b[K-Wincompatible-pointer-types\u001b[m\u001b[K]\n",
" if (cudaSuccess != cudaHostAlloc(\u001b[01;35m\u001b[K&\u001b[m\u001b[Kl->delta, l->batch*l->outputs * sizeof(float), cudaHostRegisterMapped)) {\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"In file included from \u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime.h:96:0\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[Kinclude/darknet.h:41\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.h:5\u001b[m\u001b[K,\n",
" from \u001b[01m\u001b[K./src/gaussian_yolo_layer.c:7\u001b[m\u001b[K:\n",
"\u001b[01m\u001b[K/usr/local/cuda/include/cuda_runtime_api.h:4391:39:\u001b[m\u001b[K \u001b[01;36m\u001b[Knote: \u001b[m\u001b[Kexpected ‘\u001b[01m\u001b[Kvoid **\u001b[m\u001b[K’ but argument is of type ‘\u001b[01m\u001b[Kfloat **\u001b[m\u001b[K’\n",
" extern __host__ cudaError_t CUDARTAPI \u001b[01;36m\u001b[KcudaHostAlloc\u001b[m\u001b[K(void **pHost, size_t size, unsigned int flags);\n",
" \u001b[01;36m\u001b[K^~~~~~~~~~~~~\u001b[m\u001b[K\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/upsample_layer.c -o obj/upsample_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/lstm_layer.c -o obj/lstm_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/conv_lstm_layer.c -o obj/conv_lstm_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/scale_channels_layer.c -o obj/scale_channels_layer.o\n",
"gcc -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF -c ./src/sam_layer.c -o obj/sam_layer.o\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/convolutional_kernels.cu -o obj/convolutional_kernels.o\n",
"\u001b[01m\u001b[K./src/convolutional_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_convolutional_layer_gpu(convolutional_layer, network_state)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/convolutional_kernels.cu:853:40:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kcomparison between signed and unsigned integer expressions [\u001b[01;35m\u001b[K-Wsign-compare\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K if (*state.net.max_output16_size < l.\u001b[m\u001b[Knweights) {\n",
" \u001b[01;35m\u001b[K~~~~~~~~~~~~~~~~~~~~~~~^~~~~~~~~~~~~~\u001b[m\u001b[K\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/activation_kernels.cu -o obj/activation_kernels.o\n",
"./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
"\n",
"./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
"\n",
"./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
"\n",
"./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
"\n",
"./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
"\n",
"./src/activation_kernels.cu(263): warning: variable \"MISH_THRESHOLD\" was declared but never referenced\n",
"\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/im2col_kernels.cu -o obj/im2col_kernels.o\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/col2im_kernels.cu -o obj/col2im_kernels.o\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/blas_kernels.cu -o obj/blas_kernels.o\n",
"./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
"\n",
"./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
"\n",
"./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
"\n",
"./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
"\n",
"./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
"\n",
"./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1086): warning: variable \"out_index\" was declared but never referenced\n",
"\n",
"./src/blas_kernels.cu(1130): warning: variable \"step\" was set but never used\n",
"\n",
"./src/blas_kernels.cu(1736): warning: variable \"stage_id\" was declared but never referenced\n",
"\n",
"\u001b[01m\u001b[K./src/blas_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kvoid backward_shortcut_multilayer_gpu(int, int, int, int*, float**, float*, float*, float*, float*, int, float*, float**, WEIGHTS_NORMALIZATION_T)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/blas_kernels.cu:1130:5:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kstep\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[Kint \u001b[m\u001b[Kstep = 0;\n",
" \u001b[01;35m\u001b[K^~~~\u001b[m\u001b[K\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/crop_layer_kernels.cu -o obj/crop_layer_kernels.o\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/dropout_layer_kernels.cu -o obj/dropout_layer_kernels.o\n",
"./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(140): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(245): warning: variable \"cur_scale\" was declared but never referenced\n",
"\n",
"./src/dropout_layer_kernels.cu(262): warning: variable \"block_prob\" was declared but never referenced\n",
"\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/maxpool_layer_kernels.cu -o obj/maxpool_layer_kernels.o\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/network_kernels.cu -o obj/network_kernels.o\n",
"./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
"\n",
"./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
"\n",
"./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
"\n",
"./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
"\n",
"./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
"\n",
"./src/network_kernels.cu(364): warning: variable \"l\" was declared but never referenced\n",
"\n",
"\u001b[01m\u001b[K./src/network_kernels.cu:\u001b[m\u001b[K In function ‘\u001b[01m\u001b[Kfloat train_network_datum_gpu(network, float*, float*)\u001b[m\u001b[K’:\n",
"\u001b[01m\u001b[K./src/network_kernels.cu:364:7:\u001b[m\u001b[K \u001b[01;35m\u001b[Kwarning: \u001b[m\u001b[Kvariable ‘\u001b[01m\u001b[Kl\u001b[m\u001b[K’ set but not used [\u001b[01;35m\u001b[K-Wunused-but-set-variable\u001b[m\u001b[K]\n",
" \u001b[01;35m\u001b[K \u001b[m\u001b[K layer l = net.layers[net.n - 1];\n",
" \u001b[01;35m\u001b[K^\u001b[m\u001b[K\n",
"nvcc -gencode arch=compute_30,code=sm_30 -gencode arch=compute_35,code=sm_35 -gencode arch=compute_50,code=[sm_50,compute_50] -gencode arch=compute_52,code=[sm_52,compute_52] -gencode arch=compute_61,code=[sm_61,compute_61] -gencode arch=compute_70,code=[sm_70,compute_70] -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF --compiler-options \"-Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF\" -c ./src/avgpool_layer_kernels.cu -o obj/avgpool_layer_kernels.o\n",
"g++ -std=c++11 -std=c++11 -Iinclude/ -I3rdparty/stb/include -DOPENCV `pkg-config --cflags opencv4 2> /dev/null || pkg-config --cflags opencv` -DGPU -I/usr/local/cuda/include/ -DCUDNN -DCUDNN_HALF -Wall -Wfatal-errors -Wno-unused-result -Wno-unknown-pragmas -fPIC -Ofast -DOPENCV -DGPU -DCUDNN -I/usr/local/cudnn/include -DCUDNN_HALF obj/image_opencv.o obj/http_stream.o obj/gemm.o obj/utils.o obj/dark_cuda.o obj/convolutional_layer.o obj/list.o obj/image.o obj/activations.o obj/im2col.o obj/col2im.o obj/blas.o obj/crop_layer.o obj/dropout_layer.o obj/maxpool_layer.o obj/softmax_layer.o obj/data.o obj/matrix.o obj/network.o obj/connected_layer.o obj/cost_layer.o obj/parser.o obj/option_list.o obj/darknet.o obj/detection_layer.o obj/captcha.o obj/route_layer.o obj/writing.o obj/box.o obj/nightmare.o obj/normalization_layer.o obj/avgpool_layer.o obj/coco.o obj/dice.o obj/yolo.o obj/detector.o obj/layer.o obj/compare.o obj/classifier.o obj/local_layer.o obj/swag.o obj/shortcut_layer.o obj/activation_layer.o obj/rnn_layer.o obj/gru_layer.o obj/rnn.o obj/rnn_vid.o obj/crnn_layer.o obj/demo.o obj/tag.o obj/cifar.o obj/go.o obj/batchnorm_layer.o obj/art.o obj/region_layer.o obj/reorg_layer.o obj/reorg_old_layer.o obj/super.o obj/voxel.o obj/tree.o obj/yolo_layer.o obj/gaussian_yolo_layer.o obj/upsample_layer.o obj/lstm_layer.o obj/conv_lstm_layer.o obj/scale_channels_layer.o obj/sam_layer.o obj/convolutional_kernels.o obj/activation_kernels.o obj/im2col_kernels.o obj/col2im_kernels.o obj/blas_kernels.o obj/crop_layer_kernels.o obj/dropout_layer_kernels.o obj/maxpool_layer_kernels.o obj/network_kernels.o obj/avgpool_layer_kernels.o -o darknet -lm -pthread `pkg-config --libs opencv4 2> /dev/null || pkg-config --libs opencv` -L/usr/local/cuda/lib64 -lcuda -lcudart -lcublas -lcurand -L/usr/local/cudnn/lib64 -lcudnn -lstdc++\n",
"\u001b[K |████████████████████████████████| 61kB 4.5MB/s \n",
"\u001b[K |████████████████████████████████| 36.7MB 82kB/s \n",
"\u001b[K |████████████████████████████████| 102kB 13.7MB/s \n",
"\u001b[?25h Building wheel for contextvars (setup.py) ... \u001b[?25l\u001b[?25hdone\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "yXaDc1ZVf4bX",
"outputId": "40df53b7-d344-4323-eacd-a052b05a0e51",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"!./darknet detector test cfg/coco.data cfg/yolov4.cfg yolov4.weights data/person.jpg"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
" CUDA-version: 10010 (10010), cuDNN: 7.6.5, CUDNN_HALF=1, GPU count: 1 \n",
" CUDNN_HALF=1 \n",
" OpenCV version: 3.2.0\n",
" 0 : compute_capability = 750, cudnn_half = 1, GPU: Tesla T4 \n",
"net.optimized_memory = 0 \n",
"mini_batch = 1, batch = 8, time_steps = 1, train = 0 \n",
" layer filters size/strd(dil) input output\n",
" 0 conv 32 3 x 3/ 1 608 x 608 x 3 -> 608 x 608 x 32 0.639 BF\n",
" 1 conv 64 3 x 3/ 2 608 x 608 x 32 -> 304 x 304 x 64 3.407 BF\n",
" 2 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n",
" 3 route 1 \t\t -> 304 x 304 x 64 \n",
" 4 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n",
" 5 conv 32 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 32 0.379 BF\n",
" 6 conv 64 3 x 3/ 1 304 x 304 x 32 -> 304 x 304 x 64 3.407 BF\n",
" 7 Shortcut Layer: 4, wt = 0, wn = 0, outputs: 304 x 304 x 64 0.006 BF\n",
" 8 conv 64 1 x 1/ 1 304 x 304 x 64 -> 304 x 304 x 64 0.757 BF\n",
" 9 route 8 2 \t -> 304 x 304 x 128 \n",
" 10 conv 64 1 x 1/ 1 304 x 304 x 128 -> 304 x 304 x 64 1.514 BF\n",
" 11 conv 128 3 x 3/ 2 304 x 304 x 64 -> 152 x 152 x 128 3.407 BF\n",
" 12 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n",
" 13 route 11 \t\t -> 152 x 152 x 128 \n",
" 14 conv 64 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 64 0.379 BF\n",
" 15 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n",
" 16 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n",
" 17 Shortcut Layer: 14, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n",
" 18 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n",
" 19 conv 64 3 x 3/ 1 152 x 152 x 64 -> 152 x 152 x 64 1.703 BF\n",
" 20 Shortcut Layer: 17, wt = 0, wn = 0, outputs: 152 x 152 x 64 0.001 BF\n",
" 21 conv 64 1 x 1/ 1 152 x 152 x 64 -> 152 x 152 x 64 0.189 BF\n",
" 22 route 21 12 \t -> 152 x 152 x 128 \n",
" 23 conv 128 1 x 1/ 1 152 x 152 x 128 -> 152 x 152 x 128 0.757 BF\n",
" 24 conv 256 3 x 3/ 2 152 x 152 x 128 -> 76 x 76 x 256 3.407 BF\n",
" 25 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n",
" 26 route 24 \t\t -> 76 x 76 x 256 \n",
" 27 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n",
" 28 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 29 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 30 Shortcut Layer: 27, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 31 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 32 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 33 Shortcut Layer: 30, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 34 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 35 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 36 Shortcut Layer: 33, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 37 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 38 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 39 Shortcut Layer: 36, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 40 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 41 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 42 Shortcut Layer: 39, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 43 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 44 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 45 Shortcut Layer: 42, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 46 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 47 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 48 Shortcut Layer: 45, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 49 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 50 conv 128 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 128 1.703 BF\n",
" 51 Shortcut Layer: 48, wt = 0, wn = 0, outputs: 76 x 76 x 128 0.001 BF\n",
" 52 conv 128 1 x 1/ 1 76 x 76 x 128 -> 76 x 76 x 128 0.189 BF\n",
" 53 route 52 25 \t -> 76 x 76 x 256 \n",
" 54 conv 256 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 256 0.757 BF\n",
" 55 conv 512 3 x 3/ 2 76 x 76 x 256 -> 38 x 38 x 512 3.407 BF\n",
" 56 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 57 route 55 \t\t -> 38 x 38 x 512 \n",
" 58 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 59 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 60 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 61 Shortcut Layer: 58, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 62 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 63 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 64 Shortcut Layer: 61, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 65 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 66 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 67 Shortcut Layer: 64, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 68 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 69 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 70 Shortcut Layer: 67, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 71 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 72 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 73 Shortcut Layer: 70, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 74 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 75 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 76 Shortcut Layer: 73, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 77 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 78 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 79 Shortcut Layer: 76, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 80 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 81 conv 256 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 256 1.703 BF\n",
" 82 Shortcut Layer: 79, wt = 0, wn = 0, outputs: 38 x 38 x 256 0.000 BF\n",
" 83 conv 256 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 256 0.189 BF\n",
" 84 route 83 56 \t -> 38 x 38 x 512 \n",
" 85 conv 512 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 512 0.757 BF\n",
" 86 conv 1024 3 x 3/ 2 38 x 38 x 512 -> 19 x 19 x1024 3.407 BF\n",
" 87 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 88 route 86 \t\t -> 19 x 19 x1024 \n",
" 89 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 90 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n",
" 91 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n",
" 92 Shortcut Layer: 89, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n",
" 93 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n",
" 94 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n",
" 95 Shortcut Layer: 92, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n",
" 96 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n",
" 97 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n",
" 98 Shortcut Layer: 95, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n",
" 99 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n",
" 100 conv 512 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x 512 1.703 BF\n",
" 101 Shortcut Layer: 98, wt = 0, wn = 0, outputs: 19 x 19 x 512 0.000 BF\n",
" 102 conv 512 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.189 BF\n",
" 103 route 102 87 \t -> 19 x 19 x1024 \n",
" 104 conv 1024 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x1024 0.757 BF\n",
" 105 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 106 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n",
" 107 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 108 max 5x 5/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.005 BF\n",
" 109 route 107 \t\t -> 19 x 19 x 512 \n",
" 110 max 9x 9/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.015 BF\n",
" 111 route 107 \t\t -> 19 x 19 x 512 \n",
" 112 max 13x13/ 1 19 x 19 x 512 -> 19 x 19 x 512 0.031 BF\n",
" 113 route 112 110 108 107 \t -> 19 x 19 x2048 \n",
" 114 conv 512 1 x 1/ 1 19 x 19 x2048 -> 19 x 19 x 512 0.757 BF\n",
" 115 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n",
" 116 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 117 conv 256 1 x 1/ 1 19 x 19 x 512 -> 19 x 19 x 256 0.095 BF\n",
" 118 upsample 2x 19 x 19 x 256 -> 38 x 38 x 256\n",
" 119 route 85 \t\t -> 38 x 38 x 512 \n",
" 120 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 121 route 120 118 \t -> 38 x 38 x 512 \n",
" 122 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 123 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n",
" 124 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 125 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n",
" 126 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 127 conv 128 1 x 1/ 1 38 x 38 x 256 -> 38 x 38 x 128 0.095 BF\n",
" 128 upsample 2x 38 x 38 x 128 -> 76 x 76 x 128\n",
" 129 route 54 \t\t -> 76 x 76 x 256 \n",
" 130 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n",
" 131 route 130 128 \t -> 76 x 76 x 256 \n",
" 132 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n",
" 133 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n",
" 134 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n",
" 135 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n",
" 136 conv 128 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 128 0.379 BF\n",
" 137 conv 256 3 x 3/ 1 76 x 76 x 128 -> 76 x 76 x 256 3.407 BF\n",
" 138 conv 255 1 x 1/ 1 76 x 76 x 256 -> 76 x 76 x 255 0.754 BF\n",
" 139 yolo\n",
"[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.20\n",
"nms_kind: greedynms (1), beta = 0.600000 \n",
" 140 route 136 \t\t -> 76 x 76 x 128 \n",
" 141 conv 256 3 x 3/ 2 76 x 76 x 128 -> 38 x 38 x 256 0.852 BF\n",
" 142 route 141 126 \t -> 38 x 38 x 512 \n",
" 143 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 144 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n",
" 145 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 146 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n",
" 147 conv 256 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 256 0.379 BF\n",
" 148 conv 512 3 x 3/ 1 38 x 38 x 256 -> 38 x 38 x 512 3.407 BF\n",
" 149 conv 255 1 x 1/ 1 38 x 38 x 512 -> 38 x 38 x 255 0.377 BF\n",
" 150 yolo\n",
"[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.10\n",
"nms_kind: greedynms (1), beta = 0.600000 \n",
" 151 route 147 \t\t -> 38 x 38 x 256 \n",
" 152 conv 512 3 x 3/ 2 38 x 38 x 256 -> 19 x 19 x 512 0.852 BF\n",
" 153 route 152 116 \t -> 19 x 19 x1024 \n",
" 154 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 155 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n",
" 156 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 157 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n",
" 158 conv 512 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 512 0.379 BF\n",
" 159 conv 1024 3 x 3/ 1 19 x 19 x 512 -> 19 x 19 x1024 3.407 BF\n",
" 160 conv 255 1 x 1/ 1 19 x 19 x1024 -> 19 x 19 x 255 0.189 BF\n",
" 161 yolo\n",
"[yolo] params: iou loss: ciou (4), iou_norm: 0.07, obj_norm: 1.00, cls_norm: 1.00, delta_norm: 1.00, scale_x_y: 1.05\n",
"nms_kind: greedynms (1), beta = 0.600000 \n",
"Total BFLOPS 128.459 \n",
"avg_outputs = 1068395 \n",
" Allocate additional workspace_size = 52.43 MB \n",
"Loading weights from yolov4.weights...\n",
" seen 64, trained: 32032 K-images (500 Kilo-batches_64) \n",
"Done! Loaded 162 layers from weights-file \n",
" Detection layer: 139 - type = 28 \n",
" Detection layer: 150 - type = 28 \n",
" Detection layer: 161 - type = 28 \n",
"data/person.jpg: Predicted in 54.624000 milli-seconds.\n",
"dog: 99%\n",
"person: 100%\n",
"horse: 98%\n",
"Unable to init server: Could not connect: Connection refused\n",
"\n",
"(predictions:1180): Gtk-\u001b[1;33mWARNING\u001b[0m **: \u001b[34m10:30:32.437\u001b[0m: cannot open display: \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "cGC9JimSgqjU",
"outputId": "5a31b0d2-7b85-4421-a66a-74501ae1a290",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"%%writefile data/obj.names\n",
"bus\n",
"truck"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Writing data/obj.names\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "856G9No2gsUh",
"outputId": "9ad56759-f849-447e-e4ea-5fb744c4d68c",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 34
}
},
"source": [
"%%writefile data/obj.data\n",
"classes = 2\n",
"train = data/train.txt\n",
"valid = data/val.txt\n",
"names = data/obj.names\n",
"backup = backup/"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"Writing data/obj.data\n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "kGVALMhdguOJ"
},
"source": [
"!mkdir -p data/obj\n",
"!cp -r open-images-bus-trucks/images/* data/obj/\n",
"!cp -r open-images-bus-trucks/yolo_labels/all/{train,val}.txt data/\n",
"!cp -r open-images-bus-trucks/yolo_labels/all/labels/*.txt data/obj/"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "VqUxBoqdgyqY"
},
"source": [
"# create a copy of existing configuration and modify it in place\n",
"!cp cfg/yolov4-tiny-custom.cfg cfg/yolov4-tiny-bus-trucks.cfg\n",
"# max_batches to 4000 (since the dataset is small enough)\n",
"!sed -i 's/max_batches = 500200/max_batches=4000/' cfg/yolov4-tiny-bus-trucks.cfg\n",
"# number of sub-batches per batch\n",
"!sed -i 's/subdivisions=1/subdivisions=16/' cfg/yolov4-tiny-bus-trucks.cfg\n",
"# number of batches after which learning rate is decayed\n",
"!sed -i 's/steps=400000,450000/steps=3200,3600/' cfg/yolov4-tiny-bus-trucks.cfg\n",
"# number of classes is 2 as opposed to 80 (which is the number of COCO classes)\n",
"!sed -i 's/classes=80/classes=2/g' cfg/yolov4-tiny-bus-trucks.cfg\n",
"# in the classification and regression heads, change number of output convolution filters\n",
"# from 255 -> 21 and 57 -> 33, since we have fewer classes we don't need as many filters\n",
"!sed -i 's/filters=255/filters=21/g' cfg/yolov4-tiny-bus-trucks.cfg\n",
"!sed -i 's/filters=57/filters=33/g' cfg/yolov4-tiny-bus-trucks.cfg"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "mzAvyu9rg1La"
},
"source": [
"!wget --quiet https://github.com/AlexeyAB/darknet/releases/download/darknet_yolo_v4_pre/yolov4-tiny.conv.29\n",
"!cp yolov4-tiny.conv.29 build/darknet/x64/"
],
"execution_count": null,
"outputs": []
},
{
"cell_type": "code",
"metadata": {
"id": "ygX63Lp4g22E",
"outputId": "aee3e0c8-b0ed-4b7b-a425-a3cc21d274c3",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"!./darknet detector train data/obj.data cfg/yolov4-tiny-bus-trucks.cfg yolov4-tiny.conv.29 -dont_show -mapLastAt"
],
"execution_count": null,
"outputs": [
{
"output_type": "stream",
"text": [
"\u001b[1;30;43mStreaming output truncated to the last 5000 lines.\u001b[0m\n",
" total_bbox = 28861, rewritten_bbox = 0.266796 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.253837, GIOU: 0.226252), Class: 0.512278, Obj: 0.035657, No Obj: 0.003119, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.006398, iou_loss = 0.030554, total_loss = 1.036951 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.272861, GIOU: 0.124810), Class: 0.489438, Obj: 0.001360, No Obj: 0.001263, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.592821, iou_loss = 0.524624, total_loss = 2.117445 \n",
" total_bbox = 28868, rewritten_bbox = 0.266731 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.441726, GIOU: 0.394106), Class: 0.537547, Obj: 0.008450, No Obj: 0.002911, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.765674, iou_loss = 0.170821, total_loss = 1.936495 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.533825, GIOU: 0.491214), Class: 0.521896, Obj: 0.012841, No Obj: 0.001172, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.368438, iou_loss = 0.671705, total_loss = 1.040143 \n",
" total_bbox = 28874, rewritten_bbox = 0.266676 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382269, GIOU: 0.309470), Class: 0.629325, Obj: 0.042290, No Obj: 0.003029, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.565169, iou_loss = 0.080733, total_loss = 1.645902 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.365048, GIOU: 0.285791), Class: 0.571612, Obj: 0.011964, No Obj: 0.001190, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.358244, iou_loss = 0.044649, total_loss = 0.402892 \n",
" total_bbox = 28880, rewritten_bbox = 0.266620 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.535298, GIOU: 0.535298), Class: 0.499331, Obj: 0.030466, No Obj: 0.003717, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.029361, iou_loss = 0.013425, total_loss = 1.042786 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.467882, GIOU: 0.338437), Class: 0.605530, Obj: 0.001293, No Obj: 0.001252, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.338981, iou_loss = 0.098903, total_loss = 0.437884 \n",
" total_bbox = 28884, rewritten_bbox = 0.266584 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.470582, GIOU: 0.441136), Class: 0.371564, Obj: 0.015112, No Obj: 0.003044, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.201463, iou_loss = 0.018884, total_loss = 1.220347 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.723136, GIOU: 0.707338), Class: 0.313135, Obj: 0.017850, No Obj: 0.001196, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.405795, iou_loss = 0.304098, total_loss = 0.709893 \n",
" total_bbox = 28888, rewritten_bbox = 0.266547 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.387959, GIOU: 0.285123), Class: 0.590540, Obj: 0.036874, No Obj: 0.002810, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.691791, iou_loss = 0.047101, total_loss = 1.738892 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.296154, GIOU: 0.214362), Class: 0.339423, Obj: 0.015787, No Obj: 0.001203, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.393765, iou_loss = 0.067662, total_loss = 0.461428 \n",
" total_bbox = 28894, rewritten_bbox = 0.266491 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.526562, GIOU: 0.524654), Class: 0.438935, Obj: 0.021853, No Obj: 0.003123, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.560410, iou_loss = 0.056092, total_loss = 1.616503 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.135189, GIOU: 0.135188), Class: 0.279803, Obj: 0.002760, No Obj: 0.001205, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.942092, iou_loss = 0.106651, total_loss = 1.048743 \n",
" total_bbox = 28900, rewritten_bbox = 0.266436 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320022, GIOU: 0.316257), Class: 0.493751, Obj: 0.012688, No Obj: 0.002913, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.496334, iou_loss = 0.012537, total_loss = 1.508871 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001142, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004037, iou_loss = 0.000000, total_loss = 0.004037 \n",
" total_bbox = 28904, rewritten_bbox = 0.266399 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.489614, GIOU: 0.365447), Class: 0.519569, Obj: 0.027149, No Obj: 0.002897, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.442864, iou_loss = 0.030585, total_loss = 1.473449 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001188, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004445, iou_loss = 0.000000, total_loss = 0.004445 \n",
" total_bbox = 28908, rewritten_bbox = 0.266362 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.306132, GIOU: 0.196339), Class: 0.444803, Obj: 0.009835, No Obj: 0.002846, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.130649, iou_loss = 0.007332, total_loss = 1.137981 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.245273, GIOU: -0.010869), Class: 0.537630, Obj: 0.007265, No Obj: 0.001175, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.359903, iou_loss = 0.031515, total_loss = 0.391418 \n",
" total_bbox = 28912, rewritten_bbox = 0.266325 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.394766, GIOU: 0.339255), Class: 0.441486, Obj: 0.019701, No Obj: 0.003321, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.234875, iou_loss = 0.042199, total_loss = 1.277074 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001153, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004289, iou_loss = 0.000000, total_loss = 0.004289 \n",
" total_bbox = 28915, rewritten_bbox = 0.266298 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.280854, GIOU: 0.257344), Class: 0.412369, Obj: 0.040883, No Obj: 0.003208, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.521522, iou_loss = 0.016498, total_loss = 1.538020 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001157, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004316, iou_loss = 0.000000, total_loss = 0.004316 \n",
" total_bbox = 28919, rewritten_bbox = 0.266261 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 362: 0.934549, 0.937617 avg loss, 0.000045 rate, 0.699102 seconds, 23168 images, 0.733729 hours left\n",
"Loaded: 0.000034 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.448610, GIOU: 0.445866), Class: 0.485650, Obj: 0.011371, No Obj: 0.003369, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.923500, iou_loss = 0.051312, total_loss = 1.974813 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.614888, GIOU: 0.585954), Class: 0.667435, Obj: 0.001292, No Obj: 0.001187, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.374570, iou_loss = 1.724198, total_loss = 2.098768 \n",
" total_bbox = 28925, rewritten_bbox = 0.266206 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435136, GIOU: 0.358443), Class: 0.424091, Obj: 0.017206, No Obj: 0.003650, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.483307, iou_loss = 0.012915, total_loss = 1.496221 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001260, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005421, iou_loss = 0.000000, total_loss = 0.005421 \n",
" total_bbox = 28929, rewritten_bbox = 0.266169 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.391600, GIOU: 0.321144), Class: 0.535812, Obj: 0.034205, No Obj: 0.003485, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.412027, iou_loss = 0.064033, total_loss = 1.476060 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.488761, GIOU: 0.421873), Class: 0.509812, Obj: 0.002310, No Obj: 0.001289, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.383464, iou_loss = 0.311149, total_loss = 0.694613 \n",
" total_bbox = 28934, rewritten_bbox = 0.266123 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.180581, GIOU: 0.110969), Class: 0.389600, Obj: 0.005704, No Obj: 0.003073, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.587782, iou_loss = 0.004598, total_loss = 1.592380 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.460108, GIOU: 0.196255), Class: 0.531565, Obj: 0.004338, No Obj: 0.001194, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.771863, iou_loss = 0.385480, total_loss = 1.157343 \n",
" total_bbox = 28940, rewritten_bbox = 0.266068 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.214250, GIOU: 0.153725), Class: 0.388996, Obj: 0.011725, No Obj: 0.002709, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 2.098289, iou_loss = 0.010231, total_loss = 2.108521 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.395889, GIOU: 0.348407), Class: 0.460599, Obj: 0.003519, No Obj: 0.001169, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.140527, iou_loss = 0.435309, total_loss = 1.575835 \n",
" total_bbox = 28948, rewritten_bbox = 0.265994 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353016, GIOU: 0.353016), Class: 0.528913, Obj: 0.033090, No Obj: 0.003868, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.531164, iou_loss = 0.039120, total_loss = 1.570284 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.255418, GIOU: -0.020225), Class: 0.545577, Obj: 0.002209, No Obj: 0.001255, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.428879, iou_loss = 0.030973, total_loss = 0.459853 \n",
" total_bbox = 28953, rewritten_bbox = 0.265948 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.430717, GIOU: 0.399653), Class: 0.422071, Obj: 0.027147, No Obj: 0.003600, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.358299, iou_loss = 0.247144, total_loss = 2.605443 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001333, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005608, iou_loss = 0.000000, total_loss = 0.005608 \n",
" total_bbox = 28959, rewritten_bbox = 0.265893 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.540863, GIOU: 0.485145), Class: 0.438879, Obj: 0.012542, No Obj: 0.003138, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.183125, iou_loss = 0.042062, total_loss = 1.225187 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.277940, GIOU: 0.061389), Class: 0.591289, Obj: 0.004850, No Obj: 0.001116, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.702684, iou_loss = 0.186999, total_loss = 0.889682 \n",
" total_bbox = 28964, rewritten_bbox = 0.265847 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.283700, GIOU: 0.172195), Class: 0.544809, Obj: 0.036974, No Obj: 0.003418, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.291369, iou_loss = 0.025056, total_loss = 1.316424 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001270, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004877, iou_loss = 0.000000, total_loss = 0.004877 \n",
" total_bbox = 28968, rewritten_bbox = 0.265811 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.220989, GIOU: 0.182786), Class: 0.414377, Obj: 0.016286, No Obj: 0.002872, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.691219, iou_loss = 0.018529, total_loss = 1.709748 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001211, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005081, iou_loss = 0.000000, total_loss = 0.005081 \n",
" total_bbox = 28972, rewritten_bbox = 0.265774 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.381501, GIOU: 0.371898), Class: 0.506319, Obj: 0.017740, No Obj: 0.003274, .5R: 0.166667, .75R: 0.166667, count: 6, class_loss = 2.197924, iou_loss = 0.050464, total_loss = 2.248388 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.312988, GIOU: 0.080408), Class: 0.460406, Obj: 0.001226, No Obj: 0.001144, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.376165, iou_loss = 0.031775, total_loss = 0.407940 \n",
" total_bbox = 28979, rewritten_bbox = 0.265710 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.455090, GIOU: 0.409190), Class: 0.453891, Obj: 0.015117, No Obj: 0.002899, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 2.111001, iou_loss = 0.100150, total_loss = 2.211151 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.150302, GIOU: -0.391976), Class: 0.637772, Obj: 0.003045, No Obj: 0.001151, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.353958, iou_loss = 0.006121, total_loss = 0.360079 \n",
" total_bbox = 28985, rewritten_bbox = 0.265655 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.302482, GIOU: 0.173551), Class: 0.545379, Obj: 0.020941, No Obj: 0.003346, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.820899, iou_loss = 0.088818, total_loss = 1.909717 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001188, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004533, iou_loss = 0.000000, total_loss = 0.004533 \n",
" total_bbox = 28990, rewritten_bbox = 0.265609 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399911, GIOU: 0.320562), Class: 0.593815, Obj: 0.038373, No Obj: 0.002798, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.417154, iou_loss = 0.042938, total_loss = 1.460092 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001151, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004526, iou_loss = 0.000000, total_loss = 0.004526 \n",
" total_bbox = 28994, rewritten_bbox = 0.265572 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.335900, GIOU: 0.305574), Class: 0.451781, Obj: 0.061313, No Obj: 0.003717, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.462179, iou_loss = 0.009912, total_loss = 1.472090 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001245, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005273, iou_loss = 0.000000, total_loss = 0.005273 \n",
" total_bbox = 28998, rewritten_bbox = 0.265536 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.533936, GIOU: 0.449403), Class: 0.385543, Obj: 0.054549, No Obj: 0.002931, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.587392, iou_loss = 0.028086, total_loss = 1.615478 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.196722, GIOU: -0.253225), Class: 0.692551, Obj: 0.001082, No Obj: 0.001168, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.323836, iou_loss = 0.012713, total_loss = 0.336549 \n",
" total_bbox = 29003, rewritten_bbox = 0.265490 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 363: 1.010213, 0.944876 avg loss, 0.000045 rate, 0.760841 seconds, 23232 images, 0.733457 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.312688, GIOU: 0.159817), Class: 0.611701, Obj: 0.015594, No Obj: 0.003482, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.514331, iou_loss = 0.017600, total_loss = 1.531931 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.322909, GIOU: 0.188215), Class: 0.679236, Obj: 0.011664, No Obj: 0.001412, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.718729, iou_loss = 0.136318, total_loss = 0.855047 \n",
" total_bbox = 29009, rewritten_bbox = 0.265435 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.508940, GIOU: 0.502069), Class: 0.608322, Obj: 0.053780, No Obj: 0.003066, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.273480, iou_loss = 0.069244, total_loss = 1.342724 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.412381, GIOU: 0.287393), Class: 0.645963, Obj: 0.003086, No Obj: 0.001139, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.383510, iou_loss = 0.288727, total_loss = 0.672237 \n",
" total_bbox = 29014, rewritten_bbox = 0.265389 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300554, GIOU: 0.254261), Class: 0.546830, Obj: 0.018743, No Obj: 0.003329, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.352287, iou_loss = 0.010685, total_loss = 1.362973 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001147, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005362, iou_loss = 0.000000, total_loss = 0.005362 \n",
" total_bbox = 29018, rewritten_bbox = 0.265353 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.288257, GIOU: 0.141573), Class: 0.667400, Obj: 0.023303, No Obj: 0.002896, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.326470, iou_loss = 0.020542, total_loss = 1.347013 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.160337, GIOU: -0.348038), Class: 0.272839, Obj: 0.006591, No Obj: 0.001235, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.951658, iou_loss = 0.023948, total_loss = 0.975606 \n",
" total_bbox = 29024, rewritten_bbox = 0.265298 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339541, GIOU: 0.254646), Class: 0.478719, Obj: 0.019631, No Obj: 0.002871, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.444101, iou_loss = 0.025194, total_loss = 1.469295 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001246, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005607, iou_loss = 0.000000, total_loss = 0.005607 \n",
" total_bbox = 29028, rewritten_bbox = 0.265261 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.318470, GIOU: 0.294286), Class: 0.495099, Obj: 0.017245, No Obj: 0.003042, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.485141, iou_loss = 0.038315, total_loss = 1.523456 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001202, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005115, iou_loss = 0.000000, total_loss = 0.005115 \n",
" total_bbox = 29032, rewritten_bbox = 0.265225 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.245807, GIOU: 0.107390), Class: 0.544631, Obj: 0.030266, No Obj: 0.002929, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.396359, iou_loss = 0.005603, total_loss = 1.401963 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.503706, GIOU: 0.394776), Class: 0.496944, Obj: 0.003955, No Obj: 0.001214, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.400818, iou_loss = 0.124905, total_loss = 0.525723 \n",
" total_bbox = 29037, rewritten_bbox = 0.265179 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.426830, GIOU: 0.395980), Class: 0.439137, Obj: 0.028181, No Obj: 0.002903, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.888419, iou_loss = 0.028860, total_loss = 0.917279 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.627812, GIOU: 0.617110), Class: 0.399516, Obj: 0.004640, No Obj: 0.001200, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.813440, iou_loss = 0.507488, total_loss = 1.320928 \n",
" total_bbox = 29041, rewritten_bbox = 0.265142 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.264163, GIOU: 0.073018), Class: 0.668640, Obj: 0.059361, No Obj: 0.003208, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.177292, iou_loss = 0.022350, total_loss = 1.199642 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001144, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004145, iou_loss = 0.000000, total_loss = 0.004145 \n",
" total_bbox = 29045, rewritten_bbox = 0.265106 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.306181, GIOU: 0.172895), Class: 0.677122, Obj: 0.033545, No Obj: 0.003040, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.946061, iou_loss = 0.031798, total_loss = 0.977859 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.346791, GIOU: 0.198701), Class: 0.405105, Obj: 0.006949, No Obj: 0.001220, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.874669, iou_loss = 0.168814, total_loss = 1.043483 \n",
" total_bbox = 29050, rewritten_bbox = 0.265060 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.487852, GIOU: 0.423843), Class: 0.564627, Obj: 0.064046, No Obj: 0.003262, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.411049, iou_loss = 0.101918, total_loss = 1.512966 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.191656, GIOU: 0.110409), Class: 0.720956, Obj: 0.004828, No Obj: 0.001265, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.657558, iou_loss = 0.890713, total_loss = 1.548271 \n",
" total_bbox = 29056, rewritten_bbox = 0.265005 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.254683, GIOU: -0.013937), Class: 0.623988, Obj: 0.012776, No Obj: 0.003238, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.682979, iou_loss = 0.002731, total_loss = 0.685710 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.449065, GIOU: 0.342829), Class: 0.515673, Obj: 0.008408, No Obj: 0.001330, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.591171, iou_loss = 1.374432, total_loss = 3.965603 \n",
" total_bbox = 29065, rewritten_bbox = 0.264923 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427216, GIOU: 0.305240), Class: 0.475071, Obj: 0.025716, No Obj: 0.003106, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.649013, iou_loss = 0.063712, total_loss = 1.712725 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.478703, GIOU: 0.379841), Class: 0.513915, Obj: 0.002260, No Obj: 0.001213, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.423911, iou_loss = 0.096804, total_loss = 0.520715 \n",
" total_bbox = 29070, rewritten_bbox = 0.264878 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.282894, GIOU: 0.084293), Class: 0.534053, Obj: 0.021566, No Obj: 0.002748, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.789716, iou_loss = 0.073662, total_loss = 1.863378 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.412363, GIOU: 0.355915), Class: 0.519473, Obj: 0.006008, No Obj: 0.001289, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.785364, iou_loss = 1.164175, total_loss = 2.949539 \n",
" total_bbox = 29080, rewritten_bbox = 0.264787 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.332170, GIOU: 0.332170), Class: 0.580713, Obj: 0.027204, No Obj: 0.003128, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.989604, iou_loss = 0.039565, total_loss = 1.029169 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.234086, GIOU: 0.064691), Class: 0.358324, Obj: 0.010747, No Obj: 0.001233, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.447558, iou_loss = 0.580799, total_loss = 1.028358 \n",
" total_bbox = 29084, rewritten_bbox = 0.264750 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.330655, GIOU: 0.305707), Class: 0.454943, Obj: 0.023768, No Obj: 0.003410, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.806742, iou_loss = 0.031136, total_loss = 1.837878 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001172, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004618, iou_loss = 0.000000, total_loss = 0.004618 \n",
" total_bbox = 29089, rewritten_bbox = 0.264705 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 364: 0.984730, 0.948862 avg loss, 0.000046 rate, 0.726639 seconds, 23296 images, 0.733809 hours left\n",
"Loaded: 0.000063 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.476838, GIOU: 0.451699), Class: 0.531154, Obj: 0.038520, No Obj: 0.002611, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.016373, iou_loss = 0.073447, total_loss = 1.089820 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.061297, GIOU: 0.061298), Class: 0.517293, Obj: 0.003786, No Obj: 0.001165, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.429719, iou_loss = 0.004771, total_loss = 0.434490 \n",
" total_bbox = 29093, rewritten_bbox = 0.264668 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.220251, GIOU: 0.029941), Class: 0.337150, Obj: 0.017551, No Obj: 0.003560, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.670733, iou_loss = 0.012505, total_loss = 1.683238 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001092, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003866, iou_loss = 0.000000, total_loss = 0.003866 \n",
" total_bbox = 29097, rewritten_bbox = 0.264632 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398441, GIOU: 0.398441), Class: 0.667216, Obj: 0.007073, No Obj: 0.003098, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.292459, iou_loss = 0.052491, total_loss = 1.344950 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.271726, GIOU: -0.015844), Class: 0.517496, Obj: 0.003591, No Obj: 0.001215, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.732693, iou_loss = 1.360245, total_loss = 2.092938 \n",
" total_bbox = 29103, rewritten_bbox = 0.264578 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.189710, GIOU: -0.072540), Class: 0.395620, Obj: 0.014565, No Obj: 0.002883, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.801461, iou_loss = 0.019764, total_loss = 1.821225 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001092, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003888, iou_loss = 0.000000, total_loss = 0.003888 \n",
" total_bbox = 29107, rewritten_bbox = 0.264541 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.387292, GIOU: 0.289715), Class: 0.603780, Obj: 0.028189, No Obj: 0.002964, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.830011, iou_loss = 0.056728, total_loss = 1.886739 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001132, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004182, iou_loss = 0.000000, total_loss = 0.004182 \n",
" total_bbox = 29112, rewritten_bbox = 0.264496 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382222, GIOU: 0.372122), Class: 0.395978, Obj: 0.011066, No Obj: 0.002773, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.501620, iou_loss = 0.021885, total_loss = 1.523504 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001185, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005043, iou_loss = 0.000000, total_loss = 0.005043 \n",
" total_bbox = 29116, rewritten_bbox = 0.264459 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409525, GIOU: 0.282844), Class: 0.431296, Obj: 0.041046, No Obj: 0.003336, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.575488, iou_loss = 0.023549, total_loss = 1.599037 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.229842, GIOU: 0.089796), Class: 0.644777, Obj: 0.003831, No Obj: 0.001187, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.058544, iou_loss = 0.551409, total_loss = 1.609952 \n",
" total_bbox = 29123, rewritten_bbox = 0.264396 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.274188, GIOU: 0.023457), Class: 0.384359, Obj: 0.019668, No Obj: 0.003002, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.910987, iou_loss = 0.050086, total_loss = 1.961074 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.533452, GIOU: 0.520417), Class: 0.480175, Obj: 0.005527, No Obj: 0.001181, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.764355, iou_loss = 1.450952, total_loss = 2.215307 \n",
" total_bbox = 29130, rewritten_bbox = 0.264332 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271805, GIOU: -0.025111), Class: 0.509935, Obj: 0.015570, No Obj: 0.003054, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.400644, iou_loss = 0.039796, total_loss = 1.440440 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001202, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005717, iou_loss = 0.000000, total_loss = 0.005717 \n",
" total_bbox = 29134, rewritten_bbox = 0.264296 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.311270, GIOU: 0.302812), Class: 0.462104, Obj: 0.018418, No Obj: 0.003437, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.034231, iou_loss = 0.036954, total_loss = 1.071185 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.240529, GIOU: 0.240529), Class: 0.686696, Obj: 0.010048, No Obj: 0.001140, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.336497, iou_loss = 0.050548, total_loss = 0.387045 \n",
" total_bbox = 29138, rewritten_bbox = 0.264260 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402299, GIOU: 0.402299), Class: 0.563202, Obj: 0.041375, No Obj: 0.003313, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.812441, iou_loss = 0.008502, total_loss = 0.820943 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.307133, GIOU: 0.078472), Class: 0.420539, Obj: 0.000898, No Obj: 0.001122, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.381960, iou_loss = 0.030078, total_loss = 0.412039 \n",
" total_bbox = 29141, rewritten_bbox = 0.264233 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359956, GIOU: 0.338584), Class: 0.412801, Obj: 0.013415, No Obj: 0.002949, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.188323, iou_loss = 0.053492, total_loss = 1.241814 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.373934, GIOU: 0.223260), Class: 0.566083, Obj: 0.002260, No Obj: 0.001114, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.778106, iou_loss = 0.898099, total_loss = 2.676205 \n",
" total_bbox = 29149, rewritten_bbox = 0.264160 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.310526, GIOU: 0.221342), Class: 0.527886, Obj: 0.012461, No Obj: 0.003255, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.269675, iou_loss = 0.007253, total_loss = 1.276927 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.215233, GIOU: -0.027188), Class: 0.608309, Obj: 0.000498, No Obj: 0.001131, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.708808, iou_loss = 0.125730, total_loss = 0.834538 \n",
" total_bbox = 29154, rewritten_bbox = 0.264115 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.254056, GIOU: 0.219297), Class: 0.542953, Obj: 0.021018, No Obj: 0.003061, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.123631, iou_loss = 0.011420, total_loss = 2.135051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001086, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003659, iou_loss = 0.000000, total_loss = 0.003659 \n",
" total_bbox = 29160, rewritten_bbox = 0.264060 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.275132, GIOU: 0.265928), Class: 0.614730, Obj: 0.024937, No Obj: 0.003309, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.393744, iou_loss = 0.065369, total_loss = 1.459113 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.402679, GIOU: 0.276448), Class: 0.573210, Obj: 0.010622, No Obj: 0.001149, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.186262, iou_loss = 0.312387, total_loss = 1.498649 \n",
" total_bbox = 29167, rewritten_bbox = 0.263997 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292113, GIOU: 0.041211), Class: 0.483187, Obj: 0.010885, No Obj: 0.003077, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.447484, iou_loss = 0.018088, total_loss = 1.465572 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.207372, GIOU: -0.109592), Class: 0.314975, Obj: 0.001897, No Obj: 0.001147, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.458468, iou_loss = 0.043761, total_loss = 0.502229 \n",
" total_bbox = 29172, rewritten_bbox = 0.263952 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 365: 0.982658, 0.952241 avg loss, 0.000046 rate, 0.728106 seconds, 23360 images, 0.733811 hours left\n",
"Loaded: 0.000042 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.272185, GIOU: 0.013247), Class: 0.542558, Obj: 0.064888, No Obj: 0.003423, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.711398, iou_loss = 0.021253, total_loss = 1.732651 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001114, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004161, iou_loss = 0.000000, total_loss = 0.004161 \n",
" total_bbox = 29177, rewritten_bbox = 0.263907 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.437112, GIOU: 0.427153), Class: 0.597619, Obj: 0.030032, No Obj: 0.003289, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.309948, iou_loss = 0.029940, total_loss = 1.339888 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.209358, GIOU: -0.187225), Class: 0.610680, Obj: 0.002578, No Obj: 0.001083, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.362519, iou_loss = 0.219514, total_loss = 0.582033 \n",
" total_bbox = 29182, rewritten_bbox = 0.263861 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.553689, GIOU: 0.451352), Class: 0.535534, Obj: 0.021873, No Obj: 0.003415, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.751003, iou_loss = 0.035950, total_loss = 0.786953 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.374223, GIOU: 0.290427), Class: 0.478831, Obj: 0.003118, No Obj: 0.001409, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.375958, iou_loss = 0.440366, total_loss = 0.816324 \n",
" total_bbox = 29185, rewritten_bbox = 0.263834 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.380397, GIOU: 0.322765), Class: 0.383863, Obj: 0.008228, No Obj: 0.002908, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.976762, iou_loss = 0.066873, total_loss = 2.043636 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001174, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004652, iou_loss = 0.000000, total_loss = 0.004652 \n",
" total_bbox = 29190, rewritten_bbox = 0.263789 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392882, GIOU: 0.269611), Class: 0.448683, Obj: 0.008650, No Obj: 0.002584, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.046636, iou_loss = 0.051932, total_loss = 2.098567 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.307735, GIOU: 0.307734), Class: 0.637662, Obj: 0.001680, No Obj: 0.001087, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.321727, iou_loss = 0.082740, total_loss = 0.404467 \n",
" total_bbox = 29196, rewritten_bbox = 0.263735 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357196, GIOU: 0.132905), Class: 0.607172, Obj: 0.025178, No Obj: 0.003377, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.241899, iou_loss = 0.016278, total_loss = 1.258177 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001204, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005239, iou_loss = 0.000000, total_loss = 0.005239 \n",
" total_bbox = 29200, rewritten_bbox = 0.263699 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.491018, GIOU: 0.475497), Class: 0.430664, Obj: 0.022972, No Obj: 0.003069, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 2.064499, iou_loss = 0.093361, total_loss = 2.157860 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001179, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004458, iou_loss = 0.000000, total_loss = 0.004458 \n",
" total_bbox = 29205, rewritten_bbox = 0.263653 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.273125, GIOU: 0.167219), Class: 0.526037, Obj: 0.014782, No Obj: 0.002601, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.477616, iou_loss = 0.014372, total_loss = 1.491987 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001194, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005091, iou_loss = 0.000000, total_loss = 0.005091 \n",
" total_bbox = 29209, rewritten_bbox = 0.263617 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413721, GIOU: 0.396140), Class: 0.561283, Obj: 0.028809, No Obj: 0.003001, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.088995, iou_loss = 0.070891, total_loss = 2.159886 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.221485, GIOU: 0.138235), Class: 0.430439, Obj: 0.001011, No Obj: 0.001078, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.416358, iou_loss = 0.029565, total_loss = 0.445923 \n",
" total_bbox = 29216, rewritten_bbox = 0.263554 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.457835, GIOU: 0.378244), Class: 0.386019, Obj: 0.018521, No Obj: 0.002785, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.760373, iou_loss = 0.093097, total_loss = 0.853470 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.123677, GIOU: -0.094998), Class: 0.692628, Obj: 0.004910, No Obj: 0.001131, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.642418, iou_loss = 0.041796, total_loss = 0.684214 \n",
" total_bbox = 29220, rewritten_bbox = 0.263518 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.351270, GIOU: 0.274019), Class: 0.351133, Obj: 0.029852, No Obj: 0.003481, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.219425, iou_loss = 0.008988, total_loss = 1.228413 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001198, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004977, iou_loss = 0.000000, total_loss = 0.004977 \n",
" total_bbox = 29223, rewritten_bbox = 0.263491 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.302634, GIOU: 0.248300), Class: 0.580239, Obj: 0.021189, No Obj: 0.002705, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.309237, iou_loss = 0.032677, total_loss = 1.341914 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001146, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005351, iou_loss = 0.000000, total_loss = 0.005351 \n",
" total_bbox = 29227, rewritten_bbox = 0.263455 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.416994, GIOU: 0.345724), Class: 0.475087, Obj: 0.015307, No Obj: 0.003075, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.939353, iou_loss = 0.046269, total_loss = 1.985622 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.046216, GIOU: -0.157938), Class: 0.467907, Obj: 0.001813, No Obj: 0.001103, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.375494, iou_loss = 0.278342, total_loss = 0.653836 \n",
" total_bbox = 29233, rewritten_bbox = 0.263401 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.337463, GIOU: 0.233087), Class: 0.550322, Obj: 0.012135, No Obj: 0.002660, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.753425, iou_loss = 0.011227, total_loss = 1.764652 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001073, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003579, iou_loss = 0.000000, total_loss = 0.003579 \n",
" total_bbox = 29238, rewritten_bbox = 0.263356 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.174967, GIOU: 0.035890), Class: 0.528167, Obj: 0.010899, No Obj: 0.002992, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.579985, iou_loss = 0.008244, total_loss = 1.588229 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.219615, GIOU: 0.078250), Class: 0.661517, Obj: 0.001509, No Obj: 0.001108, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.359971, iou_loss = 0.045030, total_loss = 0.405001 \n",
" total_bbox = 29243, rewritten_bbox = 0.263311 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.516868, GIOU: 0.460637), Class: 0.424909, Obj: 0.032309, No Obj: 0.003121, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.144394, iou_loss = 0.013157, total_loss = 1.157551 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.348172, GIOU: 0.191061), Class: 0.313120, Obj: 0.013466, No Obj: 0.001183, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.414596, iou_loss = 0.107875, total_loss = 0.522471 \n",
" total_bbox = 29247, rewritten_bbox = 0.263275 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 366: 0.873808, 0.944398 avg loss, 0.000047 rate, 0.709191 seconds, 23424 images, 0.733825 hours left\n",
"Loaded: 0.000048 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.258775, GIOU: 0.197565), Class: 0.426841, Obj: 0.005835, No Obj: 0.002508, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.303087, iou_loss = 0.090128, total_loss = 2.393214 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.204183, GIOU: -0.110021), Class: 0.447714, Obj: 0.002780, No Obj: 0.001172, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.874095, iou_loss = 1.479473, total_loss = 2.353568 \n",
" total_bbox = 29255, rewritten_bbox = 0.263203 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435768, GIOU: 0.435768), Class: 0.396382, Obj: 0.015201, No Obj: 0.002881, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.977142, iou_loss = 0.022884, total_loss = 2.000026 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001117, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003990, iou_loss = 0.000000, total_loss = 0.003990 \n",
" total_bbox = 29260, rewritten_bbox = 0.263158 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392425, GIOU: 0.292211), Class: 0.415916, Obj: 0.039603, No Obj: 0.002955, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.521301, iou_loss = 0.017578, total_loss = 1.538879 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001137, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004455, iou_loss = 0.000000, total_loss = 0.004455 \n",
" total_bbox = 29264, rewritten_bbox = 0.263122 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.583179, GIOU: 0.503405), Class: 0.550234, Obj: 0.070576, No Obj: 0.003844, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.428811, iou_loss = 0.041341, total_loss = 1.470152 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.831458), Class: 0.511631, Obj: 0.002226, No Obj: 0.001131, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.346285, iou_loss = 0.000131, total_loss = 0.346416 \n",
" total_bbox = 29269, rewritten_bbox = 0.263077 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.368638, GIOU: 0.307028), Class: 0.629597, Obj: 0.011403, No Obj: 0.002798, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 1.946490, iou_loss = 0.030199, total_loss = 1.976689 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001075, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003626, iou_loss = 0.000000, total_loss = 0.003626 \n",
" total_bbox = 29275, rewritten_bbox = 0.263023 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.358119, GIOU: 0.329268), Class: 0.605673, Obj: 0.020336, No Obj: 0.003511, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.055794, iou_loss = 0.042736, total_loss = 1.098529 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.241109, GIOU: 0.045559), Class: 0.562074, Obj: 0.007999, No Obj: 0.001133, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.760486, iou_loss = 0.930782, total_loss = 1.691268 \n",
" total_bbox = 29280, rewritten_bbox = 0.262978 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.326266, GIOU: 0.319634), Class: 0.472693, Obj: 0.017995, No Obj: 0.002950, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.134662, iou_loss = 0.030087, total_loss = 1.164749 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.369637, GIOU: 0.259551), Class: 0.550443, Obj: 0.034005, No Obj: 0.001124, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.376141, iou_loss = 0.046510, total_loss = 0.422651 \n",
" total_bbox = 29284, rewritten_bbox = 0.262942 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.464595, GIOU: 0.415488), Class: 0.441375, Obj: 0.011416, No Obj: 0.003558, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.519132, iou_loss = 0.098138, total_loss = 1.617270 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001204, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005281, iou_loss = 0.000000, total_loss = 0.005281 \n",
" total_bbox = 29288, rewritten_bbox = 0.262906 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.251093, GIOU: 0.239689), Class: 0.509131, Obj: 0.007196, No Obj: 0.002914, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.534610, iou_loss = 0.008466, total_loss = 1.543076 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001184, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004409, iou_loss = 0.000000, total_loss = 0.004409 \n",
" total_bbox = 29292, rewritten_bbox = 0.262870 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.611712, GIOU: 0.599667), Class: 0.511752, Obj: 0.041919, No Obj: 0.003132, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.355451, iou_loss = 0.104894, total_loss = 1.460346 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001166, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004463, iou_loss = 0.000000, total_loss = 0.004463 \n",
" total_bbox = 29296, rewritten_bbox = 0.262835 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.389732, GIOU: 0.284249), Class: 0.432276, Obj: 0.010599, No Obj: 0.002845, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.737812, iou_loss = 0.044269, total_loss = 2.782081 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.482310, GIOU: 0.482310), Class: 0.576586, Obj: 0.000978, No Obj: 0.001132, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.337113, iou_loss = 0.644651, total_loss = 0.981764 \n",
" total_bbox = 29304, rewritten_bbox = 0.262763 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.405010, GIOU: 0.401330), Class: 0.438626, Obj: 0.012077, No Obj: 0.002939, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.222245, iou_loss = 0.035283, total_loss = 1.257528 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.441125, GIOU: 0.309991), Class: 0.571793, Obj: 0.001937, No Obj: 0.001054, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347102, iou_loss = 0.079184, total_loss = 0.426286 \n",
" total_bbox = 29308, rewritten_bbox = 0.262727 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.282191, GIOU: 0.026629), Class: 0.504683, Obj: 0.020269, No Obj: 0.003001, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.964235, iou_loss = 0.021599, total_loss = 1.985834 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001237, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005388, iou_loss = 0.000000, total_loss = 0.005388 \n",
" total_bbox = 29313, rewritten_bbox = 0.262682 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477385, GIOU: 0.450187), Class: 0.515815, Obj: 0.016866, No Obj: 0.003020, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.580604, iou_loss = 0.140245, total_loss = 1.720849 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001196, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004678, iou_loss = 0.000000, total_loss = 0.004678 \n",
" total_bbox = 29317, rewritten_bbox = 0.262646 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.363059, GIOU: 0.252272), Class: 0.423056, Obj: 0.024389, No Obj: 0.003188, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.591249, iou_loss = 0.025376, total_loss = 1.616625 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001114, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004296, iou_loss = 0.000000, total_loss = 0.004296 \n",
" total_bbox = 29321, rewritten_bbox = 0.262610 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.269628, GIOU: 0.194807), Class: 0.588656, Obj: 0.005508, No Obj: 0.002417, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.764374, iou_loss = 0.047704, total_loss = 1.812078 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.316509, GIOU: 0.259605), Class: 0.526947, Obj: 0.001744, No Obj: 0.001085, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.413331, iou_loss = 1.484688, total_loss = 2.898019 \n",
" total_bbox = 29330, rewritten_bbox = 0.262530 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 367: 0.980922, 0.948050 avg loss, 0.000047 rate, 0.725805 seconds, 23488 images, 0.733646 hours left\n",
"Loaded: 0.000041 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.559317, GIOU: 0.556830), Class: 0.524459, Obj: 0.017354, No Obj: 0.002926, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.831920, iou_loss = 0.048043, total_loss = 0.879964 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.194590, GIOU: 0.087387), Class: 0.547930, Obj: 0.002667, No Obj: 0.001120, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.536205, iou_loss = 2.582464, total_loss = 4.118669 \n",
" total_bbox = 29336, rewritten_bbox = 0.262476 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.229371, GIOU: 0.199769), Class: 0.578351, Obj: 0.028577, No Obj: 0.003043, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.528459, iou_loss = 0.049386, total_loss = 1.577845 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.382786, GIOU: 0.312288), Class: 0.517207, Obj: 0.032059, No Obj: 0.001274, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354027, iou_loss = 0.179329, total_loss = 0.533356 \n",
" total_bbox = 29341, rewritten_bbox = 0.262431 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271836, GIOU: 0.210844), Class: 0.617131, Obj: 0.010530, No Obj: 0.002582, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.988674, iou_loss = 0.011171, total_loss = 0.999846 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.634631, GIOU: 0.599471), Class: 0.361526, Obj: 0.009926, No Obj: 0.001088, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.409765, iou_loss = 0.433241, total_loss = 0.843006 \n",
" total_bbox = 29345, rewritten_bbox = 0.262396 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.484680, GIOU: 0.444293), Class: 0.480853, Obj: 0.015060, No Obj: 0.002882, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 2.689190, iou_loss = 0.054153, total_loss = 2.743343 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001180, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004598, iou_loss = 0.000000, total_loss = 0.004598 \n",
" total_bbox = 29352, rewritten_bbox = 0.262333 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.216434, GIOU: -0.024569), Class: 0.456053, Obj: 0.029606, No Obj: 0.002648, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.134684, iou_loss = 0.013397, total_loss = 1.148081 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.209698, GIOU: -0.213352), Class: 0.323567, Obj: 0.014706, No Obj: 0.001108, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.414597, iou_loss = 0.015215, total_loss = 0.429812 \n",
" total_bbox = 29356, rewritten_bbox = 0.262297 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.606840, GIOU: 0.586996), Class: 0.302768, Obj: 0.019159, No Obj: 0.003032, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.275234, iou_loss = 0.017110, total_loss = 1.292344 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001078, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003730, iou_loss = 0.000000, total_loss = 0.003730 \n",
" total_bbox = 29359, rewritten_bbox = 0.262271 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.368398, GIOU: 0.231082), Class: 0.357223, Obj: 0.025973, No Obj: 0.003048, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.208774, iou_loss = 0.024504, total_loss = 2.233278 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.498103, GIOU: 0.496192), Class: 0.525970, Obj: 0.001699, No Obj: 0.001103, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.330219, iou_loss = 0.481221, total_loss = 0.811439 \n",
" total_bbox = 29365, rewritten_bbox = 0.262217 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.445945, GIOU: 0.384920), Class: 0.593541, Obj: 0.009603, No Obj: 0.003132, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.188612, iou_loss = 0.197072, total_loss = 2.385684 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.285698, GIOU: 0.178190), Class: 0.424035, Obj: 0.001896, No Obj: 0.001098, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.755611, iou_loss = 1.486614, total_loss = 2.242225 \n",
" total_bbox = 29373, rewritten_bbox = 0.262146 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.419988, GIOU: 0.349222), Class: 0.484162, Obj: 0.006390, No Obj: 0.002863, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.532001, iou_loss = 0.024125, total_loss = 1.556127 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001083, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003797, iou_loss = 0.000000, total_loss = 0.003797 \n",
" total_bbox = 29377, rewritten_bbox = 0.262110 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.417593, GIOU: 0.377497), Class: 0.417419, Obj: 0.031253, No Obj: 0.003081, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.208324, iou_loss = 0.011678, total_loss = 1.220002 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.123772, GIOU: 0.082072), Class: 0.415258, Obj: 0.001259, No Obj: 0.001121, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.279276, iou_loss = 4.111019, total_loss = 5.390295 \n",
" total_bbox = 29383, rewritten_bbox = 0.262056 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.341754, GIOU: 0.180184), Class: 0.597185, Obj: 0.028860, No Obj: 0.003143, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.166749, iou_loss = 0.005414, total_loss = 1.172162 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001168, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004379, iou_loss = 0.000000, total_loss = 0.004379 \n",
" total_bbox = 29386, rewritten_bbox = 0.262030 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.540870, GIOU: 0.508116), Class: 0.386498, Obj: 0.018406, No Obj: 0.002932, .5R: 1.000000, .75R: 0.000000, count: 4, class_loss = 1.667852, iou_loss = 0.073932, total_loss = 1.741784 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.286401, GIOU: 0.109454), Class: 0.232212, Obj: 0.002556, No Obj: 0.001059, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.498036, iou_loss = 0.044309, total_loss = 0.542345 \n",
" total_bbox = 29391, rewritten_bbox = 0.261985 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.264414, GIOU: 0.257178), Class: 0.537748, Obj: 0.024324, No Obj: 0.003289, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.410864, iou_loss = 0.006510, total_loss = 1.417374 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001040, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003440, iou_loss = 0.000000, total_loss = 0.003440 \n",
" total_bbox = 29395, rewritten_bbox = 0.261949 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.192563, GIOU: 0.086570), Class: 0.603018, Obj: 0.039776, No Obj: 0.002883, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.625308, iou_loss = 0.031815, total_loss = 1.657122 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001148, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.008578, iou_loss = 0.000000, total_loss = 0.008578 \n",
" total_bbox = 29400, rewritten_bbox = 0.261905 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.374968, GIOU: 0.309071), Class: 0.466904, Obj: 0.019050, No Obj: 0.002900, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.140992, iou_loss = 0.020875, total_loss = 1.161867 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.383353, GIOU: 0.246744), Class: 0.575106, Obj: 0.002459, No Obj: 0.001093, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.787390, iou_loss = 1.682972, total_loss = 2.470363 \n",
" total_bbox = 29405, rewritten_bbox = 0.261860 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359420, GIOU: 0.248754), Class: 0.475156, Obj: 0.012616, No Obj: 0.002803, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.960170, iou_loss = 0.041050, total_loss = 2.001220 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.286525, GIOU: 0.091574), Class: 0.376339, Obj: 0.003021, No Obj: 0.001050, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.424581, iou_loss = 0.024492, total_loss = 0.449073 \n",
" total_bbox = 29411, rewritten_bbox = 0.261807 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 368: 0.989590, 0.952204 avg loss, 0.000048 rate, 0.700420 seconds, 23552 images, 0.733635 hours left\n",
"Loaded: 0.000036 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308322, GIOU: 0.291155), Class: 0.589305, Obj: 0.024259, No Obj: 0.002887, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.016682, iou_loss = 0.058720, total_loss = 2.075402 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001106, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004352, iou_loss = 0.000000, total_loss = 0.004352 \n",
" total_bbox = 29417, rewritten_bbox = 0.261753 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.340923, GIOU: 0.271586), Class: 0.346833, Obj: 0.029189, No Obj: 0.003510, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.753637, iou_loss = 0.021441, total_loss = 1.775078 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.361152, GIOU: 0.348538), Class: 0.506983, Obj: 0.001115, No Obj: 0.001246, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.370388, iou_loss = 0.115126, total_loss = 0.485513 \n",
" total_bbox = 29422, rewritten_bbox = 0.261709 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.311887, GIOU: 0.311696), Class: 0.287149, Obj: 0.018864, No Obj: 0.003117, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.823538, iou_loss = 0.002960, total_loss = 0.826498 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.550025, GIOU: 0.492343), Class: 0.578624, Obj: 0.001974, No Obj: 0.001100, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.673667, iou_loss = 0.357810, total_loss = 1.031477 \n",
" total_bbox = 29426, rewritten_bbox = 0.261673 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.255165, GIOU: 0.175498), Class: 0.486930, Obj: 0.010565, No Obj: 0.002618, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.782195, iou_loss = 0.057515, total_loss = 1.839709 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001135, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005117, iou_loss = 0.000000, total_loss = 0.005117 \n",
" total_bbox = 29431, rewritten_bbox = 0.261629 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.384147, GIOU: 0.356661), Class: 0.510212, Obj: 0.033048, No Obj: 0.003106, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.458196, iou_loss = 0.016958, total_loss = 1.475154 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001066, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003982, iou_loss = 0.000000, total_loss = 0.003982 \n",
" total_bbox = 29435, rewritten_bbox = 0.261593 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.331087, GIOU: 0.320853), Class: 0.574613, Obj: 0.028528, No Obj: 0.003311, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.773651, iou_loss = 0.118511, total_loss = 1.892161 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001186, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004257, iou_loss = 0.000000, total_loss = 0.004257 \n",
" total_bbox = 29440, rewritten_bbox = 0.261549 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.267866, GIOU: 0.124895), Class: 0.462044, Obj: 0.013668, No Obj: 0.002884, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 3.003158, iou_loss = 0.038181, total_loss = 3.041338 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001117, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004119, iou_loss = 0.000000, total_loss = 0.004119 \n",
" total_bbox = 29447, rewritten_bbox = 0.261487 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.483773, GIOU: 0.453336), Class: 0.354207, Obj: 0.062060, No Obj: 0.003192, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.181214, iou_loss = 0.023660, total_loss = 1.204874 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001124, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004348, iou_loss = 0.000000, total_loss = 0.004348 \n",
" total_bbox = 29450, rewritten_bbox = 0.261460 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.254015, GIOU: 0.009001), Class: 0.409269, Obj: 0.030367, No Obj: 0.003425, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.384586, iou_loss = 0.020137, total_loss = 2.404723 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.585104, GIOU: 0.559249), Class: 0.704245, Obj: 0.003232, No Obj: 0.001140, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.333894, iou_loss = 0.292064, total_loss = 0.625958 \n",
" total_bbox = 29457, rewritten_bbox = 0.261398 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.227347, GIOU: 0.174628), Class: 0.597211, Obj: 0.011276, No Obj: 0.002418, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.412053, iou_loss = 0.025001, total_loss = 1.437054 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001058, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003853, iou_loss = 0.000000, total_loss = 0.003853 \n",
" total_bbox = 29461, rewritten_bbox = 0.261362 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.303337, GIOU: 0.303337), Class: 0.383455, Obj: 0.048324, No Obj: 0.003077, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.787556, iou_loss = 0.004209, total_loss = 0.791765 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.490767, GIOU: 0.482192), Class: 0.602792, Obj: 0.010359, No Obj: 0.001070, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.688954, iou_loss = 0.783218, total_loss = 1.472172 \n",
" total_bbox = 29465, rewritten_bbox = 0.261327 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413458, GIOU: 0.289556), Class: 0.425569, Obj: 0.013296, No Obj: 0.002880, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.538253, iou_loss = 0.149452, total_loss = 1.687705 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001145, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004209, iou_loss = 0.000000, total_loss = 0.004209 \n",
" total_bbox = 29469, rewritten_bbox = 0.261292 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.261722, GIOU: 0.098986), Class: 0.468104, Obj: 0.042207, No Obj: 0.003013, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.420801, iou_loss = 0.020521, total_loss = 1.441321 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001035, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003423, iou_loss = 0.000000, total_loss = 0.003423 \n",
" total_bbox = 29473, rewritten_bbox = 0.261256 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402546, GIOU: 0.268997), Class: 0.566490, Obj: 0.047809, No Obj: 0.003287, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.800539, iou_loss = 0.047177, total_loss = 1.847716 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.194560, GIOU: -0.116989), Class: 0.565889, Obj: 0.002787, No Obj: 0.001179, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.707528, iou_loss = 0.865990, total_loss = 1.573518 \n",
" total_bbox = 29480, rewritten_bbox = 0.261194 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.503807, GIOU: 0.502006), Class: 0.304095, Obj: 0.020443, No Obj: 0.002880, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.638889, iou_loss = 0.249349, total_loss = 1.888238 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.301777, GIOU: 0.198792), Class: 0.588436, Obj: 0.004617, No Obj: 0.001114, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.724055, iou_loss = 0.849883, total_loss = 1.573938 \n",
" total_bbox = 29486, rewritten_bbox = 0.261141 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.313059, GIOU: 0.201079), Class: 0.560143, Obj: 0.018820, No Obj: 0.003024, .5R: 0.125000, .75R: 0.000000, count: 8, class_loss = 2.964318, iou_loss = 0.082387, total_loss = 3.046705 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001108, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003982, iou_loss = 0.000000, total_loss = 0.003982 \n",
" total_bbox = 29494, rewritten_bbox = 0.261070 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 369: 0.985222, 0.955506 avg loss, 0.000048 rate, 0.694538 seconds, 23616 images, 0.733366 hours left\n",
"Loaded: 0.000031 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.520604, GIOU: 0.480288), Class: 0.492619, Obj: 0.056118, No Obj: 0.003295, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.371045, iou_loss = 0.035502, total_loss = 1.406547 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.246042, GIOU: 0.166435), Class: 0.621237, Obj: 0.001495, No Obj: 0.001042, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.393197, iou_loss = 0.084159, total_loss = 0.477355 \n",
" total_bbox = 29499, rewritten_bbox = 0.261026 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421658, GIOU: 0.384605), Class: 0.549850, Obj: 0.022463, No Obj: 0.002936, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.796328, iou_loss = 0.178596, total_loss = 1.974923 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001045, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003456, iou_loss = 0.000000, total_loss = 0.003456 \n",
" total_bbox = 29504, rewritten_bbox = 0.260982 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.354823, GIOU: 0.341573), Class: 0.631159, Obj: 0.013152, No Obj: 0.002431, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.394095, iou_loss = 0.062560, total_loss = 1.456655 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.206572, GIOU: 0.047118), Class: 0.486028, Obj: 0.003134, No Obj: 0.001105, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.579143, iou_loss = 0.116480, total_loss = 1.695623 \n",
" total_bbox = 29512, rewritten_bbox = 0.260911 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415357, GIOU: 0.332803), Class: 0.464895, Obj: 0.023074, No Obj: 0.003050, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.518202, iou_loss = 0.097382, total_loss = 2.615584 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.451806, GIOU: 0.451806), Class: 0.386911, Obj: 0.001540, No Obj: 0.001330, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.446213, iou_loss = 0.177953, total_loss = 0.624166 \n",
" total_bbox = 29519, rewritten_bbox = 0.260849 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.419499, GIOU: 0.387374), Class: 0.534009, Obj: 0.021896, No Obj: 0.002829, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.621251, iou_loss = 0.012288, total_loss = 1.633539 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.219398, GIOU: -0.185358), Class: 0.442580, Obj: 0.000884, No Obj: 0.001047, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.379513, iou_loss = 0.014687, total_loss = 0.394200 \n",
" total_bbox = 29524, rewritten_bbox = 0.260805 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392076, GIOU: 0.392062), Class: 0.389001, Obj: 0.011515, No Obj: 0.002916, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.492235, iou_loss = 0.053817, total_loss = 1.546052 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001037, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003827, iou_loss = 0.000000, total_loss = 0.003827 \n",
" total_bbox = 29528, rewritten_bbox = 0.260769 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403339, GIOU: 0.200311), Class: 0.526897, Obj: 0.012431, No Obj: 0.002693, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.078955, iou_loss = 0.056306, total_loss = 1.135262 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.504985, GIOU: 0.446682), Class: 0.316727, Obj: 0.003445, No Obj: 0.001148, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.443087, iou_loss = 0.651097, total_loss = 1.094185 \n",
" total_bbox = 29532, rewritten_bbox = 0.260734 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.379336, GIOU: 0.314037), Class: 0.511376, Obj: 0.027103, No Obj: 0.002749, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.604269, iou_loss = 0.014565, total_loss = 1.618834 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001056, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003491, iou_loss = 0.000000, total_loss = 0.003491 \n",
" total_bbox = 29536, rewritten_bbox = 0.260699 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444298, GIOU: 0.389514), Class: 0.522187, Obj: 0.030476, No Obj: 0.002564, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.481337, iou_loss = 0.064180, total_loss = 1.545517 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001127, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004155, iou_loss = 0.000000, total_loss = 0.004155 \n",
" total_bbox = 29540, rewritten_bbox = 0.260664 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359930, GIOU: 0.296226), Class: 0.594848, Obj: 0.018259, No Obj: 0.002652, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.039318, iou_loss = 0.009434, total_loss = 1.048752 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.373044, GIOU: 0.272388), Class: 0.505823, Obj: 0.020041, No Obj: 0.001074, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.433379, iou_loss = 0.081033, total_loss = 0.514411 \n",
" total_bbox = 29544, rewritten_bbox = 0.260628 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.317535, GIOU: 0.116412), Class: 0.666386, Obj: 0.034021, No Obj: 0.003160, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.401992, iou_loss = 0.021059, total_loss = 1.423051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001150, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004914, iou_loss = 0.000000, total_loss = 0.004914 \n",
" total_bbox = 29548, rewritten_bbox = 0.260593 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.213187, GIOU: 0.144813), Class: 0.467879, Obj: 0.039672, No Obj: 0.003096, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.415692, iou_loss = 0.042904, total_loss = 1.458597 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.259793, GIOU: 0.100840), Class: 0.371742, Obj: 0.003366, No Obj: 0.001118, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.817332, iou_loss = 1.859921, total_loss = 2.677253 \n",
" total_bbox = 29554, rewritten_bbox = 0.260540 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.257910, GIOU: -0.048642), Class: 0.457408, Obj: 0.025001, No Obj: 0.003348, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.850314, iou_loss = 0.044783, total_loss = 1.895097 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001179, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005144, iou_loss = 0.000000, total_loss = 0.005144 \n",
" total_bbox = 29559, rewritten_bbox = 0.260496 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359197, GIOU: 0.283638), Class: 0.484747, Obj: 0.013435, No Obj: 0.002831, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.851624, iou_loss = 0.094295, total_loss = 1.945919 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.244534, GIOU: 0.216569), Class: 0.571357, Obj: 0.002244, No Obj: 0.001018, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.329903, iou_loss = 0.017191, total_loss = 0.347094 \n",
" total_bbox = 29565, rewritten_bbox = 0.260443 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395153, GIOU: 0.392883), Class: 0.501927, Obj: 0.029166, No Obj: 0.003425, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.793842, iou_loss = 0.055948, total_loss = 1.849790 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.133229, GIOU: 0.133229), Class: 0.514447, Obj: 0.002768, No Obj: 0.001081, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.349963, iou_loss = 0.017417, total_loss = 0.367380 \n",
" total_bbox = 29571, rewritten_bbox = 0.260390 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.549724, GIOU: 0.486038), Class: 0.532400, Obj: 0.019509, No Obj: 0.002677, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.115215, iou_loss = 0.016772, total_loss = 1.131987 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.588596, GIOU: 0.523720), Class: 0.387367, Obj: 0.004409, No Obj: 0.001069, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.388618, iou_loss = 0.183188, total_loss = 0.571806 \n",
" total_bbox = 29575, rewritten_bbox = 0.260355 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 370: 0.959406, 0.955896 avg loss, 0.000049 rate, 0.687225 seconds, 23680 images, 0.733038 hours left\n",
"Loaded: 0.000043 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410664, GIOU: 0.407707), Class: 0.583272, Obj: 0.033743, No Obj: 0.002834, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.111721, iou_loss = 0.037884, total_loss = 1.149605 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001143, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004631, iou_loss = 0.000000, total_loss = 0.004631 \n",
" total_bbox = 29578, rewritten_bbox = 0.260329 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.719112, GIOU: 0.698560), Class: 0.429120, Obj: 0.024448, No Obj: 0.003082, .5R: 1.000000, .75R: 0.666667, count: 3, class_loss = 1.181697, iou_loss = 0.061625, total_loss = 1.243322 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.207027, GIOU: -0.158484), Class: 0.547803, Obj: 0.000607, No Obj: 0.001130, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.711164, iou_loss = 0.074690, total_loss = 0.785854 \n",
" total_bbox = 29583, rewritten_bbox = 0.260285 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.408264, GIOU: 0.379512), Class: 0.348537, Obj: 0.021043, No Obj: 0.002887, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.680708, iou_loss = 0.009680, total_loss = 1.690387 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.559902, GIOU: 0.521943), Class: 0.406169, Obj: 0.001824, No Obj: 0.001025, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.466558, iou_loss = 0.221555, total_loss = 0.688113 \n",
" total_bbox = 29588, rewritten_bbox = 0.260241 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.660122, GIOU: 0.652441), Class: 0.561248, Obj: 0.023024, No Obj: 0.003343, .5R: 1.000000, .75R: 0.500000, count: 4, class_loss = 1.324261, iou_loss = 0.054094, total_loss = 1.378354 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001103, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004251, iou_loss = 0.000000, total_loss = 0.004251 \n",
" total_bbox = 29592, rewritten_bbox = 0.260205 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.479131, GIOU: 0.385142), Class: 0.591695, Obj: 0.011684, No Obj: 0.002818, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.709367, iou_loss = 0.068891, total_loss = 1.778258 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001115, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005198, iou_loss = 0.000000, total_loss = 0.005198 \n",
" total_bbox = 29597, rewritten_bbox = 0.260161 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.376468, GIOU: 0.140404), Class: 0.601157, Obj: 0.015039, No Obj: 0.002796, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.862139, iou_loss = 0.088446, total_loss = 1.950585 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001245, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004900, iou_loss = 0.000000, total_loss = 0.004900 \n",
" total_bbox = 29602, rewritten_bbox = 0.260118 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.387582, GIOU: 0.227746), Class: 0.422724, Obj: 0.015594, No Obj: 0.003029, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.585361, iou_loss = 0.021938, total_loss = 1.607299 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.449411, GIOU: 0.414905), Class: 0.373401, Obj: 0.004423, No Obj: 0.001090, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.429318, iou_loss = 0.133016, total_loss = 0.562334 \n",
" total_bbox = 29607, rewritten_bbox = 0.260074 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370269, GIOU: 0.298928), Class: 0.404783, Obj: 0.032099, No Obj: 0.002971, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.559212, iou_loss = 0.014435, total_loss = 1.573647 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001029, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003546, iou_loss = 0.000000, total_loss = 0.003546 \n",
" total_bbox = 29611, rewritten_bbox = 0.260038 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.209043, GIOU: 0.209043), Class: 0.518555, Obj: 0.001825, No Obj: 0.002930, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.125553, iou_loss = 0.005535, total_loss = 1.131088 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.208350, GIOU: -0.065478), Class: 0.331680, Obj: 0.001163, No Obj: 0.001002, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.384472, iou_loss = 0.191293, total_loss = 0.575765 \n",
" total_bbox = 29616, rewritten_bbox = 0.263371 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307087, GIOU: 0.256786), Class: 0.403638, Obj: 0.012460, No Obj: 0.002972, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.885774, iou_loss = 0.010699, total_loss = 1.896473 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001076, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003947, iou_loss = 0.000000, total_loss = 0.003947 \n",
" total_bbox = 29621, rewritten_bbox = 0.263327 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308047, GIOU: 0.249380), Class: 0.481789, Obj: 0.027360, No Obj: 0.002795, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.022685, iou_loss = 0.063678, total_loss = 2.086364 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.208421, GIOU: 0.208421), Class: 0.602537, Obj: 0.001351, No Obj: 0.001047, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.373991, iou_loss = 0.037888, total_loss = 0.411879 \n",
" total_bbox = 29627, rewritten_bbox = 0.263273 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.360447, GIOU: 0.264854), Class: 0.580109, Obj: 0.011696, No Obj: 0.002445, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.824801, iou_loss = 0.067201, total_loss = 1.892002 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.590708, GIOU: 0.590709), Class: 0.424426, Obj: 0.001664, No Obj: 0.001053, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.384631, iou_loss = 0.304102, total_loss = 0.688733 \n",
" total_bbox = 29633, rewritten_bbox = 0.263220 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.578162, GIOU: 0.511996), Class: 0.575989, Obj: 0.009700, No Obj: 0.002933, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.088379, iou_loss = 0.012818, total_loss = 1.101197 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001126, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004548, iou_loss = 0.000000, total_loss = 0.004548 \n",
" total_bbox = 29636, rewritten_bbox = 0.263193 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382485, GIOU: 0.300928), Class: 0.355333, Obj: 0.041108, No Obj: 0.003016, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.559670, iou_loss = 0.011254, total_loss = 1.570925 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001074, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003749, iou_loss = 0.000000, total_loss = 0.003749 \n",
" total_bbox = 29640, rewritten_bbox = 0.263158 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353796, GIOU: 0.353796), Class: 0.592907, Obj: 0.015382, No Obj: 0.003155, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.056488, iou_loss = 0.056687, total_loss = 1.113175 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.391321, GIOU: 0.271420), Class: 0.580342, Obj: 0.002597, No Obj: 0.001198, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.764702, iou_loss = 0.230032, total_loss = 0.994733 \n",
" total_bbox = 29645, rewritten_bbox = 0.263113 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.304034, GIOU: 0.118976), Class: 0.397280, Obj: 0.013106, No Obj: 0.002543, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 2.175343, iou_loss = 0.101867, total_loss = 2.277210 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.363525, GIOU: 0.306501), Class: 0.595037, Obj: 0.002865, No Obj: 0.001063, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.695471, iou_loss = 1.820072, total_loss = 2.515543 \n",
" total_bbox = 29652, rewritten_bbox = 0.263051 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 371: 0.914073, 0.951714 avg loss, 0.000049 rate, 0.728746 seconds, 23744 images, 0.732637 hours left\n",
"Loaded: 0.000054 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.475769, GIOU: 0.455645), Class: 0.500654, Obj: 0.078079, No Obj: 0.003203, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.490833, iou_loss = 0.037957, total_loss = 1.528790 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.326163, GIOU: 0.212933), Class: 0.573731, Obj: 0.018411, No Obj: 0.001061, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.349627, iou_loss = 0.061530, total_loss = 0.411157 \n",
" total_bbox = 29657, rewritten_bbox = 0.263007 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.333370, GIOU: 0.165414), Class: 0.545381, Obj: 0.052399, No Obj: 0.002895, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.057693, iou_loss = 0.068605, total_loss = 1.126298 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.381720, GIOU: 0.264298), Class: 0.437996, Obj: 0.005926, No Obj: 0.001055, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.701987, iou_loss = 0.733737, total_loss = 2.435724 \n",
" total_bbox = 29664, rewritten_bbox = 0.262945 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.304912, GIOU: 0.187944), Class: 0.521198, Obj: 0.016514, No Obj: 0.003264, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.748458, iou_loss = 0.022952, total_loss = 1.771410 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001249, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.007335, iou_loss = 0.000000, total_loss = 0.007335 \n",
" total_bbox = 29669, rewritten_bbox = 0.262901 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.362224, GIOU: 0.277875), Class: 0.576972, Obj: 0.011387, No Obj: 0.002721, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.737965, iou_loss = 0.052731, total_loss = 1.790696 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001105, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005305, iou_loss = 0.000000, total_loss = 0.005305 \n",
" total_bbox = 29674, rewritten_bbox = 0.262856 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.509619, GIOU: 0.479050), Class: 0.549900, Obj: 0.037674, No Obj: 0.003105, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.068082, iou_loss = 0.079927, total_loss = 1.148009 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.365598, GIOU: 0.365598), Class: 0.578547, Obj: 0.002245, No Obj: 0.001101, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354619, iou_loss = 0.115908, total_loss = 0.470527 \n",
" total_bbox = 29678, rewritten_bbox = 0.262821 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.500400, GIOU: 0.393178), Class: 0.656265, Obj: 0.015653, No Obj: 0.003025, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.728227, iou_loss = 0.047645, total_loss = 1.775872 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001048, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004299, iou_loss = 0.000000, total_loss = 0.004299 \n",
" total_bbox = 29683, rewritten_bbox = 0.262777 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.265895, GIOU: 0.236562), Class: 0.501775, Obj: 0.018227, No Obj: 0.002765, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.178026, iou_loss = 0.010176, total_loss = 1.188202 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.296510, GIOU: 0.152933), Class: 0.492557, Obj: 0.016189, No Obj: 0.001043, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.355074, iou_loss = 0.026531, total_loss = 0.381605 \n",
" total_bbox = 29687, rewritten_bbox = 0.262741 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402075, GIOU: 0.310303), Class: 0.437125, Obj: 0.052492, No Obj: 0.002913, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.161608, iou_loss = 0.020227, total_loss = 1.181835 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.264705, GIOU: 0.040853), Class: 0.492154, Obj: 0.011125, No Obj: 0.001122, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.743821, iou_loss = 0.047740, total_loss = 0.791561 \n",
" total_bbox = 29692, rewritten_bbox = 0.262697 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402902, GIOU: 0.291978), Class: 0.473778, Obj: 0.053286, No Obj: 0.003197, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.091776, iou_loss = 0.097167, total_loss = 1.188943 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001280, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.006972, iou_loss = 0.000000, total_loss = 0.006972 \n",
" total_bbox = 29695, rewritten_bbox = 0.262670 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.329806, GIOU: 0.329806), Class: 0.413518, Obj: 0.005650, No Obj: 0.002805, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.792871, iou_loss = 0.006024, total_loss = 0.798895 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.267577, GIOU: -0.005361), Class: 0.380540, Obj: 0.001669, No Obj: 0.001060, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.458018, iou_loss = 0.033434, total_loss = 0.491452 \n",
" total_bbox = 29698, rewritten_bbox = 0.262644 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.269794, GIOU: 0.073699), Class: 0.451809, Obj: 0.074805, No Obj: 0.003439, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.102886, iou_loss = 0.011369, total_loss = 1.114255 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.242047, GIOU: -0.015184), Class: 0.453119, Obj: 0.002631, No Obj: 0.001132, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.319381, iou_loss = 3.996743, total_loss = 5.316124 \n",
" total_bbox = 29704, rewritten_bbox = 0.262591 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.192321, GIOU: 0.190815), Class: 0.551473, Obj: 0.022202, No Obj: 0.003116, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.354981, iou_loss = 0.028940, total_loss = 1.383921 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001114, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003967, iou_loss = 0.000000, total_loss = 0.003967 \n",
" total_bbox = 29708, rewritten_bbox = 0.262556 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.196582, GIOU: 0.071546), Class: 0.430251, Obj: 0.026019, No Obj: 0.002773, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.802719, iou_loss = 0.004184, total_loss = 0.806903 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.263714, GIOU: 0.037500), Class: 0.529885, Obj: 0.014828, No Obj: 0.001082, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.711173, iou_loss = 0.102294, total_loss = 0.813468 \n",
" total_bbox = 29712, rewritten_bbox = 0.262520 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.338820, GIOU: 0.306907), Class: 0.545832, Obj: 0.063801, No Obj: 0.003043, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.398282, iou_loss = 0.023490, total_loss = 1.421772 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001062, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003808, iou_loss = 0.000000, total_loss = 0.003808 \n",
" total_bbox = 29716, rewritten_bbox = 0.262485 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.251149, GIOU: 0.204751), Class: 0.396619, Obj: 0.019446, No Obj: 0.003587, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.514556, iou_loss = 0.012294, total_loss = 1.526850 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001104, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004325, iou_loss = 0.000000, total_loss = 0.004325 \n",
" total_bbox = 29720, rewritten_bbox = 0.262450 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.283898, GIOU: 0.141241), Class: 0.530006, Obj: 0.041621, No Obj: 0.002989, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.447549, iou_loss = 0.018835, total_loss = 1.466383 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.326509, GIOU: 0.326509), Class: 0.294894, Obj: 0.003751, No Obj: 0.001103, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.470650, iou_loss = 0.092746, total_loss = 0.563396 \n",
" total_bbox = 29725, rewritten_bbox = 0.262405 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 372: 0.858490, 0.942391 avg loss, 0.000050 rate, 0.684471 seconds, 23808 images, 0.732658 hours left\n",
"Loaded: 0.000058 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352865, GIOU: 0.189579), Class: 0.702712, Obj: 0.029190, No Obj: 0.002827, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.607775, iou_loss = 0.010629, total_loss = 0.618403 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.465983, GIOU: 0.349698), Class: 0.648464, Obj: 0.001797, No Obj: 0.001069, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.084552, iou_loss = 0.977641, total_loss = 2.062192 \n",
" total_bbox = 29730, rewritten_bbox = 0.262361 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.273755, GIOU: 0.073256), Class: 0.582136, Obj: 0.019359, No Obj: 0.002811, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.775466, iou_loss = 0.017914, total_loss = 1.793380 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001120, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003913, iou_loss = 0.000000, total_loss = 0.003913 \n",
" total_bbox = 29735, rewritten_bbox = 0.262317 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292060, GIOU: 0.216011), Class: 0.478113, Obj: 0.004384, No Obj: 0.002705, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.146198, iou_loss = 0.037244, total_loss = 1.183441 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.218590, GIOU: 0.186081), Class: 0.286056, Obj: 0.006191, No Obj: 0.001107, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.488417, iou_loss = 0.030348, total_loss = 0.518765 \n",
" total_bbox = 29739, rewritten_bbox = 0.262282 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.312160, GIOU: 0.268419), Class: 0.566023, Obj: 0.018718, No Obj: 0.003044, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.370461, iou_loss = 0.011540, total_loss = 1.382001 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.521978, GIOU: 0.521978), Class: 0.617527, Obj: 0.002949, No Obj: 0.001128, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.382156, iou_loss = 0.237558, total_loss = 0.619714 \n",
" total_bbox = 29744, rewritten_bbox = 0.262238 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.342567, GIOU: 0.290090), Class: 0.490050, Obj: 0.023014, No Obj: 0.002892, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.538925, iou_loss = 0.076398, total_loss = 2.615323 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000991, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003284, iou_loss = 0.000000, total_loss = 0.003284 \n",
" total_bbox = 29750, rewritten_bbox = 0.262185 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339925, GIOU: 0.282931), Class: 0.441213, Obj: 0.014952, No Obj: 0.003214, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.849050, iou_loss = 0.024918, total_loss = 1.873968 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001025, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003956, iou_loss = 0.000000, total_loss = 0.003956 \n",
" total_bbox = 29755, rewritten_bbox = 0.262141 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.366954, GIOU: 0.331341), Class: 0.484709, Obj: 0.038032, No Obj: 0.003080, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.474130, iou_loss = 0.048545, total_loss = 1.522675 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001073, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003859, iou_loss = 0.000000, total_loss = 0.003859 \n",
" total_bbox = 29759, rewritten_bbox = 0.262106 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.407669, GIOU: 0.298944), Class: 0.453961, Obj: 0.016480, No Obj: 0.002889, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.518986, iou_loss = 0.136885, total_loss = 1.655871 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001092, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003970, iou_loss = 0.000000, total_loss = 0.003970 \n",
" total_bbox = 29763, rewritten_bbox = 0.262070 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.257060, GIOU: 0.157228), Class: 0.443073, Obj: 0.008671, No Obj: 0.002774, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 2.142362, iou_loss = 0.021084, total_loss = 2.163446 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000985, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003055, iou_loss = 0.000000, total_loss = 0.003055 \n",
" total_bbox = 29768, rewritten_bbox = 0.262026 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.446334, GIOU: 0.427090), Class: 0.502936, Obj: 0.012748, No Obj: 0.003300, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.397492, iou_loss = 0.067286, total_loss = 1.464778 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001019, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003431, iou_loss = 0.000000, total_loss = 0.003431 \n",
" total_bbox = 29772, rewritten_bbox = 0.261991 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422712, GIOU: 0.397216), Class: 0.660217, Obj: 0.016963, No Obj: 0.002753, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.102722, iou_loss = 0.105428, total_loss = 2.208150 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001053, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004204, iou_loss = 0.000000, total_loss = 0.004204 \n",
" total_bbox = 29778, rewritten_bbox = 0.261938 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.423259, GIOU: 0.330939), Class: 0.553871, Obj: 0.045488, No Obj: 0.002854, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.490790, iou_loss = 0.033985, total_loss = 1.524776 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.436154, GIOU: 0.347531), Class: 0.481673, Obj: 0.003015, No Obj: 0.001006, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.798246, iou_loss = 0.341810, total_loss = 1.140057 \n",
" total_bbox = 29784, rewritten_bbox = 0.261886 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.338262, GIOU: 0.307120), Class: 0.658487, Obj: 0.018870, No Obj: 0.003030, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.649366, iou_loss = 0.012569, total_loss = 0.661935 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.418318, GIOU: 0.361527), Class: 0.486836, Obj: 0.001317, No Obj: 0.001017, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.171893, iou_loss = 0.436841, total_loss = 1.608734 \n",
" total_bbox = 29789, rewritten_bbox = 0.261842 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.505629, GIOU: 0.461827), Class: 0.451319, Obj: 0.030718, No Obj: 0.002714, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.427937, iou_loss = 0.045458, total_loss = 1.473395 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.161227, GIOU: -0.360409), Class: 0.282804, Obj: 0.000847, No Obj: 0.000987, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.469647, iou_loss = 0.007803, total_loss = 0.477450 \n",
" total_bbox = 29794, rewritten_bbox = 0.261798 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.251931, GIOU: 0.163175), Class: 0.537603, Obj: 0.027258, No Obj: 0.002684, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.082959, iou_loss = 0.016492, total_loss = 1.099452 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.350625, GIOU: 0.350292), Class: 0.424962, Obj: 0.005446, No Obj: 0.001026, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.772868, iou_loss = 0.402037, total_loss = 1.174905 \n",
" total_bbox = 29799, rewritten_bbox = 0.261754 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.291348, GIOU: 0.266473), Class: 0.604352, Obj: 0.028442, No Obj: 0.002916, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.387945, iou_loss = 0.018729, total_loss = 1.406675 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.268624, GIOU: -0.032618), Class: 0.516524, Obj: 0.002971, No Obj: 0.001012, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.716089, iou_loss = 0.158764, total_loss = 0.874853 \n",
" total_bbox = 29805, rewritten_bbox = 0.261701 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 373: 0.942123, 0.942365 avg loss, 0.000051 rate, 0.686167 seconds, 23872 images, 0.732230 hours left\n",
"Loaded: 0.000062 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.458506, GIOU: 0.330021), Class: 0.777474, Obj: 0.011751, No Obj: 0.003192, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.772745, iou_loss = 0.031805, total_loss = 0.804550 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.337522, GIOU: 0.267991), Class: 0.580897, Obj: 0.011427, No Obj: 0.001093, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.755561, iou_loss = 0.477694, total_loss = 1.233256 \n",
" total_bbox = 29809, rewritten_bbox = 0.261666 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.419272, GIOU: 0.216023), Class: 0.500322, Obj: 0.034515, No Obj: 0.003239, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.470257, iou_loss = 0.055444, total_loss = 1.525701 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.143031, GIOU: 0.143030), Class: 0.686585, Obj: 0.001374, No Obj: 0.001120, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.440229, iou_loss = 0.017902, total_loss = 0.458130 \n",
" total_bbox = 29814, rewritten_bbox = 0.261622 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429398, GIOU: 0.303216), Class: 0.542778, Obj: 0.030137, No Obj: 0.002976, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.711669, iou_loss = 0.051689, total_loss = 1.763358 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.616412, GIOU: 0.592626), Class: 0.518491, Obj: 0.001163, No Obj: 0.001028, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.341840, iou_loss = 0.777276, total_loss = 1.119116 \n",
" total_bbox = 29820, rewritten_bbox = 0.261569 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325566, GIOU: 0.235283), Class: 0.553161, Obj: 0.009413, No Obj: 0.002991, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.508460, iou_loss = 0.039591, total_loss = 1.548051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001020, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003330, iou_loss = 0.000000, total_loss = 0.003330 \n",
" total_bbox = 29824, rewritten_bbox = 0.261534 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.376499, GIOU: 0.346174), Class: 0.515138, Obj: 0.020784, No Obj: 0.002917, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.487145, iou_loss = 0.041701, total_loss = 1.528846 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001015, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003430, iou_loss = 0.000000, total_loss = 0.003430 \n",
" total_bbox = 29828, rewritten_bbox = 0.261499 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.405561, GIOU: 0.404629), Class: 0.275874, Obj: 0.007564, No Obj: 0.002457, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.715125, iou_loss = 0.017244, total_loss = 1.732368 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000980, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003022, iou_loss = 0.000000, total_loss = 0.003022 \n",
" total_bbox = 29832, rewritten_bbox = 0.261464 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.356362, GIOU: 0.286008), Class: 0.505710, Obj: 0.062609, No Obj: 0.002937, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.249332, iou_loss = 0.452909, total_loss = 2.702241 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001049, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003747, iou_loss = 0.000000, total_loss = 0.003747 \n",
" total_bbox = 29838, rewritten_bbox = 0.261412 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429916, GIOU: 0.306107), Class: 0.600712, Obj: 0.016267, No Obj: 0.002773, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.054758, iou_loss = 0.087733, total_loss = 1.142491 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.480410, GIOU: 0.480410), Class: 0.421401, Obj: 0.008308, No Obj: 0.001019, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.422635, iou_loss = 0.200490, total_loss = 0.623125 \n",
" total_bbox = 29842, rewritten_bbox = 0.261377 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.579720, GIOU: 0.561196), Class: 0.472917, Obj: 0.025102, No Obj: 0.003022, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.610462, iou_loss = 0.064497, total_loss = 1.674959 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001096, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004071, iou_loss = 0.000000, total_loss = 0.004071 \n",
" total_bbox = 29846, rewritten_bbox = 0.261342 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409807, GIOU: 0.407329), Class: 0.397207, Obj: 0.018316, No Obj: 0.003524, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.632625, iou_loss = 0.015426, total_loss = 1.648051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001025, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003479, iou_loss = 0.000000, total_loss = 0.003479 \n",
" total_bbox = 29850, rewritten_bbox = 0.261307 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.318421, GIOU: 0.252578), Class: 0.566778, Obj: 0.010606, No Obj: 0.002720, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 2.477393, iou_loss = 0.040154, total_loss = 2.517548 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.202015, GIOU: -0.238286), Class: 0.456795, Obj: 0.000648, No Obj: 0.000983, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.350134, iou_loss = 0.013727, total_loss = 0.363861 \n",
" total_bbox = 29858, rewritten_bbox = 0.261237 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.186138, GIOU: 0.039863), Class: 0.531050, Obj: 0.019228, No Obj: 0.002882, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.576773, iou_loss = 0.014848, total_loss = 1.591621 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001161, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005052, iou_loss = 0.000000, total_loss = 0.005052 \n",
" total_bbox = 29862, rewritten_bbox = 0.261202 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.539184, GIOU: 0.532570), Class: 0.546327, Obj: 0.034439, No Obj: 0.002712, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.096901, iou_loss = 0.058307, total_loss = 1.155208 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.486276, GIOU: 0.461022), Class: 0.711924, Obj: 0.005239, No Obj: 0.001097, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.322163, iou_loss = 0.135040, total_loss = 0.457203 \n",
" total_bbox = 29866, rewritten_bbox = 0.261167 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.226094, GIOU: 0.129833), Class: 0.385880, Obj: 0.046629, No Obj: 0.002948, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.870589, iou_loss = 0.051081, total_loss = 1.921670 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.186718, GIOU: -0.202499), Class: 0.439918, Obj: 0.000623, No Obj: 0.000975, .5R: 0.125000, .75R: 0.000000, count: 8, class_loss = 2.658647, iou_loss = 6.014377, total_loss = 8.673024 \n",
" total_bbox = 29879, rewritten_bbox = 0.264400 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.267811, GIOU: 0.111283), Class: 0.406929, Obj: 0.019450, No Obj: 0.002849, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.674582, iou_loss = 0.012136, total_loss = 2.686719 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001066, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003460, iou_loss = 0.000000, total_loss = 0.003460 \n",
" total_bbox = 29886, rewritten_bbox = 0.264338 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.473660, GIOU: 0.458950), Class: 0.410709, Obj: 0.017983, No Obj: 0.002697, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.186927, iou_loss = 0.074002, total_loss = 1.260930 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.420689, GIOU: 0.420689), Class: 0.524832, Obj: 0.001031, No Obj: 0.001034, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.356808, iou_loss = 0.152285, total_loss = 0.509092 \n",
" total_bbox = 29890, rewritten_bbox = 0.264302 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 374: 1.001079, 0.948236 avg loss, 0.000051 rate, 0.699613 seconds, 23936 images, 0.731821 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.384674, GIOU: 0.384674), Class: 0.502153, Obj: 0.025337, No Obj: 0.002475, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.174276, iou_loss = 0.026052, total_loss = 1.200328 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.426646, GIOU: 0.359887), Class: 0.459461, Obj: 0.000791, No Obj: 0.000987, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.712565, iou_loss = 0.207822, total_loss = 0.920386 \n",
" total_bbox = 29895, rewritten_bbox = 0.264258 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300799, GIOU: 0.295336), Class: 0.477003, Obj: 0.048730, No Obj: 0.003177, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.406078, iou_loss = 0.018306, total_loss = 1.424384 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.274230, GIOU: 0.274230), Class: 0.556013, Obj: 0.003079, No Obj: 0.001048, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.377749, iou_loss = 0.065524, total_loss = 0.443273 \n",
" total_bbox = 29900, rewritten_bbox = 0.264214 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.301308, GIOU: 0.301308), Class: 0.417394, Obj: 0.015456, No Obj: 0.002959, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.059457, iou_loss = 0.031116, total_loss = 2.090574 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.406798, GIOU: 0.352110), Class: 0.341522, Obj: 0.002395, No Obj: 0.000995, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.444885, iou_loss = 0.144741, total_loss = 0.589626 \n",
" total_bbox = 29906, rewritten_bbox = 0.264161 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.362073, GIOU: 0.293421), Class: 0.480962, Obj: 0.035652, No Obj: 0.003044, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.614811, iou_loss = 0.033723, total_loss = 1.648535 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.309521, GIOU: 0.270468), Class: 0.439520, Obj: 0.008933, No Obj: 0.000984, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.399516, iou_loss = 0.228470, total_loss = 0.627986 \n",
" total_bbox = 29911, rewritten_bbox = 0.264117 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.211133, GIOU: 0.144020), Class: 0.780405, Obj: 0.018554, No Obj: 0.002955, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.887537, iou_loss = 0.006517, total_loss = 0.894054 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.835438), Class: 0.448975, Obj: 0.001656, No Obj: 0.001035, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.386988, iou_loss = 0.069289, total_loss = 0.456277 \n",
" total_bbox = 29915, rewritten_bbox = 0.264082 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.290277, GIOU: 0.119460), Class: 0.420239, Obj: 0.007711, No Obj: 0.002587, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.178633, iou_loss = 0.004856, total_loss = 1.183489 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.297178, GIOU: 0.182953), Class: 0.552512, Obj: 0.001286, No Obj: 0.001053, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.060065, iou_loss = 2.886536, total_loss = 3.946600 \n",
" total_bbox = 29921, rewritten_bbox = 0.264029 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.290076, GIOU: 0.244830), Class: 0.588670, Obj: 0.024599, No Obj: 0.003077, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.394064, iou_loss = 0.034993, total_loss = 1.429057 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.420776, GIOU: 0.402101), Class: 0.563506, Obj: 0.015368, No Obj: 0.001068, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.713902, iou_loss = 0.240998, total_loss = 0.954901 \n",
" total_bbox = 29927, rewritten_bbox = 0.263976 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.285928, GIOU: 0.218672), Class: 0.501144, Obj: 0.019075, No Obj: 0.003160, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.285955, iou_loss = 0.033247, total_loss = 2.319201 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001106, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004117, iou_loss = 0.000000, total_loss = 0.004117 \n",
" total_bbox = 29933, rewritten_bbox = 0.263923 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.299479, GIOU: 0.175662), Class: 0.539321, Obj: 0.028097, No Obj: 0.003653, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.317346, iou_loss = 0.004548, total_loss = 1.321894 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001009, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003958, iou_loss = 0.000000, total_loss = 0.003958 \n",
" total_bbox = 29937, rewritten_bbox = 0.263887 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415761, GIOU: 0.263510), Class: 0.526812, Obj: 0.033722, No Obj: 0.002880, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.340359, iou_loss = 0.095135, total_loss = 1.435494 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.403451, GIOU: 0.237308), Class: 0.489132, Obj: 0.003342, No Obj: 0.000976, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.176169, iou_loss = 0.975200, total_loss = 2.151370 \n",
" total_bbox = 29944, rewritten_bbox = 0.263826 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.386751, GIOU: 0.230750), Class: 0.427284, Obj: 0.024839, No Obj: 0.003441, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.943466, iou_loss = 0.088426, total_loss = 2.031892 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001048, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003989, iou_loss = 0.000000, total_loss = 0.003989 \n",
" total_bbox = 29949, rewritten_bbox = 0.263782 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409489, GIOU: 0.275371), Class: 0.579058, Obj: 0.038342, No Obj: 0.003306, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.396451, iou_loss = 0.031481, total_loss = 1.427932 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.425044, GIOU: 0.370671), Class: 0.482438, Obj: 0.001011, No Obj: 0.001000, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.800195, iou_loss = 0.609961, total_loss = 1.410155 \n",
" total_bbox = 29955, rewritten_bbox = 0.263729 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.235602, GIOU: 0.170855), Class: 0.483556, Obj: 0.021191, No Obj: 0.003213, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.857549, iou_loss = 0.017110, total_loss = 1.874659 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000996, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004026, iou_loss = 0.000000, total_loss = 0.004026 \n",
" total_bbox = 29960, rewritten_bbox = 0.263685 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.459149, GIOU: 0.383795), Class: 0.586406, Obj: 0.019997, No Obj: 0.003234, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.105194, iou_loss = 0.201734, total_loss = 2.306927 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001018, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004166, iou_loss = 0.000000, total_loss = 0.004166 \n",
" total_bbox = 29966, rewritten_bbox = 0.263632 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392671, GIOU: 0.358573), Class: 0.484615, Obj: 0.030197, No Obj: 0.002972, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.814723, iou_loss = 0.104207, total_loss = 1.918930 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.449075, GIOU: 0.429334), Class: 0.539603, Obj: 0.001548, No Obj: 0.001063, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.733808, iou_loss = 0.866107, total_loss = 1.599915 \n",
" total_bbox = 29973, rewritten_bbox = 0.263571 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.189861, GIOU: 0.073370), Class: 0.417362, Obj: 0.020092, No Obj: 0.003055, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.113647, iou_loss = 0.008237, total_loss = 1.121884 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.046658, GIOU: -0.623247), Class: 0.518186, Obj: 0.001219, No Obj: 0.001070, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.870617, iou_loss = 0.004859, total_loss = 0.875476 \n",
" total_bbox = 29978, rewritten_bbox = 0.263527 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 375: 1.028342, 0.956247 avg loss, 0.000052 rate, 0.738443 seconds, 24000 images, 0.731550 hours left\n",
"Loaded: 0.000043 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.354196, GIOU: 0.339719), Class: 0.449076, Obj: 0.049905, No Obj: 0.003141, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.083049, iou_loss = 0.011859, total_loss = 1.094908 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.481469, GIOU: 0.381159), Class: 0.529486, Obj: 0.006790, No Obj: 0.000983, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.406313, iou_loss = 0.131587, total_loss = 0.537900 \n",
" total_bbox = 29982, rewritten_bbox = 0.263491 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382729, GIOU: 0.238423), Class: 0.427934, Obj: 0.010202, No Obj: 0.003011, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.462939, iou_loss = 0.049465, total_loss = 2.512404 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.424519, GIOU: 0.408872), Class: 0.549535, Obj: 0.003452, No Obj: 0.001017, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.103654, iou_loss = 1.347219, total_loss = 2.450873 \n",
" total_bbox = 29991, rewritten_bbox = 0.263412 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.223194, GIOU: 0.127510), Class: 0.560519, Obj: 0.024529, No Obj: 0.002713, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.357708, iou_loss = 0.010818, total_loss = 1.368526 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001058, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004006, iou_loss = 0.000000, total_loss = 0.004006 \n",
" total_bbox = 29995, rewritten_bbox = 0.263377 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.393818, GIOU: 0.381778), Class: 0.405619, Obj: 0.035210, No Obj: 0.002774, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.202266, iou_loss = 0.027411, total_loss = 1.229677 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.280852, GIOU: 0.118288), Class: 0.655340, Obj: 0.005405, No Obj: 0.001004, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.621508, iou_loss = 0.164300, total_loss = 0.785807 \n",
" total_bbox = 30000, rewritten_bbox = 0.263333 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.449631, GIOU: 0.444435), Class: 0.388334, Obj: 0.052026, No Obj: 0.002844, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.500362, iou_loss = 0.053728, total_loss = 1.554090 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000991, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003311, iou_loss = 0.000000, total_loss = 0.003311 \n",
" total_bbox = 30004, rewritten_bbox = 0.263298 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.391028, GIOU: 0.364144), Class: 0.458112, Obj: 0.010700, No Obj: 0.002675, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.490834, iou_loss = 0.037761, total_loss = 1.528595 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.426530, GIOU: 0.426530), Class: 0.680376, Obj: 0.046819, No Obj: 0.001113, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.311898, iou_loss = 0.158621, total_loss = 0.470519 \n",
" total_bbox = 30009, rewritten_bbox = 0.263254 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.449583, GIOU: 0.359869), Class: 0.524684, Obj: 0.033276, No Obj: 0.002900, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.404289, iou_loss = 0.023094, total_loss = 1.427382 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000949, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003222, iou_loss = 0.000000, total_loss = 0.003222 \n",
" total_bbox = 30013, rewritten_bbox = 0.263219 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399314, GIOU: 0.307512), Class: 0.441321, Obj: 0.022067, No Obj: 0.002850, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.248661, iou_loss = 0.010664, total_loss = 1.259326 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.425201, GIOU: 0.290616), Class: 0.488730, Obj: 0.003803, No Obj: 0.001027, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.397555, iou_loss = 0.070772, total_loss = 0.468327 \n",
" total_bbox = 30017, rewritten_bbox = 0.263184 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.275411, GIOU: 0.254210), Class: 0.474462, Obj: 0.077512, No Obj: 0.002712, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.979938, iou_loss = 0.022610, total_loss = 1.002547 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.486072, GIOU: 0.434410), Class: 0.471532, Obj: 0.001289, No Obj: 0.001008, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.880131, iou_loss = 2.370088, total_loss = 4.250219 \n",
" total_bbox = 30025, rewritten_bbox = 0.263114 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.432370, GIOU: 0.415359), Class: 0.448904, Obj: 0.031854, No Obj: 0.002884, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.519195, iou_loss = 0.089586, total_loss = 1.608781 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.436822, GIOU: 0.307901), Class: 0.465375, Obj: 0.019911, No Obj: 0.001060, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.407410, iou_loss = 0.100774, total_loss = 0.508183 \n",
" total_bbox = 30030, rewritten_bbox = 0.263070 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.482275, GIOU: 0.481410), Class: 0.489721, Obj: 0.012823, No Obj: 0.003185, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.347217, iou_loss = 0.014328, total_loss = 1.361545 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000966, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003032, iou_loss = 0.000000, total_loss = 0.003032 \n",
" total_bbox = 30034, rewritten_bbox = 0.263035 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.149936, GIOU: 0.063916), Class: 0.503211, Obj: 0.013762, No Obj: 0.002505, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.055134, iou_loss = 0.005699, total_loss = 1.060833 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.125486), Class: 0.464202, Obj: 0.002964, No Obj: 0.001033, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.365818, iou_loss = 0.274695, total_loss = 0.640513 \n",
" total_bbox = 30038, rewritten_bbox = 0.263000 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.336323, GIOU: 0.318472), Class: 0.631672, Obj: 0.006865, No Obj: 0.002998, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.395520, iou_loss = 0.015422, total_loss = 1.410942 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.415600, GIOU: 0.287031), Class: 0.505922, Obj: 0.003242, No Obj: 0.001054, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.353976, iou_loss = 0.387175, total_loss = 0.741151 \n",
" total_bbox = 30043, rewritten_bbox = 0.262956 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.329740, GIOU: 0.258202), Class: 0.464741, Obj: 0.009540, No Obj: 0.003143, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.085706, iou_loss = 0.029340, total_loss = 1.115046 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.393450, GIOU: 0.291869), Class: 0.522368, Obj: 0.001649, No Obj: 0.000990, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.776279, iou_loss = 0.343040, total_loss = 1.119319 \n",
" total_bbox = 30048, rewritten_bbox = 0.262913 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.347279, GIOU: 0.304904), Class: 0.555367, Obj: 0.017989, No Obj: 0.002850, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.714619, iou_loss = 0.091110, total_loss = 1.805729 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000946, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002902, iou_loss = 0.000000, total_loss = 0.002902 \n",
" total_bbox = 30053, rewritten_bbox = 0.262869 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.310801, GIOU: 0.204176), Class: 0.498292, Obj: 0.043989, No Obj: 0.003067, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.514369, iou_loss = 0.027496, total_loss = 1.541865 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.545309, GIOU: 0.477019), Class: 0.316475, Obj: 0.010547, No Obj: 0.001019, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.445704, iou_loss = 0.268004, total_loss = 0.713708 \n",
" total_bbox = 30058, rewritten_bbox = 0.262825 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 376: 0.929550, 0.953577 avg loss, 0.000052 rate, 0.678796 seconds, 24064 images, 0.731671 hours left\n",
"Loaded: 0.000065 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.460834, GIOU: 0.422677), Class: 0.541361, Obj: 0.020393, No Obj: 0.002819, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.450542, iou_loss = 0.139555, total_loss = 1.590096 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000941, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002910, iou_loss = 0.000000, total_loss = 0.002910 \n",
" total_bbox = 30062, rewritten_bbox = 0.262790 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.313075, GIOU: 0.148574), Class: 0.445068, Obj: 0.025369, No Obj: 0.003171, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.895824, iou_loss = 0.055331, total_loss = 1.951156 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.296047, GIOU: 0.053382), Class: 0.389439, Obj: 0.008381, No Obj: 0.001031, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.400830, iou_loss = 0.027501, total_loss = 0.428331 \n",
" total_bbox = 30068, rewritten_bbox = 0.262738 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.349696, GIOU: 0.246300), Class: 0.445920, Obj: 0.025117, No Obj: 0.002927, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.603279, iou_loss = 0.007022, total_loss = 1.610301 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001029, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003871, iou_loss = 0.000000, total_loss = 0.003871 \n",
" total_bbox = 30072, rewritten_bbox = 0.262703 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.540234, GIOU: 0.476737), Class: 0.539063, Obj: 0.053137, No Obj: 0.003223, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.376516, iou_loss = 0.048134, total_loss = 1.424650 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.172018, GIOU: 0.172018), Class: 0.382060, Obj: 0.000475, No Obj: 0.000952, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.413583, iou_loss = 0.025725, total_loss = 0.439309 \n",
" total_bbox = 30077, rewritten_bbox = 0.262659 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.529126, GIOU: 0.526281), Class: 0.421955, Obj: 0.014163, No Obj: 0.003297, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.265853, iou_loss = 0.046074, total_loss = 1.311927 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.297509, GIOU: 0.151194), Class: 0.322628, Obj: 0.001331, No Obj: 0.000991, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.435020, iou_loss = 0.152068, total_loss = 0.587088 \n",
" total_bbox = 30081, rewritten_bbox = 0.262624 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.361769, GIOU: 0.303958), Class: 0.507044, Obj: 0.027391, No Obj: 0.003167, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.789572, iou_loss = 0.087081, total_loss = 1.876652 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.406249, GIOU: 0.406249), Class: 0.529891, Obj: 0.003478, No Obj: 0.001043, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.359466, iou_loss = 0.144022, total_loss = 0.503488 \n",
" total_bbox = 30087, rewritten_bbox = 0.262572 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292422, GIOU: 0.258242), Class: 0.439380, Obj: 0.018017, No Obj: 0.003405, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.500198, iou_loss = 0.012666, total_loss = 1.512864 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001017, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003454, iou_loss = 0.000000, total_loss = 0.003454 \n",
" total_bbox = 30091, rewritten_bbox = 0.262537 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353375, GIOU: 0.284978), Class: 0.485688, Obj: 0.063340, No Obj: 0.003194, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.383011, iou_loss = 0.060544, total_loss = 1.443556 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001034, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003988, iou_loss = 0.000000, total_loss = 0.003988 \n",
" total_bbox = 30095, rewritten_bbox = 0.262502 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.321353, GIOU: 0.229080), Class: 0.588639, Obj: 0.039314, No Obj: 0.003065, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.159552, iou_loss = 0.016429, total_loss = 1.175981 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.115223, GIOU: -0.218349), Class: 0.631186, Obj: 0.004109, No Obj: 0.000983, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347823, iou_loss = 0.019470, total_loss = 0.367293 \n",
" total_bbox = 30099, rewritten_bbox = 0.262467 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410064, GIOU: 0.393521), Class: 0.677404, Obj: 0.034047, No Obj: 0.003183, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.251239, iou_loss = 0.034589, total_loss = 1.285828 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000989, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003174, iou_loss = 0.000000, total_loss = 0.003174 \n",
" total_bbox = 30103, rewritten_bbox = 0.262432 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.433702, GIOU: 0.318429), Class: 0.531071, Obj: 0.013112, No Obj: 0.003133, .5R: 0.285714, .75R: 0.142857, count: 7, class_loss = 2.614758, iou_loss = 0.029429, total_loss = 2.644187 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001021, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003651, iou_loss = 0.000000, total_loss = 0.003651 \n",
" total_bbox = 30110, rewritten_bbox = 0.262371 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438343, GIOU: 0.390167), Class: 0.536390, Obj: 0.005487, No Obj: 0.002601, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.089485, iou_loss = 0.096337, total_loss = 1.185821 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.495377, GIOU: 0.475819), Class: 0.658278, Obj: 0.007900, No Obj: 0.001014, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.708660, iou_loss = 0.448617, total_loss = 1.157277 \n",
" total_bbox = 30115, rewritten_bbox = 0.262328 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.600559, GIOU: 0.560736), Class: 0.391227, Obj: 0.016680, No Obj: 0.002602, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.873953, iou_loss = 0.038712, total_loss = 0.912665 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.241526, GIOU: -0.188130), Class: 0.648376, Obj: 0.001391, No Obj: 0.001065, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.608307, iou_loss = 0.259608, total_loss = 0.867915 \n",
" total_bbox = 30119, rewritten_bbox = 0.262293 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325330, GIOU: 0.321640), Class: 0.657862, Obj: 0.010590, No Obj: 0.002376, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.758782, iou_loss = 0.018066, total_loss = 1.776847 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000955, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003117, iou_loss = 0.000000, total_loss = 0.003117 \n",
" total_bbox = 30124, rewritten_bbox = 0.262249 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.249254, GIOU: -0.108284), Class: 0.544157, Obj: 0.022411, No Obj: 0.003172, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.508751, iou_loss = 0.008482, total_loss = 1.517233 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.389687, GIOU: 0.240698), Class: 0.714454, Obj: 0.001993, No Obj: 0.001017, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.315803, iou_loss = 0.054735, total_loss = 0.370538 \n",
" total_bbox = 30129, rewritten_bbox = 0.262206 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.465968, GIOU: 0.444837), Class: 0.519329, Obj: 0.018200, No Obj: 0.002996, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.526635, iou_loss = 0.021927, total_loss = 1.548562 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.197964, GIOU: -0.202938), Class: 0.595377, Obj: 0.001546, No Obj: 0.001037, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.706139, iou_loss = 0.954622, total_loss = 1.660761 \n",
" total_bbox = 30136, rewritten_bbox = 0.265463 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 377: 0.895211, 0.947740 avg loss, 0.000053 rate, 0.695161 seconds, 24128 images, 0.731188 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345466, GIOU: 0.323807), Class: 0.506061, Obj: 0.066577, No Obj: 0.002974, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.958281, iou_loss = 0.102615, total_loss = 1.060896 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.250533, GIOU: -0.023393), Class: 0.361685, Obj: 0.001313, No Obj: 0.001022, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.781129, iou_loss = 0.040348, total_loss = 0.821477 \n",
" total_bbox = 30141, rewritten_bbox = 0.265419 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.388836, GIOU: 0.345674), Class: 0.527860, Obj: 0.026332, No Obj: 0.003219, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.701308, iou_loss = 0.016898, total_loss = 0.718206 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.042532, GIOU: -0.552594), Class: 0.411485, Obj: 0.002708, No Obj: 0.001016, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.216652, iou_loss = 0.096839, total_loss = 1.313492 \n",
" total_bbox = 30146, rewritten_bbox = 0.265375 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.341440, GIOU: 0.186679), Class: 0.492586, Obj: 0.048822, No Obj: 0.003067, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.115076, iou_loss = 0.005370, total_loss = 1.120446 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.250957, GIOU: 0.053186), Class: 0.393796, Obj: 0.004494, No Obj: 0.000967, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.259954, iou_loss = 0.146493, total_loss = 1.406447 \n",
" total_bbox = 30152, rewritten_bbox = 0.265322 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.400105, GIOU: 0.257663), Class: 0.426042, Obj: 0.017016, No Obj: 0.002940, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.386777, iou_loss = 0.058018, total_loss = 2.444796 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.331827, GIOU: 0.146405), Class: 0.746944, Obj: 0.000656, No Obj: 0.000996, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.295036, iou_loss = 0.056266, total_loss = 0.351303 \n",
" total_bbox = 30159, rewritten_bbox = 0.265261 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.424102, GIOU: 0.417163), Class: 0.588522, Obj: 0.080723, No Obj: 0.002903, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.664708, iou_loss = 0.014006, total_loss = 0.678713 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.318365, GIOU: 0.221287), Class: 0.664176, Obj: 0.025211, No Obj: 0.001011, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.642588, iou_loss = 0.533166, total_loss = 1.175753 \n",
" total_bbox = 30163, rewritten_bbox = 0.265226 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.446056, GIOU: 0.332372), Class: 0.484640, Obj: 0.013472, No Obj: 0.002697, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.666339, iou_loss = 0.072792, total_loss = 1.739131 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.334912, GIOU: 0.093984), Class: 0.564587, Obj: 0.005202, No Obj: 0.000935, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.372497, iou_loss = 0.139649, total_loss = 0.512145 \n",
" total_bbox = 30168, rewritten_bbox = 0.265182 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409177, GIOU: 0.314318), Class: 0.437396, Obj: 0.013567, No Obj: 0.003312, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.951793, iou_loss = 0.025634, total_loss = 1.977428 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000951, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003071, iou_loss = 0.000000, total_loss = 0.003071 \n",
" total_bbox = 30173, rewritten_bbox = 0.265138 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.301607, GIOU: 0.227737), Class: 0.469651, Obj: 0.030427, No Obj: 0.002790, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.438720, iou_loss = 0.012500, total_loss = 1.451220 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000906, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002606, iou_loss = 0.000000, total_loss = 0.002606 \n",
" total_bbox = 30177, rewritten_bbox = 0.265103 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.474454, GIOU: 0.463636), Class: 0.507950, Obj: 0.045311, No Obj: 0.002758, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.487935, iou_loss = 0.025019, total_loss = 1.512954 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.237632, GIOU: 0.237632), Class: 0.585982, Obj: 0.006156, No Obj: 0.000941, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.393335, iou_loss = 0.050938, total_loss = 0.444273 \n",
" total_bbox = 30182, rewritten_bbox = 0.265059 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.295482, GIOU: 0.208454), Class: 0.599191, Obj: 0.022123, No Obj: 0.002883, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.096972, iou_loss = 0.059224, total_loss = 2.156196 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.523393, GIOU: 0.511462), Class: 0.574334, Obj: 0.001532, No Obj: 0.000979, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.338985, iou_loss = 0.843406, total_loss = 1.182390 \n",
" total_bbox = 30189, rewritten_bbox = 0.264997 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.240296, GIOU: 0.083954), Class: 0.419026, Obj: 0.027674, No Obj: 0.003366, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.404279, iou_loss = 0.011595, total_loss = 1.415875 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001086, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004669, iou_loss = 0.000000, total_loss = 0.004669 \n",
" total_bbox = 30193, rewritten_bbox = 0.264962 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359320, GIOU: 0.244336), Class: 0.356420, Obj: 0.012080, No Obj: 0.002962, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.681025, iou_loss = 0.086287, total_loss = 1.767312 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.390221, GIOU: 0.197910), Class: 0.567527, Obj: 0.001504, No Obj: 0.000993, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.356232, iou_loss = 0.202864, total_loss = 0.559097 \n",
" total_bbox = 30198, rewritten_bbox = 0.264918 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427359, GIOU: 0.418659), Class: 0.505223, Obj: 0.009057, No Obj: 0.002488, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.810356, iou_loss = 0.123985, total_loss = 1.934342 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.443557, GIOU: 0.398217), Class: 0.525781, Obj: 0.003712, No Obj: 0.000987, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.337540, iou_loss = 0.119645, total_loss = 0.457184 \n",
" total_bbox = 30204, rewritten_bbox = 0.264866 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.356474, GIOU: 0.203660), Class: 0.501715, Obj: 0.025754, No Obj: 0.002711, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.474527, iou_loss = 0.051519, total_loss = 1.526045 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000957, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003113, iou_loss = 0.000000, total_loss = 0.003113 \n",
" total_bbox = 30208, rewritten_bbox = 0.264830 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.223629, GIOU: 0.133086), Class: 0.317589, Obj: 0.016713, No Obj: 0.003325, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.706601, iou_loss = 0.030355, total_loss = 2.736956 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.262890, GIOU: 0.133089), Class: 0.538972, Obj: 0.006753, No Obj: 0.000998, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.711656, iou_loss = 0.074536, total_loss = 0.786192 \n",
" total_bbox = 30216, rewritten_bbox = 0.264760 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307335, GIOU: 0.245313), Class: 0.458283, Obj: 0.014397, No Obj: 0.003291, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.574585, iou_loss = 0.021976, total_loss = 1.596560 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001016, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003625, iou_loss = 0.000000, total_loss = 0.003625 \n",
" total_bbox = 30220, rewritten_bbox = 0.264725 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 378: 1.004835, 0.953450 avg loss, 0.000053 rate, 0.720556 seconds, 24192 images, 0.730873 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.289248, GIOU: 0.168174), Class: 0.409973, Obj: 0.006918, No Obj: 0.002621, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.131855, iou_loss = 0.032125, total_loss = 1.163980 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.244249, GIOU: 0.008151), Class: 0.568109, Obj: 0.002515, No Obj: 0.001027, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.368351, iou_loss = 0.202675, total_loss = 0.571026 \n",
" total_bbox = 30224, rewritten_bbox = 0.264690 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344828, GIOU: 0.227530), Class: 0.477489, Obj: 0.030944, No Obj: 0.002868, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.434995, iou_loss = 0.044958, total_loss = 1.479952 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000948, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003075, iou_loss = 0.000000, total_loss = 0.003075 \n",
" total_bbox = 30228, rewritten_bbox = 0.264655 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.387537, GIOU: 0.349119), Class: 0.442539, Obj: 0.009613, No Obj: 0.002754, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.434789, iou_loss = 0.028981, total_loss = 1.463770 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.443480, GIOU: 0.297054), Class: 0.655709, Obj: 0.001060, No Obj: 0.000982, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.324650, iou_loss = 0.093380, total_loss = 0.418029 \n",
" total_bbox = 30233, rewritten_bbox = 0.264612 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.412099, GIOU: 0.321527), Class: 0.437698, Obj: 0.024190, No Obj: 0.003027, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.509585, iou_loss = 0.096871, total_loss = 1.606456 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000953, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003452, iou_loss = 0.000000, total_loss = 0.003452 \n",
" total_bbox = 30237, rewritten_bbox = 0.264577 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.283444, GIOU: 0.147676), Class: 0.341144, Obj: 0.040126, No Obj: 0.002797, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.710772, iou_loss = 0.025560, total_loss = 1.736332 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001002, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003593, iou_loss = 0.000000, total_loss = 0.003593 \n",
" total_bbox = 30241, rewritten_bbox = 0.264542 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.523044, GIOU: 0.478536), Class: 0.522695, Obj: 0.004323, No Obj: 0.002669, .5R: 1.000000, .75R: 0.000000, count: 3, class_loss = 1.134707, iou_loss = 0.118284, total_loss = 1.252992 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.239293, GIOU: -0.129641), Class: 0.354984, Obj: 0.001757, No Obj: 0.000966, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.478664, iou_loss = 0.067158, total_loss = 0.545822 \n",
" total_bbox = 30245, rewritten_bbox = 0.264507 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.464354, GIOU: 0.378380), Class: 0.395977, Obj: 0.011747, No Obj: 0.003413, .5R: 0.500000, .75R: 0.333333, count: 6, class_loss = 2.436313, iou_loss = 0.056173, total_loss = 2.492486 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000946, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002950, iou_loss = 0.000000, total_loss = 0.002950 \n",
" total_bbox = 30251, rewritten_bbox = 0.264454 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.522812, GIOU: 0.478133), Class: 0.533227, Obj: 0.070578, No Obj: 0.003632, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.671985, iou_loss = 0.048456, total_loss = 1.720441 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001000, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003357, iou_loss = 0.000000, total_loss = 0.003357 \n",
" total_bbox = 30256, rewritten_bbox = 0.264410 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369003, GIOU: 0.332603), Class: 0.525724, Obj: 0.010161, No Obj: 0.003129, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.518694, iou_loss = 0.045534, total_loss = 1.564228 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000938, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002890, iou_loss = 0.000000, total_loss = 0.002890 \n",
" total_bbox = 30260, rewritten_bbox = 0.264375 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.317550, GIOU: 0.274107), Class: 0.476616, Obj: 0.047609, No Obj: 0.002828, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.132479, iou_loss = 0.063656, total_loss = 2.196135 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.194410, GIOU: -0.150781), Class: 0.535716, Obj: 0.001105, No Obj: 0.000999, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.745919, iou_loss = 0.105480, total_loss = 0.851400 \n",
" total_bbox = 30268, rewritten_bbox = 0.264306 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.386862, GIOU: 0.382283), Class: 0.510637, Obj: 0.019530, No Obj: 0.002839, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.457639, iou_loss = 0.079776, total_loss = 1.537415 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000944, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003337, iou_loss = 0.000000, total_loss = 0.003337 \n",
" total_bbox = 30272, rewritten_bbox = 0.264271 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.384974, GIOU: 0.315198), Class: 0.479568, Obj: 0.019590, No Obj: 0.002762, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.555784, iou_loss = 0.055450, total_loss = 1.611234 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.479620, GIOU: 0.373317), Class: 0.399128, Obj: 0.006910, No Obj: 0.001012, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.788835, iou_loss = 0.248712, total_loss = 1.037547 \n",
" total_bbox = 30278, rewritten_bbox = 0.264218 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.462059, GIOU: 0.367906), Class: 0.471092, Obj: 0.012860, No Obj: 0.002766, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 2.310470, iou_loss = 0.067370, total_loss = 2.377840 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000951, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003105, iou_loss = 0.000000, total_loss = 0.003105 \n",
" total_bbox = 30284, rewritten_bbox = 0.264166 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.277493, GIOU: 0.216857), Class: 0.398280, Obj: 0.013076, No Obj: 0.002793, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.660098, iou_loss = 0.010077, total_loss = 1.670175 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001008, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004175, iou_loss = 0.000000, total_loss = 0.004175 \n",
" total_bbox = 30288, rewritten_bbox = 0.264131 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.367033, GIOU: 0.260625), Class: 0.404663, Obj: 0.027169, No Obj: 0.002924, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.553811, iou_loss = 0.008437, total_loss = 1.562248 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000942, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003562, iou_loss = 0.000000, total_loss = 0.003562 \n",
" total_bbox = 30292, rewritten_bbox = 0.264096 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.235786, GIOU: 0.056771), Class: 0.525558, Obj: 0.022955, No Obj: 0.002525, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.474193, iou_loss = 0.038390, total_loss = 1.512582 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.802758, GIOU: 0.797762), Class: 0.498293, Obj: 0.028897, No Obj: 0.000988, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 0.383878, iou_loss = 0.618857, total_loss = 1.002735 \n",
" total_bbox = 30297, rewritten_bbox = 0.264053 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 379: 0.921544, 0.950259 avg loss, 0.000054 rate, 0.678999 seconds, 24256 images, 0.730815 hours left\n",
"Loaded: 0.000063 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390432, GIOU: 0.357502), Class: 0.494254, Obj: 0.027009, No Obj: 0.002542, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.885674, iou_loss = 0.204987, total_loss = 2.090662 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000992, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003912, iou_loss = 0.000000, total_loss = 0.003912 \n",
" total_bbox = 30302, rewritten_bbox = 0.264009 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.394082, GIOU: 0.380801), Class: 0.462518, Obj: 0.009809, No Obj: 0.003205, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.826172, iou_loss = 0.077029, total_loss = 1.903202 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.675761, GIOU: 0.661251), Class: 0.631261, Obj: 0.001119, No Obj: 0.000955, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.325596, iou_loss = 1.657277, total_loss = 1.982873 \n",
" total_bbox = 30308, rewritten_bbox = 0.263957 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.361462, GIOU: 0.250489), Class: 0.471392, Obj: 0.010497, No Obj: 0.002587, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.051134, iou_loss = 0.020393, total_loss = 1.071527 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.300966, GIOU: 0.220914), Class: 0.318629, Obj: 0.012013, No Obj: 0.000964, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.461989, iou_loss = 0.062831, total_loss = 0.524820 \n",
" total_bbox = 30312, rewritten_bbox = 0.263922 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370524, GIOU: 0.291028), Class: 0.645526, Obj: 0.013890, No Obj: 0.002887, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.057718, iou_loss = 0.080091, total_loss = 2.137809 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.377421, GIOU: 0.251997), Class: 0.374516, Obj: 0.003118, No Obj: 0.000973, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.379310, iou_loss = 0.128246, total_loss = 0.507557 \n",
" total_bbox = 30319, rewritten_bbox = 0.263861 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.210038, GIOU: 0.200407), Class: 0.442716, Obj: 0.019724, No Obj: 0.003023, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.526950, iou_loss = 0.012149, total_loss = 1.539099 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.343892, GIOU: 0.343892), Class: 0.561870, Obj: 0.001491, No Obj: 0.001083, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.707936, iou_loss = 0.241137, total_loss = 0.949073 \n",
" total_bbox = 30325, rewritten_bbox = 0.263809 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.448854, GIOU: 0.377833), Class: 0.413163, Obj: 0.012062, No Obj: 0.003110, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.460762, iou_loss = 0.110574, total_loss = 1.571335 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.407987, GIOU: 0.393693), Class: 0.486985, Obj: 0.003902, No Obj: 0.001006, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.665771, iou_loss = 0.280107, total_loss = 0.945878 \n",
" total_bbox = 30331, rewritten_bbox = 0.263757 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372950, GIOU: 0.372950), Class: 0.557521, Obj: 0.006737, No Obj: 0.003157, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.150113, iou_loss = 0.026523, total_loss = 1.176635 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001015, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003749, iou_loss = 0.000000, total_loss = 0.003749 \n",
" total_bbox = 30334, rewritten_bbox = 0.263730 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.488950, GIOU: 0.440144), Class: 0.343691, Obj: 0.017551, No Obj: 0.003101, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.263672, iou_loss = 0.024723, total_loss = 1.288395 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.289180, GIOU: 0.203540), Class: 0.577097, Obj: 0.003313, No Obj: 0.000943, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.682099, iou_loss = 0.117751, total_loss = 0.799850 \n",
" total_bbox = 30339, rewritten_bbox = 0.263687 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.169727, GIOU: 0.024749), Class: 0.481139, Obj: 0.022088, No Obj: 0.002853, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.760054, iou_loss = 0.006431, total_loss = 0.766485 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.294475, GIOU: 0.018182), Class: 0.475551, Obj: 0.005304, No Obj: 0.000988, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.990637, iou_loss = 0.984361, total_loss = 2.974998 \n",
" total_bbox = 30346, rewritten_bbox = 0.263626 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325124, GIOU: 0.222599), Class: 0.609514, Obj: 0.028497, No Obj: 0.002982, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.033970, iou_loss = 0.020740, total_loss = 1.054710 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.285455, GIOU: 0.214944), Class: 0.518460, Obj: 0.006287, No Obj: 0.000964, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.732955, iou_loss = 0.212799, total_loss = 0.945754 \n",
" total_bbox = 30351, rewritten_bbox = 0.263583 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.284309, GIOU: 0.187718), Class: 0.445158, Obj: 0.013240, No Obj: 0.002798, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 2.053665, iou_loss = 0.021072, total_loss = 2.074737 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.254279, GIOU: -0.023661), Class: 0.522204, Obj: 0.000751, No Obj: 0.000988, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.357591, iou_loss = 0.030686, total_loss = 0.388277 \n",
" total_bbox = 30357, rewritten_bbox = 0.263531 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.237248, GIOU: 0.030668), Class: 0.427276, Obj: 0.011224, No Obj: 0.003407, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.602663, iou_loss = 0.006708, total_loss = 1.609371 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.061279, GIOU: -0.590743), Class: 0.468263, Obj: 0.001305, No Obj: 0.000945, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.344869, iou_loss = 1.733051, total_loss = 2.077921 \n",
" total_bbox = 30362, rewritten_bbox = 0.263487 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.517372, GIOU: 0.504982), Class: 0.443479, Obj: 0.010168, No Obj: 0.003036, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.834560, iou_loss = 0.040850, total_loss = 1.875410 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000975, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003332, iou_loss = 0.000000, total_loss = 0.003332 \n",
" total_bbox = 30367, rewritten_bbox = 0.263444 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.224366, GIOU: 0.144925), Class: 0.685696, Obj: 0.044860, No Obj: 0.003325, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.239456, iou_loss = 0.013343, total_loss = 1.252799 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001022, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004288, iou_loss = 0.000000, total_loss = 0.004288 \n",
" total_bbox = 30371, rewritten_bbox = 0.263409 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.368633, GIOU: 0.332638), Class: 0.497542, Obj: 0.028966, No Obj: 0.002795, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.508135, iou_loss = 0.046250, total_loss = 1.554384 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.128401, GIOU: -0.384727), Class: 0.348603, Obj: 0.001548, No Obj: 0.000999, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.419134, iou_loss = 0.003942, total_loss = 0.423076 \n",
" total_bbox = 30376, rewritten_bbox = 0.263366 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369405, GIOU: 0.305554), Class: 0.338001, Obj: 0.030089, No Obj: 0.003192, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.622753, iou_loss = 0.008909, total_loss = 1.631662 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.421540, GIOU: 0.352190), Class: 0.510413, Obj: 0.001862, No Obj: 0.001026, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.751374, iou_loss = 0.612110, total_loss = 1.363484 \n",
" total_bbox = 30382, rewritten_bbox = 0.263314 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 380: 1.001186, 0.955352 avg loss, 0.000054 rate, 0.700212 seconds, 24320 images, 0.730336 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.310919, GIOU: 0.230898), Class: 0.528559, Obj: 0.021286, No Obj: 0.002781, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.496060, iou_loss = 0.035514, total_loss = 1.531574 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000951, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003135, iou_loss = 0.000000, total_loss = 0.003135 \n",
" total_bbox = 30386, rewritten_bbox = 0.263279 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.347609, GIOU: 0.225914), Class: 0.585364, Obj: 0.059840, No Obj: 0.002701, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.972054, iou_loss = 0.027543, total_loss = 0.999597 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.399788, GIOU: 0.336370), Class: 0.464141, Obj: 0.025196, No Obj: 0.000996, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.763143, iou_loss = 0.224386, total_loss = 0.987529 \n",
" total_bbox = 30391, rewritten_bbox = 0.263236 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.215357, GIOU: 0.119072), Class: 0.523620, Obj: 0.038290, No Obj: 0.003316, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.546353, iou_loss = 0.009898, total_loss = 1.556251 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.243693, GIOU: -0.000581), Class: 0.500595, Obj: 0.000807, No Obj: 0.000913, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.353369, iou_loss = 0.066224, total_loss = 0.419592 \n",
" total_bbox = 30396, rewritten_bbox = 0.263193 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.326924, GIOU: 0.263217), Class: 0.508193, Obj: 0.047779, No Obj: 0.003355, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.398918, iou_loss = 0.097504, total_loss = 1.496423 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000956, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002974, iou_loss = 0.000000, total_loss = 0.002974 \n",
" total_bbox = 30400, rewritten_bbox = 0.263158 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.473476, GIOU: 0.307436), Class: 0.543508, Obj: 0.064847, No Obj: 0.003190, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.722031, iou_loss = 0.020090, total_loss = 0.742120 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.299742, GIOU: 0.114104), Class: 0.490750, Obj: 0.000940, No Obj: 0.001030, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.268894, iou_loss = 4.748097, total_loss = 7.016992 \n",
" total_bbox = 30408, rewritten_bbox = 0.263089 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.342498, GIOU: 0.266307), Class: 0.396161, Obj: 0.059947, No Obj: 0.002754, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.188151, iou_loss = 0.019730, total_loss = 1.207881 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.465268, GIOU: 0.344853), Class: 0.642892, Obj: 0.004432, No Obj: 0.000923, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.320090, iou_loss = 0.092833, total_loss = 0.412923 \n",
" total_bbox = 30412, rewritten_bbox = 0.263054 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.420422, GIOU: 0.395960), Class: 0.474310, Obj: 0.020048, No Obj: 0.002788, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.484261, iou_loss = 0.069551, total_loss = 1.553812 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.171610, GIOU: -0.031299), Class: 0.485111, Obj: 0.000591, No Obj: 0.000943, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.361121, iou_loss = 0.884602, total_loss = 1.245723 \n",
" total_bbox = 30417, rewritten_bbox = 0.263011 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399184, GIOU: 0.219066), Class: 0.693683, Obj: 0.013379, No Obj: 0.002733, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.074619, iou_loss = 0.067386, total_loss = 1.142005 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.324639, GIOU: 0.250565), Class: 0.427523, Obj: 0.004573, No Obj: 0.000964, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.120936, iou_loss = 0.647043, total_loss = 1.767979 \n",
" total_bbox = 30423, rewritten_bbox = 0.262959 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.350765, GIOU: 0.267108), Class: 0.403829, Obj: 0.020637, No Obj: 0.003088, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.953444, iou_loss = 0.020085, total_loss = 1.973530 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.293034, GIOU: 0.293034), Class: 0.735209, Obj: 0.004709, No Obj: 0.000871, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.329598, iou_loss = 0.074884, total_loss = 0.404482 \n",
" total_bbox = 30429, rewritten_bbox = 0.262907 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.196569, GIOU: 0.115906), Class: 0.573697, Obj: 0.073878, No Obj: 0.003522, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.245859, iou_loss = 0.015773, total_loss = 1.261632 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000975, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003181, iou_loss = 0.000000, total_loss = 0.003181 \n",
" total_bbox = 30433, rewritten_bbox = 0.262873 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.459323, GIOU: 0.359669), Class: 0.570309, Obj: 0.016176, No Obj: 0.002673, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.471530, iou_loss = 0.028023, total_loss = 1.499553 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.277962, GIOU: 0.018184), Class: 0.446912, Obj: 0.001524, No Obj: 0.000958, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.145029, iou_loss = 0.349586, total_loss = 1.494615 \n",
" total_bbox = 30440, rewritten_bbox = 0.262812 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444084, GIOU: 0.407009), Class: 0.483317, Obj: 0.014989, No Obj: 0.002868, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.040429, iou_loss = 0.034024, total_loss = 1.074453 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.390755, GIOU: 0.382143), Class: 0.598514, Obj: 0.001758, No Obj: 0.001045, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.772114, iou_loss = 0.349313, total_loss = 1.121427 \n",
" total_bbox = 30445, rewritten_bbox = 0.262769 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271103, GIOU: 0.271103), Class: 0.534595, Obj: 0.024574, No Obj: 0.002621, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.998293, iou_loss = 0.008041, total_loss = 1.006334 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.245960, GIOU: 0.154742), Class: 0.499496, Obj: 0.001196, No Obj: 0.000946, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.159318, iou_loss = 0.491001, total_loss = 1.650319 \n",
" total_bbox = 30451, rewritten_bbox = 0.262717 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.362245, GIOU: 0.219754), Class: 0.444553, Obj: 0.044987, No Obj: 0.002947, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.468927, iou_loss = 0.004596, total_loss = 0.473523 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.657571, GIOU: 0.639653), Class: 0.558877, Obj: 0.017726, No Obj: 0.000983, .5R: 1.000000, .75R: 0.500000, count: 2, class_loss = 0.700341, iou_loss = 0.696820, total_loss = 1.397161 \n",
" total_bbox = 30454, rewritten_bbox = 0.262691 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.430388, GIOU: 0.385207), Class: 0.349759, Obj: 0.028193, No Obj: 0.003220, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.594109, iou_loss = 0.064631, total_loss = 1.658740 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000955, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003341, iou_loss = 0.000000, total_loss = 0.003341 \n",
" total_bbox = 30458, rewritten_bbox = 0.262657 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.350559, GIOU: 0.120860), Class: 0.542845, Obj: 0.022869, No Obj: 0.002765, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.330356, iou_loss = 0.004543, total_loss = 0.334900 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.348690, GIOU: 0.296112), Class: 0.577009, Obj: 0.003021, No Obj: 0.000932, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.685593, iou_loss = 0.275675, total_loss = 0.961269 \n",
" total_bbox = 30461, rewritten_bbox = 0.262631 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 381: 0.915491, 0.951366 avg loss, 0.000055 rate, 0.675371 seconds, 24384 images, 0.730075 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.222063, GIOU: 0.222063), Class: 0.449353, Obj: 0.009716, No Obj: 0.002935, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.116024, iou_loss = 0.002941, total_loss = 1.118964 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.260382, GIOU: 0.201475), Class: 0.643600, Obj: 0.002873, No Obj: 0.000925, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.342075, iou_loss = 0.019693, total_loss = 0.361767 \n",
" total_bbox = 30465, rewritten_bbox = 0.262596 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.459380, GIOU: 0.374571), Class: 0.479620, Obj: 0.012540, No Obj: 0.002544, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.458092, iou_loss = 0.043488, total_loss = 1.501580 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.516676, GIOU: 0.486324), Class: 0.423965, Obj: 0.003783, No Obj: 0.000962, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.422335, iou_loss = 0.773451, total_loss = 1.195786 \n",
" total_bbox = 30470, rewritten_bbox = 0.262553 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.585699, GIOU: 0.553553), Class: 0.405584, Obj: 0.015646, No Obj: 0.002691, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.519564, iou_loss = 0.083892, total_loss = 1.603456 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000972, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003505, iou_loss = 0.000000, total_loss = 0.003505 \n",
" total_bbox = 30474, rewritten_bbox = 0.262519 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.518778, GIOU: 0.425963), Class: 0.614888, Obj: 0.028158, No Obj: 0.002898, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.383163, iou_loss = 0.021291, total_loss = 1.404454 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.464529, GIOU: 0.418961), Class: 0.560175, Obj: 0.001513, No Obj: 0.000925, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.345470, iou_loss = 0.326616, total_loss = 0.672086 \n",
" total_bbox = 30479, rewritten_bbox = 0.262476 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.381085, GIOU: 0.225395), Class: 0.517807, Obj: 0.055907, No Obj: 0.003227, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.449351, iou_loss = 0.024936, total_loss = 1.474288 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.444146, GIOU: 0.382556), Class: 0.542534, Obj: 0.003063, No Obj: 0.000932, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.679751, iou_loss = 0.826362, total_loss = 1.506113 \n",
" total_bbox = 30485, rewritten_bbox = 0.262424 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.420978, GIOU: 0.398422), Class: 0.424900, Obj: 0.029619, No Obj: 0.003076, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.090711, iou_loss = 0.053311, total_loss = 1.144022 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.448376, GIOU: 0.448376), Class: 0.404811, Obj: 0.000499, No Obj: 0.000935, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.372871, iou_loss = 0.175312, total_loss = 0.548183 \n",
" total_bbox = 30489, rewritten_bbox = 0.262390 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352503, GIOU: 0.269718), Class: 0.537015, Obj: 0.064978, No Obj: 0.003026, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.351164, iou_loss = 0.013242, total_loss = 1.364406 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000915, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002976, iou_loss = 0.000000, total_loss = 0.002976 \n",
" total_bbox = 30493, rewritten_bbox = 0.262355 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.231079, GIOU: 0.105895), Class: 0.520429, Obj: 0.014799, No Obj: 0.002819, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.601209, iou_loss = 0.007365, total_loss = 1.608574 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.568728, GIOU: 0.509360), Class: 0.509696, Obj: 0.004919, No Obj: 0.000915, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.129539, iou_loss = 1.425432, total_loss = 2.554971 \n",
" total_bbox = 30500, rewritten_bbox = 0.262295 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.478776, GIOU: 0.478776), Class: 0.447974, Obj: 0.064663, No Obj: 0.003133, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.766111, iou_loss = 0.037758, total_loss = 0.803869 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.224453, GIOU: 0.163250), Class: 0.633199, Obj: 0.001372, No Obj: 0.000929, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.751598, iou_loss = 1.186780, total_loss = 1.938378 \n",
" total_bbox = 30504, rewritten_bbox = 0.262261 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435643, GIOU: 0.401286), Class: 0.400211, Obj: 0.086871, No Obj: 0.003517, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.535589, iou_loss = 0.027703, total_loss = 1.563292 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000938, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003110, iou_loss = 0.000000, total_loss = 0.003110 \n",
" total_bbox = 30508, rewritten_bbox = 0.262226 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.371447, GIOU: 0.272711), Class: 0.502502, Obj: 0.026081, No Obj: 0.002742, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.496559, iou_loss = 0.058193, total_loss = 1.554753 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.282814, GIOU: 0.062835), Class: 0.625848, Obj: 0.004757, No Obj: 0.000979, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.783979, iou_loss = 0.084919, total_loss = 0.868898 \n",
" total_bbox = 30514, rewritten_bbox = 0.262175 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409113, GIOU: 0.355136), Class: 0.581075, Obj: 0.045742, No Obj: 0.003006, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.057201, iou_loss = 0.051014, total_loss = 1.108214 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.286207, GIOU: 0.077821), Class: 0.430609, Obj: 0.003838, No Obj: 0.000965, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.096646, iou_loss = 0.263752, total_loss = 1.360398 \n",
" total_bbox = 30520, rewritten_bbox = 0.262123 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.460077, GIOU: 0.341551), Class: 0.458684, Obj: 0.032583, No Obj: 0.002580, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.503166, iou_loss = 0.032351, total_loss = 1.535516 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000921, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002932, iou_loss = 0.000000, total_loss = 0.002932 \n",
" total_bbox = 30524, rewritten_bbox = 0.262089 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.262264, GIOU: 0.168182), Class: 0.519314, Obj: 0.018224, No Obj: 0.002609, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.577453, iou_loss = 0.011584, total_loss = 1.589037 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000928, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003776, iou_loss = 0.000000, total_loss = 0.003776 \n",
" total_bbox = 30528, rewritten_bbox = 0.262055 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.283812, GIOU: 0.283812), Class: 0.428583, Obj: 0.177983, No Obj: 0.003463, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.099137, iou_loss = 0.014688, total_loss = 1.113825 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.264623, GIOU: -0.052384), Class: 0.417373, Obj: 0.000513, No Obj: 0.000942, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.371943, iou_loss = 0.085483, total_loss = 0.457426 \n",
" total_bbox = 30532, rewritten_bbox = 0.262020 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.282520, GIOU: 0.282520), Class: 0.403449, Obj: 0.018573, No Obj: 0.003066, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.801063, iou_loss = 0.002530, total_loss = 0.803593 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.266919, GIOU: 0.019715), Class: 0.502960, Obj: 0.016102, No Obj: 0.000960, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.523968, iou_loss = 0.389163, total_loss = 1.913131 \n",
" total_bbox = 30538, rewritten_bbox = 0.261969 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 382: 0.904260, 0.946655 avg loss, 0.000056 rate, 0.675186 seconds, 24448 images, 0.729564 hours left\n",
"Loaded: 0.000064 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.331659, GIOU: 0.128861), Class: 0.570014, Obj: 0.028248, No Obj: 0.002794, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.165815, iou_loss = 0.011234, total_loss = 1.177049 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.281523, GIOU: 0.115454), Class: 0.397493, Obj: 0.003755, No Obj: 0.000914, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.395099, iou_loss = 0.023681, total_loss = 0.418779 \n",
" total_bbox = 30542, rewritten_bbox = 0.261934 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.535622, GIOU: 0.514149), Class: 0.527382, Obj: 0.017725, No Obj: 0.003093, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.384125, iou_loss = 0.014554, total_loss = 1.398679 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000990, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003741, iou_loss = 0.000000, total_loss = 0.003741 \n",
" total_bbox = 30546, rewritten_bbox = 0.261900 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370302, GIOU: 0.294862), Class: 0.621191, Obj: 0.031446, No Obj: 0.003215, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.274563, iou_loss = 0.006576, total_loss = 1.281139 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.334705, GIOU: 0.097346), Class: 0.838096, Obj: 0.002796, No Obj: 0.000937, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.298262, iou_loss = 0.134954, total_loss = 0.433215 \n",
" total_bbox = 30551, rewritten_bbox = 0.261857 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.236923, GIOU: 0.227527), Class: 0.463123, Obj: 0.012988, No Obj: 0.003550, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.549919, iou_loss = 0.022422, total_loss = 1.572341 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.466656, GIOU: 0.428185), Class: 0.432447, Obj: 0.001489, No Obj: 0.000982, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.403366, iou_loss = 0.407191, total_loss = 0.810557 \n",
" total_bbox = 30556, rewritten_bbox = 0.261814 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.367826, GIOU: 0.364871), Class: 0.504187, Obj: 0.020393, No Obj: 0.003321, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.084806, iou_loss = 0.011593, total_loss = 1.096399 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000974, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.006441, iou_loss = 0.000000, total_loss = 0.006441 \n",
" total_bbox = 30559, rewritten_bbox = 0.261789 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.366346, GIOU: 0.263868), Class: 0.521274, Obj: 0.046908, No Obj: 0.003047, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.478837, iou_loss = 0.030916, total_loss = 1.509754 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000870, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002536, iou_loss = 0.000000, total_loss = 0.002536 \n",
" total_bbox = 30563, rewritten_bbox = 0.261754 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.506508, GIOU: 0.482016), Class: 0.554337, Obj: 0.023201, No Obj: 0.002629, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.444475, iou_loss = 0.059331, total_loss = 1.503806 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.311076, GIOU: 0.218569), Class: 0.514224, Obj: 0.012988, No Obj: 0.000983, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.771275, iou_loss = 0.106698, total_loss = 0.877973 \n",
" total_bbox = 30569, rewritten_bbox = 0.261703 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.335077, GIOU: 0.334192), Class: 0.512617, Obj: 0.057608, No Obj: 0.002770, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.410152, iou_loss = 0.025074, total_loss = 1.435226 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000957, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002814, iou_loss = 0.000000, total_loss = 0.002814 \n",
" total_bbox = 30573, rewritten_bbox = 0.261669 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.348217, GIOU: 0.251526), Class: 0.601068, Obj: 0.041110, No Obj: 0.002980, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.825830, iou_loss = 0.078537, total_loss = 1.904367 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.382877, GIOU: 0.382877), Class: 0.725177, Obj: 0.000916, No Obj: 0.000888, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.348849, iou_loss = 0.127934, total_loss = 0.476782 \n",
" total_bbox = 30579, rewritten_bbox = 0.261617 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346271, GIOU: 0.195711), Class: 0.394761, Obj: 0.009894, No Obj: 0.002930, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.245585, iou_loss = 0.047132, total_loss = 1.292717 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.136267, GIOU: 0.136267), Class: 0.361734, Obj: 0.017612, No Obj: 0.000986, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.407591, iou_loss = 0.047592, total_loss = 0.455183 \n",
" total_bbox = 30583, rewritten_bbox = 0.261583 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.318541, GIOU: 0.178333), Class: 0.398872, Obj: 0.016281, No Obj: 0.003325, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.601423, iou_loss = 0.009485, total_loss = 1.610907 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.404870, GIOU: 0.320936), Class: 0.376706, Obj: 0.006834, No Obj: 0.000944, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.703234, iou_loss = 2.829282, total_loss = 4.532516 \n",
" total_bbox = 30591, rewritten_bbox = 0.261515 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.469304, GIOU: 0.436535), Class: 0.396514, Obj: 0.008948, No Obj: 0.002671, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.525364, iou_loss = 0.039023, total_loss = 1.564388 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000939, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003139, iou_loss = 0.000000, total_loss = 0.003139 \n",
" total_bbox = 30595, rewritten_bbox = 0.261481 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372205, GIOU: 0.300162), Class: 0.488758, Obj: 0.010601, No Obj: 0.002971, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.760266, iou_loss = 0.055302, total_loss = 1.815568 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.415750, GIOU: 0.356864), Class: 0.425425, Obj: 0.001551, No Obj: 0.000952, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.841507, iou_loss = 0.256294, total_loss = 1.097801 \n",
" total_bbox = 30602, rewritten_bbox = 0.261421 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.389497, GIOU: 0.271714), Class: 0.567519, Obj: 0.013505, No Obj: 0.002530, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.553916, iou_loss = 0.047087, total_loss = 1.601002 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.396232, GIOU: 0.259181), Class: 0.514601, Obj: 0.003749, No Obj: 0.000941, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.733480, iou_loss = 0.246322, total_loss = 0.979802 \n",
" total_bbox = 30608, rewritten_bbox = 0.261370 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.389086, GIOU: 0.231244), Class: 0.495235, Obj: 0.064074, No Obj: 0.003428, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.384844, iou_loss = 0.039656, total_loss = 1.424500 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000982, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003587, iou_loss = 0.000000, total_loss = 0.003587 \n",
" total_bbox = 30612, rewritten_bbox = 0.261335 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357505, GIOU: 0.269067), Class: 0.443341, Obj: 0.025356, No Obj: 0.002714, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.303953, iou_loss = 0.239985, total_loss = 2.543938 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000919, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004166, iou_loss = 0.000000, total_loss = 0.004166 \n",
" total_bbox = 30618, rewritten_bbox = 0.261284 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 383: 0.943749, 0.946365 avg loss, 0.000056 rate, 0.677681 seconds, 24512 images, 0.729054 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.388384, GIOU: 0.313496), Class: 0.451197, Obj: 0.014326, No Obj: 0.002625, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.632284, iou_loss = 0.043327, total_loss = 1.675612 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.164922, GIOU: 0.052010), Class: 0.530693, Obj: 0.002880, No Obj: 0.000923, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.765457, iou_loss = 2.633768, total_loss = 3.399225 \n",
" total_bbox = 30624, rewritten_bbox = 0.261233 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.270473, GIOU: 0.061166), Class: 0.492473, Obj: 0.032535, No Obj: 0.003401, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.946605, iou_loss = 0.012876, total_loss = 1.959480 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.384760, GIOU: 0.318366), Class: 0.611322, Obj: 0.006386, No Obj: 0.000950, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.670366, iou_loss = 0.348049, total_loss = 1.018415 \n",
" total_bbox = 30631, rewritten_bbox = 0.261173 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.411277, GIOU: 0.335859), Class: 0.392588, Obj: 0.087834, No Obj: 0.003866, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.539178, iou_loss = 0.068314, total_loss = 1.607492 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.224412, GIOU: -0.040738), Class: 0.545663, Obj: 0.002360, No Obj: 0.001014, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.407484, iou_loss = 0.054920, total_loss = 0.462403 \n",
" total_bbox = 30636, rewritten_bbox = 0.261131 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.379616, GIOU: 0.305163), Class: 0.531243, Obj: 0.012865, No Obj: 0.002065, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.425718, iou_loss = 0.137843, total_loss = 1.563561 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.518788, GIOU: 0.418656), Class: 0.391856, Obj: 0.004817, No Obj: 0.000957, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.385287, iou_loss = 0.430994, total_loss = 0.816281 \n",
" total_bbox = 30642, rewritten_bbox = 0.264343 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413577, GIOU: 0.362607), Class: 0.557414, Obj: 0.014877, No Obj: 0.002630, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.253159, iou_loss = 0.068928, total_loss = 2.322086 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000883, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002706, iou_loss = 0.000000, total_loss = 0.002706 \n",
" total_bbox = 30648, rewritten_bbox = 0.264291 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.453103, GIOU: 0.353943), Class: 0.492289, Obj: 0.036787, No Obj: 0.002982, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.498205, iou_loss = 0.054688, total_loss = 1.552893 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.278073, GIOU: -0.039521), Class: 0.386985, Obj: 0.003138, No Obj: 0.001010, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.385327, iou_loss = 0.026486, total_loss = 0.411813 \n",
" total_bbox = 30653, rewritten_bbox = 0.264248 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325121, GIOU: 0.209796), Class: 0.527284, Obj: 0.019272, No Obj: 0.002791, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.180972, iou_loss = 0.144298, total_loss = 2.325270 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001053, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004334, iou_loss = 0.000000, total_loss = 0.004334 \n",
" total_bbox = 30659, rewritten_bbox = 0.264196 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.171182, GIOU: 0.145878), Class: 0.611985, Obj: 0.022258, No Obj: 0.002565, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.334785, iou_loss = 0.029053, total_loss = 1.363838 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.234007, GIOU: -0.125363), Class: 0.227515, Obj: 0.000291, No Obj: 0.000892, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.478501, iou_loss = 0.022039, total_loss = 0.500540 \n",
" total_bbox = 30664, rewritten_bbox = 0.264153 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.441691, GIOU: 0.418911), Class: 0.431188, Obj: 0.016684, No Obj: 0.002951, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.874525, iou_loss = 0.033636, total_loss = 1.908161 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.411731, GIOU: 0.239424), Class: 0.599266, Obj: 0.002129, No Obj: 0.000905, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.413666, iou_loss = 0.076269, total_loss = 0.489935 \n",
" total_bbox = 30670, rewritten_bbox = 0.264102 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.400062, GIOU: 0.354971), Class: 0.407263, Obj: 0.023450, No Obj: 0.002616, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.810635, iou_loss = 0.008270, total_loss = 0.818904 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.566473, GIOU: 0.547906), Class: 0.523565, Obj: 0.002100, No Obj: 0.000878, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.342689, iou_loss = 0.249827, total_loss = 0.592516 \n",
" total_bbox = 30673, rewritten_bbox = 0.264076 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.288036, GIOU: 0.163193), Class: 0.639574, Obj: 0.041028, No Obj: 0.003086, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.227339, iou_loss = 0.020317, total_loss = 1.247655 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.314839, GIOU: 0.099112), Class: 0.394053, Obj: 0.013981, No Obj: 0.000946, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.384562, iou_loss = 0.053377, total_loss = 0.437939 \n",
" total_bbox = 30678, rewritten_bbox = 0.264033 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369199, GIOU: 0.332201), Class: 0.583933, Obj: 0.048321, No Obj: 0.003532, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.255361, iou_loss = 0.028600, total_loss = 1.283961 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.535138, GIOU: 0.488153), Class: 0.437421, Obj: 0.003381, No Obj: 0.000903, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.394125, iou_loss = 0.177743, total_loss = 0.571867 \n",
" total_bbox = 30683, rewritten_bbox = 0.263990 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.313529, GIOU: 0.294840), Class: 0.464555, Obj: 0.033011, No Obj: 0.002735, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.486527, iou_loss = 0.033946, total_loss = 1.520473 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.437084, GIOU: 0.422763), Class: 0.532643, Obj: 0.001584, No Obj: 0.000943, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.364316, iou_loss = 1.270576, total_loss = 1.634892 \n",
" total_bbox = 30688, rewritten_bbox = 0.263947 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.562258, GIOU: 0.562258), Class: 0.503493, Obj: 0.033187, No Obj: 0.003015, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.140787, iou_loss = 0.025781, total_loss = 1.166568 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.221223, GIOU: 0.055450), Class: 0.366565, Obj: 0.000771, No Obj: 0.001013, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.236372, iou_loss = 2.452165, total_loss = 3.688536 \n",
" total_bbox = 30694, rewritten_bbox = 0.263895 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.374135, GIOU: 0.342855), Class: 0.356675, Obj: 0.021898, No Obj: 0.003299, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.102785, iou_loss = 0.079078, total_loss = 2.181864 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.430034, GIOU: 0.373893), Class: 0.510712, Obj: 0.003042, No Obj: 0.000997, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.411352, iou_loss = 0.431330, total_loss = 0.842682 \n",
" total_bbox = 30700, rewritten_bbox = 0.263844 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415258, GIOU: 0.349383), Class: 0.406808, Obj: 0.009306, No Obj: 0.003215, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.197787, iou_loss = 0.009675, total_loss = 1.207462 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.478674, GIOU: 0.478118), Class: 0.564310, Obj: 0.002458, No Obj: 0.000940, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.752030, iou_loss = 0.407949, total_loss = 1.159979 \n",
" total_bbox = 30705, rewritten_bbox = 0.263801 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 384: 1.019832, 0.953712 avg loss, 0.000057 rate, 0.701766 seconds, 24576 images, 0.728573 hours left\n",
"Loaded: 0.000076 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271456, GIOU: 0.196757), Class: 0.516641, Obj: 0.036245, No Obj: 0.002624, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.404785, iou_loss = 0.009410, total_loss = 1.414195 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000837, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002290, iou_loss = 0.000000, total_loss = 0.002290 \n",
" total_bbox = 30709, rewritten_bbox = 0.263766 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.506641, GIOU: 0.500581), Class: 0.440820, Obj: 0.046059, No Obj: 0.003363, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.988998, iou_loss = 0.191141, total_loss = 2.180140 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000993, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003399, iou_loss = 0.000000, total_loss = 0.003399 \n",
" total_bbox = 30714, rewritten_bbox = 0.263723 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353516, GIOU: 0.348326), Class: 0.446973, Obj: 0.019899, No Obj: 0.003056, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.118709, iou_loss = 0.009825, total_loss = 1.128534 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000964, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003122, iou_loss = 0.000000, total_loss = 0.003122 \n",
" total_bbox = 30718, rewritten_bbox = 0.266944 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308079, GIOU: 0.285080), Class: 0.535085, Obj: 0.018291, No Obj: 0.002440, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.432250, iou_loss = 0.037035, total_loss = 1.469286 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000882, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002576, iou_loss = 0.000000, total_loss = 0.002576 \n",
" total_bbox = 30722, rewritten_bbox = 0.266910 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409879, GIOU: 0.332259), Class: 0.481571, Obj: 0.050337, No Obj: 0.002938, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.778619, iou_loss = 0.063075, total_loss = 1.841694 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.419036, GIOU: 0.275947), Class: 0.726896, Obj: 0.002916, No Obj: 0.000954, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.307479, iou_loss = 0.489876, total_loss = 0.797355 \n",
" total_bbox = 30728, rewritten_bbox = 0.266858 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.432908, GIOU: 0.365087), Class: 0.393870, Obj: 0.030811, No Obj: 0.002834, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.163066, iou_loss = 0.035211, total_loss = 1.198277 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.462378, GIOU: 0.365076), Class: 0.468268, Obj: 0.001151, No Obj: 0.000938, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.769223, iou_loss = 0.637786, total_loss = 1.407009 \n",
" total_bbox = 30733, rewritten_bbox = 0.266814 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.362708, GIOU: 0.250885), Class: 0.459991, Obj: 0.023380, No Obj: 0.003546, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.787415, iou_loss = 0.002961, total_loss = 0.790377 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.439275, GIOU: 0.254508), Class: 0.443602, Obj: 0.007904, No Obj: 0.000966, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.803311, iou_loss = 0.378600, total_loss = 1.181911 \n",
" total_bbox = 30737, rewritten_bbox = 0.266779 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.471621, GIOU: 0.380750), Class: 0.540710, Obj: 0.027277, No Obj: 0.002718, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.789506, iou_loss = 0.051284, total_loss = 1.840789 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.245843, GIOU: 0.245843), Class: 0.783680, Obj: 0.000896, No Obj: 0.000929, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.289099, iou_loss = 0.052645, total_loss = 0.341743 \n",
" total_bbox = 30743, rewritten_bbox = 0.266727 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346777, GIOU: 0.246604), Class: 0.388262, Obj: 0.018864, No Obj: 0.002918, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.555643, iou_loss = 0.080820, total_loss = 1.636463 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000906, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002794, iou_loss = 0.000000, total_loss = 0.002794 \n",
" total_bbox = 30747, rewritten_bbox = 0.266693 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.380640, GIOU: 0.342578), Class: 0.639322, Obj: 0.050455, No Obj: 0.003103, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.339710, iou_loss = 0.059671, total_loss = 1.399381 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000900, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003279, iou_loss = 0.000000, total_loss = 0.003279 \n",
" total_bbox = 30751, rewritten_bbox = 0.266658 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.432929, GIOU: 0.337345), Class: 0.493150, Obj: 0.034613, No Obj: 0.003308, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.457765, iou_loss = 0.025985, total_loss = 1.483750 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.280398, GIOU: 0.062600), Class: 0.503834, Obj: 0.006989, No Obj: 0.000998, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.390164, iou_loss = 0.041215, total_loss = 0.431380 \n",
" total_bbox = 30756, rewritten_bbox = 0.266615 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.500947, GIOU: 0.438715), Class: 0.543231, Obj: 0.023382, No Obj: 0.002383, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.144578, iou_loss = 0.025780, total_loss = 1.170357 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.508939, GIOU: 0.423158), Class: 0.549514, Obj: 0.010976, No Obj: 0.000930, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.359763, iou_loss = 0.115359, total_loss = 0.475122 \n",
" total_bbox = 30760, rewritten_bbox = 0.266580 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.290254, GIOU: 0.233050), Class: 0.526993, Obj: 0.039757, No Obj: 0.002623, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.811801, iou_loss = 0.040670, total_loss = 1.852470 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000917, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002851, iou_loss = 0.000000, total_loss = 0.002851 \n",
" total_bbox = 30765, rewritten_bbox = 0.266537 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.272563, GIOU: 0.042108), Class: 0.449135, Obj: 0.042118, No Obj: 0.003275, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.662555, iou_loss = 0.018744, total_loss = 1.681298 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.395854, GIOU: 0.346227), Class: 0.497982, Obj: 0.000061, No Obj: 0.000901, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.424470, iou_loss = 0.323157, total_loss = 0.747627 \n",
" total_bbox = 30770, rewritten_bbox = 0.266493 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.385641, GIOU: 0.292661), Class: 0.566435, Obj: 0.009136, No Obj: 0.002951, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.460401, iou_loss = 0.088070, total_loss = 1.548471 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.157736, GIOU: 0.157737), Class: 0.793644, Obj: 0.004805, No Obj: 0.001001, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.374399, iou_loss = 0.022224, total_loss = 0.396623 \n",
" total_bbox = 30775, rewritten_bbox = 0.266450 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.224737, GIOU: 0.001518), Class: 0.470149, Obj: 0.013041, No Obj: 0.002245, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.481130, iou_loss = 0.020584, total_loss = 1.501714 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.228885, GIOU: 0.140377), Class: 0.439780, Obj: 0.003132, No Obj: 0.000901, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.802401, iou_loss = 0.326192, total_loss = 1.128594 \n",
" total_bbox = 30781, rewritten_bbox = 0.266398 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 385: 0.881124, 0.946453 avg loss, 0.000057 rate, 0.693706 seconds, 24640 images, 0.728337 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.342748, GIOU: 0.336794), Class: 0.559291, Obj: 0.033322, No Obj: 0.002713, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.998440, iou_loss = 0.035040, total_loss = 1.033480 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000963, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003675, iou_loss = 0.000000, total_loss = 0.003675 \n",
" total_bbox = 30784, rewritten_bbox = 0.266372 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.293852, GIOU: 0.089730), Class: 0.510980, Obj: 0.028923, No Obj: 0.002844, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.486804, iou_loss = 0.028018, total_loss = 1.514822 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.263683, GIOU: -0.011878), Class: 0.585054, Obj: 0.002908, No Obj: 0.000919, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.330179, iou_loss = 0.190389, total_loss = 0.520568 \n",
" total_bbox = 30789, rewritten_bbox = 0.266329 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.321635, GIOU: 0.200725), Class: 0.595306, Obj: 0.009251, No Obj: 0.002881, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.686960, iou_loss = 0.022864, total_loss = 1.709824 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.178130, GIOU: 0.032483), Class: 0.508113, Obj: 0.002149, No Obj: 0.000919, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.384605, iou_loss = 0.022441, total_loss = 0.407046 \n",
" total_bbox = 30795, rewritten_bbox = 0.266277 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.482477, GIOU: 0.420675), Class: 0.445564, Obj: 0.018688, No Obj: 0.003200, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.452119, iou_loss = 0.022028, total_loss = 1.474148 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000933, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003146, iou_loss = 0.000000, total_loss = 0.003146 \n",
" total_bbox = 30799, rewritten_bbox = 0.266242 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.723746, GIOU: 0.723483), Class: 0.511880, Obj: 0.014296, No Obj: 0.002904, .5R: 1.000000, .75R: 0.500000, count: 2, class_loss = 0.801499, iou_loss = 0.068771, total_loss = 0.870270 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.453933, GIOU: 0.365474), Class: 0.604269, Obj: 0.004786, No Obj: 0.000905, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.368661, iou_loss = 0.435800, total_loss = 0.804461 \n",
" total_bbox = 30802, rewritten_bbox = 0.266216 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395534, GIOU: 0.250312), Class: 0.531206, Obj: 0.044381, No Obj: 0.003658, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.261627, iou_loss = 0.141913, total_loss = 2.403541 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000927, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003537, iou_loss = 0.000000, total_loss = 0.003537 \n",
" total_bbox = 30808, rewritten_bbox = 0.266165 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.221170, GIOU: 0.038353), Class: 0.710530, Obj: 0.011407, No Obj: 0.002197, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.139844, iou_loss = 0.007857, total_loss = 1.147701 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.422628, GIOU: 0.284110), Class: 0.337804, Obj: 0.001992, No Obj: 0.000890, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.525136, iou_loss = 0.093107, total_loss = 0.618243 \n",
" total_bbox = 30812, rewritten_bbox = 0.266130 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.207961, GIOU: 0.195604), Class: 0.469387, Obj: 0.024218, No Obj: 0.003147, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.487469, iou_loss = 0.011276, total_loss = 1.498744 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.728684, GIOU: 0.708560), Class: 0.517138, Obj: 0.001407, No Obj: 0.000884, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.339793, iou_loss = 0.327557, total_loss = 0.667350 \n",
" total_bbox = 30817, rewritten_bbox = 0.266087 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402850, GIOU: 0.354003), Class: 0.443323, Obj: 0.029802, No Obj: 0.002961, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 2.096085, iou_loss = 0.053489, total_loss = 2.149574 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000924, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003120, iou_loss = 0.000000, total_loss = 0.003120 \n",
" total_bbox = 30822, rewritten_bbox = 0.266044 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.245958, GIOU: 0.207395), Class: 0.484347, Obj: 0.016514, No Obj: 0.002948, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.167405, iou_loss = 0.002883, total_loss = 1.170288 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.178211, GIOU: -0.308261), Class: 0.394382, Obj: 0.002593, No Obj: 0.000922, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.415989, iou_loss = 0.009740, total_loss = 0.425729 \n",
" total_bbox = 30826, rewritten_bbox = 0.266009 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.418580, GIOU: 0.393153), Class: 0.670326, Obj: 0.028514, No Obj: 0.003386, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.074187, iou_loss = 0.016216, total_loss = 1.090403 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.163444, GIOU: 0.119948), Class: 0.668964, Obj: 0.003007, No Obj: 0.000857, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.394042, iou_loss = 0.068696, total_loss = 0.462738 \n",
" total_bbox = 30830, rewritten_bbox = 0.265975 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.460422, GIOU: 0.408938), Class: 0.533385, Obj: 0.015760, No Obj: 0.002857, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.688104, iou_loss = 0.043778, total_loss = 0.731881 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.198879, GIOU: 0.131118), Class: 0.377896, Obj: 0.003053, No Obj: 0.000858, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.769524, iou_loss = 0.056950, total_loss = 0.826474 \n",
" total_bbox = 30834, rewritten_bbox = 0.265940 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339183, GIOU: 0.227264), Class: 0.515829, Obj: 0.029054, No Obj: 0.003474, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.811293, iou_loss = 0.029282, total_loss = 1.840575 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000912, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002933, iou_loss = 0.000000, total_loss = 0.002933 \n",
" total_bbox = 30839, rewritten_bbox = 0.265897 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.457286, GIOU: 0.402812), Class: 0.530015, Obj: 0.017768, No Obj: 0.002411, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.467663, iou_loss = 0.122476, total_loss = 1.590139 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.351695, GIOU: 0.298754), Class: 0.433726, Obj: 0.003024, No Obj: 0.000880, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.759141, iou_loss = 1.192876, total_loss = 1.952016 \n",
" total_bbox = 30845, rewritten_bbox = 0.265845 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.445854, GIOU: 0.303983), Class: 0.635012, Obj: 0.032500, No Obj: 0.002918, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.588556, iou_loss = 0.065111, total_loss = 1.653667 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000909, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002800, iou_loss = 0.000000, total_loss = 0.002800 \n",
" total_bbox = 30850, rewritten_bbox = 0.265802 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.371290, GIOU: 0.357821), Class: 0.493584, Obj: 0.020561, No Obj: 0.002843, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.501058, iou_loss = 0.010019, total_loss = 1.511077 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000856, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002635, iou_loss = 0.000000, total_loss = 0.002635 \n",
" total_bbox = 30854, rewritten_bbox = 0.265768 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 386: 0.853025, 0.937110 avg loss, 0.000058 rate, 0.698739 seconds, 24704 images, 0.728021 hours left\n",
"Loaded: 0.000033 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.437160, GIOU: 0.356370), Class: 0.466082, Obj: 0.048150, No Obj: 0.003098, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.800300, iou_loss = 0.177593, total_loss = 1.977893 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000887, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002768, iou_loss = 0.000000, total_loss = 0.002768 \n",
" total_bbox = 30859, rewritten_bbox = 0.265725 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.488747, GIOU: 0.485919), Class: 0.403266, Obj: 0.028902, No Obj: 0.003142, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.816447, iou_loss = 0.007069, total_loss = 0.823516 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.389678, GIOU: 0.382031), Class: 0.403138, Obj: 0.020970, No Obj: 0.000877, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.346990, iou_loss = 0.106642, total_loss = 0.453632 \n",
" total_bbox = 30862, rewritten_bbox = 0.265699 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.531196, GIOU: 0.432300), Class: 0.563364, Obj: 0.157453, No Obj: 0.003377, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.215072, iou_loss = 0.173770, total_loss = 1.388842 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.001029, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004020, iou_loss = 0.000000, total_loss = 0.004020 \n",
" total_bbox = 30866, rewritten_bbox = 0.265664 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.281395, GIOU: 0.249804), Class: 0.508647, Obj: 0.016482, No Obj: 0.002388, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.238994, iou_loss = 0.022301, total_loss = 2.261295 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.336726, GIOU: 0.336727), Class: 0.663841, Obj: 0.001662, No Obj: 0.000811, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.339829, iou_loss = 0.307940, total_loss = 0.647769 \n",
" total_bbox = 30873, rewritten_bbox = 0.265604 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.275793, GIOU: 0.091278), Class: 0.438645, Obj: 0.025149, No Obj: 0.002452, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.530789, iou_loss = 0.010849, total_loss = 1.541638 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000873, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002631, iou_loss = 0.000000, total_loss = 0.002631 \n",
" total_bbox = 30877, rewritten_bbox = 0.265570 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472054, GIOU: 0.438383), Class: 0.507113, Obj: 0.164335, No Obj: 0.003009, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 0.879525, iou_loss = 0.038397, total_loss = 0.917922 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.195739, GIOU: -0.076108), Class: 0.539864, Obj: 0.000432, No Obj: 0.000925, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.108475, iou_loss = 0.196000, total_loss = 1.304475 \n",
" total_bbox = 30883, rewritten_bbox = 0.265518 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.367973, GIOU: 0.269365), Class: 0.423331, Obj: 0.024875, No Obj: 0.002995, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.515989, iou_loss = 0.006063, total_loss = 1.522051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000866, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002560, iou_loss = 0.000000, total_loss = 0.002560 \n",
" total_bbox = 30888, rewritten_bbox = 0.268713 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.358920, GIOU: 0.209395), Class: 0.484358, Obj: 0.019874, No Obj: 0.002829, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.184702, iou_loss = 0.056825, total_loss = 1.241527 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.318183, GIOU: 0.225069), Class: 0.480556, Obj: 0.004041, No Obj: 0.000940, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.254651, iou_loss = 1.959506, total_loss = 3.214157 \n",
" total_bbox = 30894, rewritten_bbox = 0.268661 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.523434, GIOU: 0.522356), Class: 0.445686, Obj: 0.092540, No Obj: 0.003470, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.343177, iou_loss = 0.092403, total_loss = 1.435580 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000954, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003220, iou_loss = 0.000000, total_loss = 0.003220 \n",
" total_bbox = 30898, rewritten_bbox = 0.268626 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.505088, GIOU: 0.494096), Class: 0.513086, Obj: 0.063398, No Obj: 0.002925, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.703115, iou_loss = 0.109387, total_loss = 1.812502 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000901, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002951, iou_loss = 0.000000, total_loss = 0.002951 \n",
" total_bbox = 30903, rewritten_bbox = 0.268582 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.277563, GIOU: 0.277563), Class: 0.462791, Obj: 0.045282, No Obj: 0.002840, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.096102, iou_loss = 0.003936, total_loss = 1.100037 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.452512, GIOU: 0.352942), Class: 0.255335, Obj: 0.003801, No Obj: 0.000861, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.503439, iou_loss = 0.081523, total_loss = 0.584962 \n",
" total_bbox = 30907, rewritten_bbox = 0.268548 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.407286, GIOU: 0.237812), Class: 0.391058, Obj: 0.013704, No Obj: 0.002857, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.614894, iou_loss = 0.017424, total_loss = 1.632318 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.464529, GIOU: 0.385071), Class: 0.359129, Obj: 0.005225, No Obj: 0.000880, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.484578, iou_loss = 1.138313, total_loss = 1.622891 \n",
" total_bbox = 30912, rewritten_bbox = 0.268504 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.274655, GIOU: 0.202764), Class: 0.458261, Obj: 0.034994, No Obj: 0.002624, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.783875, iou_loss = 0.044852, total_loss = 1.828728 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.267263, GIOU: 0.031220), Class: 0.538800, Obj: 0.008784, No Obj: 0.000874, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.321897, iou_loss = 0.082946, total_loss = 0.404842 \n",
" total_bbox = 30918, rewritten_bbox = 0.268452 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.467795, GIOU: 0.333457), Class: 0.533609, Obj: 0.044744, No Obj: 0.002769, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.927549, iou_loss = 0.173548, total_loss = 2.101098 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.382169, GIOU: 0.332611), Class: 0.411481, Obj: 0.001616, No Obj: 0.000945, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.409165, iou_loss = 0.249877, total_loss = 0.659042 \n",
" total_bbox = 30924, rewritten_bbox = 0.268400 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372011, GIOU: 0.256661), Class: 0.576872, Obj: 0.017134, No Obj: 0.003213, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.999024, iou_loss = 0.038079, total_loss = 1.037103 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.360388, GIOU: 0.308370), Class: 0.577757, Obj: 0.005714, No Obj: 0.000900, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.371703, iou_loss = 0.067944, total_loss = 0.439647 \n",
" total_bbox = 30928, rewritten_bbox = 0.268365 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.215079, GIOU: -0.015080), Class: 0.515700, Obj: 0.037562, No Obj: 0.003273, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 1.948196, iou_loss = 0.077202, total_loss = 2.025398 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.525378, GIOU: 0.492299), Class: 0.567175, Obj: 0.013176, No Obj: 0.000941, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.073959, iou_loss = 1.042586, total_loss = 2.116545 \n",
" total_bbox = 30937, rewritten_bbox = 0.271520 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 387: 0.940984, 0.937497 avg loss, 0.000059 rate, 0.681633 seconds, 24768 images, 0.727755 hours left\n",
"Loaded: 0.000068 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.406959, GIOU: 0.288390), Class: 0.376411, Obj: 0.029253, No Obj: 0.002950, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.538327, iou_loss = 0.148689, total_loss = 1.687016 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.463376, GIOU: 0.446409), Class: 0.522880, Obj: 0.009229, No Obj: 0.000896, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.745055, iou_loss = 0.501606, total_loss = 1.246661 \n",
" total_bbox = 30943, rewritten_bbox = 0.271467 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.423492, GIOU: 0.323827), Class: 0.619772, Obj: 0.018149, No Obj: 0.002804, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.100017, iou_loss = 0.019883, total_loss = 1.119900 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.210636, GIOU: -0.193066), Class: 0.432884, Obj: 0.003780, No Obj: 0.000877, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.343172, iou_loss = 0.046453, total_loss = 0.389625 \n",
" total_bbox = 30947, rewritten_bbox = 0.271432 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.500446, GIOU: 0.446618), Class: 0.390867, Obj: 0.018869, No Obj: 0.002883, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.486537, iou_loss = 0.042857, total_loss = 1.529394 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000856, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002430, iou_loss = 0.000000, total_loss = 0.002430 \n",
" total_bbox = 30951, rewritten_bbox = 0.271397 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.340011, GIOU: 0.272740), Class: 0.662054, Obj: 0.015232, No Obj: 0.002653, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.973394, iou_loss = 0.028806, total_loss = 1.002200 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000847, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002461, iou_loss = 0.000000, total_loss = 0.002461 \n",
" total_bbox = 30954, rewritten_bbox = 0.271370 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.335995, GIOU: 0.230935), Class: 0.557732, Obj: 0.024694, No Obj: 0.002381, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.413203, iou_loss = 0.067186, total_loss = 1.480388 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000858, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002772, iou_loss = 0.000000, total_loss = 0.002772 \n",
" total_bbox = 30958, rewritten_bbox = 0.271335 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.614249, GIOU: 0.573556), Class: 0.598068, Obj: 0.068125, No Obj: 0.002697, .5R: 1.000000, .75R: 0.000000, count: 3, class_loss = 1.101110, iou_loss = 0.055285, total_loss = 1.156396 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.081605, GIOU: -0.186394), Class: 0.316204, Obj: 0.002609, No Obj: 0.000898, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.491844, iou_loss = 2.503533, total_loss = 2.995377 \n",
" total_bbox = 30962, rewritten_bbox = 0.271300 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.430122, GIOU: 0.301043), Class: 0.473714, Obj: 0.022831, No Obj: 0.002798, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.882266, iou_loss = 0.050382, total_loss = 1.932648 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.303636, GIOU: 0.252499), Class: 0.613548, Obj: 0.001684, No Obj: 0.000907, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.700708, iou_loss = 0.162871, total_loss = 0.863579 \n",
" total_bbox = 30969, rewritten_bbox = 0.271239 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.482325, GIOU: 0.378390), Class: 0.535624, Obj: 0.024753, No Obj: 0.002767, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.404745, iou_loss = 0.031528, total_loss = 1.436273 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000825, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002291, iou_loss = 0.000000, total_loss = 0.002291 \n",
" total_bbox = 30973, rewritten_bbox = 0.271204 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.611330, GIOU: 0.609473), Class: 0.439739, Obj: 0.037464, No Obj: 0.002864, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.020959, iou_loss = 0.018520, total_loss = 1.039479 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.383693, GIOU: 0.198585), Class: 0.394910, Obj: 0.005384, No Obj: 0.000863, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.491413, iou_loss = 0.069205, total_loss = 0.560618 \n",
" total_bbox = 30977, rewritten_bbox = 0.271169 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.365798, GIOU: 0.239641), Class: 0.538260, Obj: 0.027430, No Obj: 0.002339, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.065883, iou_loss = 0.008450, total_loss = 1.074333 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.190683, GIOU: 0.073535), Class: 0.475661, Obj: 0.005573, No Obj: 0.000889, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.238866, iou_loss = 1.194060, total_loss = 2.432926 \n",
" total_bbox = 30983, rewritten_bbox = 0.271116 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322580, GIOU: 0.154618), Class: 0.629669, Obj: 0.021007, No Obj: 0.003278, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.347140, iou_loss = 0.016976, total_loss = 1.364116 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000923, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002893, iou_loss = 0.000000, total_loss = 0.002893 \n",
" total_bbox = 30987, rewritten_bbox = 0.271081 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.365165, GIOU: 0.328479), Class: 0.624364, Obj: 0.044572, No Obj: 0.003283, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 1.947221, iou_loss = 0.164126, total_loss = 2.111347 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.353284, GIOU: 0.279146), Class: 0.460606, Obj: 0.005723, No Obj: 0.000901, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.881489, iou_loss = 0.288432, total_loss = 1.169921 \n",
" total_bbox = 30995, rewritten_bbox = 0.271011 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.363126, GIOU: 0.233699), Class: 0.552754, Obj: 0.057078, No Obj: 0.002743, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.688099, iou_loss = 0.026266, total_loss = 0.714365 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.544564, GIOU: 0.499693), Class: 0.299766, Obj: 0.000128, No Obj: 0.000946, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.454014, iou_loss = 0.288689, total_loss = 0.742703 \n",
" total_bbox = 30998, rewritten_bbox = 0.270985 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.416508, GIOU: 0.327089), Class: 0.509902, Obj: 0.036116, No Obj: 0.002636, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.849188, iou_loss = 0.028556, total_loss = 1.877744 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.252757, GIOU: -0.080160), Class: 0.472128, Obj: 0.001127, No Obj: 0.000848, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.393496, iou_loss = 0.026074, total_loss = 0.419569 \n",
" total_bbox = 31004, rewritten_bbox = 0.270933 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.368297, GIOU: 0.336788), Class: 0.533535, Obj: 0.014292, No Obj: 0.002794, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.725846, iou_loss = 0.053132, total_loss = 1.778978 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.283950, GIOU: 0.224251), Class: 0.543075, Obj: 0.003833, No Obj: 0.000880, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.059065, iou_loss = 1.945306, total_loss = 3.004370 \n",
" total_bbox = 31012, rewritten_bbox = 0.270863 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.527609, GIOU: 0.488631), Class: 0.446525, Obj: 0.023150, No Obj: 0.002640, .5R: 0.666667, .75R: 0.166667, count: 6, class_loss = 2.450291, iou_loss = 0.103804, total_loss = 2.554096 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.188289, GIOU: 0.173293), Class: 0.602219, Obj: 0.001595, No Obj: 0.000830, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.311848, iou_loss = 0.038624, total_loss = 0.350473 \n",
" total_bbox = 31019, rewritten_bbox = 0.270802 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 388: 0.950414, 0.938789 avg loss, 0.000059 rate, 0.705049 seconds, 24832 images, 0.727319 hours left\n",
"Loaded: 0.000032 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.426064, GIOU: 0.414250), Class: 0.394516, Obj: 0.044186, No Obj: 0.002956, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.570178, iou_loss = 0.026279, total_loss = 1.596457 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000851, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002471, iou_loss = 0.000000, total_loss = 0.002471 \n",
" total_bbox = 31023, rewritten_bbox = 0.270767 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.330784, GIOU: 0.321928), Class: 0.420151, Obj: 0.019860, No Obj: 0.002705, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.498988, iou_loss = 0.058439, total_loss = 1.557428 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.236205, GIOU: -0.133104), Class: 0.591181, Obj: 0.008409, No Obj: 0.000842, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.675096, iou_loss = 0.042556, total_loss = 0.717652 \n",
" total_bbox = 31029, rewritten_bbox = 0.270714 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.277119, GIOU: 0.049111), Class: 0.374966, Obj: 0.031525, No Obj: 0.003050, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.270214, iou_loss = 0.005024, total_loss = 1.275238 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.294561, GIOU: 0.207217), Class: 0.523769, Obj: 0.005920, No Obj: 0.000859, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.348404, iou_loss = 0.333075, total_loss = 0.681479 \n",
" total_bbox = 31033, rewritten_bbox = 0.270680 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.633633, GIOU: 0.629362), Class: 0.553650, Obj: 0.028480, No Obj: 0.002696, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.509161, iou_loss = 0.173739, total_loss = 1.682900 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000902, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003347, iou_loss = 0.000000, total_loss = 0.003347 \n",
" total_bbox = 31037, rewritten_bbox = 0.270645 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.324509, GIOU: 0.212505), Class: 0.522017, Obj: 0.008169, No Obj: 0.002156, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.200031, iou_loss = 0.116022, total_loss = 2.316054 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.227642, GIOU: -0.062206), Class: 0.579782, Obj: 0.000547, No Obj: 0.000866, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.366132, iou_loss = 0.069212, total_loss = 0.435344 \n",
" total_bbox = 31044, rewritten_bbox = 0.270584 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395295, GIOU: 0.304940), Class: 0.422738, Obj: 0.055320, No Obj: 0.002520, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.785758, iou_loss = 0.070864, total_loss = 1.856622 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000881, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003978, iou_loss = 0.000000, total_loss = 0.003978 \n",
" total_bbox = 31049, rewritten_bbox = 0.270540 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403740, GIOU: 0.398476), Class: 0.614568, Obj: 0.017204, No Obj: 0.002561, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.366169, iou_loss = 0.088472, total_loss = 1.454642 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.327079, GIOU: 0.259428), Class: 0.449096, Obj: 0.003256, No Obj: 0.000913, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.919477, iou_loss = 1.993813, total_loss = 3.913290 \n",
" total_bbox = 31058, rewritten_bbox = 0.270462 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320792, GIOU: 0.233243), Class: 0.525804, Obj: 0.036371, No Obj: 0.002903, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 1.915887, iou_loss = 0.040021, total_loss = 1.955908 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000859, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002432, iou_loss = 0.000000, total_loss = 0.002432 \n",
" total_bbox = 31064, rewritten_bbox = 0.273629 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.371009, GIOU: 0.370828), Class: 0.487956, Obj: 0.011871, No Obj: 0.002781, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.436549, iou_loss = 0.011633, total_loss = 1.448182 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000924, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003390, iou_loss = 0.000000, total_loss = 0.003390 \n",
" total_bbox = 31068, rewritten_bbox = 0.273593 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.522432, GIOU: 0.458040), Class: 0.485137, Obj: 0.025694, No Obj: 0.002888, .5R: 0.571429, .75R: 0.000000, count: 7, class_loss = 2.633331, iou_loss = 0.210237, total_loss = 2.843568 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000855, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002613, iou_loss = 0.000000, total_loss = 0.002613 \n",
" total_bbox = 31075, rewritten_bbox = 0.273532 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.326755, GIOU: 0.153074), Class: 0.481030, Obj: 0.015077, No Obj: 0.003020, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.530675, iou_loss = 0.051008, total_loss = 1.581684 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000802, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002408, iou_loss = 0.000000, total_loss = 0.002408 \n",
" total_bbox = 31079, rewritten_bbox = 0.273497 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382044, GIOU: 0.331498), Class: 0.568683, Obj: 0.088321, No Obj: 0.002764, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.476627, iou_loss = 0.047763, total_loss = 1.524390 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.478158, GIOU: 0.389581), Class: 0.378658, Obj: 0.002360, No Obj: 0.000899, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.813378, iou_loss = 0.527755, total_loss = 1.341133 \n",
" total_bbox = 31086, rewritten_bbox = 0.273435 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.387243, GIOU: 0.333971), Class: 0.521210, Obj: 0.057104, No Obj: 0.003098, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.449939, iou_loss = 0.022723, total_loss = 1.472662 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000881, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002742, iou_loss = 0.000000, total_loss = 0.002742 \n",
" total_bbox = 31090, rewritten_bbox = 0.273400 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.310223, GIOU: 0.253077), Class: 0.505070, Obj: 0.016293, No Obj: 0.002653, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.067237, iou_loss = 0.004349, total_loss = 1.071586 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.455176, GIOU: 0.444314), Class: 0.255393, Obj: 0.002746, No Obj: 0.000832, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.850085, iou_loss = 0.612737, total_loss = 1.462821 \n",
" total_bbox = 31095, rewritten_bbox = 0.273356 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.542735, GIOU: 0.542735), Class: 0.448059, Obj: 0.069631, No Obj: 0.003259, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.673829, iou_loss = 0.010278, total_loss = 0.684107 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.561349, GIOU: 0.523591), Class: 0.520799, Obj: 0.009616, No Obj: 0.000900, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.784131, iou_loss = 0.960367, total_loss = 1.744499 \n",
" total_bbox = 31099, rewritten_bbox = 0.273321 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.511861, GIOU: 0.472321), Class: 0.575417, Obj: 0.017027, No Obj: 0.002679, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.348049, iou_loss = 0.059880, total_loss = 1.407929 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000917, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003041, iou_loss = 0.000000, total_loss = 0.003041 \n",
" total_bbox = 31103, rewritten_bbox = 0.273286 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 389: 0.961248, 0.941035 avg loss, 0.000060 rate, 0.718107 seconds, 24896 images, 0.727121 hours left\n",
"Loaded: 0.000073 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.309564, GIOU: 0.227227), Class: 0.631529, Obj: 0.037180, No Obj: 0.002924, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.013036, iou_loss = 0.037815, total_loss = 1.050851 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.196120, GIOU: -0.199669), Class: 0.551445, Obj: 0.003517, No Obj: 0.000826, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.425049, iou_loss = 0.805164, total_loss = 2.230212 \n",
" total_bbox = 31110, rewritten_bbox = 0.273224 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.290920, GIOU: 0.137530), Class: 0.554596, Obj: 0.014842, No Obj: 0.002403, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.697372, iou_loss = 0.020703, total_loss = 1.718076 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.294478, GIOU: 0.263495), Class: 0.507421, Obj: 0.008491, No Obj: 0.000827, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.397999, iou_loss = 0.294929, total_loss = 0.692929 \n",
" total_bbox = 31115, rewritten_bbox = 0.273180 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307572, GIOU: 0.292352), Class: 0.543907, Obj: 0.018471, No Obj: 0.002804, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.503166, iou_loss = 0.041053, total_loss = 1.544218 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.355084, GIOU: 0.243346), Class: 0.485720, Obj: 0.001401, No Obj: 0.000836, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.744512, iou_loss = 0.521674, total_loss = 1.266186 \n",
" total_bbox = 31121, rewritten_bbox = 0.273127 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308594, GIOU: 0.148643), Class: 0.382966, Obj: 0.014611, No Obj: 0.002745, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.995729, iou_loss = 0.086971, total_loss = 2.082700 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000842, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002556, iou_loss = 0.000000, total_loss = 0.002556 \n",
" total_bbox = 31126, rewritten_bbox = 0.273084 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.425816, GIOU: 0.226052), Class: 0.569423, Obj: 0.012736, No Obj: 0.002710, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.453640, iou_loss = 0.014756, total_loss = 1.468396 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.254846, GIOU: 0.254847), Class: 0.477824, Obj: 0.000577, No Obj: 0.000810, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.341900, iou_loss = 0.056897, total_loss = 0.398797 \n",
" total_bbox = 31131, rewritten_bbox = 0.273040 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.441522, GIOU: 0.395225), Class: 0.488009, Obj: 0.013758, No Obj: 0.002157, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.502423, iou_loss = 0.032486, total_loss = 1.534910 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000822, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002159, iou_loss = 0.000000, total_loss = 0.002159 \n",
" total_bbox = 31135, rewritten_bbox = 0.273005 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402065, GIOU: 0.371137), Class: 0.549219, Obj: 0.036571, No Obj: 0.002577, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.457693, iou_loss = 0.046431, total_loss = 1.504125 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000916, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003693, iou_loss = 0.000000, total_loss = 0.003693 \n",
" total_bbox = 31139, rewritten_bbox = 0.272970 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.471420, GIOU: 0.463263), Class: 0.539260, Obj: 0.017071, No Obj: 0.002936, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.143957, iou_loss = 0.027556, total_loss = 1.171513 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.557659, GIOU: 0.525217), Class: 0.386166, Obj: 0.004922, No Obj: 0.000922, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.428528, iou_loss = 0.203316, total_loss = 0.631844 \n",
" total_bbox = 31143, rewritten_bbox = 0.272935 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.593255, GIOU: 0.546886), Class: 0.591585, Obj: 0.092099, No Obj: 0.002651, .5R: 1.000000, .75R: 0.000000, count: 4, class_loss = 1.323122, iou_loss = 0.102201, total_loss = 1.425323 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000845, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002496, iou_loss = 0.000000, total_loss = 0.002496 \n",
" total_bbox = 31147, rewritten_bbox = 0.272899 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392836, GIOU: 0.307432), Class: 0.422223, Obj: 0.039314, No Obj: 0.002807, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.119371, iou_loss = 0.019350, total_loss = 1.138721 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.669088, GIOU: 0.642536), Class: 0.495361, Obj: 0.002851, No Obj: 0.000891, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.379057, iou_loss = 0.978455, total_loss = 1.357512 \n",
" total_bbox = 31151, rewritten_bbox = 0.272864 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.312145, GIOU: 0.306309), Class: 0.508244, Obj: 0.009656, No Obj: 0.002475, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.453345, iou_loss = 0.144361, total_loss = 1.597706 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000836, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002973, iou_loss = 0.000000, total_loss = 0.002973 \n",
" total_bbox = 31155, rewritten_bbox = 0.272829 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.161255, GIOU: 0.161255), Class: 0.443955, Obj: 0.028703, No Obj: 0.002646, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.119811, iou_loss = 0.026653, total_loss = 1.146464 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.379104, GIOU: 0.336693), Class: 0.600443, Obj: 0.000436, No Obj: 0.000816, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.676281, iou_loss = 0.650180, total_loss = 1.326461 \n",
" total_bbox = 31160, rewritten_bbox = 0.272786 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.458551, GIOU: 0.434208), Class: 0.590024, Obj: 0.044741, No Obj: 0.002904, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.346248, iou_loss = 0.044462, total_loss = 1.390710 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000889, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002854, iou_loss = 0.000000, total_loss = 0.002854 \n",
" total_bbox = 31164, rewritten_bbox = 0.272751 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436686, GIOU: 0.409058), Class: 0.432907, Obj: 0.017577, No Obj: 0.002415, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.243838, iou_loss = 0.053769, total_loss = 1.297607 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.329018, GIOU: 0.117984), Class: 0.426118, Obj: 0.006362, No Obj: 0.000839, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.367806, iou_loss = 0.384595, total_loss = 0.752401 \n",
" total_bbox = 31168, rewritten_bbox = 0.272716 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346503, GIOU: 0.324178), Class: 0.521487, Obj: 0.021452, No Obj: 0.002347, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.812928, iou_loss = 0.053120, total_loss = 1.866049 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.161802, GIOU: -0.039298), Class: 0.609084, Obj: 0.007412, No Obj: 0.000892, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.709835, iou_loss = 0.028556, total_loss = 0.738391 \n",
" total_bbox = 31175, rewritten_bbox = 0.272654 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357164, GIOU: 0.251225), Class: 0.458638, Obj: 0.050783, No Obj: 0.003269, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.526349, iou_loss = 0.011786, total_loss = 1.538135 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000852, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002417, iou_loss = 0.000000, total_loss = 0.002417 \n",
" total_bbox = 31179, rewritten_bbox = 0.272619 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 390: 0.889950, 0.935927 avg loss, 0.000060 rate, 0.723688 seconds, 24960 images, 0.727053 hours left\n",
"Loaded: 0.000045 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.330690, GIOU: 0.235474), Class: 0.536451, Obj: 0.011845, No Obj: 0.002844, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.181754, iou_loss = 0.071650, total_loss = 2.253404 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000859, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002983, iou_loss = 0.000000, total_loss = 0.002983 \n",
" total_bbox = 31185, rewritten_bbox = 0.272567 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.448689, GIOU: 0.277384), Class: 0.561662, Obj: 0.011625, No Obj: 0.002719, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.442259, iou_loss = 0.082466, total_loss = 1.524725 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.336444, GIOU: 0.211923), Class: 0.731305, Obj: 0.004208, No Obj: 0.000804, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.335835, iou_loss = 0.125878, total_loss = 0.461713 \n",
" total_bbox = 31190, rewritten_bbox = 0.272523 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.400104, GIOU: 0.383098), Class: 0.565643, Obj: 0.009643, No Obj: 0.002617, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.380395, iou_loss = 0.179881, total_loss = 1.560276 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.278098, GIOU: -0.035108), Class: 0.496706, Obj: 0.001823, No Obj: 0.000864, .5R: 0.090909, .75R: 0.000000, count: 11, class_loss = 4.035104, iou_loss = 0.875964, total_loss = 4.911068 \n",
" total_bbox = 31205, rewritten_bbox = 0.275597 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.327177, GIOU: 0.111451), Class: 0.620042, Obj: 0.093807, No Obj: 0.003637, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.279221, iou_loss = 0.115982, total_loss = 1.395203 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000912, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003132, iou_loss = 0.000000, total_loss = 0.003132 \n",
" total_bbox = 31209, rewritten_bbox = 0.275562 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.195098, GIOU: 0.094889), Class: 0.529175, Obj: 0.006582, No Obj: 0.002589, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.399361, iou_loss = 0.020126, total_loss = 1.419487 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000857, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003427, iou_loss = 0.000000, total_loss = 0.003427 \n",
" total_bbox = 31213, rewritten_bbox = 0.275526 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.363510, GIOU: 0.179661), Class: 0.483984, Obj: 0.027261, No Obj: 0.002371, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.249718, iou_loss = 0.028505, total_loss = 1.278223 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000814, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002580, iou_loss = 0.000000, total_loss = 0.002580 \n",
" total_bbox = 31216, rewritten_bbox = 0.275500 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.222289, GIOU: 0.001948), Class: 0.520498, Obj: 0.009163, No Obj: 0.002770, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.369481, iou_loss = 0.014394, total_loss = 1.383875 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000818, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003322, iou_loss = 0.000000, total_loss = 0.003322 \n",
" total_bbox = 31220, rewritten_bbox = 0.275464 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.248790, GIOU: 0.172374), Class: 0.523542, Obj: 0.019138, No Obj: 0.002467, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.630477, iou_loss = 0.010960, total_loss = 1.641438 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000822, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002157, iou_loss = 0.000000, total_loss = 0.002157 \n",
" total_bbox = 31224, rewritten_bbox = 0.275429 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.400645, GIOU: 0.250335), Class: 0.523970, Obj: 0.020725, No Obj: 0.002724, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.334366, iou_loss = 0.019311, total_loss = 1.353676 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000891, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004197, iou_loss = 0.000000, total_loss = 0.004197 \n",
" total_bbox = 31228, rewritten_bbox = 0.275394 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.499262, GIOU: 0.469835), Class: 0.607287, Obj: 0.016670, No Obj: 0.002552, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 2.190755, iou_loss = 0.070097, total_loss = 2.260852 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000796, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002164, iou_loss = 0.000000, total_loss = 0.002164 \n",
" total_bbox = 31234, rewritten_bbox = 0.275341 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.501040, GIOU: 0.428111), Class: 0.536710, Obj: 0.018248, No Obj: 0.002732, .5R: 0.500000, .75R: 0.500000, count: 4, class_loss = 1.446244, iou_loss = 0.023756, total_loss = 1.470000 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.299474, GIOU: 0.032794), Class: 0.581103, Obj: 0.002484, No Obj: 0.000804, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.653158, iou_loss = 0.078686, total_loss = 0.731844 \n",
" total_bbox = 31240, rewritten_bbox = 0.275288 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436328, GIOU: 0.422826), Class: 0.484046, Obj: 0.026033, No Obj: 0.002978, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.458613, iou_loss = 0.020206, total_loss = 1.478819 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000886, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002787, iou_loss = 0.000000, total_loss = 0.002787 \n",
" total_bbox = 31244, rewritten_bbox = 0.275253 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.405342, GIOU: 0.340506), Class: 0.486710, Obj: 0.017747, No Obj: 0.002779, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.804584, iou_loss = 0.045441, total_loss = 1.850024 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.434180, GIOU: 0.384883), Class: 0.635791, Obj: 0.001109, No Obj: 0.000924, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.312097, iou_loss = 2.148846, total_loss = 2.460943 \n",
" total_bbox = 31250, rewritten_bbox = 0.275200 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.327000, GIOU: 0.232150), Class: 0.558852, Obj: 0.050090, No Obj: 0.003085, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.542989, iou_loss = 0.029105, total_loss = 1.572094 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.273980, GIOU: 0.208196), Class: 0.458054, Obj: 0.004821, No Obj: 0.000821, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.394479, iou_loss = 0.044836, total_loss = 0.439315 \n",
" total_bbox = 31255, rewritten_bbox = 0.275156 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427462, GIOU: 0.348557), Class: 0.537930, Obj: 0.064677, No Obj: 0.003348, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.466331, iou_loss = 0.048496, total_loss = 1.514827 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.168205, GIOU: -0.018301), Class: 0.288758, Obj: 0.001317, No Obj: 0.000877, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.430168, iou_loss = 0.148977, total_loss = 0.579145 \n",
" total_bbox = 31260, rewritten_bbox = 0.275112 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271952, GIOU: 0.268892), Class: 0.641272, Obj: 0.026416, No Obj: 0.002617, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.392620, iou_loss = 0.021440, total_loss = 1.414061 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.378069, GIOU: 0.378069), Class: 0.448509, Obj: 0.003315, No Obj: 0.000844, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.448047, iou_loss = 0.124594, total_loss = 0.572640 \n",
" total_bbox = 31265, rewritten_bbox = 0.275068 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 391: 0.983368, 0.940671 avg loss, 0.000061 rate, 0.709970 seconds, 25024 images, 0.727040 hours left\n",
"Loaded: 0.000035 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.297892, GIOU: 0.094639), Class: 0.415875, Obj: 0.007548, No Obj: 0.002663, .5R: 0.000000, .75R: 0.000000, count: 7, class_loss = 2.666802, iou_loss = 0.061206, total_loss = 2.728008 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.205330, GIOU: -0.067583), Class: 0.516666, Obj: 0.000428, No Obj: 0.000837, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 2.169851, iou_loss = 2.548003, total_loss = 4.717854 \n",
" total_bbox = 31279, rewritten_bbox = 0.278142 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.543212, GIOU: 0.524775), Class: 0.516958, Obj: 0.040518, No Obj: 0.003324, .5R: 0.750000, .75R: 0.500000, count: 4, class_loss = 1.462028, iou_loss = 0.066405, total_loss = 1.528433 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000827, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002362, iou_loss = 0.000000, total_loss = 0.002362 \n",
" total_bbox = 31283, rewritten_bbox = 0.278106 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410474, GIOU: 0.378080), Class: 0.585738, Obj: 0.027023, No Obj: 0.002658, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.685728, iou_loss = 0.105351, total_loss = 1.791079 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.693038, GIOU: 0.660183), Class: 0.606870, Obj: 0.010718, No Obj: 0.000832, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.375183, iou_loss = 0.301279, total_loss = 0.676463 \n",
" total_bbox = 31289, rewritten_bbox = 0.278053 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.259601, GIOU: 0.259602), Class: 0.473751, Obj: 0.014790, No Obj: 0.002540, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.970117, iou_loss = 0.008306, total_loss = 1.978423 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000839, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002617, iou_loss = 0.000000, total_loss = 0.002617 \n",
" total_bbox = 31294, rewritten_bbox = 0.278009 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382111, GIOU: 0.296026), Class: 0.534670, Obj: 0.020621, No Obj: 0.002851, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.136990, iou_loss = 0.017808, total_loss = 1.154798 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.220686, GIOU: 0.098069), Class: 0.429731, Obj: 0.003203, No Obj: 0.000829, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.174126, iou_loss = 1.058985, total_loss = 2.233111 \n",
" total_bbox = 31300, rewritten_bbox = 0.277955 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339031, GIOU: 0.296589), Class: 0.451238, Obj: 0.014881, No Obj: 0.002489, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.557498, iou_loss = 0.015167, total_loss = 1.572666 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000796, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002390, iou_loss = 0.000000, total_loss = 0.002390 \n",
" total_bbox = 31304, rewritten_bbox = 0.277920 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320483, GIOU: 0.170950), Class: 0.531948, Obj: 0.015551, No Obj: 0.002574, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.178931, iou_loss = 0.042153, total_loss = 2.221084 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000889, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003020, iou_loss = 0.000000, total_loss = 0.003020 \n",
" total_bbox = 31310, rewritten_bbox = 0.277866 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.388354, GIOU: 0.388354), Class: 0.537539, Obj: 0.036283, No Obj: 0.002803, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.405593, iou_loss = 0.022924, total_loss = 1.428517 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.428800, GIOU: 0.268981), Class: 0.431371, Obj: 0.002071, No Obj: 0.000804, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.379480, iou_loss = 0.081985, total_loss = 0.461465 \n",
" total_bbox = 31315, rewritten_bbox = 0.277822 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.258441, GIOU: 0.029140), Class: 0.451347, Obj: 0.015304, No Obj: 0.002432, .5R: 0.000000, .75R: 0.000000, count: 7, class_loss = 2.608451, iou_loss = 0.031279, total_loss = 2.639730 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.299495, GIOU: 0.053396), Class: 0.480188, Obj: 0.004504, No Obj: 0.000800, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.843429, iou_loss = 0.133721, total_loss = 0.977150 \n",
" total_bbox = 31324, rewritten_bbox = 0.280935 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.329270, GIOU: 0.261892), Class: 0.543364, Obj: 0.045355, No Obj: 0.003383, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.807528, iou_loss = 0.057364, total_loss = 1.864892 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.669963, GIOU: 0.647707), Class: 0.549878, Obj: 0.010284, No Obj: 0.000858, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.361720, iou_loss = 0.954426, total_loss = 1.316146 \n",
" total_bbox = 31330, rewritten_bbox = 0.280881 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352628, GIOU: 0.274035), Class: 0.547173, Obj: 0.071431, No Obj: 0.003190, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.333850, iou_loss = 0.031428, total_loss = 1.365278 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.341969, GIOU: 0.218104), Class: 0.549178, Obj: 0.004232, No Obj: 0.000832, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.149087, iou_loss = 1.230831, total_loss = 2.379918 \n",
" total_bbox = 31337, rewritten_bbox = 0.280818 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.453731, GIOU: 0.355628), Class: 0.488049, Obj: 0.029752, No Obj: 0.002983, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.466763, iou_loss = 0.135771, total_loss = 1.602535 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.174783, GIOU: 0.174783), Class: 0.502342, Obj: 0.001037, No Obj: 0.000839, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.371443, iou_loss = 0.026590, total_loss = 0.398033 \n",
" total_bbox = 31342, rewritten_bbox = 0.280773 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.572774, GIOU: 0.454372), Class: 0.330186, Obj: 0.038555, No Obj: 0.002577, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.227290, iou_loss = 0.017002, total_loss = 1.244291 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.491879, GIOU: 0.491880), Class: 0.548698, Obj: 0.004220, No Obj: 0.000816, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.353128, iou_loss = 0.210903, total_loss = 0.564031 \n",
" total_bbox = 31346, rewritten_bbox = 0.280738 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.380354, GIOU: 0.330580), Class: 0.482756, Obj: 0.014735, No Obj: 0.003062, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.398951, iou_loss = 0.021264, total_loss = 1.420216 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.476231, GIOU: 0.353430), Class: 0.512302, Obj: 0.005738, No Obj: 0.000897, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.391400, iou_loss = 1.202744, total_loss = 2.594143 \n",
" total_bbox = 31354, rewritten_bbox = 0.280666 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.529112, GIOU: 0.518359), Class: 0.516926, Obj: 0.023129, No Obj: 0.003196, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.795388, iou_loss = 0.085498, total_loss = 1.880886 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.379735, GIOU: 0.192525), Class: 0.238195, Obj: 0.017824, No Obj: 0.000867, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.469707, iou_loss = 0.053799, total_loss = 0.523506 \n",
" total_bbox = 31360, rewritten_bbox = 0.280612 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.356524, GIOU: 0.299615), Class: 0.431239, Obj: 0.013139, No Obj: 0.003025, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.584320, iou_loss = 0.023231, total_loss = 1.607551 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.480336, GIOU: 0.426025), Class: 0.557074, Obj: 0.001426, No Obj: 0.000824, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.329406, iou_loss = 0.305673, total_loss = 0.635079 \n",
" total_bbox = 31365, rewritten_bbox = 0.280567 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 392: 1.155045, 0.962108 avg loss, 0.000062 rate, 0.694975 seconds, 25088 images, 0.726888 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344365, GIOU: 0.328899), Class: 0.628500, Obj: 0.047784, No Obj: 0.002713, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.365950, iou_loss = 0.023013, total_loss = 1.388962 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.439713, GIOU: 0.439713), Class: 0.358648, Obj: 0.011946, No Obj: 0.000901, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.462992, iou_loss = 0.169257, total_loss = 0.632249 \n",
" total_bbox = 31370, rewritten_bbox = 0.280523 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.232533, GIOU: 0.100564), Class: 0.516285, Obj: 0.012446, No Obj: 0.002815, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.801646, iou_loss = 0.025133, total_loss = 1.826779 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000827, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002732, iou_loss = 0.000000, total_loss = 0.002732 \n",
" total_bbox = 31375, rewritten_bbox = 0.280478 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.485829, GIOU: 0.440225), Class: 0.506023, Obj: 0.027987, No Obj: 0.002787, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.824648, iou_loss = 0.096743, total_loss = 1.921390 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000810, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002486, iou_loss = 0.000000, total_loss = 0.002486 \n",
" total_bbox = 31380, rewritten_bbox = 0.280433 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359931, GIOU: 0.310956), Class: 0.416547, Obj: 0.069043, No Obj: 0.002764, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.135457, iou_loss = 0.026242, total_loss = 1.161699 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.471828, GIOU: 0.456418), Class: 0.377387, Obj: 0.064893, No Obj: 0.000837, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.363067, iou_loss = 0.202834, total_loss = 0.565902 \n",
" total_bbox = 31384, rewritten_bbox = 0.280398 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.258750, GIOU: 0.245068), Class: 0.495500, Obj: 0.053358, No Obj: 0.002791, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.115249, iou_loss = 0.030882, total_loss = 1.146131 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.592701, GIOU: 0.555291), Class: 0.505697, Obj: 0.000965, No Obj: 0.000809, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.352299, iou_loss = 0.520955, total_loss = 0.873254 \n",
" total_bbox = 31388, rewritten_bbox = 0.280362 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.351385, GIOU: 0.139946), Class: 0.536041, Obj: 0.046457, No Obj: 0.003485, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.518871, iou_loss = 0.062154, total_loss = 1.581025 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.273109, GIOU: -0.052570), Class: 0.678881, Obj: 0.002289, No Obj: 0.000899, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.344739, iou_loss = 0.027048, total_loss = 0.371786 \n",
" total_bbox = 31393, rewritten_bbox = 0.280317 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.242143, GIOU: 0.178781), Class: 0.542894, Obj: 0.075938, No Obj: 0.003337, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.484822, iou_loss = 0.015429, total_loss = 1.500250 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000877, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002737, iou_loss = 0.000000, total_loss = 0.002737 \n",
" total_bbox = 31397, rewritten_bbox = 0.280282 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.312458, GIOU: 0.267371), Class: 0.535315, Obj: 0.033541, No Obj: 0.002425, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.058781, iou_loss = 0.025333, total_loss = 1.084114 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.147397, GIOU: 0.147398), Class: 0.506058, Obj: 0.002080, No Obj: 0.000827, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.764656, iou_loss = 0.411161, total_loss = 1.175816 \n",
" total_bbox = 31402, rewritten_bbox = 0.280237 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.215938, GIOU: 0.215938), Class: 0.403614, Obj: 0.027768, No Obj: 0.002588, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.200635, iou_loss = 0.002277, total_loss = 1.202912 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000839, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002855, iou_loss = 0.000000, total_loss = 0.002855 \n",
" total_bbox = 31405, rewritten_bbox = 0.280210 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.420882, GIOU: 0.288771), Class: 0.380252, Obj: 0.037223, No Obj: 0.003438, .5R: 0.428571, .75R: 0.142857, count: 7, class_loss = 2.054034, iou_loss = 0.037218, total_loss = 2.091252 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.372223, GIOU: 0.217644), Class: 0.439786, Obj: 0.004214, No Obj: 0.000872, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.138219, iou_loss = 1.030956, total_loss = 2.169175 \n",
" total_bbox = 31416, rewritten_bbox = 0.289661 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.317860, GIOU: 0.271303), Class: 0.515476, Obj: 0.037901, No Obj: 0.002596, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.776605, iou_loss = 0.055385, total_loss = 1.831990 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000834, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003181, iou_loss = 0.000000, total_loss = 0.003181 \n",
" total_bbox = 31421, rewritten_bbox = 0.289615 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.380667, GIOU: 0.336600), Class: 0.436000, Obj: 0.024737, No Obj: 0.002766, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.830408, iou_loss = 0.127040, total_loss = 1.957449 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000860, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003091, iou_loss = 0.000000, total_loss = 0.003091 \n",
" total_bbox = 31426, rewritten_bbox = 0.289569 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.512354, GIOU: 0.485997), Class: 0.535589, Obj: 0.034525, No Obj: 0.002805, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.478081, iou_loss = 0.023326, total_loss = 1.501407 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000798, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002010, iou_loss = 0.000000, total_loss = 0.002010 \n",
" total_bbox = 31430, rewritten_bbox = 0.289532 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429846, GIOU: 0.419243), Class: 0.603877, Obj: 0.011589, No Obj: 0.002285, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.393741, iou_loss = 0.123557, total_loss = 1.517297 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.366970, GIOU: 0.365551), Class: 0.332797, Obj: 0.000605, No Obj: 0.000838, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.528039, iou_loss = 0.396655, total_loss = 0.924694 \n",
" total_bbox = 31435, rewritten_bbox = 0.289486 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.489038, GIOU: 0.445462), Class: 0.599917, Obj: 0.126087, No Obj: 0.002976, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.255242, iou_loss = 0.031464, total_loss = 1.286706 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000788, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003255, iou_loss = 0.000000, total_loss = 0.003255 \n",
" total_bbox = 31439, rewritten_bbox = 0.289449 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.326371, GIOU: 0.285028), Class: 0.475898, Obj: 0.022535, No Obj: 0.002351, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.184322, iou_loss = 0.078591, total_loss = 1.262913 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.359804, GIOU: 0.214931), Class: 0.555750, Obj: 0.005722, No Obj: 0.000829, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.719627, iou_loss = 0.687707, total_loss = 1.407334 \n",
" total_bbox = 31444, rewritten_bbox = 0.289403 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 393: 0.888817, 0.954779 avg loss, 0.000062 rate, 0.714231 seconds, 25152 images, 0.726585 hours left\n",
"Loaded: 0.000049 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.269016, GIOU: 0.222296), Class: 0.613568, Obj: 0.015566, No Obj: 0.002684, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.523859, iou_loss = 0.016894, total_loss = 1.540753 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000861, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002561, iou_loss = 0.000000, total_loss = 0.002561 \n",
" total_bbox = 31448, rewritten_bbox = 0.289367 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.256892, GIOU: 0.130221), Class: 0.464500, Obj: 0.027788, No Obj: 0.002556, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.567359, iou_loss = 0.011522, total_loss = 1.578882 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000781, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002214, iou_loss = 0.000000, total_loss = 0.002214 \n",
" total_bbox = 31452, rewritten_bbox = 0.289330 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436863, GIOU: 0.405153), Class: 0.493501, Obj: 0.036684, No Obj: 0.003082, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.520646, iou_loss = 0.029689, total_loss = 1.550336 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.379880, GIOU: 0.215610), Class: 0.497981, Obj: 0.007902, No Obj: 0.000867, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.358603, iou_loss = 0.349367, total_loss = 0.707970 \n",
" total_bbox = 31457, rewritten_bbox = 0.289284 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.266883, GIOU: 0.266883), Class: 0.481396, Obj: 0.030584, No Obj: 0.002805, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.525940, iou_loss = 0.019241, total_loss = 1.545181 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.225261, GIOU: -0.107466), Class: 0.414044, Obj: 0.001537, No Obj: 0.000872, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.397595, iou_loss = 0.022832, total_loss = 0.420427 \n",
" total_bbox = 31462, rewritten_bbox = 0.289238 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.377358, GIOU: 0.208014), Class: 0.516730, Obj: 0.024447, No Obj: 0.002636, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.415645, iou_loss = 0.024007, total_loss = 1.439653 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.247991, GIOU: -0.057915), Class: 0.580994, Obj: 0.006539, No Obj: 0.000862, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.381013, iou_loss = 0.099272, total_loss = 0.480284 \n",
" total_bbox = 31467, rewritten_bbox = 0.289192 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.518710, GIOU: 0.455417), Class: 0.517706, Obj: 0.060665, No Obj: 0.002542, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.044630, iou_loss = 0.100984, total_loss = 1.145614 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.435079, GIOU: 0.355660), Class: 0.539296, Obj: 0.005839, No Obj: 0.000879, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.883177, iou_loss = 0.809460, total_loss = 2.692637 \n",
" total_bbox = 31475, rewritten_bbox = 0.289118 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.593250, GIOU: 0.501199), Class: 0.513720, Obj: 0.024265, No Obj: 0.002833, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.389706, iou_loss = 0.030031, total_loss = 1.419737 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000830, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002721, iou_loss = 0.000000, total_loss = 0.002721 \n",
" total_bbox = 31479, rewritten_bbox = 0.289082 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.371506, GIOU: 0.280365), Class: 0.597310, Obj: 0.047350, No Obj: 0.002424, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.969392, iou_loss = 0.009042, total_loss = 0.978434 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.307342, GIOU: 0.278862), Class: 0.504292, Obj: 0.009892, No Obj: 0.000893, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.511310, iou_loss = 0.531247, total_loss = 2.042557 \n",
" total_bbox = 31486, rewritten_bbox = 0.289017 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436954, GIOU: 0.368665), Class: 0.396891, Obj: 0.030103, No Obj: 0.002852, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.685295, iou_loss = 0.059954, total_loss = 1.745249 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000816, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002498, iou_loss = 0.000000, total_loss = 0.002498 \n",
" total_bbox = 31490, rewritten_bbox = 0.288981 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.455327, GIOU: 0.428725), Class: 0.547331, Obj: 0.058287, No Obj: 0.002570, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 0.984143, iou_loss = 0.022807, total_loss = 1.006950 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.276677, GIOU: 0.084353), Class: 0.415609, Obj: 0.002338, No Obj: 0.000866, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.207290, iou_loss = 0.178770, total_loss = 1.386060 \n",
" total_bbox = 31496, rewritten_bbox = 0.288926 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.171663, GIOU: -0.098351), Class: 0.599507, Obj: 0.025628, No Obj: 0.003246, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.076511, iou_loss = 0.004985, total_loss = 1.081497 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.462694, GIOU: 0.338944), Class: 0.482368, Obj: 0.003750, No Obj: 0.000893, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.381659, iou_loss = 0.332076, total_loss = 0.713735 \n",
" total_bbox = 31500, rewritten_bbox = 0.288889 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.655420, GIOU: 0.637640), Class: 0.530938, Obj: 0.151481, No Obj: 0.003755, .5R: 1.000000, .75R: 0.000000, count: 4, class_loss = 1.372240, iou_loss = 0.110736, total_loss = 1.482976 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000877, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002625, iou_loss = 0.000000, total_loss = 0.002625 \n",
" total_bbox = 31504, rewritten_bbox = 0.288852 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.506254, GIOU: 0.330827), Class: 0.505951, Obj: 0.006732, No Obj: 0.002113, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.051058, iou_loss = 0.027832, total_loss = 1.078890 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.407754, GIOU: 0.360302), Class: 0.646711, Obj: 0.022551, No Obj: 0.000842, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347804, iou_loss = 0.092287, total_loss = 0.440090 \n",
" total_bbox = 31508, rewritten_bbox = 0.288816 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369724, GIOU: 0.350046), Class: 0.511161, Obj: 0.021743, No Obj: 0.002540, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.502921, iou_loss = 0.016395, total_loss = 1.519316 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000816, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002355, iou_loss = 0.000000, total_loss = 0.002355 \n",
" total_bbox = 31512, rewritten_bbox = 0.288779 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.547741, GIOU: 0.537402), Class: 0.512034, Obj: 0.025453, No Obj: 0.002259, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.453163, iou_loss = 0.148163, total_loss = 1.601327 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.327333, GIOU: 0.205004), Class: 0.556372, Obj: 0.003016, No Obj: 0.000822, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.688311, iou_loss = 1.294966, total_loss = 1.983277 \n",
" total_bbox = 31518, rewritten_bbox = 0.288724 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.432329, GIOU: 0.411322), Class: 0.479763, Obj: 0.010212, No Obj: 0.003078, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.083226, iou_loss = 0.031215, total_loss = 1.114440 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000792, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002143, iou_loss = 0.000000, total_loss = 0.002143 \n",
" total_bbox = 31521, rewritten_bbox = 0.288696 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 394: 0.893960, 0.948697 avg loss, 0.000063 rate, 0.732531 seconds, 25216 images, 0.726475 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.253353, GIOU: 0.253353), Class: 0.443261, Obj: 0.019625, No Obj: 0.002645, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.460359, iou_loss = 0.023641, total_loss = 1.484000 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000829, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002651, iou_loss = 0.000000, total_loss = 0.002651 \n",
" total_bbox = 31525, rewritten_bbox = 0.288660 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413557, GIOU: 0.344451), Class: 0.609288, Obj: 0.009483, No Obj: 0.002927, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.379875, iou_loss = 0.164020, total_loss = 1.543895 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.588057, GIOU: 0.583247), Class: 0.471247, Obj: 0.003990, No Obj: 0.000834, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.397599, iou_loss = 0.277876, total_loss = 0.675475 \n",
" total_bbox = 31530, rewritten_bbox = 0.288614 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444976, GIOU: 0.393613), Class: 0.513922, Obj: 0.033226, No Obj: 0.003098, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.920839, iou_loss = 0.073345, total_loss = 1.994184 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000799, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002770, iou_loss = 0.000000, total_loss = 0.002770 \n",
" total_bbox = 31535, rewritten_bbox = 0.288568 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.265780, GIOU: 0.178131), Class: 0.453131, Obj: 0.028544, No Obj: 0.002231, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.708265, iou_loss = 0.006934, total_loss = 0.715199 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.240893, GIOU: -0.138059), Class: 0.542552, Obj: 0.012117, No Obj: 0.000845, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.733370, iou_loss = 0.058650, total_loss = 0.792020 \n",
" total_bbox = 31539, rewritten_bbox = 0.288532 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.254193, GIOU: 0.254192), Class: 0.581727, Obj: 0.016925, No Obj: 0.002239, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.005246, iou_loss = 0.013255, total_loss = 1.018502 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.433925, GIOU: 0.344455), Class: 0.500887, Obj: 0.003767, No Obj: 0.000813, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.853021, iou_loss = 2.193462, total_loss = 4.046483 \n",
" total_bbox = 31547, rewritten_bbox = 0.288458 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.482055, GIOU: 0.441988), Class: 0.599125, Obj: 0.020005, No Obj: 0.002236, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.401298, iou_loss = 0.078457, total_loss = 1.479756 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.358754, GIOU: 0.358754), Class: 0.346624, Obj: 0.000868, No Obj: 0.000827, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.431974, iou_loss = 0.111827, total_loss = 0.543802 \n",
" total_bbox = 31552, rewritten_bbox = 0.288413 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.408311, GIOU: 0.394428), Class: 0.421433, Obj: 0.029203, No Obj: 0.002876, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.954034, iou_loss = 0.128842, total_loss = 2.082876 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.274947, GIOU: 0.175838), Class: 0.553889, Obj: 0.004679, No Obj: 0.000782, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.097338, iou_loss = 1.104422, total_loss = 2.201760 \n",
" total_bbox = 31560, rewritten_bbox = 0.288340 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.396463, GIOU: 0.357879), Class: 0.458663, Obj: 0.015088, No Obj: 0.003036, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.449393, iou_loss = 0.062650, total_loss = 1.512044 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000813, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002785, iou_loss = 0.000000, total_loss = 0.002785 \n",
" total_bbox = 31564, rewritten_bbox = 0.288303 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.448409, GIOU: 0.417848), Class: 0.424195, Obj: 0.011397, No Obj: 0.002204, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.597648, iou_loss = 0.014456, total_loss = 1.612104 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000782, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002265, iou_loss = 0.000000, total_loss = 0.002265 \n",
" total_bbox = 31568, rewritten_bbox = 0.288267 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.302027, GIOU: 0.213272), Class: 0.447725, Obj: 0.007132, No Obj: 0.002279, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.785320, iou_loss = 0.002003, total_loss = 0.787323 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.176553, GIOU: -0.033453), Class: 0.485310, Obj: 0.001048, No Obj: 0.000811, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.750232, iou_loss = 0.064288, total_loss = 0.814520 \n",
" total_bbox = 31572, rewritten_bbox = 0.288230 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.362893, GIOU: 0.250003), Class: 0.409732, Obj: 0.021238, No Obj: 0.002265, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.234350, iou_loss = 0.004854, total_loss = 1.239204 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000773, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002048, iou_loss = 0.000000, total_loss = 0.002048 \n",
" total_bbox = 31575, rewritten_bbox = 0.288203 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.272956, GIOU: 0.130144), Class: 0.487361, Obj: 0.031623, No Obj: 0.002736, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.899101, iou_loss = 0.096455, total_loss = 1.995555 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.024543, GIOU: 0.024543), Class: 0.640154, Obj: 0.001194, No Obj: 0.000885, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347866, iou_loss = 0.000529, total_loss = 0.348395 \n",
" total_bbox = 31581, rewritten_bbox = 0.288148 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.278404, GIOU: 0.274942), Class: 0.628510, Obj: 0.032594, No Obj: 0.003204, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.288374, iou_loss = 0.009850, total_loss = 1.298224 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000819, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002366, iou_loss = 0.000000, total_loss = 0.002366 \n",
" total_bbox = 31585, rewritten_bbox = 0.288111 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.463292, GIOU: 0.326515), Class: 0.688828, Obj: 0.034317, No Obj: 0.003218, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.085393, iou_loss = 0.107136, total_loss = 1.192529 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.641001, GIOU: 0.611555), Class: 0.560519, Obj: 0.004487, No Obj: 0.000869, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.655134, iou_loss = 0.634763, total_loss = 1.289897 \n",
" total_bbox = 31590, rewritten_bbox = 0.288066 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.484140, GIOU: 0.454386), Class: 0.527969, Obj: 0.082473, No Obj: 0.003361, .5R: 0.800000, .75R: 0.000000, count: 5, class_loss = 1.785993, iou_loss = 0.107005, total_loss = 1.892999 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000854, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002832, iou_loss = 0.000000, total_loss = 0.002832 \n",
" total_bbox = 31595, rewritten_bbox = 0.288020 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.343187, GIOU: 0.339445), Class: 0.501186, Obj: 0.047140, No Obj: 0.002934, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.048429, iou_loss = 0.008527, total_loss = 1.056955 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.614545, GIOU: 0.584291), Class: 0.251504, Obj: 0.006890, No Obj: 0.000741, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.457215, iou_loss = 0.337548, total_loss = 0.794763 \n",
" total_bbox = 31599, rewritten_bbox = 0.287984 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 395: 0.906919, 0.944519 avg loss, 0.000064 rate, 0.696283 seconds, 25280 images, 0.726549 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.223991, GIOU: 0.223812), Class: 0.411484, Obj: 0.058830, No Obj: 0.002949, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.424552, iou_loss = 0.011692, total_loss = 1.436244 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.376321, GIOU: 0.233153), Class: 0.620787, Obj: 0.003913, No Obj: 0.000784, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.995194, iou_loss = 2.320618, total_loss = 3.315812 \n",
" total_bbox = 31606, rewritten_bbox = 0.287920 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369797, GIOU: 0.285308), Class: 0.476998, Obj: 0.009268, No Obj: 0.002246, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.065230, iou_loss = 0.030688, total_loss = 2.095917 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000774, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002284, iou_loss = 0.000000, total_loss = 0.002284 \n",
" total_bbox = 31611, rewritten_bbox = 0.287874 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.623050, GIOU: 0.612581), Class: 0.363925, Obj: 0.039174, No Obj: 0.003145, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.432764, iou_loss = 0.005593, total_loss = 0.438357 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.322494, GIOU: 0.277361), Class: 0.542145, Obj: 0.002171, No Obj: 0.000818, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.714791, iou_loss = 0.749749, total_loss = 1.464540 \n",
" total_bbox = 31614, rewritten_bbox = 0.287847 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.411599, GIOU: 0.379773), Class: 0.528860, Obj: 0.017959, No Obj: 0.002742, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.406100, iou_loss = 0.009676, total_loss = 1.415776 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000800, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002499, iou_loss = 0.000000, total_loss = 0.002499 \n",
" total_bbox = 31618, rewritten_bbox = 0.287811 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.221981, GIOU: 0.221981), Class: 0.413236, Obj: 0.027342, No Obj: 0.002777, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.097099, iou_loss = 0.002438, total_loss = 1.099537 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.253670, GIOU: 0.253669), Class: 0.281017, Obj: 0.000590, No Obj: 0.000795, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.466432, iou_loss = 0.177116, total_loss = 0.643548 \n",
" total_bbox = 31622, rewritten_bbox = 0.287774 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357599, GIOU: 0.276860), Class: 0.455290, Obj: 0.024908, No Obj: 0.002471, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.127160, iou_loss = 0.072882, total_loss = 1.200042 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.360312, GIOU: 0.296257), Class: 0.429474, Obj: 0.004673, No Obj: 0.000821, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.160713, iou_loss = 2.687833, total_loss = 4.848547 \n",
" total_bbox = 31630, rewritten_bbox = 0.287702 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.393608, GIOU: 0.331211), Class: 0.575793, Obj: 0.012486, No Obj: 0.002849, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.356651, iou_loss = 0.040005, total_loss = 1.396656 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000868, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002886, iou_loss = 0.000000, total_loss = 0.002886 \n",
" total_bbox = 31634, rewritten_bbox = 0.287665 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352946, GIOU: 0.249071), Class: 0.515681, Obj: 0.025325, No Obj: 0.002719, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.181703, iou_loss = 0.051852, total_loss = 2.233554 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.267744, GIOU: 0.031781), Class: 0.756175, Obj: 0.002537, No Obj: 0.000837, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.331624, iou_loss = 0.035918, total_loss = 0.367543 \n",
" total_bbox = 31641, rewritten_bbox = 0.287602 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.491058, GIOU: 0.458359), Class: 0.517381, Obj: 0.039189, No Obj: 0.003190, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 1.723034, iou_loss = 0.093318, total_loss = 1.816352 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.383720, GIOU: 0.364762), Class: 0.744388, Obj: 0.002949, No Obj: 0.000768, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.377751, iou_loss = 0.231468, total_loss = 0.609219 \n",
" total_bbox = 31647, rewritten_bbox = 0.287547 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.347438, GIOU: 0.274569), Class: 0.477329, Obj: 0.014785, No Obj: 0.002060, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.819058, iou_loss = 0.020790, total_loss = 1.839847 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.254885, GIOU: 0.167581), Class: 0.409648, Obj: 0.002815, No Obj: 0.000775, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.210956, iou_loss = 2.716642, total_loss = 3.927599 \n",
" total_bbox = 31654, rewritten_bbox = 0.287483 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.492311, GIOU: 0.482713), Class: 0.668166, Obj: 0.055431, No Obj: 0.003203, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.214824, iou_loss = 0.037358, total_loss = 1.252182 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000803, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002594, iou_loss = 0.000000, total_loss = 0.002594 \n",
" total_bbox = 31658, rewritten_bbox = 0.287447 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.412390, GIOU: 0.375256), Class: 0.566396, Obj: 0.027811, No Obj: 0.002734, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.036239, iou_loss = 0.019354, total_loss = 1.055593 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.747094, GIOU: 0.728595), Class: 0.264896, Obj: 0.006034, No Obj: 0.000798, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.439498, iou_loss = 0.387786, total_loss = 0.827284 \n",
" total_bbox = 31662, rewritten_bbox = 0.287411 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435590, GIOU: 0.278704), Class: 0.570367, Obj: 0.076837, No Obj: 0.003074, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.632716, iou_loss = 0.062840, total_loss = 1.695556 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.558680, GIOU: 0.499828), Class: 0.218192, Obj: 0.004213, No Obj: 0.000828, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.499303, iou_loss = 0.160271, total_loss = 0.659574 \n",
" total_bbox = 31668, rewritten_bbox = 0.287356 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.321769, GIOU: 0.279909), Class: 0.544610, Obj: 0.143592, No Obj: 0.002782, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.289407, iou_loss = 0.073317, total_loss = 1.362724 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.548314, GIOU: 0.512087), Class: 0.419933, Obj: 0.013599, No Obj: 0.000892, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.368317, iou_loss = 0.340682, total_loss = 0.708999 \n",
" total_bbox = 31673, rewritten_bbox = 0.287311 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.418080, GIOU: 0.337083), Class: 0.365660, Obj: 0.031159, No Obj: 0.002964, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.543791, iou_loss = 0.039532, total_loss = 1.583323 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000770, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002299, iou_loss = 0.000000, total_loss = 0.002299 \n",
" total_bbox = 31677, rewritten_bbox = 0.287275 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.251600, GIOU: 0.128756), Class: 0.472132, Obj: 0.015572, No Obj: 0.002580, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.998387, iou_loss = 0.043481, total_loss = 2.041867 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000815, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003079, iou_loss = 0.000000, total_loss = 0.003079 \n",
" total_bbox = 31682, rewritten_bbox = 0.287229 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 396: 0.975171, 0.947585 avg loss, 0.000064 rate, 0.705616 seconds, 25344 images, 0.726256 hours left\n",
"Loaded: 0.000076 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409980, GIOU: 0.342741), Class: 0.543418, Obj: 0.065995, No Obj: 0.002894, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.232151, iou_loss = 0.032816, total_loss = 1.264967 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000778, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002142, iou_loss = 0.000000, total_loss = 0.002142 \n",
" total_bbox = 31686, rewritten_bbox = 0.287193 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325456, GIOU: 0.200536), Class: 0.498253, Obj: 0.021503, No Obj: 0.003170, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.192010, iou_loss = 0.048261, total_loss = 2.240272 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.380456, GIOU: 0.268357), Class: 0.441886, Obj: 0.010536, No Obj: 0.000813, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.741717, iou_loss = 0.431183, total_loss = 1.172900 \n",
" total_bbox = 31694, rewritten_bbox = 0.287121 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421336, GIOU: 0.388498), Class: 0.513052, Obj: 0.045545, No Obj: 0.002811, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.843903, iou_loss = 0.114942, total_loss = 1.958845 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000823, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003078, iou_loss = 0.000000, total_loss = 0.003078 \n",
" total_bbox = 31699, rewritten_bbox = 0.287075 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339403, GIOU: 0.201066), Class: 0.523211, Obj: 0.021490, No Obj: 0.002813, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.410848, iou_loss = 0.094911, total_loss = 1.505759 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.520331, GIOU: 0.445594), Class: 0.413485, Obj: 0.022169, No Obj: 0.000812, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.380006, iou_loss = 0.121263, total_loss = 0.501269 \n",
" total_bbox = 31704, rewritten_bbox = 0.287030 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.245402, GIOU: 0.112423), Class: 0.617681, Obj: 0.029210, No Obj: 0.002505, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.318058, iou_loss = 0.006427, total_loss = 1.324485 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000817, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002397, iou_loss = 0.000000, total_loss = 0.002397 \n",
" total_bbox = 31708, rewritten_bbox = 0.286994 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.385010, GIOU: 0.254309), Class: 0.497295, Obj: 0.017730, No Obj: 0.002529, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.446569, iou_loss = 0.047798, total_loss = 1.494367 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000761, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002137, iou_loss = 0.000000, total_loss = 0.002137 \n",
" total_bbox = 31712, rewritten_bbox = 0.286958 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.350831, GIOU: 0.201044), Class: 0.532368, Obj: 0.029916, No Obj: 0.002538, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.024549, iou_loss = 0.086469, total_loss = 2.111018 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.489083, GIOU: 0.399977), Class: 0.475880, Obj: 0.005063, No Obj: 0.000818, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.379787, iou_loss = 0.101969, total_loss = 0.481756 \n",
" total_bbox = 31719, rewritten_bbox = 0.286894 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.258478, GIOU: 0.135423), Class: 0.452961, Obj: 0.059171, No Obj: 0.002582, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.345094, iou_loss = 0.072661, total_loss = 1.417755 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000891, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003340, iou_loss = 0.000000, total_loss = 0.003340 \n",
" total_bbox = 31723, rewritten_bbox = 0.286858 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.316029, GIOU: 0.244120), Class: 0.452154, Obj: 0.030559, No Obj: 0.002810, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.215356, iou_loss = 0.057817, total_loss = 2.273172 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.287493, GIOU: 0.152805), Class: 0.339323, Obj: 0.001849, No Obj: 0.000799, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.440706, iou_loss = 1.250991, total_loss = 1.691697 \n",
" total_bbox = 31730, rewritten_bbox = 0.286795 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370515, GIOU: 0.365002), Class: 0.517240, Obj: 0.041763, No Obj: 0.002900, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.492472, iou_loss = 0.010596, total_loss = 1.503068 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000866, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002460, iou_loss = 0.000000, total_loss = 0.002460 \n",
" total_bbox = 31734, rewritten_bbox = 0.286759 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.433610, GIOU: 0.404548), Class: 0.576588, Obj: 0.109369, No Obj: 0.004314, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.024466, iou_loss = 0.017410, total_loss = 1.041875 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.447795, GIOU: 0.332444), Class: 0.584648, Obj: 0.001178, No Obj: 0.000869, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.110026, iou_loss = 0.591419, total_loss = 1.701445 \n",
" total_bbox = 31740, rewritten_bbox = 0.286704 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409341, GIOU: 0.409341), Class: 0.606927, Obj: 0.009733, No Obj: 0.002532, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.731163, iou_loss = 0.026050, total_loss = 0.757212 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.259190, GIOU: 0.243739), Class: 0.590456, Obj: 0.003592, No Obj: 0.000862, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.756130, iou_loss = 0.898740, total_loss = 1.654869 \n",
" total_bbox = 31744, rewritten_bbox = 0.286668 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.309754, GIOU: 0.195235), Class: 0.360959, Obj: 0.065962, No Obj: 0.003372, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.831932, iou_loss = 0.002345, total_loss = 0.834277 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.325367, GIOU: 0.199115), Class: 0.457736, Obj: 0.021210, No Obj: 0.000898, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.749141, iou_loss = 0.104082, total_loss = 0.853223 \n",
" total_bbox = 31748, rewritten_bbox = 0.286632 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.417705, GIOU: 0.338776), Class: 0.448970, Obj: 0.011662, No Obj: 0.002379, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.379665, iou_loss = 0.131011, total_loss = 2.510677 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.442396, GIOU: 0.326040), Class: 0.678240, Obj: 0.000678, No Obj: 0.000761, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.335728, iou_loss = 0.304638, total_loss = 0.640366 \n",
" total_bbox = 31755, rewritten_bbox = 0.286569 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409152, GIOU: 0.362828), Class: 0.489545, Obj: 0.015367, No Obj: 0.002621, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.867581, iou_loss = 0.017045, total_loss = 1.884626 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000793, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002178, iou_loss = 0.000000, total_loss = 0.002178 \n",
" total_bbox = 31760, rewritten_bbox = 0.286524 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.440241, GIOU: 0.424801), Class: 0.448848, Obj: 0.039307, No Obj: 0.002803, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.540343, iou_loss = 0.037980, total_loss = 1.578323 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000771, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001919, iou_loss = 0.000000, total_loss = 0.001919 \n",
" total_bbox = 31764, rewritten_bbox = 0.286488 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 397: 0.939739, 0.946800 avg loss, 0.000065 rate, 0.678762 seconds, 25408 images, 0.726058 hours left\n",
"Loaded: 0.000051 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344072, GIOU: 0.295059), Class: 0.456574, Obj: 0.024181, No Obj: 0.002740, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.567824, iou_loss = 0.073476, total_loss = 1.641299 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000840, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003895, iou_loss = 0.000000, total_loss = 0.003895 \n",
" total_bbox = 31768, rewritten_bbox = 0.286452 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.514446, GIOU: 0.465227), Class: 0.478253, Obj: 0.073347, No Obj: 0.003015, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.006272, iou_loss = 0.028037, total_loss = 1.034309 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.407221, GIOU: 0.243473), Class: 0.610919, Obj: 0.004440, No Obj: 0.000826, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.381823, iou_loss = 0.064936, total_loss = 0.446759 \n",
" total_bbox = 31772, rewritten_bbox = 0.286416 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319706, GIOU: 0.310998), Class: 0.532882, Obj: 0.044980, No Obj: 0.003402, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.199009, iou_loss = 0.065396, total_loss = 2.264406 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000897, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003290, iou_loss = 0.000000, total_loss = 0.003290 \n",
" total_bbox = 31778, rewritten_bbox = 0.286362 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.534549, GIOU: 0.490879), Class: 0.495173, Obj: 0.027608, No Obj: 0.003336, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.616937, iou_loss = 0.031545, total_loss = 1.648482 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000742, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002112, iou_loss = 0.000000, total_loss = 0.002112 \n",
" total_bbox = 31782, rewritten_bbox = 0.286326 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.265895, GIOU: 0.199593), Class: 0.690269, Obj: 0.057521, No Obj: 0.002557, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.161152, iou_loss = 0.051354, total_loss = 1.212506 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.565313, GIOU: 0.565313), Class: 0.496785, Obj: 0.009285, No Obj: 0.000781, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.380287, iou_loss = 0.276063, total_loss = 0.656350 \n",
" total_bbox = 31787, rewritten_bbox = 0.286281 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.371655, GIOU: 0.266935), Class: 0.441620, Obj: 0.042003, No Obj: 0.003286, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.268610, iou_loss = 0.123750, total_loss = 2.392359 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000812, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003391, iou_loss = 0.000000, total_loss = 0.003391 \n",
" total_bbox = 31793, rewritten_bbox = 0.286227 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.497307, GIOU: 0.446061), Class: 0.481415, Obj: 0.042162, No Obj: 0.003250, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.075013, iou_loss = 0.008417, total_loss = 1.083430 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.219815, GIOU: 0.219815), Class: 0.637292, Obj: 0.001022, No Obj: 0.000787, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347710, iou_loss = 0.042206, total_loss = 0.389916 \n",
" total_bbox = 31797, rewritten_bbox = 0.286191 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.482259, GIOU: 0.481415), Class: 0.471880, Obj: 0.038719, No Obj: 0.003004, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 1.885631, iou_loss = 0.125224, total_loss = 2.010855 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000776, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002702, iou_loss = 0.000000, total_loss = 0.002702 \n",
" total_bbox = 31802, rewritten_bbox = 0.286146 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.461055, GIOU: 0.394677), Class: 0.451548, Obj: 0.022669, No Obj: 0.003034, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.200262, iou_loss = 0.115345, total_loss = 2.315607 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.170452, GIOU: 0.074529), Class: 0.490219, Obj: 0.003224, No Obj: 0.000798, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354862, iou_loss = 0.016952, total_loss = 0.371815 \n",
" total_bbox = 31809, rewritten_bbox = 0.286083 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.401671, GIOU: 0.363465), Class: 0.660476, Obj: 0.078475, No Obj: 0.003570, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.304124, iou_loss = 0.045588, total_loss = 1.349712 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000805, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002459, iou_loss = 0.000000, total_loss = 0.002459 \n",
" total_bbox = 31813, rewritten_bbox = 0.286047 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403865, GIOU: 0.400797), Class: 0.524047, Obj: 0.048296, No Obj: 0.002644, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.124091, iou_loss = 0.206649, total_loss = 2.330739 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.350553, GIOU: 0.195025), Class: 0.491114, Obj: 0.005947, No Obj: 0.000795, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.403787, iou_loss = 0.065725, total_loss = 0.469511 \n",
" total_bbox = 31820, rewritten_bbox = 0.285984 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.163453, GIOU: 0.030549), Class: 0.722398, Obj: 0.023726, No Obj: 0.002316, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.773943, iou_loss = 0.001899, total_loss = 0.775842 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.332044, GIOU: 0.276828), Class: 0.501728, Obj: 0.002651, No Obj: 0.000789, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.233912, iou_loss = 1.889070, total_loss = 3.122982 \n",
" total_bbox = 31825, rewritten_bbox = 0.285939 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357614, GIOU: 0.249385), Class: 0.585793, Obj: 0.042758, No Obj: 0.002597, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.041237, iou_loss = 0.101581, total_loss = 2.142818 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000785, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002156, iou_loss = 0.000000, total_loss = 0.002156 \n",
" total_bbox = 31831, rewritten_bbox = 0.285885 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.160742, GIOU: 0.160742), Class: 0.530551, Obj: 0.013674, No Obj: 0.002376, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.702248, iou_loss = 0.000828, total_loss = 0.703076 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.475714, GIOU: 0.475592), Class: 0.468020, Obj: 0.011760, No Obj: 0.000907, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.740060, iou_loss = 0.531448, total_loss = 1.271508 \n",
" total_bbox = 31835, rewritten_bbox = 0.285849 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472939, GIOU: 0.419157), Class: 0.450070, Obj: 0.051406, No Obj: 0.003041, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.471651, iou_loss = 0.030569, total_loss = 1.502220 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000765, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002350, iou_loss = 0.000000, total_loss = 0.002350 \n",
" total_bbox = 31839, rewritten_bbox = 0.285813 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.494769, GIOU: 0.451311), Class: 0.541330, Obj: 0.015778, No Obj: 0.002474, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.020982, iou_loss = 0.017966, total_loss = 1.038947 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.290830, GIOU: 0.280150), Class: 0.544594, Obj: 0.014970, No Obj: 0.000770, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.394639, iou_loss = 0.082316, total_loss = 0.476956 \n",
" total_bbox = 31843, rewritten_bbox = 0.285777 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 398: 0.904372, 0.942557 avg loss, 0.000065 rate, 0.696327 seconds, 25472 images, 0.725592 hours left\n",
"Loaded: 0.000045 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.309981, GIOU: 0.309981), Class: 0.432982, Obj: 0.012333, No Obj: 0.002856, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.752430, iou_loss = 0.005143, total_loss = 0.757573 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.367254, GIOU: 0.153085), Class: 0.597355, Obj: 0.006425, No Obj: 0.000802, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.307426, iou_loss = 0.176272, total_loss = 0.483698 \n",
" total_bbox = 31846, rewritten_bbox = 0.285750 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.504578, GIOU: 0.448596), Class: 0.529221, Obj: 0.010129, No Obj: 0.002102, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.098616, iou_loss = 0.167843, total_loss = 2.266459 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000747, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002265, iou_loss = 0.000000, total_loss = 0.002265 \n",
" total_bbox = 31852, rewritten_bbox = 0.285696 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.195298, GIOU: 0.037812), Class: 0.287215, Obj: 0.006168, No Obj: 0.002170, .5R: 0.000000, .75R: 0.000000, count: 7, class_loss = 3.215273, iou_loss = 0.017639, total_loss = 3.232912 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000747, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001957, iou_loss = 0.000000, total_loss = 0.001957 \n",
" total_bbox = 31859, rewritten_bbox = 0.285634 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.280500, GIOU: 0.195764), Class: 0.525950, Obj: 0.026900, No Obj: 0.003346, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.615448, iou_loss = 0.068891, total_loss = 1.684340 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.493946, GIOU: 0.461865), Class: 0.361314, Obj: 0.004941, No Obj: 0.000805, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.485234, iou_loss = 0.437914, total_loss = 0.923148 \n",
" total_bbox = 31864, rewritten_bbox = 0.285589 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.235878, GIOU: 0.189878), Class: 0.421574, Obj: 0.013280, No Obj: 0.002846, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 2.022771, iou_loss = 0.011002, total_loss = 2.033773 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.403148, GIOU: 0.372371), Class: 0.360950, Obj: 0.002584, No Obj: 0.000800, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.418896, iou_loss = 0.117905, total_loss = 0.536801 \n",
" total_bbox = 31870, rewritten_bbox = 0.285535 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.360400, GIOU: 0.354484), Class: 0.540857, Obj: 0.013929, No Obj: 0.002684, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.670114, iou_loss = 0.169675, total_loss = 1.839789 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000799, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002720, iou_loss = 0.000000, total_loss = 0.002720 \n",
" total_bbox = 31875, rewritten_bbox = 0.285490 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.388072, GIOU: 0.194080), Class: 0.448236, Obj: 0.038951, No Obj: 0.002697, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.139039, iou_loss = 0.013360, total_loss = 1.152399 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.377101, GIOU: 0.314223), Class: 0.485030, Obj: 0.000994, No Obj: 0.000776, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.388710, iou_loss = 0.136076, total_loss = 0.524786 \n",
" total_bbox = 31879, rewritten_bbox = 0.285454 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.195386, GIOU: -0.052694), Class: 0.582027, Obj: 0.013072, No Obj: 0.002343, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.080786, iou_loss = 0.005441, total_loss = 1.086227 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.241745, GIOU: 0.026993), Class: 0.404875, Obj: 0.002113, No Obj: 0.000759, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.349740, iou_loss = 0.016540, total_loss = 0.366281 \n",
" total_bbox = 31883, rewritten_bbox = 0.285419 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300163, GIOU: 0.185089), Class: 0.440127, Obj: 0.038874, No Obj: 0.002524, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.440392, iou_loss = 0.009764, total_loss = 1.450157 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000769, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002732, iou_loss = 0.000000, total_loss = 0.002732 \n",
" total_bbox = 31887, rewritten_bbox = 0.285383 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.348108, GIOU: 0.229751), Class: 0.585502, Obj: 0.004710, No Obj: 0.002731, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.897154, iou_loss = 0.024775, total_loss = 1.921930 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000772, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002127, iou_loss = 0.000000, total_loss = 0.002127 \n",
" total_bbox = 31892, rewritten_bbox = 0.285338 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.350241, GIOU: 0.316996), Class: 0.505921, Obj: 0.015691, No Obj: 0.002335, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.606285, iou_loss = 0.017213, total_loss = 1.623498 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000740, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001906, iou_loss = 0.000000, total_loss = 0.001906 \n",
" total_bbox = 31896, rewritten_bbox = 0.285302 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421789, GIOU: 0.403445), Class: 0.587493, Obj: 0.017726, No Obj: 0.002537, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.631775, iou_loss = 0.030792, total_loss = 1.662567 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000777, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002130, iou_loss = 0.000000, total_loss = 0.002130 \n",
" total_bbox = 31901, rewritten_bbox = 0.285258 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.381829, GIOU: 0.234560), Class: 0.531699, Obj: 0.045268, No Obj: 0.002787, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 2.555366, iou_loss = 0.135148, total_loss = 2.690514 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000756, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002124, iou_loss = 0.000000, total_loss = 0.002124 \n",
" total_bbox = 31908, rewritten_bbox = 0.285195 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.377881, GIOU: 0.375546), Class: 0.577769, Obj: 0.015290, No Obj: 0.002689, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.444253, iou_loss = 0.047123, total_loss = 1.491375 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000707, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001727, iou_loss = 0.000000, total_loss = 0.001727 \n",
" total_bbox = 31912, rewritten_bbox = 0.285159 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.379715, GIOU: 0.345920), Class: 0.599945, Obj: 0.053138, No Obj: 0.002974, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.329099, iou_loss = 0.029565, total_loss = 1.358664 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000748, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001968, iou_loss = 0.000000, total_loss = 0.001968 \n",
" total_bbox = 31916, rewritten_bbox = 0.285123 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.293253, GIOU: 0.232265), Class: 0.490859, Obj: 0.038805, No Obj: 0.003030, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.338216, iou_loss = 0.012734, total_loss = 1.350950 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.674729, GIOU: 0.648793), Class: 0.424005, Obj: 0.006311, No Obj: 0.000802, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.360187, iou_loss = 0.324106, total_loss = 0.684293 \n",
" total_bbox = 31921, rewritten_bbox = 0.285079 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 399: 0.919248, 0.940226 avg loss, 0.000066 rate, 0.698082 seconds, 25536 images, 0.725304 hours left\n",
"Loaded: 0.000072 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398854, GIOU: 0.284180), Class: 0.488327, Obj: 0.011651, No Obj: 0.002263, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.463647, iou_loss = 0.142806, total_loss = 1.606454 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000773, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002087, iou_loss = 0.000000, total_loss = 0.002087 \n",
" total_bbox = 31925, rewritten_bbox = 0.285043 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357581, GIOU: 0.295604), Class: 0.610895, Obj: 0.044096, No Obj: 0.002799, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.346872, iou_loss = 0.054000, total_loss = 1.400872 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000791, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002447, iou_loss = 0.000000, total_loss = 0.002447 \n",
" total_bbox = 31929, rewritten_bbox = 0.285007 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.500568, GIOU: 0.490711), Class: 0.513115, Obj: 0.035111, No Obj: 0.002698, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.406025, iou_loss = 0.046962, total_loss = 1.452987 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000766, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002045, iou_loss = 0.000000, total_loss = 0.002045 \n",
" total_bbox = 31933, rewritten_bbox = 0.284972 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.356560, GIOU: 0.174437), Class: 0.603591, Obj: 0.015681, No Obj: 0.003165, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.745117, iou_loss = 0.043676, total_loss = 1.788793 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000842, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003066, iou_loss = 0.000000, total_loss = 0.003066 \n",
" total_bbox = 31938, rewritten_bbox = 0.284927 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.383708, GIOU: 0.314560), Class: 0.542306, Obj: 0.035335, No Obj: 0.002906, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.361573, iou_loss = 0.024892, total_loss = 1.386465 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.367104, GIOU: 0.258211), Class: 0.410570, Obj: 0.011969, No Obj: 0.000870, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.189594, iou_loss = 1.668164, total_loss = 2.857757 \n",
" total_bbox = 31945, rewritten_bbox = 0.284865 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.434420, GIOU: 0.358863), Class: 0.461505, Obj: 0.056313, No Obj: 0.002981, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.843499, iou_loss = 0.047016, total_loss = 1.890515 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000735, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001957, iou_loss = 0.000000, total_loss = 0.001957 \n",
" total_bbox = 31950, rewritten_bbox = 0.284820 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.378769, GIOU: 0.240457), Class: 0.438770, Obj: 0.053864, No Obj: 0.002770, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.196799, iou_loss = 0.071668, total_loss = 2.268467 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.585837, GIOU: 0.554705), Class: 0.450915, Obj: 0.002164, No Obj: 0.000754, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.821911, iou_loss = 0.628165, total_loss = 1.450076 \n",
" total_bbox = 31958, rewritten_bbox = 0.284749 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.418563, GIOU: 0.363208), Class: 0.500462, Obj: 0.052228, No Obj: 0.002425, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.106526, iou_loss = 0.061792, total_loss = 1.168318 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.262760, GIOU: 0.262760), Class: 0.626762, Obj: 0.004096, No Obj: 0.000781, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.389562, iou_loss = 0.186996, total_loss = 0.576558 \n",
" total_bbox = 31962, rewritten_bbox = 0.284713 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.469727, GIOU: 0.374181), Class: 0.453102, Obj: 0.027919, No Obj: 0.002771, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.841452, iou_loss = 0.091404, total_loss = 1.932856 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000804, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004514, iou_loss = 0.000000, total_loss = 0.004514 \n",
" total_bbox = 31967, rewritten_bbox = 0.284669 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.305088, GIOU: 0.289145), Class: 0.480353, Obj: 0.026144, No Obj: 0.002877, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.869243, iou_loss = 0.030535, total_loss = 1.899778 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000777, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002388, iou_loss = 0.000000, total_loss = 0.002388 \n",
" total_bbox = 31972, rewritten_bbox = 0.284624 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353193, GIOU: 0.330685), Class: 0.488514, Obj: 0.006982, No Obj: 0.002575, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.476354, iou_loss = 0.097500, total_loss = 1.573855 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.202083, GIOU: 0.002460), Class: 0.602394, Obj: 0.010272, No Obj: 0.000896, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.099410, iou_loss = 0.158469, total_loss = 1.257879 \n",
" total_bbox = 31979, rewritten_bbox = 0.284562 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.309677, GIOU: 0.214238), Class: 0.378477, Obj: 0.018099, No Obj: 0.002554, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.601337, iou_loss = 0.071994, total_loss = 1.673331 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000745, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002285, iou_loss = 0.000000, total_loss = 0.002285 \n",
" total_bbox = 31983, rewritten_bbox = 0.284526 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.495094, GIOU: 0.426677), Class: 0.476354, Obj: 0.014959, No Obj: 0.002175, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.171380, iou_loss = 0.076042, total_loss = 1.247422 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000772, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002223, iou_loss = 0.000000, total_loss = 0.002223 \n",
" total_bbox = 31986, rewritten_bbox = 0.284499 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.439417, GIOU: 0.387404), Class: 0.455351, Obj: 0.046361, No Obj: 0.002759, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.745742, iou_loss = 0.285927, total_loss = 2.031668 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000807, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003713, iou_loss = 0.000000, total_loss = 0.003713 \n",
" total_bbox = 31991, rewritten_bbox = 0.284455 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357508, GIOU: 0.203073), Class: 0.501581, Obj: 0.009061, No Obj: 0.002486, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.566326, iou_loss = 0.051738, total_loss = 1.618065 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.401588, GIOU: 0.339485), Class: 0.397472, Obj: 0.001711, No Obj: 0.000754, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.767296, iou_loss = 0.730758, total_loss = 1.498054 \n",
" total_bbox = 31997, rewritten_bbox = 0.284402 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413306, GIOU: 0.332500), Class: 0.392884, Obj: 0.019633, No Obj: 0.002741, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.497709, iou_loss = 0.037082, total_loss = 1.534792 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000770, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002181, iou_loss = 0.000000, total_loss = 0.002181 \n",
" total_bbox = 32001, rewritten_bbox = 0.284366 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 400: 0.930013, 0.939205 avg loss, 0.000067 rate, 0.692045 seconds, 25600 images, 0.725034 hours left\n",
"Saving weights to backup//yolov4-tiny-bus-trucks_last.weights\n",
"Loaded: 0.000035 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.341662, GIOU: 0.287012), Class: 0.497822, Obj: 0.016407, No Obj: 0.003215, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.291272, iou_loss = 0.099999, total_loss = 2.391272 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.657798, GIOU: 0.648152), Class: 0.485727, Obj: 0.002166, No Obj: 0.000832, .5R: 1.000000, .75R: 0.500000, count: 2, class_loss = 0.745423, iou_loss = 0.712015, total_loss = 1.457438 \n",
" total_bbox = 32009, rewritten_bbox = 0.284295 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.225029, GIOU: 0.043660), Class: 0.481774, Obj: 0.031954, No Obj: 0.002679, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.548840, iou_loss = 0.003093, total_loss = 1.551933 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000737, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001980, iou_loss = 0.000000, total_loss = 0.001980 \n",
" total_bbox = 32013, rewritten_bbox = 0.284260 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.525231, GIOU: 0.445408), Class: 0.451454, Obj: 0.027442, No Obj: 0.002924, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.522971, iou_loss = 0.069623, total_loss = 1.592594 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000730, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002230, iou_loss = 0.000000, total_loss = 0.002230 \n",
" total_bbox = 32017, rewritten_bbox = 0.284224 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.537125, GIOU: 0.514779), Class: 0.452585, Obj: 0.041453, No Obj: 0.002772, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.412977, iou_loss = 0.033176, total_loss = 1.446154 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000707, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001886, iou_loss = 0.000000, total_loss = 0.001886 \n",
" total_bbox = 32021, rewritten_bbox = 0.284189 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.354622, GIOU: 0.160663), Class: 0.527238, Obj: 0.016203, No Obj: 0.002620, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.246012, iou_loss = 0.051144, total_loss = 2.297156 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.419575, GIOU: 0.303047), Class: 0.542577, Obj: 0.004599, No Obj: 0.000748, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.709012, iou_loss = 1.019238, total_loss = 1.728249 \n",
" total_bbox = 32029, rewritten_bbox = 0.284118 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.268086, GIOU: 0.257287), Class: 0.496963, Obj: 0.021573, No Obj: 0.003059, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.942322, iou_loss = 0.042526, total_loss = 1.984848 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.347918, GIOU: 0.139514), Class: 0.490327, Obj: 0.001144, No Obj: 0.000818, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.360491, iou_loss = 0.252261, total_loss = 0.612752 \n",
" total_bbox = 32035, rewritten_bbox = 0.284064 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.378306, GIOU: 0.320386), Class: 0.510755, Obj: 0.075630, No Obj: 0.002712, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 2.532986, iou_loss = 0.144419, total_loss = 2.677405 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000849, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005482, iou_loss = 0.000000, total_loss = 0.005482 \n",
" total_bbox = 32042, rewritten_bbox = 0.284002 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.396864, GIOU: 0.374620), Class: 0.487010, Obj: 0.051090, No Obj: 0.003116, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.117607, iou_loss = 0.007317, total_loss = 1.124924 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.417175, GIOU: 0.283931), Class: 0.596221, Obj: 0.000600, No Obj: 0.000748, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.384053, iou_loss = 0.092978, total_loss = 0.477031 \n",
" total_bbox = 32046, rewritten_bbox = 0.283967 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345201, GIOU: 0.343104), Class: 0.501439, Obj: 0.026635, No Obj: 0.002724, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.763856, iou_loss = 0.009408, total_loss = 0.773264 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.262636, GIOU: 0.262636), Class: 0.480853, Obj: 0.006515, No Obj: 0.000715, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.761022, iou_loss = 0.122596, total_loss = 0.883618 \n",
" total_bbox = 32050, rewritten_bbox = 0.283931 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427232, GIOU: 0.278840), Class: 0.502960, Obj: 0.009567, No Obj: 0.002894, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.547383, iou_loss = 0.044933, total_loss = 1.592316 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.265826, GIOU: -0.058606), Class: 0.411449, Obj: 0.003944, No Obj: 0.000858, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.759087, iou_loss = 0.729447, total_loss = 1.488534 \n",
" total_bbox = 32056, rewritten_bbox = 0.283878 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.478565, GIOU: 0.459082), Class: 0.471056, Obj: 0.078816, No Obj: 0.002401, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.067438, iou_loss = 0.023484, total_loss = 1.090922 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.363371, GIOU: 0.155088), Class: 0.639940, Obj: 0.000962, No Obj: 0.000745, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.355945, iou_loss = 0.059305, total_loss = 0.415250 \n",
" total_bbox = 32060, rewritten_bbox = 0.283843 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.340654, GIOU: 0.283495), Class: 0.556483, Obj: 0.008503, No Obj: 0.002641, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.832853, iou_loss = 0.052183, total_loss = 1.885036 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.126435, GIOU: -0.427186), Class: 0.543139, Obj: 0.000271, No Obj: 0.000764, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.365142, iou_loss = 0.014579, total_loss = 0.379721 \n",
" total_bbox = 32066, rewritten_bbox = 0.283790 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436805, GIOU: 0.432295), Class: 0.571379, Obj: 0.029074, No Obj: 0.003065, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.411580, iou_loss = 0.028881, total_loss = 1.440461 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.295545, GIOU: 0.132680), Class: 0.557893, Obj: 0.002824, No Obj: 0.000808, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.355612, iou_loss = 0.141858, total_loss = 0.497470 \n",
" total_bbox = 32071, rewritten_bbox = 0.283745 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.343801, GIOU: 0.289731), Class: 0.478697, Obj: 0.028557, No Obj: 0.002888, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.839237, iou_loss = 0.029820, total_loss = 1.869057 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.281528, GIOU: 0.252892), Class: 0.364770, Obj: 0.000838, No Obj: 0.000777, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.378666, iou_loss = 0.118826, total_loss = 0.497492 \n",
" total_bbox = 32077, rewritten_bbox = 0.283692 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.237852, GIOU: 0.193584), Class: 0.674089, Obj: 0.016604, No Obj: 0.002783, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.358164, iou_loss = 0.004884, total_loss = 1.363048 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000765, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002778, iou_loss = 0.000000, total_loss = 0.002778 \n",
" total_bbox = 32081, rewritten_bbox = 0.283657 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.321124, GIOU: 0.308770), Class: 0.642437, Obj: 0.022614, No Obj: 0.002579, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.293102, iou_loss = 0.053725, total_loss = 1.346828 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.469719, GIOU: 0.349550), Class: 0.444865, Obj: 0.003012, No Obj: 0.000796, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.371118, iou_loss = 0.114268, total_loss = 0.485386 \n",
" total_bbox = 32086, rewritten_bbox = 0.283613 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 401: 0.987137, 0.943998 avg loss, 0.000067 rate, 0.704116 seconds, 25664 images, 0.724705 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.268315, GIOU: 0.164317), Class: 0.469157, Obj: 0.011385, No Obj: 0.002181, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.088207, iou_loss = 0.022568, total_loss = 1.110775 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.405528, GIOU: 0.405528), Class: 0.337648, Obj: 0.016472, No Obj: 0.000749, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.400689, iou_loss = 0.142368, total_loss = 0.543057 \n",
" total_bbox = 32090, rewritten_bbox = 0.283577 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.467959, GIOU: 0.321460), Class: 0.641430, Obj: 0.028056, No Obj: 0.002283, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.678047, iou_loss = 0.037637, total_loss = 1.715684 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.303653, GIOU: 0.084422), Class: 0.350132, Obj: 0.005484, No Obj: 0.000741, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.404037, iou_loss = 0.045637, total_loss = 0.449674 \n",
" total_bbox = 32096, rewritten_bbox = 0.283524 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390031, GIOU: 0.329757), Class: 0.467353, Obj: 0.008241, No Obj: 0.002914, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.529460, iou_loss = 0.100632, total_loss = 1.630092 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000917, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003329, iou_loss = 0.000000, total_loss = 0.003329 \n",
" total_bbox = 32100, rewritten_bbox = 0.283489 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345811, GIOU: 0.314719), Class: 0.525349, Obj: 0.012156, No Obj: 0.002897, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.672589, iou_loss = 0.017176, total_loss = 1.689764 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000738, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002586, iou_loss = 0.000000, total_loss = 0.002586 \n",
" total_bbox = 32104, rewritten_bbox = 0.283454 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.343242, GIOU: 0.287535), Class: 0.549962, Obj: 0.065517, No Obj: 0.003371, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.770604, iou_loss = 0.049520, total_loss = 1.820124 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000737, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002092, iou_loss = 0.000000, total_loss = 0.002092 \n",
" total_bbox = 32109, rewritten_bbox = 0.283410 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.256112, GIOU: 0.247016), Class: 0.508137, Obj: 0.022257, No Obj: 0.002723, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.874788, iou_loss = 0.042331, total_loss = 1.917120 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.463711, GIOU: 0.376728), Class: 0.454392, Obj: 0.013226, No Obj: 0.000803, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.194931, iou_loss = 2.349262, total_loss = 3.544192 \n",
" total_bbox = 32117, rewritten_bbox = 0.283339 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395056, GIOU: 0.373499), Class: 0.457711, Obj: 0.038028, No Obj: 0.002929, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.384598, iou_loss = 0.021260, total_loss = 1.405858 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000704, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001755, iou_loss = 0.000000, total_loss = 0.001755 \n",
" total_bbox = 32121, rewritten_bbox = 0.283304 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.290845, GIOU: 0.253618), Class: 0.615819, Obj: 0.043608, No Obj: 0.003194, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.034982, iou_loss = 0.014744, total_loss = 1.049727 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.565784, GIOU: 0.531063), Class: 0.334814, Obj: 0.012478, No Obj: 0.000809, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.424450, iou_loss = 0.163788, total_loss = 0.588237 \n",
" total_bbox = 32125, rewritten_bbox = 0.283268 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.318510, GIOU: -0.001007), Class: 0.474659, Obj: 0.046369, No Obj: 0.002791, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.791714, iou_loss = 0.027605, total_loss = 1.819319 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.139919, GIOU: -0.153382), Class: 0.467060, Obj: 0.001334, No Obj: 0.000742, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.728443, iou_loss = 0.071748, total_loss = 0.800191 \n",
" total_bbox = 32132, rewritten_bbox = 0.283207 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.313738, GIOU: 0.196055), Class: 0.506943, Obj: 0.022566, No Obj: 0.003127, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.417263, iou_loss = 0.034182, total_loss = 1.451445 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000789, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003948, iou_loss = 0.000000, total_loss = 0.003948 \n",
" total_bbox = 32136, rewritten_bbox = 0.283172 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.378430, GIOU: 0.332527), Class: 0.628241, Obj: 0.026520, No Obj: 0.002805, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.688169, iou_loss = 0.061531, total_loss = 1.749700 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.098748, GIOU: -0.209041), Class: 0.512125, Obj: 0.000370, No Obj: 0.000789, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.429295, iou_loss = 0.018847, total_loss = 0.448142 \n",
" total_bbox = 32142, rewritten_bbox = 0.283119 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.350732, GIOU: 0.159688), Class: 0.344607, Obj: 0.065676, No Obj: 0.002676, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.299872, iou_loss = 0.006469, total_loss = 1.306340 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000722, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001898, iou_loss = 0.000000, total_loss = 0.001898 \n",
" total_bbox = 32145, rewritten_bbox = 0.283092 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.294979, GIOU: 0.282018), Class: 0.498448, Obj: 0.041421, No Obj: 0.002086, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.360137, iou_loss = 0.065759, total_loss = 1.425896 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.145680, GIOU: -0.410262), Class: 0.598101, Obj: 0.000110, No Obj: 0.000741, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.389739, iou_loss = 0.020044, total_loss = 0.409783 \n",
" total_bbox = 32150, rewritten_bbox = 0.283048 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.317802, GIOU: 0.175513), Class: 0.551888, Obj: 0.034871, No Obj: 0.002477, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.403693, iou_loss = 0.025725, total_loss = 1.429418 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.403008, GIOU: 0.326441), Class: 0.432395, Obj: 0.000437, No Obj: 0.000769, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.390274, iou_loss = 0.661212, total_loss = 1.051486 \n",
" total_bbox = 32155, rewritten_bbox = 0.283004 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.268590, GIOU: -0.003003), Class: 0.622064, Obj: 0.102914, No Obj: 0.003222, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.973419, iou_loss = 0.004690, total_loss = 0.978109 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.231554, GIOU: 0.080663), Class: 0.484126, Obj: 0.001898, No Obj: 0.000735, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.393443, iou_loss = 0.062701, total_loss = 0.456144 \n",
" total_bbox = 32159, rewritten_bbox = 0.282969 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.305747, GIOU: 0.248868), Class: 0.479736, Obj: 0.024029, No Obj: 0.002819, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.528375, iou_loss = 0.027129, total_loss = 1.555504 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.372922, GIOU: 0.349220), Class: 0.618382, Obj: 0.001628, No Obj: 0.000755, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.666796, iou_loss = 0.327661, total_loss = 0.994457 \n",
" total_bbox = 32165, rewritten_bbox = 0.282916 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 402: 0.913781, 0.940976 avg loss, 0.000068 rate, 0.677994 seconds, 25728 images, 0.724498 hours left\n",
"Loaded: 0.000044 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382515, GIOU: 0.306584), Class: 0.544834, Obj: 0.033993, No Obj: 0.002550, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.375855, iou_loss = 0.030804, total_loss = 1.406659 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000796, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003876, iou_loss = 0.000000, total_loss = 0.003876 \n",
" total_bbox = 32169, rewritten_bbox = 0.282881 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422062, GIOU: 0.260344), Class: 0.544667, Obj: 0.027623, No Obj: 0.002835, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.413985, iou_loss = 0.025062, total_loss = 1.439048 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000784, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002684, iou_loss = 0.000000, total_loss = 0.002684 \n",
" total_bbox = 32173, rewritten_bbox = 0.282846 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.471930, GIOU: 0.330309), Class: 0.403479, Obj: 0.057849, No Obj: 0.002732, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.188883, iou_loss = 0.038502, total_loss = 1.227386 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.401750, GIOU: 0.359446), Class: 0.392547, Obj: 0.027066, No Obj: 0.000754, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.398765, iou_loss = 0.068159, total_loss = 0.466924 \n",
" total_bbox = 32177, rewritten_bbox = 0.282811 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.358007, GIOU: 0.247310), Class: 0.461671, Obj: 0.021208, No Obj: 0.002400, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.537197, iou_loss = 0.015633, total_loss = 1.552830 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000734, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002237, iou_loss = 0.000000, total_loss = 0.002237 \n",
" total_bbox = 32181, rewritten_bbox = 0.282776 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.368994, GIOU: 0.350801), Class: 0.476658, Obj: 0.011609, No Obj: 0.002574, .5R: 0.250000, .75R: 0.000000, count: 8, class_loss = 2.988469, iou_loss = 0.082627, total_loss = 3.071096 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.188633, GIOU: -0.092288), Class: 0.454424, Obj: 0.000537, No Obj: 0.000747, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.794350, iou_loss = 0.083257, total_loss = 0.877607 \n",
" total_bbox = 32191, rewritten_bbox = 0.282688 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.183060, GIOU: 0.141500), Class: 0.521058, Obj: 0.012244, No Obj: 0.002594, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.460024, iou_loss = 0.005464, total_loss = 1.465488 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.322384, GIOU: 0.318977), Class: 0.555448, Obj: 0.008503, No Obj: 0.000812, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.723660, iou_loss = 0.178023, total_loss = 0.901683 \n",
" total_bbox = 32197, rewritten_bbox = 0.282635 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.397702, GIOU: 0.276309), Class: 0.531110, Obj: 0.056513, No Obj: 0.002864, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.446376, iou_loss = 0.034765, total_loss = 1.481141 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.189407, GIOU: 0.189407), Class: 0.488786, Obj: 0.000877, No Obj: 0.000764, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.778922, iou_loss = 0.371083, total_loss = 1.150005 \n",
" total_bbox = 32203, rewritten_bbox = 0.282582 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415634, GIOU: 0.348127), Class: 0.421862, Obj: 0.037353, No Obj: 0.002377, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.481648, iou_loss = 0.116038, total_loss = 1.597685 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.266220, GIOU: 0.266220), Class: 0.506925, Obj: 0.003543, No Obj: 0.000788, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354579, iou_loss = 0.061563, total_loss = 0.416142 \n",
" total_bbox = 32208, rewritten_bbox = 0.282539 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.423330, GIOU: 0.394317), Class: 0.466407, Obj: 0.035826, No Obj: 0.002569, .5R: 0.571429, .75R: 0.000000, count: 7, class_loss = 2.509128, iou_loss = 0.159641, total_loss = 2.668768 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000735, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002174, iou_loss = 0.000000, total_loss = 0.002174 \n",
" total_bbox = 32215, rewritten_bbox = 0.282477 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.425167, GIOU: 0.338708), Class: 0.474737, Obj: 0.028478, No Obj: 0.002619, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.972794, iou_loss = 0.073430, total_loss = 2.046224 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000740, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002265, iou_loss = 0.000000, total_loss = 0.002265 \n",
" total_bbox = 32220, rewritten_bbox = 0.282433 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.551299, GIOU: 0.495205), Class: 0.548074, Obj: 0.033601, No Obj: 0.002496, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.045209, iou_loss = 0.032992, total_loss = 1.078201 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.052960, GIOU: -0.455985), Class: 0.506507, Obj: 0.000777, No Obj: 0.000691, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.824893, iou_loss = 0.007021, total_loss = 0.831914 \n",
" total_bbox = 32225, rewritten_bbox = 0.282389 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.358370, GIOU: 0.293740), Class: 0.503425, Obj: 0.043709, No Obj: 0.003012, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.723033, iou_loss = 0.033308, total_loss = 1.756340 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000733, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002608, iou_loss = 0.000000, total_loss = 0.002608 \n",
" total_bbox = 32230, rewritten_bbox = 0.282346 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422078, GIOU: 0.420835), Class: 0.404034, Obj: 0.057357, No Obj: 0.002711, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.570238, iou_loss = 0.017711, total_loss = 1.587949 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000706, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001713, iou_loss = 0.000000, total_loss = 0.001713 \n",
" total_bbox = 32234, rewritten_bbox = 0.282311 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.231039, GIOU: 0.202972), Class: 0.481790, Obj: 0.023440, No Obj: 0.002872, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.116963, iou_loss = 0.002723, total_loss = 1.119687 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.167642, GIOU: -0.007702), Class: 0.626561, Obj: 0.001719, No Obj: 0.000832, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.320513, iou_loss = 1.089074, total_loss = 1.409587 \n",
" total_bbox = 32238, rewritten_bbox = 0.282276 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357574, GIOU: 0.311040), Class: 0.430606, Obj: 0.034867, No Obj: 0.003595, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.542766, iou_loss = 0.015993, total_loss = 1.558759 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000786, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002377, iou_loss = 0.000000, total_loss = 0.002377 \n",
" total_bbox = 32242, rewritten_bbox = 0.282241 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.531617, GIOU: 0.506167), Class: 0.493018, Obj: 0.026738, No Obj: 0.002231, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.694066, iou_loss = 0.063711, total_loss = 1.757777 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000724, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002016, iou_loss = 0.000000, total_loss = 0.002016 \n",
" total_bbox = 32247, rewritten_bbox = 0.282197 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 403: 0.954647, 0.942343 avg loss, 0.000069 rate, 0.685823 seconds, 25792 images, 0.724029 hours left\n",
"Loaded: 0.000065 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.379925, GIOU: 0.301893), Class: 0.555679, Obj: 0.058948, No Obj: 0.002433, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.276715, iou_loss = 0.039968, total_loss = 1.316682 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.477585, GIOU: 0.389067), Class: 0.350992, Obj: 0.003615, No Obj: 0.000729, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.425518, iou_loss = 0.833302, total_loss = 1.258820 \n",
" total_bbox = 32252, rewritten_bbox = 0.282153 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.294986, GIOU: 0.155069), Class: 0.472874, Obj: 0.019670, No Obj: 0.002411, .5R: 0.166667, .75R: 0.166667, count: 6, class_loss = 2.229818, iou_loss = 0.024957, total_loss = 2.254775 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000731, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002214, iou_loss = 0.000000, total_loss = 0.002214 \n",
" total_bbox = 32258, rewritten_bbox = 0.282101 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.244636, GIOU: 0.227266), Class: 0.491432, Obj: 0.026041, No Obj: 0.002300, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.441825, iou_loss = 0.055270, total_loss = 1.497094 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000768, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002498, iou_loss = 0.000000, total_loss = 0.002498 \n",
" total_bbox = 32262, rewritten_bbox = 0.282066 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353565, GIOU: 0.280203), Class: 0.350839, Obj: 0.019916, No Obj: 0.002352, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.031648, iou_loss = 0.015547, total_loss = 2.047196 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.279229, GIOU: 0.050137), Class: 0.455508, Obj: 0.002528, No Obj: 0.000711, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.390168, iou_loss = 0.039029, total_loss = 0.429196 \n",
" total_bbox = 32268, rewritten_bbox = 0.282013 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435261, GIOU: 0.334043), Class: 0.527875, Obj: 0.019850, No Obj: 0.002585, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.885152, iou_loss = 0.020363, total_loss = 1.905515 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000714, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001986, iou_loss = 0.000000, total_loss = 0.001986 \n",
" total_bbox = 32273, rewritten_bbox = 0.281969 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.324996, GIOU: 0.324996), Class: 0.566670, Obj: 0.022271, No Obj: 0.002390, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.723297, iou_loss = 0.024305, total_loss = 0.747602 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.031415, GIOU: -0.752209), Class: 0.626079, Obj: 0.000958, No Obj: 0.000750, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.341351, iou_loss = 0.001136, total_loss = 0.342487 \n",
" total_bbox = 32276, rewritten_bbox = 0.281943 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.467696, GIOU: 0.465746), Class: 0.618316, Obj: 0.020164, No Obj: 0.002458, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 0.991792, iou_loss = 0.131868, total_loss = 1.123660 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.297070, GIOU: 0.085438), Class: 0.582370, Obj: 0.002278, No Obj: 0.000777, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.708882, iou_loss = 0.146645, total_loss = 0.855527 \n",
" total_bbox = 32281, rewritten_bbox = 0.281900 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413189, GIOU: 0.413189), Class: 0.577434, Obj: 0.026848, No Obj: 0.002766, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.480395, iou_loss = 0.038777, total_loss = 1.519173 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.289776, GIOU: 0.059548), Class: 0.571282, Obj: 0.043709, No Obj: 0.000725, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.374639, iou_loss = 0.025691, total_loss = 0.400331 \n",
" total_bbox = 32286, rewritten_bbox = 0.281856 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477952, GIOU: 0.410144), Class: 0.442061, Obj: 0.036446, No Obj: 0.002663, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.117969, iou_loss = 0.016701, total_loss = 1.134670 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.588882, GIOU: 0.587805), Class: 0.427467, Obj: 0.004411, No Obj: 0.000773, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.858767, iou_loss = 1.378853, total_loss = 2.237620 \n",
" total_bbox = 32291, rewritten_bbox = 0.281812 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.262632, GIOU: 0.219255), Class: 0.541872, Obj: 0.011091, No Obj: 0.002033, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.554397, iou_loss = 0.022541, total_loss = 1.576938 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000690, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001778, iou_loss = 0.000000, total_loss = 0.001778 \n",
" total_bbox = 32295, rewritten_bbox = 0.281777 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.404467, GIOU: 0.385874), Class: 0.464962, Obj: 0.024345, No Obj: 0.003227, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.483141, iou_loss = 0.056413, total_loss = 1.539553 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.262896, GIOU: 0.262896), Class: 0.645694, Obj: 0.006483, No Obj: 0.000766, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.330415, iou_loss = 0.059541, total_loss = 0.389956 \n",
" total_bbox = 32300, rewritten_bbox = 0.281734 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.311174, GIOU: 0.217440), Class: 0.575033, Obj: 0.046371, No Obj: 0.002482, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.044058, iou_loss = 0.008131, total_loss = 1.052189 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.344103, GIOU: 0.179793), Class: 0.507110, Obj: 0.005091, No Obj: 0.000718, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.360377, iou_loss = 0.242017, total_loss = 0.602394 \n",
" total_bbox = 32304, rewritten_bbox = 0.281699 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429740, GIOU: 0.364005), Class: 0.529454, Obj: 0.019470, No Obj: 0.002390, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.159760, iou_loss = 0.166780, total_loss = 2.326541 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000729, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002209, iou_loss = 0.000000, total_loss = 0.002209 \n",
" total_bbox = 32310, rewritten_bbox = 0.281647 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.474029, GIOU: 0.446655), Class: 0.423405, Obj: 0.016379, No Obj: 0.002661, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.708054, iou_loss = 0.014507, total_loss = 1.722561 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000718, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001806, iou_loss = 0.000000, total_loss = 0.001806 \n",
" total_bbox = 32314, rewritten_bbox = 0.281612 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415492, GIOU: 0.413097), Class: 0.636694, Obj: 0.038692, No Obj: 0.002525, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.299406, iou_loss = 0.022344, total_loss = 1.321750 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000700, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001840, iou_loss = 0.000000, total_loss = 0.001840 \n",
" total_bbox = 32318, rewritten_bbox = 0.281577 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.318763, GIOU: 0.224141), Class: 0.393304, Obj: 0.078895, No Obj: 0.002835, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.104037, iou_loss = 0.019887, total_loss = 1.123924 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.240277, GIOU: 0.175848), Class: 0.746492, Obj: 0.001515, No Obj: 0.000732, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.352515, iou_loss = 0.133947, total_loss = 0.486462 \n",
" total_bbox = 32322, rewritten_bbox = 0.281542 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 404: 0.874112, 0.935520 avg loss, 0.000070 rate, 0.712564 seconds, 25856 images, 0.723642 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.206540, GIOU: 0.119204), Class: 0.522917, Obj: 0.018394, No Obj: 0.002303, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.330388, iou_loss = 0.016805, total_loss = 1.347193 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000758, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003406, iou_loss = 0.000000, total_loss = 0.003406 \n",
" total_bbox = 32326, rewritten_bbox = 0.281507 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413096, GIOU: 0.294883), Class: 0.553533, Obj: 0.078144, No Obj: 0.002988, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.697222, iou_loss = 0.038358, total_loss = 1.735580 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000705, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002076, iou_loss = 0.000000, total_loss = 0.002076 \n",
" total_bbox = 32331, rewritten_bbox = 0.281464 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.315677, GIOU: 0.300866), Class: 0.798401, Obj: 0.023264, No Obj: 0.002151, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.594951, iou_loss = 0.013555, total_loss = 0.608506 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.438694, GIOU: 0.432088), Class: 0.632880, Obj: 0.014233, No Obj: 0.000821, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.669021, iou_loss = 0.258985, total_loss = 0.928006 \n",
" total_bbox = 32335, rewritten_bbox = 0.281429 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344222, GIOU: 0.310823), Class: 0.482795, Obj: 0.018072, No Obj: 0.002714, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.445711, iou_loss = 0.028533, total_loss = 1.474244 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000750, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002397, iou_loss = 0.000000, total_loss = 0.002397 \n",
" total_bbox = 32339, rewritten_bbox = 0.281394 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.381021, GIOU: 0.311945), Class: 0.526719, Obj: 0.026718, No Obj: 0.002012, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.308002, iou_loss = 0.086265, total_loss = 1.394268 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000709, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002925, iou_loss = 0.000000, total_loss = 0.002925 \n",
" total_bbox = 32343, rewritten_bbox = 0.281359 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300483, GIOU: 0.268394), Class: 0.523875, Obj: 0.035970, No Obj: 0.002607, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.350524, iou_loss = 0.060399, total_loss = 1.410923 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000728, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001881, iou_loss = 0.000000, total_loss = 0.001881 \n",
" total_bbox = 32347, rewritten_bbox = 0.281324 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.365730, GIOU: 0.260185), Class: 0.394312, Obj: 0.024025, No Obj: 0.002819, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.157816, iou_loss = 0.044560, total_loss = 2.202375 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000710, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001812, iou_loss = 0.000000, total_loss = 0.001812 \n",
" total_bbox = 32352, rewritten_bbox = 0.281281 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.246088, GIOU: 0.165359), Class: 0.433705, Obj: 0.009072, No Obj: 0.002964, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 2.035219, iou_loss = 0.015935, total_loss = 2.051154 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000784, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002413, iou_loss = 0.000000, total_loss = 0.002413 \n",
" total_bbox = 32357, rewritten_bbox = 0.281237 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372852, GIOU: 0.360772), Class: 0.482833, Obj: 0.012445, No Obj: 0.002854, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.752004, iou_loss = 0.011646, total_loss = 1.763649 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000688, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002016, iou_loss = 0.000000, total_loss = 0.002016 \n",
" total_bbox = 32362, rewritten_bbox = 0.281194 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435777, GIOU: 0.305581), Class: 0.459580, Obj: 0.022488, No Obj: 0.001815, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.766838, iou_loss = 0.048280, total_loss = 0.815118 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.414583, GIOU: 0.406583), Class: 0.478716, Obj: 0.010029, No Obj: 0.000727, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.090218, iou_loss = 1.395584, total_loss = 2.485803 \n",
" total_bbox = 32367, rewritten_bbox = 0.281151 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359831, GIOU: 0.182780), Class: 0.545405, Obj: 0.045497, No Obj: 0.002788, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.125074, iou_loss = 0.013395, total_loss = 1.138469 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000749, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002986, iou_loss = 0.000000, total_loss = 0.002986 \n",
" total_bbox = 32370, rewritten_bbox = 0.281125 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.560155, GIOU: 0.532716), Class: 0.406813, Obj: 0.022584, No Obj: 0.002572, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.566849, iou_loss = 0.022711, total_loss = 1.589560 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.175485, GIOU: 0.175485), Class: 0.638978, Obj: 0.001073, No Obj: 0.000736, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.397196, iou_loss = 0.026848, total_loss = 0.424044 \n",
" total_bbox = 32375, rewritten_bbox = 0.281081 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.589397, GIOU: 0.520949), Class: 0.519990, Obj: 0.027535, No Obj: 0.002759, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.465109, iou_loss = 0.118243, total_loss = 1.583352 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.293871, GIOU: 0.061786), Class: 0.468831, Obj: 0.006025, No Obj: 0.000737, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.339713, iou_loss = 0.293324, total_loss = 0.633036 \n",
" total_bbox = 32380, rewritten_bbox = 0.281038 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403904, GIOU: 0.334559), Class: 0.361534, Obj: 0.024719, No Obj: 0.002605, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.664802, iou_loss = 0.032840, total_loss = 1.697642 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000721, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001926, iou_loss = 0.000000, total_loss = 0.001926 \n",
" total_bbox = 32384, rewritten_bbox = 0.281003 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357336, GIOU: 0.191480), Class: 0.426198, Obj: 0.023527, No Obj: 0.002468, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.829792, iou_loss = 0.073459, total_loss = 1.903252 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.246354, GIOU: 0.246355), Class: 0.525572, Obj: 0.003439, No Obj: 0.000794, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.362182, iou_loss = 0.053137, total_loss = 0.415319 \n",
" total_bbox = 32390, rewritten_bbox = 0.280951 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.302619, GIOU: 0.254740), Class: 0.363037, Obj: 0.086501, No Obj: 0.002956, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.934331, iou_loss = 0.017950, total_loss = 1.952281 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.293188, GIOU: 0.293188), Class: 0.463493, Obj: 0.012159, No Obj: 0.000684, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.393192, iou_loss = 0.074847, total_loss = 0.468039 \n",
" total_bbox = 32396, rewritten_bbox = 0.280899 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 405: 0.860881, 0.928056 avg loss, 0.000070 rate, 0.734388 seconds, 25920 images, 0.723524 hours left\n",
"Loaded: 0.000035 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.254202, GIOU: 0.186865), Class: 0.532458, Obj: 0.052427, No Obj: 0.002877, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.687358, iou_loss = 0.008981, total_loss = 0.696339 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.230714, GIOU: 0.108556), Class: 0.488725, Obj: 0.004514, No Obj: 0.000752, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.397644, iou_loss = 0.031361, total_loss = 0.429005 \n",
" total_bbox = 32399, rewritten_bbox = 0.280873 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319863, GIOU: 0.188052), Class: 0.566943, Obj: 0.029296, No Obj: 0.003157, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.114733, iou_loss = 0.052419, total_loss = 2.167151 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000931, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.008245, iou_loss = 0.000000, total_loss = 0.008245 \n",
" total_bbox = 32405, rewritten_bbox = 0.280821 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.367388, GIOU: 0.318800), Class: 0.509809, Obj: 0.027532, No Obj: 0.002934, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.746124, iou_loss = 0.013313, total_loss = 1.759437 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.504305, GIOU: 0.504305), Class: 0.604848, Obj: 0.001533, No Obj: 0.000769, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.311598, iou_loss = 0.221212, total_loss = 0.532810 \n",
" total_bbox = 32411, rewritten_bbox = 0.280769 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402799, GIOU: 0.304035), Class: 0.560084, Obj: 0.048726, No Obj: 0.002979, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.739113, iou_loss = 0.077132, total_loss = 1.816244 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.456133, GIOU: 0.456134), Class: 0.327876, Obj: 0.002241, No Obj: 0.000754, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.436073, iou_loss = 0.180993, total_loss = 0.617066 \n",
" total_bbox = 32417, rewritten_bbox = 0.280717 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.452106, GIOU: 0.415116), Class: 0.537959, Obj: 0.008215, No Obj: 0.002583, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.586341, iou_loss = 0.240104, total_loss = 2.826445 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000729, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002081, iou_loss = 0.000000, total_loss = 0.002081 \n",
" total_bbox = 32424, rewritten_bbox = 0.280656 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.314278, GIOU: 0.240537), Class: 0.582330, Obj: 0.072771, No Obj: 0.002921, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.247052, iou_loss = 0.028568, total_loss = 1.275620 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.278060, GIOU: 0.155927), Class: 0.525710, Obj: 0.006101, No Obj: 0.000781, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.519496, iou_loss = 2.931761, total_loss = 4.451256 \n",
" total_bbox = 32432, rewritten_bbox = 0.280587 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.360873, GIOU: 0.302776), Class: 0.572139, Obj: 0.021407, No Obj: 0.002031, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.303894, iou_loss = 0.135444, total_loss = 1.439338 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.327842, GIOU: 0.234821), Class: 0.587934, Obj: 0.004140, No Obj: 0.000830, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.796034, iou_loss = 0.201124, total_loss = 0.997157 \n",
" total_bbox = 32438, rewritten_bbox = 0.280535 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.612187, GIOU: 0.582248), Class: 0.470796, Obj: 0.045582, No Obj: 0.002557, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.109516, iou_loss = 0.126645, total_loss = 1.236161 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.357215, GIOU: 0.186009), Class: 0.709149, Obj: 0.002183, No Obj: 0.000759, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.367750, iou_loss = 0.043597, total_loss = 0.411347 \n",
" total_bbox = 32442, rewritten_bbox = 0.280501 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.246036, GIOU: -0.081877), Class: 0.509304, Obj: 0.027585, No Obj: 0.002465, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.592731, iou_loss = 0.013057, total_loss = 1.605788 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.281039, GIOU: 0.089562), Class: 0.463587, Obj: 0.005458, No Obj: 0.000762, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.719024, iou_loss = 0.205602, total_loss = 0.924626 \n",
" total_bbox = 32448, rewritten_bbox = 0.280449 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382595, GIOU: 0.211420), Class: 0.420609, Obj: 0.010492, No Obj: 0.002658, .5R: 0.142857, .75R: 0.142857, count: 7, class_loss = 2.866987, iou_loss = 0.024904, total_loss = 2.891892 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.075298, GIOU: -0.651786), Class: 0.560370, Obj: 0.000264, No Obj: 0.000713, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.420986, iou_loss = 0.005330, total_loss = 0.426315 \n",
" total_bbox = 32456, rewritten_bbox = 0.280380 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346283, GIOU: 0.336370), Class: 0.445857, Obj: 0.006007, No Obj: 0.002651, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.570583, iou_loss = 0.017596, total_loss = 1.588179 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000678, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001698, iou_loss = 0.000000, total_loss = 0.001698 \n",
" total_bbox = 32460, rewritten_bbox = 0.280345 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.534106, GIOU: 0.482279), Class: 0.489647, Obj: 0.042346, No Obj: 0.003098, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.117340, iou_loss = 0.014285, total_loss = 1.131625 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.411766, GIOU: 0.400710), Class: 0.470919, Obj: 0.004921, No Obj: 0.000725, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.384190, iou_loss = 0.631906, total_loss = 1.016096 \n",
" total_bbox = 32464, rewritten_bbox = 0.280311 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472817, GIOU: 0.347415), Class: 0.414808, Obj: 0.099612, No Obj: 0.002947, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.750447, iou_loss = 0.029819, total_loss = 0.780265 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.384688, GIOU: 0.315692), Class: 0.555087, Obj: 0.013685, No Obj: 0.000845, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.075306, iou_loss = 0.383790, total_loss = 1.459096 \n",
" total_bbox = 32469, rewritten_bbox = 0.280267 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.419868, GIOU: 0.388357), Class: 0.463184, Obj: 0.036352, No Obj: 0.002947, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 2.553379, iou_loss = 0.195430, total_loss = 2.748808 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.178043, GIOU: -0.308486), Class: 0.418652, Obj: 0.000171, No Obj: 0.000769, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.402747, iou_loss = 0.009482, total_loss = 0.412229 \n",
" total_bbox = 32477, rewritten_bbox = 0.280198 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.467838, GIOU: 0.430196), Class: 0.551408, Obj: 0.021429, No Obj: 0.002261, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.036244, iou_loss = 0.038033, total_loss = 1.074277 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.045140, GIOU: -0.654432), Class: 0.589502, Obj: 0.001072, No Obj: 0.000692, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.356418, iou_loss = 0.002777, total_loss = 0.359195 \n",
" total_bbox = 32481, rewritten_bbox = 0.280164 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.234679, GIOU: 0.202163), Class: 0.546634, Obj: 0.025929, No Obj: 0.002594, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.499519, iou_loss = 0.009486, total_loss = 1.509005 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000710, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001811, iou_loss = 0.000000, total_loss = 0.001811 \n",
" total_bbox = 32485, rewritten_bbox = 0.280129 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 406: 1.032575, 0.938508 avg loss, 0.000071 rate, 0.705653 seconds, 25984 images, 0.723623 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410730, GIOU: 0.409542), Class: 0.574725, Obj: 0.010199, No Obj: 0.002746, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.018685, iou_loss = 0.088911, total_loss = 1.107595 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.470911, GIOU: 0.362968), Class: 0.513584, Obj: 0.006572, No Obj: 0.000739, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.069109, iou_loss = 0.602399, total_loss = 1.671508 \n",
" total_bbox = 32491, rewritten_bbox = 0.280078 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.420321, GIOU: 0.341767), Class: 0.573725, Obj: 0.112658, No Obj: 0.003026, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.905648, iou_loss = 0.015473, total_loss = 0.921120 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.612822, GIOU: 0.555608), Class: 0.451309, Obj: 0.035489, No Obj: 0.000786, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.434563, iou_loss = 0.216962, total_loss = 0.651525 \n",
" total_bbox = 32495, rewritten_bbox = 0.280043 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.281890, GIOU: 0.233715), Class: 0.498743, Obj: 0.030620, No Obj: 0.002263, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.470281, iou_loss = 0.017163, total_loss = 1.487445 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000709, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002420, iou_loss = 0.000000, total_loss = 0.002420 \n",
" total_bbox = 32499, rewritten_bbox = 0.280009 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.466301, GIOU: 0.452342), Class: 0.368522, Obj: 0.081596, No Obj: 0.003225, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.499333, iou_loss = 0.028389, total_loss = 1.527722 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000724, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002527, iou_loss = 0.000000, total_loss = 0.002527 \n",
" total_bbox = 32503, rewritten_bbox = 0.279974 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436314, GIOU: 0.335220), Class: 0.587292, Obj: 0.016537, No Obj: 0.002173, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.973358, iou_loss = 0.015399, total_loss = 0.988757 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000676, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001634, iou_loss = 0.000000, total_loss = 0.001634 \n",
" total_bbox = 32506, rewritten_bbox = 0.279948 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.297885, GIOU: 0.264333), Class: 0.548351, Obj: 0.035212, No Obj: 0.002693, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.037305, iou_loss = 0.052802, total_loss = 1.090108 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.423933, GIOU: 0.420477), Class: 0.461547, Obj: 0.001037, No Obj: 0.000702, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.384201, iou_loss = 0.425881, total_loss = 0.810082 \n",
" total_bbox = 32510, rewritten_bbox = 0.279914 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.360881, GIOU: 0.242761), Class: 0.631836, Obj: 0.020676, No Obj: 0.002561, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 1.809394, iou_loss = 0.039121, total_loss = 1.848515 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000726, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002460, iou_loss = 0.000000, total_loss = 0.002460 \n",
" total_bbox = 32516, rewritten_bbox = 0.279862 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.297528, GIOU: 0.224393), Class: 0.518284, Obj: 0.027164, No Obj: 0.002865, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.516473, iou_loss = 0.046769, total_loss = 1.563242 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.261182, GIOU: 0.261182), Class: 0.651870, Obj: 0.006028, No Obj: 0.000683, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.367748, iou_loss = 0.060345, total_loss = 0.428093 \n",
" total_bbox = 32521, rewritten_bbox = 0.279819 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.355681, GIOU: 0.341755), Class: 0.487846, Obj: 0.127749, No Obj: 0.003105, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.028383, iou_loss = 0.023591, total_loss = 1.051973 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.482173, GIOU: 0.417430), Class: 0.574996, Obj: 0.035712, No Obj: 0.000743, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.322269, iou_loss = 0.203469, total_loss = 0.525738 \n",
" total_bbox = 32525, rewritten_bbox = 0.279785 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.191727, GIOU: 0.121765), Class: 0.537848, Obj: 0.037849, No Obj: 0.002865, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.498655, iou_loss = 0.016069, total_loss = 1.514724 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.127495, GIOU: -0.435147), Class: 0.464268, Obj: 0.000933, No Obj: 0.000725, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.385671, iou_loss = 0.003922, total_loss = 0.389592 \n",
" total_bbox = 32530, rewritten_bbox = 0.279742 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.231458, GIOU: 0.182153), Class: 0.522279, Obj: 0.012466, No Obj: 0.002654, .5R: 0.000000, .75R: 0.000000, count: 8, class_loss = 3.005200, iou_loss = 0.011497, total_loss = 3.016697 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.547115, GIOU: 0.490691), Class: 0.675469, Obj: 0.001750, No Obj: 0.000680, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.308284, iou_loss = 0.331604, total_loss = 0.639888 \n",
" total_bbox = 32539, rewritten_bbox = 0.279664 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.412169, GIOU: 0.363802), Class: 0.535708, Obj: 0.050691, No Obj: 0.002053, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.928215, iou_loss = 0.069014, total_loss = 0.997229 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.199576, GIOU: 0.087975), Class: 0.447270, Obj: 0.000321, No Obj: 0.000717, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.756448, iou_loss = 0.072568, total_loss = 0.829016 \n",
" total_bbox = 32544, rewritten_bbox = 0.279621 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.418072, GIOU: 0.320999), Class: 0.538833, Obj: 0.013247, No Obj: 0.002747, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.262992, iou_loss = 0.130040, total_loss = 2.393033 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.230054, GIOU: 0.230054), Class: 0.650079, Obj: 0.015823, No Obj: 0.000684, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.351998, iou_loss = 0.046673, total_loss = 0.398671 \n",
" total_bbox = 32551, rewritten_bbox = 0.279561 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.455609, GIOU: 0.419627), Class: 0.589040, Obj: 0.044495, No Obj: 0.003061, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.055724, iou_loss = 0.052062, total_loss = 1.107786 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000743, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003403, iou_loss = 0.000000, total_loss = 0.003403 \n",
" total_bbox = 32554, rewritten_bbox = 0.279536 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.314738, GIOU: 0.163541), Class: 0.477718, Obj: 0.013667, No Obj: 0.002394, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.250874, iou_loss = 0.056904, total_loss = 2.307778 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.430535, GIOU: 0.360411), Class: 0.517871, Obj: 0.012002, No Obj: 0.000705, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.750451, iou_loss = 1.001037, total_loss = 1.751488 \n",
" total_bbox = 32562, rewritten_bbox = 0.279467 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.440449, GIOU: 0.386382), Class: 0.522174, Obj: 0.028687, No Obj: 0.002625, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.842124, iou_loss = 0.180196, total_loss = 2.022320 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000743, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002774, iou_loss = 0.000000, total_loss = 0.002774 \n",
" total_bbox = 32567, rewritten_bbox = 0.279424 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 407: 0.923000, 0.936957 avg loss, 0.000072 rate, 0.709183 seconds, 26048 images, 0.723432 hours left\n",
"Loaded: 0.000050 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.512280, GIOU: 0.505065), Class: 0.605462, Obj: 0.019089, No Obj: 0.002095, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.100965, iou_loss = 0.078151, total_loss = 1.179117 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000730, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002171, iou_loss = 0.000000, total_loss = 0.002171 \n",
" total_bbox = 32570, rewritten_bbox = 0.279398 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320624, GIOU: 0.184019), Class: 0.640613, Obj: 0.023785, No Obj: 0.002455, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.761862, iou_loss = 0.035735, total_loss = 1.797596 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.450599, GIOU: 0.371229), Class: 0.299065, Obj: 0.002621, No Obj: 0.000750, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.457041, iou_loss = 0.130461, total_loss = 0.587503 \n",
" total_bbox = 32576, rewritten_bbox = 0.279347 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.600445, GIOU: 0.533375), Class: 0.513998, Obj: 0.024826, No Obj: 0.002284, .5R: 0.800000, .75R: 0.200000, count: 5, class_loss = 1.933517, iou_loss = 0.082013, total_loss = 2.015531 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000690, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001983, iou_loss = 0.000000, total_loss = 0.001983 \n",
" total_bbox = 32581, rewritten_bbox = 0.279304 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.294440, GIOU: 0.281732), Class: 0.550366, Obj: 0.018203, No Obj: 0.002198, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.481942, iou_loss = 0.010512, total_loss = 1.492454 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000704, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003198, iou_loss = 0.000000, total_loss = 0.003198 \n",
" total_bbox = 32585, rewritten_bbox = 0.279270 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.476252, GIOU: 0.401194), Class: 0.436997, Obj: 0.015805, No Obj: 0.002628, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 2.234413, iou_loss = 0.162593, total_loss = 2.397005 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.356304, GIOU: 0.282812), Class: 0.514531, Obj: 0.003338, No Obj: 0.000749, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.128958, iou_loss = 0.312148, total_loss = 1.441107 \n",
" total_bbox = 32594, rewritten_bbox = 0.279192 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.327472, GIOU: 0.324679), Class: 0.541059, Obj: 0.050079, No Obj: 0.002637, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.359275, iou_loss = 0.029165, total_loss = 1.388440 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000684, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001791, iou_loss = 0.000000, total_loss = 0.001791 \n",
" total_bbox = 32598, rewritten_bbox = 0.279158 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398964, GIOU: 0.362876), Class: 0.655974, Obj: 0.012131, No Obj: 0.002227, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.066225, iou_loss = 0.029191, total_loss = 1.095416 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.289451, GIOU: 0.186951), Class: 0.589829, Obj: 0.002197, No Obj: 0.000683, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.384602, iou_loss = 0.697730, total_loss = 2.082332 \n",
" total_bbox = 32605, rewritten_bbox = 0.279098 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344237, GIOU: 0.296597), Class: 0.318520, Obj: 0.028344, No Obj: 0.002836, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.747728, iou_loss = 0.008244, total_loss = 1.755972 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000709, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002708, iou_loss = 0.000000, total_loss = 0.002708 \n",
" total_bbox = 32609, rewritten_bbox = 0.279064 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.331378, GIOU: 0.269874), Class: 0.672262, Obj: 0.044262, No Obj: 0.002748, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.034922, iou_loss = 0.012277, total_loss = 1.047200 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.291989, GIOU: 0.139190), Class: 0.549929, Obj: 0.000468, No Obj: 0.000706, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.721685, iou_loss = 0.208828, total_loss = 0.930513 \n",
" total_bbox = 32614, rewritten_bbox = 0.279021 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.461220, GIOU: 0.454741), Class: 0.539258, Obj: 0.011206, No Obj: 0.002396, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.462212, iou_loss = 0.176890, total_loss = 1.639102 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000680, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002031, iou_loss = 0.000000, total_loss = 0.002031 \n",
" total_bbox = 32618, rewritten_bbox = 0.278987 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.406684, GIOU: 0.376295), Class: 0.498086, Obj: 0.005479, No Obj: 0.002292, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.400267, iou_loss = 0.029108, total_loss = 1.429376 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000721, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002073, iou_loss = 0.000000, total_loss = 0.002073 \n",
" total_bbox = 32622, rewritten_bbox = 0.278953 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.529702, GIOU: 0.526305), Class: 0.667927, Obj: 0.037047, No Obj: 0.002652, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.315227, iou_loss = 0.126458, total_loss = 1.441685 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.427034, GIOU: 0.325229), Class: 0.501088, Obj: 0.004670, No Obj: 0.000755, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.724886, iou_loss = 0.724428, total_loss = 1.449314 \n",
" total_bbox = 32628, rewritten_bbox = 0.278902 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.259694, GIOU: 0.108344), Class: 0.439398, Obj: 0.025541, No Obj: 0.002727, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.058487, iou_loss = 0.008281, total_loss = 1.066768 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.261537, GIOU: 0.174109), Class: 0.498622, Obj: 0.014535, No Obj: 0.000673, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.385886, iou_loss = 0.048655, total_loss = 0.434540 \n",
" total_bbox = 32632, rewritten_bbox = 0.278867 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415414, GIOU: 0.282110), Class: 0.587053, Obj: 0.067321, No Obj: 0.002904, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.254132, iou_loss = 0.029491, total_loss = 1.283622 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.529285, GIOU: 0.484634), Class: 0.427195, Obj: 0.014294, No Obj: 0.000767, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.470695, iou_loss = 0.516889, total_loss = 0.987584 \n",
" total_bbox = 32637, rewritten_bbox = 0.278825 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.418905, GIOU: 0.331690), Class: 0.521978, Obj: 0.051360, No Obj: 0.002426, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.258210, iou_loss = 0.290453, total_loss = 2.548663 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.267656, GIOU: -0.066048), Class: 0.403502, Obj: 0.012664, No Obj: 0.000758, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.387290, iou_loss = 0.025495, total_loss = 0.412786 \n",
" total_bbox = 32645, rewritten_bbox = 0.278756 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.439150, GIOU: 0.409285), Class: 0.530605, Obj: 0.096885, No Obj: 0.003221, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.326094, iou_loss = 0.023557, total_loss = 1.349651 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000709, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002193, iou_loss = 0.000000, total_loss = 0.002193 \n",
" total_bbox = 32649, rewritten_bbox = 0.278722 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 408: 0.929062, 0.936168 avg loss, 0.000072 rate, 0.727278 seconds, 26112 images, 0.723276 hours left\n",
"Loaded: 0.000084 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410097, GIOU: 0.368506), Class: 0.574671, Obj: 0.015866, No Obj: 0.002566, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.048898, iou_loss = 0.101584, total_loss = 1.150482 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.276708, GIOU: 0.172674), Class: 0.406124, Obj: 0.000695, No Obj: 0.000697, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.199643, iou_loss = 0.901517, total_loss = 2.101160 \n",
" total_bbox = 32655, rewritten_bbox = 0.278671 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.373564, GIOU: 0.306999), Class: 0.537048, Obj: 0.077788, No Obj: 0.002886, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.943770, iou_loss = 0.171882, total_loss = 1.115652 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.517301, GIOU: 0.421608), Class: 0.355073, Obj: 0.019179, No Obj: 0.000701, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.407457, iou_loss = 0.143930, total_loss = 0.551386 \n",
" total_bbox = 32659, rewritten_bbox = 0.278637 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325073, GIOU: 0.324260), Class: 0.483584, Obj: 0.011429, No Obj: 0.002485, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.883020, iou_loss = 0.023310, total_loss = 1.906330 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000680, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001965, iou_loss = 0.000000, total_loss = 0.001965 \n",
" total_bbox = 32664, rewritten_bbox = 0.278594 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.500236, GIOU: 0.441068), Class: 0.592869, Obj: 0.057767, No Obj: 0.002328, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.290397, iou_loss = 0.069505, total_loss = 1.359901 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.377774, GIOU: 0.204182), Class: 0.519531, Obj: 0.006279, No Obj: 0.000749, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.086048, iou_loss = 0.769409, total_loss = 1.855458 \n",
" total_bbox = 32671, rewritten_bbox = 0.278534 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.414336, GIOU: 0.330638), Class: 0.450935, Obj: 0.037245, No Obj: 0.002510, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.714406, iou_loss = 0.189152, total_loss = 2.903558 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.402664, GIOU: 0.383352), Class: 0.580630, Obj: 0.026422, No Obj: 0.000817, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.074344, iou_loss = 0.420769, total_loss = 1.495113 \n",
" total_bbox = 32681, rewritten_bbox = 0.278449 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.283831, GIOU: 0.283831), Class: 0.454060, Obj: 0.083490, No Obj: 0.002495, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.431988, iou_loss = 0.034487, total_loss = 1.466476 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000709, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002825, iou_loss = 0.000000, total_loss = 0.002825 \n",
" total_bbox = 32685, rewritten_bbox = 0.278415 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.210495, GIOU: 0.114479), Class: 0.580208, Obj: 0.099320, No Obj: 0.002996, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.684576, iou_loss = 0.001162, total_loss = 0.685737 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.531487, GIOU: 0.531487), Class: 0.516695, Obj: 0.014258, No Obj: 0.000794, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.341426, iou_loss = 0.245918, total_loss = 0.587344 \n",
" total_bbox = 32688, rewritten_bbox = 0.278390 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308720, GIOU: 0.213135), Class: 0.506494, Obj: 0.034742, No Obj: 0.002500, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.780697, iou_loss = 0.030946, total_loss = 1.811643 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000657, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001571, iou_loss = 0.000000, total_loss = 0.001571 \n",
" total_bbox = 32693, rewritten_bbox = 0.278347 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.550492, GIOU: 0.522691), Class: 0.626988, Obj: 0.026417, No Obj: 0.003378, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 1.754433, iou_loss = 0.171300, total_loss = 1.925734 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.474272, GIOU: 0.470637), Class: 0.654430, Obj: 0.016939, No Obj: 0.000771, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.648436, iou_loss = 0.441348, total_loss = 1.089785 \n",
" total_bbox = 32700, rewritten_bbox = 0.278287 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.575999, GIOU: 0.568785), Class: 0.527574, Obj: 0.038392, No Obj: 0.002861, .5R: 0.500000, .75R: 0.500000, count: 4, class_loss = 1.405120, iou_loss = 0.018825, total_loss = 1.423945 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000720, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002400, iou_loss = 0.000000, total_loss = 0.002400 \n",
" total_bbox = 32704, rewritten_bbox = 0.278253 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392498, GIOU: 0.284415), Class: 0.416883, Obj: 0.096414, No Obj: 0.002763, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.385538, iou_loss = 0.069766, total_loss = 1.455304 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.622170, GIOU: 0.575360), Class: 0.481221, Obj: 0.006044, No Obj: 0.000694, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.410738, iou_loss = 0.245555, total_loss = 0.656293 \n",
" total_bbox = 32709, rewritten_bbox = 0.278211 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.465160, GIOU: 0.438511), Class: 0.562602, Obj: 0.018298, No Obj: 0.002797, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.621878, iou_loss = 0.021917, total_loss = 1.643795 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.562597, GIOU: 0.548184), Class: 0.545362, Obj: 0.010665, No Obj: 0.000749, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.344238, iou_loss = 0.526772, total_loss = 0.871010 \n",
" total_bbox = 32714, rewritten_bbox = 0.278168 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415290, GIOU: 0.341058), Class: 0.565728, Obj: 0.047261, No Obj: 0.002395, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.328264, iou_loss = 0.053593, total_loss = 1.381857 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000701, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003274, iou_loss = 0.000000, total_loss = 0.003274 \n",
" total_bbox = 32718, rewritten_bbox = 0.278134 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.276947, GIOU: 0.022498), Class: 0.646078, Obj: 0.188403, No Obj: 0.002451, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.783786, iou_loss = 0.023837, total_loss = 0.807622 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.188773, GIOU: 0.182111), Class: 0.478802, Obj: 0.002637, No Obj: 0.000741, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.718764, iou_loss = 0.253153, total_loss = 0.971917 \n",
" total_bbox = 32723, rewritten_bbox = 0.278092 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.349735, GIOU: 0.099650), Class: 0.542013, Obj: 0.030072, No Obj: 0.002602, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.411866, iou_loss = 0.012298, total_loss = 1.424163 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000687, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002466, iou_loss = 0.000000, total_loss = 0.002466 \n",
" total_bbox = 32727, rewritten_bbox = 0.278058 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.445713, GIOU: 0.439969), Class: 0.495971, Obj: 0.034896, No Obj: 0.002766, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.472453, iou_loss = 0.015600, total_loss = 1.488053 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000702, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001907, iou_loss = 0.000000, total_loss = 0.001907 \n",
" total_bbox = 32731, rewritten_bbox = 0.278024 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 409: 0.920209, 0.934572 avg loss, 0.000073 rate, 0.718825 seconds, 26176 images, 0.723301 hours left\n",
"Loaded: 0.000064 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307340, GIOU: 0.298022), Class: 0.308500, Obj: 0.041209, No Obj: 0.002669, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.251281, iou_loss = 0.079914, total_loss = 1.331195 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000693, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001739, iou_loss = 0.000000, total_loss = 0.001739 \n",
" total_bbox = 32734, rewritten_bbox = 0.277998 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.275788, GIOU: 0.185077), Class: 0.523209, Obj: 0.031527, No Obj: 0.002111, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.053827, iou_loss = 0.016762, total_loss = 1.070588 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000674, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001629, iou_loss = 0.000000, total_loss = 0.001629 \n",
" total_bbox = 32737, rewritten_bbox = 0.277973 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.442958, GIOU: 0.374120), Class: 0.417069, Obj: 0.019287, No Obj: 0.002533, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.886795, iou_loss = 0.077605, total_loss = 1.964400 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000769, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001893, iou_loss = 0.000000, total_loss = 0.001893 \n",
" total_bbox = 32742, rewritten_bbox = 0.277930 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.376547, GIOU: 0.296543), Class: 0.474210, Obj: 0.022040, No Obj: 0.002307, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.651528, iou_loss = 0.029192, total_loss = 1.680720 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000697, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002826, iou_loss = 0.000000, total_loss = 0.002826 \n",
" total_bbox = 32746, rewritten_bbox = 0.277897 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438016, GIOU: 0.398096), Class: 0.439469, Obj: 0.012479, No Obj: 0.002864, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.423445, iou_loss = 0.011504, total_loss = 1.434949 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.337286, GIOU: 0.192744), Class: 0.562391, Obj: 0.006709, No Obj: 0.000758, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.688258, iou_loss = 0.133645, total_loss = 0.821903 \n",
" total_bbox = 32752, rewritten_bbox = 0.277846 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.626117, GIOU: 0.603518), Class: 0.585590, Obj: 0.084257, No Obj: 0.002558, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.674702, iou_loss = 0.041838, total_loss = 0.716540 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.329879, GIOU: 0.276893), Class: 0.396740, Obj: 0.001235, No Obj: 0.000729, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.270867, iou_loss = 1.501038, total_loss = 2.771904 \n",
" total_bbox = 32757, rewritten_bbox = 0.277803 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395552, GIOU: 0.338568), Class: 0.487316, Obj: 0.064869, No Obj: 0.003056, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.445284, iou_loss = 0.020748, total_loss = 1.466032 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000712, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003041, iou_loss = 0.000000, total_loss = 0.003041 \n",
" total_bbox = 32761, rewritten_bbox = 0.277769 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346698, GIOU: 0.338975), Class: 0.591479, Obj: 0.115626, No Obj: 0.002932, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.121456, iou_loss = 0.069510, total_loss = 1.190966 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000713, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002527, iou_loss = 0.000000, total_loss = 0.002527 \n",
" total_bbox = 32765, rewritten_bbox = 0.277735 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.483227, GIOU: 0.467443), Class: 0.575001, Obj: 0.023944, No Obj: 0.002330, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.356862, iou_loss = 0.064169, total_loss = 1.421031 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000735, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002187, iou_loss = 0.000000, total_loss = 0.002187 \n",
" total_bbox = 32769, rewritten_bbox = 0.277701 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427479, GIOU: 0.392425), Class: 0.610489, Obj: 0.041709, No Obj: 0.003191, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.357288, iou_loss = 0.018315, total_loss = 1.375604 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000682, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001705, iou_loss = 0.000000, total_loss = 0.001705 \n",
" total_bbox = 32773, rewritten_bbox = 0.277668 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369704, GIOU: 0.369704), Class: 0.536248, Obj: 0.058481, No Obj: 0.002662, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.338887, iou_loss = 0.038242, total_loss = 1.377129 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000695, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002492, iou_loss = 0.000000, total_loss = 0.002492 \n",
" total_bbox = 32777, rewritten_bbox = 0.277634 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.689934, GIOU: 0.689248), Class: 0.560082, Obj: 0.016497, No Obj: 0.002988, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.064386, iou_loss = 0.028711, total_loss = 1.093097 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000701, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001949, iou_loss = 0.000000, total_loss = 0.001949 \n",
" total_bbox = 32780, rewritten_bbox = 0.277608 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.576638, GIOU: 0.513549), Class: 0.545874, Obj: 0.046932, No Obj: 0.002414, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.329083, iou_loss = 0.064395, total_loss = 1.393478 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000646, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001442, iou_loss = 0.000000, total_loss = 0.001442 \n",
" total_bbox = 32784, rewritten_bbox = 0.277574 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.555022, GIOU: 0.484123), Class: 0.551363, Obj: 0.020230, No Obj: 0.002974, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.745673, iou_loss = 0.016902, total_loss = 0.762575 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.382064, GIOU: 0.300938), Class: 0.566842, Obj: 0.002930, No Obj: 0.000674, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.561671, iou_loss = 1.928039, total_loss = 3.489711 \n",
" total_bbox = 32790, rewritten_bbox = 0.277524 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477254, GIOU: 0.448644), Class: 0.541545, Obj: 0.038340, No Obj: 0.002548, .5R: 0.500000, .75R: 0.166667, count: 6, class_loss = 2.121169, iou_loss = 0.096308, total_loss = 2.217476 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.252052, GIOU: -0.101165), Class: 0.596302, Obj: 0.020908, No Obj: 0.000689, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.329608, iou_loss = 0.020615, total_loss = 0.350223 \n",
" total_bbox = 32797, rewritten_bbox = 0.277464 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.631776, GIOU: 0.623660), Class: 0.416088, Obj: 0.067216, No Obj: 0.002800, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.773139, iou_loss = 0.019677, total_loss = 0.792816 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.611923, GIOU: 0.569381), Class: 0.678985, Obj: 0.035562, No Obj: 0.000717, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.308592, iou_loss = 0.237945, total_loss = 0.546538 \n",
" total_bbox = 32800, rewritten_bbox = 0.277439 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 410: 0.780669, 0.919182 avg loss, 0.000074 rate, 0.716257 seconds, 26240 images, 0.723239 hours left\n",
"Loaded: 0.000047 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.207062, GIOU: 0.183271), Class: 0.499962, Obj: 0.017843, No Obj: 0.002247, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.555384, iou_loss = 0.012030, total_loss = 1.567413 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000660, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001678, iou_loss = 0.000000, total_loss = 0.001678 \n",
" total_bbox = 32804, rewritten_bbox = 0.277405 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.401178, GIOU: 0.375329), Class: 0.455973, Obj: 0.054542, No Obj: 0.002809, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.426947, iou_loss = 0.026945, total_loss = 1.453891 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000679, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001800, iou_loss = 0.000000, total_loss = 0.001800 \n",
" total_bbox = 32808, rewritten_bbox = 0.277371 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271427, GIOU: 0.270873), Class: 0.505113, Obj: 0.012261, No Obj: 0.002090, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.636214, iou_loss = 0.039567, total_loss = 1.675781 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000694, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002245, iou_loss = 0.000000, total_loss = 0.002245 \n",
" total_bbox = 32813, rewritten_bbox = 0.277329 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.470104, GIOU: 0.410134), Class: 0.572907, Obj: 0.035558, No Obj: 0.002653, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.748453, iou_loss = 0.162002, total_loss = 1.910454 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.017334, GIOU: 0.017333), Class: 0.475861, Obj: 0.002105, No Obj: 0.000690, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.415499, iou_loss = 0.000740, total_loss = 0.416239 \n",
" total_bbox = 32819, rewritten_bbox = 0.277278 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.464933, GIOU: 0.349754), Class: 0.481049, Obj: 0.068032, No Obj: 0.003488, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.756669, iou_loss = 0.008228, total_loss = 0.764897 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000780, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002783, iou_loss = 0.000000, total_loss = 0.002783 \n",
" total_bbox = 32821, rewritten_bbox = 0.277261 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.496559, GIOU: 0.477471), Class: 0.509828, Obj: 0.083421, No Obj: 0.002825, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.390068, iou_loss = 0.187623, total_loss = 1.577690 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.682374, GIOU: 0.648137), Class: 0.600516, Obj: 0.021353, No Obj: 0.000702, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.329966, iou_loss = 0.296790, total_loss = 0.626756 \n",
" total_bbox = 32826, rewritten_bbox = 0.277219 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.249632, GIOU: 0.234088), Class: 0.473780, Obj: 0.018380, No Obj: 0.003346, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.890821, iou_loss = 0.066879, total_loss = 1.957700 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.569879, GIOU: 0.513683), Class: 0.506145, Obj: 0.000937, No Obj: 0.000696, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.716588, iou_loss = 1.011211, total_loss = 1.727799 \n",
" total_bbox = 32833, rewritten_bbox = 0.277160 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390249, GIOU: 0.283677), Class: 0.542466, Obj: 0.063271, No Obj: 0.002924, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.687574, iou_loss = 0.075477, total_loss = 1.763051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000641, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001522, iou_loss = 0.000000, total_loss = 0.001522 \n",
" total_bbox = 32838, rewritten_bbox = 0.277118 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.462525, GIOU: 0.442760), Class: 0.248806, Obj: 0.023023, No Obj: 0.002930, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.687138, iou_loss = 0.054588, total_loss = 1.741726 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000677, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001863, iou_loss = 0.000000, total_loss = 0.001863 \n",
" total_bbox = 32842, rewritten_bbox = 0.277084 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.360072, GIOU: 0.315099), Class: 0.474848, Obj: 0.029182, No Obj: 0.002600, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.899172, iou_loss = 0.102543, total_loss = 2.001715 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.368294, GIOU: 0.173482), Class: 0.447351, Obj: 0.006185, No Obj: 0.000698, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354998, iou_loss = 0.063870, total_loss = 0.418868 \n",
" total_bbox = 32848, rewritten_bbox = 0.277034 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292525, GIOU: 0.241475), Class: 0.495626, Obj: 0.030565, No Obj: 0.002679, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.852566, iou_loss = 0.100668, total_loss = 1.953234 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000643, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001421, iou_loss = 0.000000, total_loss = 0.001421 \n",
" total_bbox = 32853, rewritten_bbox = 0.276991 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.414900, GIOU: 0.302888), Class: 0.430741, Obj: 0.018656, No Obj: 0.002367, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.542460, iou_loss = 0.109434, total_loss = 1.651895 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.186759, GIOU: -0.262679), Class: 0.327587, Obj: 0.001701, No Obj: 0.000629, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.860507, iou_loss = 0.048908, total_loss = 0.909415 \n",
" total_bbox = 32859, rewritten_bbox = 0.276941 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.557215, GIOU: 0.425924), Class: 0.770959, Obj: 0.036358, No Obj: 0.002399, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.023681, iou_loss = 0.033742, total_loss = 1.057423 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.495793, GIOU: 0.474275), Class: 0.837493, Obj: 0.002252, No Obj: 0.000730, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.316364, iou_loss = 0.353690, total_loss = 0.670053 \n",
" total_bbox = 32863, rewritten_bbox = 0.276907 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.431797, GIOU: 0.392793), Class: 0.607951, Obj: 0.048460, No Obj: 0.003553, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.374316, iou_loss = 0.061759, total_loss = 1.436074 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.159399, GIOU: 0.159399), Class: 0.322519, Obj: 0.007000, No Obj: 0.000650, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.370423, iou_loss = 0.022003, total_loss = 0.392427 \n",
" total_bbox = 32868, rewritten_bbox = 0.276865 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.278991, GIOU: -0.026446), Class: 0.466709, Obj: 0.006382, No Obj: 0.002513, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.482771, iou_loss = 0.002772, total_loss = 0.485544 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000758, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002305, iou_loss = 0.000000, total_loss = 0.002305 \n",
" total_bbox = 32869, rewritten_bbox = 0.276857 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.451715, GIOU: 0.361088), Class: 0.635231, Obj: 0.027283, No Obj: 0.002621, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.374826, iou_loss = 0.087191, total_loss = 1.462018 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.348010, GIOU: 0.110242), Class: 0.540374, Obj: 0.001524, No Obj: 0.000666, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.757559, iou_loss = 0.154850, total_loss = 0.912409 \n",
" total_bbox = 32875, rewritten_bbox = 0.276806 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 411: 0.866518, 0.913915 avg loss, 0.000074 rate, 0.706697 seconds, 26304 images, 0.723150 hours left\n",
"Loaded: 0.000043 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.306682, GIOU: 0.269472), Class: 0.448139, Obj: 0.016242, No Obj: 0.002810, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 2.732247, iou_loss = 0.024776, total_loss = 2.757023 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000643, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001523, iou_loss = 0.000000, total_loss = 0.001523 \n",
" total_bbox = 32882, rewritten_bbox = 0.276747 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.499070, GIOU: 0.454600), Class: 0.629578, Obj: 0.023936, No Obj: 0.002215, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.310724, iou_loss = 0.033134, total_loss = 1.343858 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.177094, GIOU: -0.015949), Class: 0.471286, Obj: 0.000818, No Obj: 0.000732, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.798661, iou_loss = 0.153557, total_loss = 0.952218 \n",
" total_bbox = 32888, rewritten_bbox = 0.276697 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.514113, GIOU: 0.474826), Class: 0.547358, Obj: 0.017263, No Obj: 0.002184, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.836471, iou_loss = 0.042611, total_loss = 1.879082 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000691, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001740, iou_loss = 0.000000, total_loss = 0.001740 \n",
" total_bbox = 32893, rewritten_bbox = 0.276655 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.383831, GIOU: 0.252948), Class: 0.475633, Obj: 0.016778, No Obj: 0.002924, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.613573, iou_loss = 0.011916, total_loss = 1.625489 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.443964, GIOU: 0.439107), Class: 0.553995, Obj: 0.001364, No Obj: 0.000709, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.775655, iou_loss = 0.938803, total_loss = 1.714458 \n",
" total_bbox = 32899, rewritten_bbox = 0.276604 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395504, GIOU: 0.317833), Class: 0.520784, Obj: 0.073101, No Obj: 0.002627, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.731318, iou_loss = 0.097824, total_loss = 1.829142 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.163459, GIOU: -0.071294), Class: 0.725128, Obj: 0.000843, No Obj: 0.000728, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.344462, iou_loss = 0.224203, total_loss = 0.568665 \n",
" total_bbox = 32905, rewritten_bbox = 0.276554 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.505792, GIOU: 0.400090), Class: 0.523464, Obj: 0.044013, No Obj: 0.002781, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.514258, iou_loss = 0.030014, total_loss = 1.544271 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.228116, GIOU: -0.089283), Class: 0.585933, Obj: 0.000942, No Obj: 0.000677, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.399056, iou_loss = 0.311005, total_loss = 0.710061 \n",
" total_bbox = 32910, rewritten_bbox = 0.276512 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.365261, GIOU: 0.357046), Class: 0.445089, Obj: 0.028021, No Obj: 0.002755, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.110691, iou_loss = 0.029768, total_loss = 1.140459 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000674, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001732, iou_loss = 0.000000, total_loss = 0.001732 \n",
" total_bbox = 32913, rewritten_bbox = 0.276486 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.276086, GIOU: 0.276086), Class: 0.560133, Obj: 0.022702, No Obj: 0.002434, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.975787, iou_loss = 0.080597, total_loss = 1.056384 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.198264, GIOU: 0.033561), Class: 0.420190, Obj: 0.002334, No Obj: 0.000706, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.795293, iou_loss = 1.743196, total_loss = 2.538489 \n",
" total_bbox = 32918, rewritten_bbox = 0.276444 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.371837, GIOU: 0.324666), Class: 0.562204, Obj: 0.045459, No Obj: 0.002605, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.426654, iou_loss = 0.026931, total_loss = 1.453585 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.066357, GIOU: -0.054800), Class: 0.529584, Obj: 0.000250, No Obj: 0.000720, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.344244, iou_loss = 0.007089, total_loss = 0.351334 \n",
" total_bbox = 32923, rewritten_bbox = 0.276403 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.656495, GIOU: 0.625902), Class: 0.474276, Obj: 0.075272, No Obj: 0.003001, .5R: 1.000000, .75R: 0.000000, count: 3, class_loss = 1.009768, iou_loss = 0.064542, total_loss = 1.074310 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.523791, GIOU: 0.428292), Class: 0.719317, Obj: 0.001694, No Obj: 0.000643, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.342439, iou_loss = 0.145116, total_loss = 0.487555 \n",
" total_bbox = 32927, rewritten_bbox = 0.276369 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.258886, GIOU: 0.150906), Class: 0.380716, Obj: 0.022102, No Obj: 0.002396, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.294488, iou_loss = 0.018292, total_loss = 1.312781 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.222087, GIOU: -0.075014), Class: 0.375749, Obj: 0.009503, No Obj: 0.000664, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.445726, iou_loss = 0.083543, total_loss = 0.529270 \n",
" total_bbox = 32931, rewritten_bbox = 0.276335 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.492052, GIOU: 0.464715), Class: 0.475048, Obj: 0.110505, No Obj: 0.003137, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.356397, iou_loss = 0.097571, total_loss = 1.453968 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.386691, GIOU: 0.314424), Class: 0.450176, Obj: 0.004113, No Obj: 0.000671, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.206619, iou_loss = 3.086421, total_loss = 4.293040 \n",
" total_bbox = 32938, rewritten_bbox = 0.276277 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.535313, GIOU: 0.481873), Class: 0.600859, Obj: 0.059540, No Obj: 0.002580, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.446704, iou_loss = 0.076570, total_loss = 1.523274 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000651, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001645, iou_loss = 0.000000, total_loss = 0.001645 \n",
" total_bbox = 32942, rewritten_bbox = 0.276243 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.420274, GIOU: 0.376943), Class: 0.488996, Obj: 0.025812, No Obj: 0.003176, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.640820, iou_loss = 0.059600, total_loss = 1.700420 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000626, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001462, iou_loss = 0.000000, total_loss = 0.001462 \n",
" total_bbox = 32946, rewritten_bbox = 0.276210 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.360880, GIOU: 0.355155), Class: 0.508608, Obj: 0.094638, No Obj: 0.003009, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.055678, iou_loss = 0.016948, total_loss = 1.072626 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.674980, GIOU: 0.651518), Class: 0.254796, Obj: 0.014149, No Obj: 0.000678, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.478876, iou_loss = 0.268354, total_loss = 0.747230 \n",
" total_bbox = 32950, rewritten_bbox = 0.276176 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.484616, GIOU: 0.377000), Class: 0.489924, Obj: 0.015285, No Obj: 0.002212, .5R: 0.666667, .75R: 0.000000, count: 6, class_loss = 2.171158, iou_loss = 0.085449, total_loss = 2.256607 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000650, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001497, iou_loss = 0.000000, total_loss = 0.001497 \n",
" total_bbox = 32956, rewritten_bbox = 0.276126 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 412: 0.951529, 0.917677 avg loss, 0.000075 rate, 0.729898 seconds, 26368 images, 0.722964 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.485538, GIOU: 0.429834), Class: 0.599378, Obj: 0.198339, No Obj: 0.003456, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.674205, iou_loss = 0.026160, total_loss = 0.700365 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.556900, GIOU: 0.535887), Class: 0.557438, Obj: 0.008296, No Obj: 0.000754, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.705290, iou_loss = 1.471241, total_loss = 2.176531 \n",
" total_bbox = 32960, rewritten_bbox = 0.276092 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.449185, GIOU: 0.373732), Class: 0.578260, Obj: 0.028277, No Obj: 0.002950, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.686679, iou_loss = 0.125928, total_loss = 1.812607 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000696, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002555, iou_loss = 0.000000, total_loss = 0.002555 \n",
" total_bbox = 32965, rewritten_bbox = 0.276050 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422531, GIOU: 0.224560), Class: 0.564205, Obj: 0.030946, No Obj: 0.002606, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.669962, iou_loss = 0.084687, total_loss = 1.754649 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000657, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001941, iou_loss = 0.000000, total_loss = 0.001941 \n",
" total_bbox = 32970, rewritten_bbox = 0.276008 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339727, GIOU: 0.109837), Class: 0.529872, Obj: 0.039208, No Obj: 0.002651, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.359408, iou_loss = 0.007717, total_loss = 1.367124 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000620, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001288, iou_loss = 0.000000, total_loss = 0.001288 \n",
" total_bbox = 32974, rewritten_bbox = 0.275975 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.351013, GIOU: 0.318182), Class: 0.578701, Obj: 0.124127, No Obj: 0.002536, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.902888, iou_loss = 0.016764, total_loss = 0.919652 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.433788, GIOU: 0.402928), Class: 0.498828, Obj: 0.000665, No Obj: 0.000646, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.379820, iou_loss = 0.426040, total_loss = 0.805859 \n",
" total_bbox = 32978, rewritten_bbox = 0.275942 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.454977, GIOU: 0.416239), Class: 0.545120, Obj: 0.057179, No Obj: 0.002645, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.681121, iou_loss = 0.059676, total_loss = 1.740798 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.365564, GIOU: 0.318416), Class: 0.536729, Obj: 0.000215, No Obj: 0.000652, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.333040, iou_loss = 0.053277, total_loss = 0.386318 \n",
" total_bbox = 32984, rewritten_bbox = 0.275891 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472230, GIOU: 0.446595), Class: 0.513414, Obj: 0.017914, No Obj: 0.002724, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.151688, iou_loss = 0.043979, total_loss = 1.195667 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000653, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001979, iou_loss = 0.000000, total_loss = 0.001979 \n",
" total_bbox = 32987, rewritten_bbox = 0.275866 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398111, GIOU: 0.265287), Class: 0.488883, Obj: 0.050550, No Obj: 0.002765, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.069189, iou_loss = 0.021679, total_loss = 1.090867 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.574008, GIOU: 0.574008), Class: 0.440950, Obj: 0.029705, No Obj: 0.000656, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.371507, iou_loss = 0.287216, total_loss = 0.658723 \n",
" total_bbox = 32991, rewritten_bbox = 0.275833 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357745, GIOU: 0.317604), Class: 0.465928, Obj: 0.048110, No Obj: 0.002995, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.146908, iou_loss = 0.021933, total_loss = 1.168841 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.640654, GIOU: 0.621200), Class: 0.453574, Obj: 0.057916, No Obj: 0.000676, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.354864, iou_loss = 0.331168, total_loss = 0.686032 \n",
" total_bbox = 32995, rewritten_bbox = 0.275799 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.447661, GIOU: 0.357746), Class: 0.424581, Obj: 0.026119, No Obj: 0.002886, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.613380, iou_loss = 0.033787, total_loss = 1.647166 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000632, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001325, iou_loss = 0.000000, total_loss = 0.001325 \n",
" total_bbox = 32999, rewritten_bbox = 0.275766 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357629, GIOU: 0.170289), Class: 0.410503, Obj: 0.043730, No Obj: 0.002692, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.880952, iou_loss = 0.061083, total_loss = 1.942035 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.225463, GIOU: -0.201004), Class: 0.390655, Obj: 0.003223, No Obj: 0.000624, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.767925, iou_loss = 0.150953, total_loss = 0.918878 \n",
" total_bbox = 33006, rewritten_bbox = 0.275707 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.297748, GIOU: 0.158958), Class: 0.504475, Obj: 0.079850, No Obj: 0.003443, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.368149, iou_loss = 0.009352, total_loss = 1.377501 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000669, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001986, iou_loss = 0.000000, total_loss = 0.001986 \n",
" total_bbox = 33010, rewritten_bbox = 0.275674 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.424164, GIOU: 0.410723), Class: 0.478510, Obj: 0.036106, No Obj: 0.002873, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.510326, iou_loss = 0.030610, total_loss = 1.540936 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000693, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003317, iou_loss = 0.000000, total_loss = 0.003317 \n",
" total_bbox = 33014, rewritten_bbox = 0.275641 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.445385, GIOU: 0.421225), Class: 0.458635, Obj: 0.012886, No Obj: 0.002041, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.613087, iou_loss = 0.069099, total_loss = 2.682187 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000655, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002012, iou_loss = 0.000000, total_loss = 0.002012 \n",
" total_bbox = 33021, rewritten_bbox = 0.275582 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382403, GIOU: 0.285001), Class: 0.358764, Obj: 0.020469, No Obj: 0.003056, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.970537, iou_loss = 0.026654, total_loss = 1.997191 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000671, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001786, iou_loss = 0.000000, total_loss = 0.001786 \n",
" total_bbox = 33026, rewritten_bbox = 0.275540 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472972, GIOU: 0.384708), Class: 0.598914, Obj: 0.099292, No Obj: 0.002777, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.617217, iou_loss = 0.082729, total_loss = 1.699946 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000711, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002782, iou_loss = 0.000000, total_loss = 0.002782 \n",
" total_bbox = 33031, rewritten_bbox = 0.275499 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 413: 0.845950, 0.910504 avg loss, 0.000076 rate, 0.694378 seconds, 26432 images, 0.723010 hours left\n",
"Loaded: 0.000037 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.373708, GIOU: 0.280193), Class: 0.473451, Obj: 0.028881, No Obj: 0.002690, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.659165, iou_loss = 0.017260, total_loss = 1.676425 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000665, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001719, iou_loss = 0.000000, total_loss = 0.001719 \n",
" total_bbox = 33035, rewritten_bbox = 0.275465 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.312635, GIOU: 0.223087), Class: 0.523985, Obj: 0.048480, No Obj: 0.002683, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.442908, iou_loss = 0.026481, total_loss = 1.469389 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000647, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001560, iou_loss = 0.000000, total_loss = 0.001560 \n",
" total_bbox = 33039, rewritten_bbox = 0.275432 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.245081, GIOU: 0.090067), Class: 0.453117, Obj: 0.033576, No Obj: 0.002367, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.519503, iou_loss = 0.051207, total_loss = 1.570710 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.259817, GIOU: 0.118250), Class: 0.547396, Obj: 0.002435, No Obj: 0.000669, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.748502, iou_loss = 0.796028, total_loss = 1.544531 \n",
" total_bbox = 33045, rewritten_bbox = 0.275382 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308714, GIOU: 0.280016), Class: 0.606114, Obj: 0.063435, No Obj: 0.002443, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.969680, iou_loss = 0.037449, total_loss = 1.007129 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.476149, GIOU: 0.373902), Class: 0.458917, Obj: 0.003345, No Obj: 0.000692, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.770425, iou_loss = 0.586593, total_loss = 1.357019 \n",
" total_bbox = 33050, rewritten_bbox = 0.275340 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.393980, GIOU: 0.335203), Class: 0.457510, Obj: 0.025619, No Obj: 0.002781, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.488445, iou_loss = 0.069075, total_loss = 1.557520 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000653, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001680, iou_loss = 0.000000, total_loss = 0.001680 \n",
" total_bbox = 33054, rewritten_bbox = 0.275307 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.428996, GIOU: 0.358994), Class: 0.518880, Obj: 0.009156, No Obj: 0.002953, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.190542, iou_loss = 0.039168, total_loss = 1.229710 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.720124, GIOU: 0.701924), Class: 0.672666, Obj: 0.054544, No Obj: 0.000690, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.285709, iou_loss = 0.784562, total_loss = 1.070271 \n",
" total_bbox = 33058, rewritten_bbox = 0.275274 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.488735, GIOU: 0.386256), Class: 0.426900, Obj: 0.044818, No Obj: 0.002744, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 2.045115, iou_loss = 0.023139, total_loss = 2.068254 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000633, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001455, iou_loss = 0.000000, total_loss = 0.001455 \n",
" total_bbox = 33063, rewritten_bbox = 0.275232 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.296773, GIOU: 0.221000), Class: 0.573173, Obj: 0.007814, No Obj: 0.002139, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.543690, iou_loss = 0.009617, total_loss = 1.553307 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000659, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001776, iou_loss = 0.000000, total_loss = 0.001776 \n",
" total_bbox = 33067, rewritten_bbox = 0.275199 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.239524, GIOU: 0.131806), Class: 0.576353, Obj: 0.062020, No Obj: 0.002751, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.387671, iou_loss = 0.015337, total_loss = 1.403008 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000695, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002080, iou_loss = 0.000000, total_loss = 0.002080 \n",
" total_bbox = 33071, rewritten_bbox = 0.275166 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325929, GIOU: 0.310327), Class: 0.612890, Obj: 0.115379, No Obj: 0.003393, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.232087, iou_loss = 0.036051, total_loss = 1.268138 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000644, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001757, iou_loss = 0.000000, total_loss = 0.001757 \n",
" total_bbox = 33075, rewritten_bbox = 0.275132 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.080129, GIOU: -0.119190), Class: 0.432870, Obj: 0.070593, No Obj: 0.003381, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.085090, iou_loss = 0.000491, total_loss = 1.085581 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.346073, GIOU: 0.333699), Class: 0.497690, Obj: 0.015372, No Obj: 0.000676, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354640, iou_loss = 0.164077, total_loss = 0.518717 \n",
" total_bbox = 33079, rewritten_bbox = 0.275099 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.650840, GIOU: 0.640454), Class: 0.437666, Obj: 0.014220, No Obj: 0.002216, .5R: 1.000000, .75R: 0.000000, count: 3, class_loss = 1.134868, iou_loss = 0.019014, total_loss = 1.153882 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.576414, GIOU: 0.576414), Class: 0.402134, Obj: 0.005992, No Obj: 0.000637, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.432693, iou_loss = 0.289654, total_loss = 0.722347 \n",
" total_bbox = 33083, rewritten_bbox = 0.275066 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.414228, GIOU: 0.284846), Class: 0.584696, Obj: 0.013461, No Obj: 0.002623, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.810622, iou_loss = 0.190500, total_loss = 2.001122 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.387503, GIOU: 0.193413), Class: 0.490820, Obj: 0.020147, No Obj: 0.000710, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.342280, iou_loss = 0.064904, total_loss = 0.407184 \n",
" total_bbox = 33089, rewritten_bbox = 0.275016 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.267277, GIOU: 0.226015), Class: 0.478554, Obj: 0.044239, No Obj: 0.003069, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.535647, iou_loss = 0.009300, total_loss = 1.544947 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000645, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001834, iou_loss = 0.000000, total_loss = 0.001834 \n",
" total_bbox = 33093, rewritten_bbox = 0.274983 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.205306, GIOU: 0.119877), Class: 0.442449, Obj: 0.028170, No Obj: 0.002190, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.158265, iou_loss = 0.008477, total_loss = 1.166742 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.143772, GIOU: -0.357074), Class: 0.536355, Obj: 0.002735, No Obj: 0.000692, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.388785, iou_loss = 0.023368, total_loss = 0.412153 \n",
" total_bbox = 33097, rewritten_bbox = 0.274949 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.379550, GIOU: 0.368461), Class: 0.455976, Obj: 0.095472, No Obj: 0.003176, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.420480, iou_loss = 0.053115, total_loss = 1.473595 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000728, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003054, iou_loss = 0.000000, total_loss = 0.003054 \n",
" total_bbox = 33101, rewritten_bbox = 0.274916 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 414: 0.819394, 0.901393 avg loss, 0.000077 rate, 0.692527 seconds, 26496 images, 0.722699 hours left\n",
"Loaded: 0.000041 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.524722, GIOU: 0.450828), Class: 0.525584, Obj: 0.025009, No Obj: 0.002705, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.516660, iou_loss = 0.087385, total_loss = 1.604045 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.413086, GIOU: 0.413086), Class: 0.427461, Obj: 0.009950, No Obj: 0.000668, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.372442, iou_loss = 0.148357, total_loss = 0.520799 \n",
" total_bbox = 33106, rewritten_bbox = 0.274875 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322106, GIOU: 0.232457), Class: 0.465966, Obj: 0.014374, No Obj: 0.002490, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.327652, iou_loss = 0.016513, total_loss = 2.344165 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000611, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001320, iou_loss = 0.000000, total_loss = 0.001320 \n",
" total_bbox = 33112, rewritten_bbox = 0.274825 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.174550, GIOU: 0.156137), Class: 0.564178, Obj: 0.028483, No Obj: 0.002530, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.072410, iou_loss = 0.001867, total_loss = 1.074277 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.482928, GIOU: 0.400436), Class: 0.480110, Obj: 0.019870, No Obj: 0.000652, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.322676, iou_loss = 0.136105, total_loss = 0.458782 \n",
" total_bbox = 33116, rewritten_bbox = 0.274792 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.310216, GIOU: 0.309938), Class: 0.411755, Obj: 0.006145, No Obj: 0.002703, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.855221, iou_loss = 0.004335, total_loss = 0.859556 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.335965, GIOU: 0.080312), Class: 0.480964, Obj: 0.001165, No Obj: 0.000723, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.700021, iou_loss = 0.168432, total_loss = 0.868453 \n",
" total_bbox = 33120, rewritten_bbox = 0.274758 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.375079, GIOU: 0.344464), Class: 0.656007, Obj: 0.009292, No Obj: 0.002301, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.734514, iou_loss = 0.007703, total_loss = 0.742216 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.350367, GIOU: 0.193217), Class: 0.490088, Obj: 0.000928, No Obj: 0.000698, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.730898, iou_loss = 0.775606, total_loss = 1.506504 \n",
" total_bbox = 33124, rewritten_bbox = 0.274725 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.494902, GIOU: 0.468630), Class: 0.625888, Obj: 0.110132, No Obj: 0.003105, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.865558, iou_loss = 0.061572, total_loss = 0.927130 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.310326, GIOU: 0.152845), Class: 0.532007, Obj: 0.004330, No Obj: 0.000806, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.094577, iou_loss = 0.707959, total_loss = 1.802536 \n",
" total_bbox = 33130, rewritten_bbox = 0.274676 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410630, GIOU: 0.396573), Class: 0.560731, Obj: 0.037855, No Obj: 0.002921, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.180699, iou_loss = 0.050041, total_loss = 2.230741 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000620, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001442, iou_loss = 0.000000, total_loss = 0.001442 \n",
" total_bbox = 33136, rewritten_bbox = 0.274626 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427277, GIOU: 0.407780), Class: 0.625599, Obj: 0.056816, No Obj: 0.002739, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.997670, iou_loss = 0.013183, total_loss = 1.010853 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.337002, GIOU: 0.178105), Class: 0.420397, Obj: 0.006851, No Obj: 0.000678, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.808749, iou_loss = 1.402646, total_loss = 2.211395 \n",
" total_bbox = 33141, rewritten_bbox = 0.274584 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.349444, GIOU: 0.345007), Class: 0.497383, Obj: 0.108356, No Obj: 0.002914, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.020333, iou_loss = 0.013502, total_loss = 1.033834 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000718, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001873, iou_loss = 0.000000, total_loss = 0.001873 \n",
" total_bbox = 33144, rewritten_bbox = 0.274559 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390490, GIOU: 0.365144), Class: 0.583958, Obj: 0.100841, No Obj: 0.002895, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.295193, iou_loss = 0.027185, total_loss = 1.322379 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.298667, GIOU: 0.078213), Class: 0.664481, Obj: 0.004219, No Obj: 0.000689, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.360613, iou_loss = 0.028213, total_loss = 0.388826 \n",
" total_bbox = 33149, rewritten_bbox = 0.274518 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.317958, GIOU: 0.317958), Class: 0.370927, Obj: 0.112073, No Obj: 0.003632, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.606135, iou_loss = 0.014530, total_loss = 1.620665 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000616, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001324, iou_loss = 0.000000, total_loss = 0.001324 \n",
" total_bbox = 33153, rewritten_bbox = 0.274485 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.531918, GIOU: 0.425245), Class: 0.533268, Obj: 0.070479, No Obj: 0.002618, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.246836, iou_loss = 0.027721, total_loss = 1.274557 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.294676, GIOU: 0.197182), Class: 0.541694, Obj: 0.028647, No Obj: 0.000688, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.729006, iou_loss = 0.861770, total_loss = 1.590777 \n",
" total_bbox = 33159, rewritten_bbox = 0.274435 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.375287, GIOU: 0.266284), Class: 0.581879, Obj: 0.050892, No Obj: 0.002492, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.262233, iou_loss = 0.113112, total_loss = 1.375345 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000670, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001931, iou_loss = 0.000000, total_loss = 0.001931 \n",
" total_bbox = 33163, rewritten_bbox = 0.274402 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339798, GIOU: 0.331326), Class: 0.609356, Obj: 0.090676, No Obj: 0.002729, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.569704, iou_loss = 0.030440, total_loss = 1.600144 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000688, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002087, iou_loss = 0.000000, total_loss = 0.002087 \n",
" total_bbox = 33168, rewritten_bbox = 0.274361 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.442153, GIOU: 0.357712), Class: 0.566972, Obj: 0.013735, No Obj: 0.002430, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.784118, iou_loss = 0.116316, total_loss = 1.900433 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000668, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003352, iou_loss = 0.000000, total_loss = 0.003352 \n",
" total_bbox = 33173, rewritten_bbox = 0.274319 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438294, GIOU: 0.326777), Class: 0.556592, Obj: 0.028750, No Obj: 0.002707, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.483821, iou_loss = 0.093383, total_loss = 2.577204 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000638, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001676, iou_loss = 0.000000, total_loss = 0.001676 \n",
" total_bbox = 33180, rewritten_bbox = 0.274262 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 415: 0.881700, 0.899424 avg loss, 0.000077 rate, 0.681704 seconds, 26560 images, 0.722371 hours left\n",
"Loaded: 0.000063 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.503025, GIOU: 0.483679), Class: 0.550448, Obj: 0.020992, No Obj: 0.002805, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.479836, iou_loss = 0.161769, total_loss = 1.641605 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000740, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002358, iou_loss = 0.000000, total_loss = 0.002358 \n",
" total_bbox = 33184, rewritten_bbox = 0.274229 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.433326, GIOU: 0.349222), Class: 0.562777, Obj: 0.017307, No Obj: 0.003244, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.900425, iou_loss = 0.098195, total_loss = 1.998620 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000637, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002500, iou_loss = 0.000000, total_loss = 0.002500 \n",
" total_bbox = 33189, rewritten_bbox = 0.274187 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300867, GIOU: 0.219441), Class: 0.534886, Obj: 0.046995, No Obj: 0.003171, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.474886, iou_loss = 0.026732, total_loss = 1.501618 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.414678, GIOU: 0.248942), Class: 0.452180, Obj: 0.002453, No Obj: 0.000664, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.133911, iou_loss = 0.427620, total_loss = 1.561531 \n",
" total_bbox = 33196, rewritten_bbox = 0.274129 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292601, GIOU: 0.027107), Class: 0.633859, Obj: 0.046584, No Obj: 0.002771, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.920371, iou_loss = 0.050510, total_loss = 0.970882 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000683, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.006719, iou_loss = 0.000000, total_loss = 0.006719 \n",
" total_bbox = 33199, rewritten_bbox = 0.274105 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.434555, GIOU: 0.382639), Class: 0.596072, Obj: 0.057855, No Obj: 0.002368, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.311139, iou_loss = 0.030839, total_loss = 1.341978 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000616, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001838, iou_loss = 0.000000, total_loss = 0.001838 \n",
" total_bbox = 33203, rewritten_bbox = 0.274072 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.289873, GIOU: 0.117708), Class: 0.487322, Obj: 0.028122, No Obj: 0.002973, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.629044, iou_loss = 0.014156, total_loss = 1.643200 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000622, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001782, iou_loss = 0.000000, total_loss = 0.001782 \n",
" total_bbox = 33207, rewritten_bbox = 0.274039 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.333213, GIOU: 0.233828), Class: 0.450032, Obj: 0.021516, No Obj: 0.002677, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.891239, iou_loss = 0.037207, total_loss = 1.928446 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.166966, GIOU: -0.207445), Class: 0.478591, Obj: 0.001785, No Obj: 0.000653, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.363134, iou_loss = 0.013503, total_loss = 0.376637 \n",
" total_bbox = 33213, rewritten_bbox = 0.273989 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.611235, GIOU: 0.543672), Class: 0.470522, Obj: 0.027478, No Obj: 0.002603, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 2.169458, iou_loss = 0.267885, total_loss = 2.437343 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000687, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001684, iou_loss = 0.000000, total_loss = 0.001684 \n",
" total_bbox = 33219, rewritten_bbox = 0.273940 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.350624, GIOU: 0.320645), Class: 0.564603, Obj: 0.065488, No Obj: 0.002550, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.704015, iou_loss = 0.057562, total_loss = 1.761576 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000637, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001728, iou_loss = 0.000000, total_loss = 0.001728 \n",
" total_bbox = 33224, rewritten_bbox = 0.273898 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.328419, GIOU: 0.296657), Class: 0.425777, Obj: 0.070877, No Obj: 0.003160, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.829690, iou_loss = 0.123944, total_loss = 0.953633 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.265653, GIOU: 0.265653), Class: 0.529852, Obj: 0.011267, No Obj: 0.000693, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.708062, iou_loss = 0.125113, total_loss = 0.833175 \n",
" total_bbox = 33228, rewritten_bbox = 0.273865 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322926, GIOU: 0.123969), Class: 0.512084, Obj: 0.026849, No Obj: 0.002542, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.874250, iou_loss = 0.014074, total_loss = 1.888324 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.250270, GIOU: 0.250270), Class: 0.311015, Obj: 0.018559, No Obj: 0.000613, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.408661, iou_loss = 0.054369, total_loss = 0.463030 \n",
" total_bbox = 33234, rewritten_bbox = 0.273816 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.452498, GIOU: 0.412429), Class: 0.461696, Obj: 0.055904, No Obj: 0.003039, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.158350, iou_loss = 0.032917, total_loss = 1.191267 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000619, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001712, iou_loss = 0.000000, total_loss = 0.001712 \n",
" total_bbox = 33237, rewritten_bbox = 0.273791 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.407565, GIOU: 0.386111), Class: 0.481882, Obj: 0.023577, No Obj: 0.002668, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.185714, iou_loss = 0.131231, total_loss = 2.316944 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.344157, GIOU: 0.197658), Class: 0.467900, Obj: 0.015863, No Obj: 0.000657, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.830367, iou_loss = 0.628905, total_loss = 1.459272 \n",
" total_bbox = 33245, rewritten_bbox = 0.273725 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.383104, GIOU: 0.249054), Class: 0.639854, Obj: 0.030660, No Obj: 0.002546, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.640242, iou_loss = 0.111817, total_loss = 1.752059 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000708, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002747, iou_loss = 0.000000, total_loss = 0.002747 \n",
" total_bbox = 33250, rewritten_bbox = 0.273684 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403977, GIOU: 0.280701), Class: 0.577146, Obj: 0.052093, No Obj: 0.002683, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.370787, iou_loss = 0.027192, total_loss = 1.397978 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.143958, GIOU: -0.046925), Class: 0.607408, Obj: 0.000783, No Obj: 0.000644, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.423929, iou_loss = 0.012554, total_loss = 0.436483 \n",
" total_bbox = 33255, rewritten_bbox = 0.273643 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.797760, GIOU: 0.797760), Class: 0.449005, Obj: 0.007896, No Obj: 0.002507, .5R: 1.000000, .75R: 1.000000, count: 2, class_loss = 0.873164, iou_loss = 0.059620, total_loss = 0.932784 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.523446, GIOU: 0.429964), Class: 0.267906, Obj: 0.021745, No Obj: 0.000635, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.432065, iou_loss = 0.147375, total_loss = 0.579440 \n",
" total_bbox = 33258, rewritten_bbox = 0.273618 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 416: 0.905784, 0.900060 avg loss, 0.000078 rate, 0.702210 seconds, 26624 images, 0.721936 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.377228, GIOU: 0.289564), Class: 0.541223, Obj: 0.064049, No Obj: 0.003033, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.763015, iou_loss = 0.030788, total_loss = 1.793802 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000606, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001451, iou_loss = 0.000000, total_loss = 0.001451 \n",
" total_bbox = 33263, rewritten_bbox = 0.273577 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.476177, GIOU: 0.462730), Class: 0.471905, Obj: 0.014316, No Obj: 0.002696, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.515295, iou_loss = 0.132037, total_loss = 1.647332 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000616, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001362, iou_loss = 0.000000, total_loss = 0.001362 \n",
" total_bbox = 33267, rewritten_bbox = 0.273544 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.364347, GIOU: 0.259817), Class: 0.444278, Obj: 0.037636, No Obj: 0.003267, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.471517, iou_loss = 0.013898, total_loss = 1.485415 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.189832, GIOU: 0.189833), Class: 0.657655, Obj: 0.078122, No Obj: 0.000692, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.307375, iou_loss = 0.034004, total_loss = 0.341378 \n",
" total_bbox = 33272, rewritten_bbox = 0.273503 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390999, GIOU: 0.335166), Class: 0.637998, Obj: 0.053878, No Obj: 0.003565, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.225672, iou_loss = 0.033626, total_loss = 1.259299 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000657, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001749, iou_loss = 0.000000, total_loss = 0.001749 \n",
" total_bbox = 33276, rewritten_bbox = 0.273470 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.178975, GIOU: 0.158810), Class: 0.508266, Obj: 0.030418, No Obj: 0.002951, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.080808, iou_loss = 0.004492, total_loss = 1.085300 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.698361, GIOU: 0.689873), Class: 0.383723, Obj: 0.037710, No Obj: 0.000603, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.410155, iou_loss = 0.408391, total_loss = 0.818545 \n",
" total_bbox = 33280, rewritten_bbox = 0.273438 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.304198, GIOU: 0.287328), Class: 0.585582, Obj: 0.091802, No Obj: 0.002890, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.310790, iou_loss = 0.021477, total_loss = 1.332267 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000611, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001359, iou_loss = 0.000000, total_loss = 0.001359 \n",
" total_bbox = 33284, rewritten_bbox = 0.273405 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.356104, GIOU: 0.356104), Class: 0.454219, Obj: 0.036104, No Obj: 0.003028, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.810919, iou_loss = 0.004641, total_loss = 0.815560 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.393972, GIOU: 0.321631), Class: 0.609885, Obj: 0.004026, No Obj: 0.000637, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.717592, iou_loss = 0.495269, total_loss = 1.212862 \n",
" total_bbox = 33288, rewritten_bbox = 0.273372 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.329679, GIOU: 0.299970), Class: 0.527388, Obj: 0.041570, No Obj: 0.003002, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.446231, iou_loss = 0.028902, total_loss = 1.475134 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000642, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002239, iou_loss = 0.000000, total_loss = 0.002239 \n",
" total_bbox = 33292, rewritten_bbox = 0.273339 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369498, GIOU: 0.369117), Class: 0.399509, Obj: 0.028484, No Obj: 0.002871, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.528810, iou_loss = 0.036234, total_loss = 1.565044 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.617600, GIOU: 0.614650), Class: 0.622781, Obj: 0.001525, No Obj: 0.000656, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.377135, iou_loss = 0.794685, total_loss = 1.171820 \n",
" total_bbox = 33297, rewritten_bbox = 0.273298 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352348, GIOU: 0.220465), Class: 0.412507, Obj: 0.047586, No Obj: 0.002942, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.620655, iou_loss = 0.042318, total_loss = 1.662973 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.215199, GIOU: 0.215198), Class: 0.547480, Obj: 0.002739, No Obj: 0.000664, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354190, iou_loss = 0.123820, total_loss = 0.478010 \n",
" total_bbox = 33302, rewritten_bbox = 0.273257 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.604647, GIOU: 0.546820), Class: 0.463612, Obj: 0.084430, No Obj: 0.002883, .5R: 1.000000, .75R: 0.250000, count: 4, class_loss = 1.428781, iou_loss = 0.138033, total_loss = 1.566814 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.244305, GIOU: 0.243117), Class: 0.386700, Obj: 0.003324, No Obj: 0.000668, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.404073, iou_loss = 0.653385, total_loss = 1.057458 \n",
" total_bbox = 33307, rewritten_bbox = 0.273216 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.361011, GIOU: 0.352672), Class: 0.457264, Obj: 0.021617, No Obj: 0.002788, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.558827, iou_loss = 0.026394, total_loss = 1.585221 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000598, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001345, iou_loss = 0.000000, total_loss = 0.001345 \n",
" total_bbox = 33311, rewritten_bbox = 0.273183 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.347359, GIOU: 0.157153), Class: 0.458451, Obj: 0.022865, No Obj: 0.002306, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.487859, iou_loss = 0.022555, total_loss = 1.510414 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000637, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002207, iou_loss = 0.000000, total_loss = 0.002207 \n",
" total_bbox = 33315, rewritten_bbox = 0.273150 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.347797, GIOU: 0.262618), Class: 0.598503, Obj: 0.020396, No Obj: 0.002517, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.437068, iou_loss = 0.014241, total_loss = 1.451308 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.202706, GIOU: 0.154395), Class: 0.525753, Obj: 0.002662, No Obj: 0.000630, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.104601, iou_loss = 0.418632, total_loss = 1.523233 \n",
" total_bbox = 33322, rewritten_bbox = 0.273093 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.361143, GIOU: 0.361142), Class: 0.530779, Obj: 0.048376, No Obj: 0.003184, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.743849, iou_loss = 0.070625, total_loss = 0.814475 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.466218, GIOU: 0.243995), Class: 0.548914, Obj: 0.032263, No Obj: 0.000698, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.654479, iou_loss = 0.346153, total_loss = 1.000633 \n",
" total_bbox = 33326, rewritten_bbox = 0.273060 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.274477, GIOU: 0.189081), Class: 0.442781, Obj: 0.023637, No Obj: 0.002289, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.111283, iou_loss = 0.022969, total_loss = 1.134252 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.484630, GIOU: 0.436210), Class: 0.505504, Obj: 0.000318, No Obj: 0.000618, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.367946, iou_loss = 0.502940, total_loss = 0.870885 \n",
" total_bbox = 33330, rewritten_bbox = 0.273027 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 417: 0.828914, 0.892945 avg loss, 0.000079 rate, 0.716599 seconds, 26688 images, 0.721709 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472583, GIOU: 0.443039), Class: 0.561495, Obj: 0.037406, No Obj: 0.002588, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.348253, iou_loss = 0.103345, total_loss = 1.451598 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.166220, GIOU: 0.046143), Class: 0.430681, Obj: 0.001139, No Obj: 0.000651, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.775118, iou_loss = 0.035856, total_loss = 0.810974 \n",
" total_bbox = 33336, rewritten_bbox = 0.272978 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.215109, GIOU: 0.215109), Class: 0.464358, Obj: 0.061200, No Obj: 0.002837, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.081732, iou_loss = 0.011177, total_loss = 1.092909 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000601, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001400, iou_loss = 0.000000, total_loss = 0.001400 \n",
" total_bbox = 33339, rewritten_bbox = 0.272954 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.303522, GIOU: 0.112126), Class: 0.596221, Obj: 0.025322, No Obj: 0.002215, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.790610, iou_loss = 0.019642, total_loss = 1.810252 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000685, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003275, iou_loss = 0.000000, total_loss = 0.003275 \n",
" total_bbox = 33344, rewritten_bbox = 0.272913 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.447842, GIOU: 0.422691), Class: 0.542300, Obj: 0.042159, No Obj: 0.002296, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.127893, iou_loss = 0.025089, total_loss = 1.152982 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.080056, GIOU: -0.082728), Class: 0.710561, Obj: 0.000263, No Obj: 0.000668, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.371730, iou_loss = 0.005413, total_loss = 0.377143 \n",
" total_bbox = 33348, rewritten_bbox = 0.272880 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271757, GIOU: 0.173922), Class: 0.492939, Obj: 0.035726, No Obj: 0.002708, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.188451, iou_loss = 0.043613, total_loss = 2.232064 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.353148, GIOU: 0.303849), Class: 0.533121, Obj: 0.001529, No Obj: 0.000620, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.675374, iou_loss = 1.102307, total_loss = 1.777681 \n",
" total_bbox = 33356, rewritten_bbox = 0.272814 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370236, GIOU: 0.274023), Class: 0.408163, Obj: 0.010450, No Obj: 0.002433, .5R: 0.100000, .75R: 0.000000, count: 10, class_loss = 3.879456, iou_loss = 0.149675, total_loss = 4.029130 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000611, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001569, iou_loss = 0.000000, total_loss = 0.001569 \n",
" total_bbox = 33366, rewritten_bbox = 0.272733 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.351511, GIOU: 0.348722), Class: 0.321089, Obj: 0.049568, No Obj: 0.002579, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.510330, iou_loss = 0.058922, total_loss = 1.569252 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.359291, GIOU: 0.141751), Class: 0.424612, Obj: 0.004519, No Obj: 0.000612, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.398534, iou_loss = 0.048898, total_loss = 0.447433 \n",
" total_bbox = 33371, rewritten_bbox = 0.272692 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.312703, GIOU: 0.169910), Class: 0.553521, Obj: 0.025902, No Obj: 0.002320, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.161953, iou_loss = 0.018453, total_loss = 1.180405 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.280105, GIOU: 0.144925), Class: 0.687622, Obj: 0.001357, No Obj: 0.000616, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.032941, iou_loss = 0.490775, total_loss = 1.523716 \n",
" total_bbox = 33377, rewritten_bbox = 0.272643 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.137928, GIOU: -0.047108), Class: 0.479644, Obj: 0.018676, No Obj: 0.002805, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.518230, iou_loss = 0.003696, total_loss = 1.521926 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000704, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002125, iou_loss = 0.000000, total_loss = 0.002125 \n",
" total_bbox = 33381, rewritten_bbox = 0.272610 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.379939, GIOU: 0.326576), Class: 0.573492, Obj: 0.011803, No Obj: 0.002618, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.060830, iou_loss = 0.010664, total_loss = 1.071494 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.201643, GIOU: 0.103804), Class: 0.502105, Obj: 0.002742, No Obj: 0.000653, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.736175, iou_loss = 2.408976, total_loss = 3.145151 \n",
" total_bbox = 33387, rewritten_bbox = 0.275556 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344050, GIOU: 0.254974), Class: 0.456558, Obj: 0.015653, No Obj: 0.002261, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.557177, iou_loss = 0.028060, total_loss = 2.585238 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000646, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001506, iou_loss = 0.000000, total_loss = 0.001506 \n",
" total_bbox = 33394, rewritten_bbox = 0.275499 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.355404, GIOU: 0.206321), Class: 0.499517, Obj: 0.028236, No Obj: 0.002484, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.574270, iou_loss = 0.043356, total_loss = 1.617627 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000631, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001528, iou_loss = 0.000000, total_loss = 0.001528 \n",
" total_bbox = 33398, rewritten_bbox = 0.275466 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.542535, GIOU: 0.520864), Class: 0.537232, Obj: 0.026221, No Obj: 0.002344, .5R: 0.666667, .75R: 0.166667, count: 6, class_loss = 2.134312, iou_loss = 0.162849, total_loss = 2.297161 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.522460, GIOU: 0.470107), Class: 0.576440, Obj: 0.004965, No Obj: 0.000623, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.354732, iou_loss = 0.769881, total_loss = 1.124612 \n",
" total_bbox = 33405, rewritten_bbox = 0.275408 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271343, GIOU: 0.229748), Class: 0.575714, Obj: 0.052786, No Obj: 0.002434, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.299779, iou_loss = 0.019673, total_loss = 1.319452 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.584531, GIOU: 0.539462), Class: 0.431702, Obj: 0.002195, No Obj: 0.000587, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.751098, iou_loss = 0.752958, total_loss = 1.504056 \n",
" total_bbox = 33411, rewritten_bbox = 0.275358 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.428284, GIOU: 0.373304), Class: 0.599654, Obj: 0.036291, No Obj: 0.002520, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.426366, iou_loss = 0.094705, total_loss = 1.521070 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.467560, GIOU: 0.419417), Class: 0.318918, Obj: 0.010952, No Obj: 0.000621, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.855106, iou_loss = 0.701540, total_loss = 1.556646 \n",
" total_bbox = 33417, rewritten_bbox = 0.275309 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319566, GIOU: 0.319566), Class: 0.636562, Obj: 0.070098, No Obj: 0.002889, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.244825, iou_loss = 0.028806, total_loss = 1.273631 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.399331, GIOU: 0.215956), Class: 0.511897, Obj: 0.001682, No Obj: 0.000649, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.360701, iou_loss = 0.066392, total_loss = 0.427092 \n",
" total_bbox = 33422, rewritten_bbox = 0.275268 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 418: 1.047686, 0.908419 avg loss, 0.000080 rate, 0.696436 seconds, 26752 images, 0.721624 hours left\n",
"Loaded: 0.000033 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307555, GIOU: 0.190426), Class: 0.640447, Obj: 0.038732, No Obj: 0.002865, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.360071, iou_loss = 0.026644, total_loss = 1.386715 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000599, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001362, iou_loss = 0.000000, total_loss = 0.001362 \n",
" total_bbox = 33426, rewritten_bbox = 0.275235 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.460518, GIOU: 0.426262), Class: 0.458337, Obj: 0.043976, No Obj: 0.002660, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.845415, iou_loss = 0.067461, total_loss = 1.912876 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.353506, GIOU: 0.141573), Class: 0.505574, Obj: 0.003181, No Obj: 0.000584, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.373617, iou_loss = 0.164536, total_loss = 0.538152 \n",
" total_bbox = 33432, rewritten_bbox = 0.275185 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.302595, GIOU: 0.097804), Class: 0.468498, Obj: 0.038706, No Obj: 0.002339, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.152005, iou_loss = 0.003960, total_loss = 1.155965 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.157919, GIOU: -0.306725), Class: 0.580936, Obj: 0.001116, No Obj: 0.000622, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.764945, iou_loss = 0.031559, total_loss = 0.796503 \n",
" total_bbox = 33437, rewritten_bbox = 0.275144 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.447421, GIOU: 0.397717), Class: 0.475175, Obj: 0.007501, No Obj: 0.001826, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 2.354985, iou_loss = 0.233936, total_loss = 2.588920 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.177570, GIOU: 0.177571), Class: 0.285363, Obj: 0.000913, No Obj: 0.000597, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.399302, iou_loss = 0.027517, total_loss = 0.426819 \n",
" total_bbox = 33445, rewritten_bbox = 0.278068 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.428385, GIOU: 0.410453), Class: 0.524428, Obj: 0.201946, No Obj: 0.003147, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 0.940042, iou_loss = 0.037461, total_loss = 0.977503 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.277903, GIOU: 0.201435), Class: 0.610312, Obj: 0.008592, No Obj: 0.000646, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.751230, iou_loss = 0.103700, total_loss = 0.854930 \n",
" total_bbox = 33450, rewritten_bbox = 0.278027 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.420811, GIOU: 0.418539), Class: 0.598871, Obj: 0.056190, No Obj: 0.002690, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.006875, iou_loss = 0.091209, total_loss = 1.098084 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.258959), Class: 0.466241, Obj: 0.000562, No Obj: 0.000603, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.359567, iou_loss = 0.066523, total_loss = 0.426090 \n",
" total_bbox = 33454, rewritten_bbox = 0.277994 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.779132, GIOU: 0.769725), Class: 0.713534, Obj: 0.016387, No Obj: 0.001828, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 0.392971, iou_loss = 0.031287, total_loss = 0.424258 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.438411, GIOU: 0.371820), Class: 0.572716, Obj: 0.013716, No Obj: 0.000647, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.712593, iou_loss = 0.836983, total_loss = 1.549576 \n",
" total_bbox = 33457, rewritten_bbox = 0.277969 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.331174, GIOU: 0.200155), Class: 0.533479, Obj: 0.024347, No Obj: 0.002738, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.726900, iou_loss = 0.031678, total_loss = 1.758578 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.616860, GIOU: 0.565456), Class: 0.584533, Obj: 0.010335, No Obj: 0.000654, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.305938, iou_loss = 0.204960, total_loss = 0.510898 \n",
" total_bbox = 33463, rewritten_bbox = 0.277919 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.304485, GIOU: 0.215067), Class: 0.576001, Obj: 0.037258, No Obj: 0.002817, .5R: 0.125000, .75R: 0.000000, count: 8, class_loss = 2.738144, iou_loss = 0.040938, total_loss = 2.779082 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000567, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001187, iou_loss = 0.000000, total_loss = 0.001187 \n",
" total_bbox = 33471, rewritten_bbox = 0.277852 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.244956, GIOU: 0.066175), Class: 0.454081, Obj: 0.007129, No Obj: 0.002937, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.218696, iou_loss = 0.017222, total_loss = 1.235919 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.162263, GIOU: -0.219286), Class: 0.493658, Obj: 0.000524, No Obj: 0.000651, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.395467, iou_loss = 0.026809, total_loss = 0.422276 \n",
" total_bbox = 33475, rewritten_bbox = 0.277819 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372333, GIOU: 0.334973), Class: 0.478583, Obj: 0.014180, No Obj: 0.002646, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.451013, iou_loss = 0.022726, total_loss = 1.473739 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000620, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001538, iou_loss = 0.000000, total_loss = 0.001538 \n",
" total_bbox = 33479, rewritten_bbox = 0.277786 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.543234, GIOU: 0.361692), Class: 0.598939, Obj: 0.033923, No Obj: 0.002555, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 0.935754, iou_loss = 0.043604, total_loss = 0.979358 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.468752, GIOU: 0.448820), Class: 0.526285, Obj: 0.018178, No Obj: 0.000675, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.829455, iou_loss = 0.523114, total_loss = 1.352569 \n",
" total_bbox = 33484, rewritten_bbox = 0.277745 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.197564, GIOU: 0.070918), Class: 0.513649, Obj: 0.020818, No Obj: 0.002789, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.468558, iou_loss = 0.011718, total_loss = 1.480276 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.258301, GIOU: 0.105290), Class: 0.518236, Obj: 0.001433, No Obj: 0.000611, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.663482, iou_loss = 0.170360, total_loss = 0.833841 \n",
" total_bbox = 33490, rewritten_bbox = 0.277695 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.433950, GIOU: 0.377371), Class: 0.402063, Obj: 0.046870, No Obj: 0.003575, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.810010, iou_loss = 0.063519, total_loss = 1.873529 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.253244, GIOU: -0.060614), Class: 0.683502, Obj: 0.001294, No Obj: 0.000636, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.642972, iou_loss = 0.098207, total_loss = 0.741179 \n",
" total_bbox = 33497, rewritten_bbox = 0.277637 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.261542, GIOU: 0.076733), Class: 0.510263, Obj: 0.014864, No Obj: 0.002532, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.408385, iou_loss = 0.010135, total_loss = 2.418520 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000622, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001626, iou_loss = 0.000000, total_loss = 0.001626 \n",
" total_bbox = 33503, rewritten_bbox = 0.277587 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.295157, GIOU: 0.223760), Class: 0.552724, Obj: 0.022643, No Obj: 0.002431, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.539644, iou_loss = 0.023896, total_loss = 1.563540 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000567, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001097, iou_loss = 0.000000, total_loss = 0.001097 \n",
" total_bbox = 33507, rewritten_bbox = 0.277554 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 419: 0.964521, 0.914029 avg loss, 0.000080 rate, 0.715421 seconds, 26816 images, 0.721338 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345743, GIOU: 0.345743), Class: 0.511459, Obj: 0.009881, No Obj: 0.003229, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.509394, iou_loss = 0.060882, total_loss = 1.570276 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.260211, GIOU: -0.066177), Class: 0.586943, Obj: 0.002586, No Obj: 0.000640, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.339109, iou_loss = 0.020903, total_loss = 0.360011 \n",
" total_bbox = 33512, rewritten_bbox = 0.277513 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.233182, GIOU: 0.125447), Class: 0.308614, Obj: 0.010181, No Obj: 0.002404, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.304259, iou_loss = 0.002318, total_loss = 1.306577 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.317705, GIOU: 0.293329), Class: 0.417709, Obj: 0.003955, No Obj: 0.000639, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.368792, iou_loss = 0.514119, total_loss = 0.882911 \n",
" total_bbox = 33516, rewritten_bbox = 0.277479 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.408705, GIOU: 0.400516), Class: 0.463632, Obj: 0.079680, No Obj: 0.003390, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.130704, iou_loss = 0.029746, total_loss = 1.160451 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.132339, GIOU: 0.132339), Class: 0.456545, Obj: 0.001742, No Obj: 0.000613, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.352480, iou_loss = 0.015401, total_loss = 0.367881 \n",
" total_bbox = 33520, rewritten_bbox = 0.277446 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320960, GIOU: 0.004001), Class: 0.474443, Obj: 0.010165, No Obj: 0.002223, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.077222, iou_loss = 0.022666, total_loss = 1.099888 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.176773, GIOU: -0.305032), Class: 0.546163, Obj: 0.001322, No Obj: 0.000594, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.376901, iou_loss = 0.032043, total_loss = 0.408944 \n",
" total_bbox = 33524, rewritten_bbox = 0.277413 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.347742, GIOU: 0.298497), Class: 0.520108, Obj: 0.024355, No Obj: 0.002552, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.485474, iou_loss = 0.034166, total_loss = 1.519639 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000583, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001369, iou_loss = 0.000000, total_loss = 0.001369 \n",
" total_bbox = 33528, rewritten_bbox = 0.277380 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.448524, GIOU: 0.291288), Class: 0.532991, Obj: 0.027382, No Obj: 0.002531, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 2.155953, iou_loss = 0.042248, total_loss = 2.198201 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000633, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002252, iou_loss = 0.000000, total_loss = 0.002252 \n",
" total_bbox = 33533, rewritten_bbox = 0.277339 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339615, GIOU: 0.228195), Class: 0.627325, Obj: 0.086944, No Obj: 0.003100, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.004231, iou_loss = 0.067891, total_loss = 1.072122 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000715, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002094, iou_loss = 0.000000, total_loss = 0.002094 \n",
" total_bbox = 33536, rewritten_bbox = 0.277314 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.387894, GIOU: 0.333520), Class: 0.536340, Obj: 0.031225, No Obj: 0.002610, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.040295, iou_loss = 0.036438, total_loss = 1.076734 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.689219, GIOU: 0.667977), Class: 0.465114, Obj: 0.002401, No Obj: 0.000611, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.380445, iou_loss = 0.704906, total_loss = 1.085351 \n",
" total_bbox = 33540, rewritten_bbox = 0.277281 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.490994, GIOU: 0.452684), Class: 0.579810, Obj: 0.043418, No Obj: 0.002879, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.683124, iou_loss = 0.104470, total_loss = 1.787594 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000641, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003136, iou_loss = 0.000000, total_loss = 0.003136 \n",
" total_bbox = 33545, rewritten_bbox = 0.277240 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322128, GIOU: 0.229047), Class: 0.435832, Obj: 0.034750, No Obj: 0.002452, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.611093, iou_loss = 0.018809, total_loss = 1.629902 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000673, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004135, iou_loss = 0.000000, total_loss = 0.004135 \n",
" total_bbox = 33549, rewritten_bbox = 0.277206 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.179347, GIOU: 0.179347), Class: 0.533323, Obj: 0.057763, No Obj: 0.002627, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.038255, iou_loss = 0.008074, total_loss = 1.046329 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.068910, GIOU: -0.226372), Class: 0.497125, Obj: 0.000577, No Obj: 0.000659, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.425325, iou_loss = 0.930351, total_loss = 1.355677 \n",
" total_bbox = 33553, rewritten_bbox = 0.277173 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.283961, GIOU: 0.271529), Class: 0.585953, Obj: 0.077554, No Obj: 0.003104, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.293541, iou_loss = 0.042223, total_loss = 1.335764 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000654, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002082, iou_loss = 0.000000, total_loss = 0.002082 \n",
" total_bbox = 33557, rewritten_bbox = 0.277140 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.469177, GIOU: 0.367546), Class: 0.462310, Obj: 0.018440, No Obj: 0.002398, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.133087, iou_loss = 0.010290, total_loss = 1.143376 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.231540, GIOU: 0.203673), Class: 0.365711, Obj: 0.003083, No Obj: 0.000656, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.820389, iou_loss = 0.050172, total_loss = 0.870561 \n",
" total_bbox = 33562, rewritten_bbox = 0.277099 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477568, GIOU: 0.464495), Class: 0.425594, Obj: 0.028258, No Obj: 0.002517, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 2.108365, iou_loss = 0.079502, total_loss = 2.187867 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000593, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001400, iou_loss = 0.000000, total_loss = 0.001400 \n",
" total_bbox = 33567, rewritten_bbox = 0.277058 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.424313, GIOU: 0.398747), Class: 0.518935, Obj: 0.025589, No Obj: 0.003220, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.743463, iou_loss = 0.030486, total_loss = 1.773949 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000601, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001327, iou_loss = 0.000000, total_loss = 0.001327 \n",
" total_bbox = 33572, rewritten_bbox = 0.277017 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344437, GIOU: 0.317140), Class: 0.464826, Obj: 0.030482, No Obj: 0.002496, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.472787, iou_loss = 0.045286, total_loss = 1.518073 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000645, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001846, iou_loss = 0.000000, total_loss = 0.001846 \n",
" total_bbox = 33576, rewritten_bbox = 0.276984 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 420: 0.816939, 0.904320 avg loss, 0.000081 rate, 0.705226 seconds, 26880 images, 0.721241 hours left\n",
"Loaded: 0.000057 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.445829, GIOU: 0.355892), Class: 0.548604, Obj: 0.068412, No Obj: 0.002462, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.659109, iou_loss = 0.217644, total_loss = 1.876753 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000604, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001325, iou_loss = 0.000000, total_loss = 0.001325 \n",
" total_bbox = 33581, rewritten_bbox = 0.276942 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427824, GIOU: 0.374608), Class: 0.573856, Obj: 0.059441, No Obj: 0.002868, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.034965, iou_loss = 0.107156, total_loss = 1.142121 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.278726, GIOU: 0.048324), Class: 0.280393, Obj: 0.000582, No Obj: 0.000592, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.448879, iou_loss = 0.038918, total_loss = 0.487797 \n",
" total_bbox = 33585, rewritten_bbox = 0.276909 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.229792, GIOU: -0.005333), Class: 0.464280, Obj: 0.029125, No Obj: 0.002380, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.454657, iou_loss = 0.006656, total_loss = 1.461313 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.283040, GIOU: 0.283040), Class: 0.685002, Obj: 0.002836, No Obj: 0.000595, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.289785, iou_loss = 0.068867, total_loss = 0.358651 \n",
" total_bbox = 33590, rewritten_bbox = 0.276868 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.247880, GIOU: 0.247410), Class: 0.635245, Obj: 0.058016, No Obj: 0.002189, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.895015, iou_loss = 0.047441, total_loss = 0.942456 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.146233, GIOU: -0.384906), Class: 0.531756, Obj: 0.000492, No Obj: 0.000642, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.395124, iou_loss = 0.007769, total_loss = 0.402894 \n",
" total_bbox = 33594, rewritten_bbox = 0.276835 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.451968, GIOU: 0.451968), Class: 0.487447, Obj: 0.012101, No Obj: 0.002032, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.443681, iou_loss = 0.015573, total_loss = 0.459253 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.351429, GIOU: 0.190376), Class: 0.571124, Obj: 0.004984, No Obj: 0.000671, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.024434, iou_loss = 0.512557, total_loss = 1.536991 \n",
" total_bbox = 33598, rewritten_bbox = 0.276802 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435081, GIOU: 0.372807), Class: 0.491531, Obj: 0.071209, No Obj: 0.003431, .5R: 0.333333, .75R: 0.111111, count: 9, class_loss = 3.184557, iou_loss = 0.188781, total_loss = 3.373338 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.420782, GIOU: 0.420782), Class: 0.556831, Obj: 0.000984, No Obj: 0.000647, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.342495, iou_loss = 0.152995, total_loss = 0.495489 \n",
" total_bbox = 33608, rewritten_bbox = 0.276720 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.239264, GIOU: 0.196864), Class: 0.383167, Obj: 0.002685, No Obj: 0.002282, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.174955, iou_loss = 0.006768, total_loss = 1.181723 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.461576, GIOU: 0.420754), Class: 0.601472, Obj: 0.000411, No Obj: 0.000602, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.316024, iou_loss = 0.545265, total_loss = 0.861289 \n",
" total_bbox = 33612, rewritten_bbox = 0.276687 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.521127, GIOU: 0.521127), Class: 0.512605, Obj: 0.045512, No Obj: 0.003063, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.459160, iou_loss = 0.021916, total_loss = 1.481076 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000603, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001446, iou_loss = 0.000000, total_loss = 0.001446 \n",
" total_bbox = 33616, rewritten_bbox = 0.276654 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392226, GIOU: 0.353427), Class: 0.490287, Obj: 0.039295, No Obj: 0.002763, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.125622, iou_loss = 0.019113, total_loss = 1.144735 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.626675, GIOU: 0.582628), Class: 0.540735, Obj: 0.009903, No Obj: 0.000605, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.664196, iou_loss = 0.995953, total_loss = 1.660148 \n",
" total_bbox = 33621, rewritten_bbox = 0.276613 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.464996, GIOU: 0.463947), Class: 0.520385, Obj: 0.019868, No Obj: 0.002251, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.453457, iou_loss = 0.058413, total_loss = 1.511870 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000601, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001351, iou_loss = 0.000000, total_loss = 0.001351 \n",
" total_bbox = 33625, rewritten_bbox = 0.276580 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.450683, GIOU: 0.404321), Class: 0.465907, Obj: 0.027051, No Obj: 0.002218, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.836798, iou_loss = 0.149928, total_loss = 1.986726 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000624, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001851, iou_loss = 0.000000, total_loss = 0.001851 \n",
" total_bbox = 33630, rewritten_bbox = 0.276539 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.239031, GIOU: -0.017198), Class: 0.560947, Obj: 0.011534, No Obj: 0.002020, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.525598, iou_loss = 0.013110, total_loss = 1.538708 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.337503, GIOU: 0.160250), Class: 0.458386, Obj: 0.001754, No Obj: 0.000650, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.384009, iou_loss = 2.889205, total_loss = 3.273214 \n",
" total_bbox = 33635, rewritten_bbox = 0.276498 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345832, GIOU: 0.238895), Class: 0.602248, Obj: 0.134791, No Obj: 0.003504, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.228346, iou_loss = 0.025336, total_loss = 1.253682 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000620, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002369, iou_loss = 0.000000, total_loss = 0.002369 \n",
" total_bbox = 33639, rewritten_bbox = 0.276465 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.350507, GIOU: 0.268173), Class: 0.604764, Obj: 0.033730, No Obj: 0.002652, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.476519, iou_loss = 0.010598, total_loss = 1.487116 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000564, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001093, iou_loss = 0.000000, total_loss = 0.001093 \n",
" total_bbox = 33643, rewritten_bbox = 0.276432 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271121, GIOU: 0.007622), Class: 0.442657, Obj: 0.074798, No Obj: 0.002583, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.756440, iou_loss = 0.012680, total_loss = 1.769120 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.328663, GIOU: 0.301207), Class: 0.600153, Obj: 0.001861, No Obj: 0.000649, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.688761, iou_loss = 0.972403, total_loss = 1.661163 \n",
" total_bbox = 33650, rewritten_bbox = 0.276374 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.419242, GIOU: 0.379922), Class: 0.582429, Obj: 0.043009, No Obj: 0.002694, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.353755, iou_loss = 0.119155, total_loss = 1.472910 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.314320, GIOU: 0.066126), Class: 0.406955, Obj: 0.010841, No Obj: 0.000624, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.227283, iou_loss = 2.845667, total_loss = 4.072950 \n",
" total_bbox = 33657, rewritten_bbox = 0.276317 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 421: 0.910692, 0.904957 avg loss, 0.000082 rate, 0.736487 seconds, 26944 images, 0.721043 hours left\n",
"Loaded: 0.000062 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.332007, GIOU: 0.258491), Class: 0.480307, Obj: 0.044993, No Obj: 0.002581, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.839677, iou_loss = 0.026209, total_loss = 1.865886 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000602, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001382, iou_loss = 0.000000, total_loss = 0.001382 \n",
" total_bbox = 33662, rewritten_bbox = 0.276276 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.190658, GIOU: 0.067932), Class: 0.562732, Obj: 0.013340, No Obj: 0.001957, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.066829, iou_loss = 0.001592, total_loss = 1.068421 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.376798, GIOU: 0.286004), Class: 0.478698, Obj: 0.000492, No Obj: 0.000577, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.443772, iou_loss = 0.113057, total_loss = 0.556830 \n",
" total_bbox = 33666, rewritten_bbox = 0.276243 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.489109, GIOU: 0.434512), Class: 0.378526, Obj: 0.015057, No Obj: 0.002873, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 2.019035, iou_loss = 0.044898, total_loss = 2.063933 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000577, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001815, iou_loss = 0.000000, total_loss = 0.001815 \n",
" total_bbox = 33671, rewritten_bbox = 0.276202 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.424056, GIOU: 0.331047), Class: 0.516122, Obj: 0.039827, No Obj: 0.002350, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.963761, iou_loss = 0.009065, total_loss = 0.972826 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.353447, GIOU: 0.173356), Class: 0.487457, Obj: 0.027334, No Obj: 0.000612, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.026291, iou_loss = 1.803989, total_loss = 2.830280 \n",
" total_bbox = 33677, rewritten_bbox = 0.276153 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.577060, GIOU: 0.576918), Class: 0.468861, Obj: 0.034069, No Obj: 0.002404, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.159317, iou_loss = 0.034513, total_loss = 1.193831 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.424187, GIOU: 0.271611), Class: 0.613525, Obj: 0.007779, No Obj: 0.000573, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347200, iou_loss = 0.086035, total_loss = 0.433235 \n",
" total_bbox = 33681, rewritten_bbox = 0.276120 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.376128, GIOU: 0.208553), Class: 0.460637, Obj: 0.033795, No Obj: 0.002640, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.232350, iou_loss = 0.061634, total_loss = 2.293984 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000609, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001528, iou_loss = 0.000000, total_loss = 0.001528 \n",
" total_bbox = 33687, rewritten_bbox = 0.276071 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.404793, GIOU: 0.274508), Class: 0.569831, Obj: 0.013295, No Obj: 0.002221, .5R: 0.166667, .75R: 0.166667, count: 6, class_loss = 2.136232, iou_loss = 0.029573, total_loss = 2.165806 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000588, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001533, iou_loss = 0.000000, total_loss = 0.001533 \n",
" total_bbox = 33693, rewritten_bbox = 0.276022 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390033, GIOU: 0.313834), Class: 0.640920, Obj: 0.029174, No Obj: 0.002462, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.705368, iou_loss = 0.048280, total_loss = 1.753648 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000593, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001767, iou_loss = 0.000000, total_loss = 0.001767 \n",
" total_bbox = 33698, rewritten_bbox = 0.275981 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.379677, GIOU: 0.238748), Class: 0.403829, Obj: 0.061576, No Obj: 0.002547, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.947217, iou_loss = 0.050546, total_loss = 1.997763 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000565, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001166, iou_loss = 0.000000, total_loss = 0.001166 \n",
" total_bbox = 33703, rewritten_bbox = 0.275940 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.442976, GIOU: 0.377085), Class: 0.565068, Obj: 0.020949, No Obj: 0.002330, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.042491, iou_loss = 0.060388, total_loss = 1.102879 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.411632, GIOU: 0.346624), Class: 0.553918, Obj: 0.002307, No Obj: 0.000593, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.677271, iou_loss = 0.443215, total_loss = 1.120486 \n",
" total_bbox = 33708, rewritten_bbox = 0.275899 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.494306, GIOU: 0.494133), Class: 0.490316, Obj: 0.091701, No Obj: 0.003298, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.338694, iou_loss = 0.022207, total_loss = 1.360901 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.589688, GIOU: 0.546338), Class: 0.533238, Obj: 0.006663, No Obj: 0.000622, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.340831, iou_loss = 0.285529, total_loss = 0.626359 \n",
" total_bbox = 33713, rewritten_bbox = 0.275858 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346423, GIOU: 0.313491), Class: 0.568918, Obj: 0.037213, No Obj: 0.002810, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.832072, iou_loss = 0.051548, total_loss = 1.883620 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.404052, GIOU: 0.346897), Class: 0.505286, Obj: 0.016031, No Obj: 0.000616, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.379418, iou_loss = 0.159153, total_loss = 0.538571 \n",
" total_bbox = 33719, rewritten_bbox = 0.275809 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382868, GIOU: 0.323127), Class: 0.555367, Obj: 0.028962, No Obj: 0.002548, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.707359, iou_loss = 0.054044, total_loss = 1.761403 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.536997, GIOU: 0.524104), Class: 0.692203, Obj: 0.002169, No Obj: 0.000594, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.349699, iou_loss = 1.113938, total_loss = 1.463637 \n",
" total_bbox = 33725, rewritten_bbox = 0.275760 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.267756, GIOU: 0.068012), Class: 0.571918, Obj: 0.032899, No Obj: 0.002516, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.353267, iou_loss = 0.011750, total_loss = 1.365016 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000611, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001364, iou_loss = 0.000000, total_loss = 0.001364 \n",
" total_bbox = 33729, rewritten_bbox = 0.275727 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.375498, GIOU: 0.314129), Class: 0.438035, Obj: 0.015136, No Obj: 0.002625, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.549089, iou_loss = 0.122213, total_loss = 1.671302 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.343344, GIOU: 0.159512), Class: 0.492951, Obj: 0.002764, No Obj: 0.000630, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.355112, iou_loss = 0.039392, total_loss = 0.394503 \n",
" total_bbox = 33734, rewritten_bbox = 0.275686 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.503175, GIOU: 0.490643), Class: 0.476556, Obj: 0.017998, No Obj: 0.002327, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.521850, iou_loss = 0.079396, total_loss = 1.601245 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.535516, GIOU: 0.521005), Class: 0.439610, Obj: 0.000648, No Obj: 0.000596, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.432055, iou_loss = 0.446754, total_loss = 0.878809 \n",
" total_bbox = 33739, rewritten_bbox = 0.275645 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 422: 0.938536, 0.908315 avg loss, 0.000083 rate, 0.700726 seconds, 27008 images, 0.721155 hours left\n",
"Loaded: 0.000054 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.323860, GIOU: 0.248703), Class: 0.501582, Obj: 0.019487, No Obj: 0.003100, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.159040, iou_loss = 0.046592, total_loss = 2.205633 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000555, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001068, iou_loss = 0.000000, total_loss = 0.001068 \n",
" total_bbox = 33745, rewritten_bbox = 0.275596 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.334580, GIOU: 0.200542), Class: 0.599173, Obj: 0.047568, No Obj: 0.002385, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.010307, iou_loss = 0.016174, total_loss = 1.026481 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.081950, GIOU: -0.638007), Class: 0.611733, Obj: 0.001011, No Obj: 0.000583, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.361799, iou_loss = 0.005801, total_loss = 0.367600 \n",
" total_bbox = 33749, rewritten_bbox = 0.275564 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.377785, GIOU: 0.297438), Class: 0.450692, Obj: 0.031236, No Obj: 0.002633, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.876021, iou_loss = 0.082180, total_loss = 1.958201 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.381639, GIOU: 0.362785), Class: 0.387052, Obj: 0.014858, No Obj: 0.000635, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.016560, iou_loss = 1.772059, total_loss = 3.788619 \n",
" total_bbox = 33759, rewritten_bbox = 0.275482 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.285640, GIOU: 0.065537), Class: 0.749279, Obj: 0.129075, No Obj: 0.003167, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.063401, iou_loss = 0.026464, total_loss = 1.089865 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000670, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002471, iou_loss = 0.000000, total_loss = 0.002471 \n",
" total_bbox = 33763, rewritten_bbox = 0.275449 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.504432, GIOU: 0.484469), Class: 0.459831, Obj: 0.050383, No Obj: 0.003108, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.899252, iou_loss = 0.078336, total_loss = 1.977588 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000591, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001385, iou_loss = 0.000000, total_loss = 0.001385 \n",
" total_bbox = 33768, rewritten_bbox = 0.275409 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.296111, GIOU: 0.130574), Class: 0.441023, Obj: 0.027150, No Obj: 0.002842, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.938116, iou_loss = 0.074490, total_loss = 2.012606 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.365269, GIOU: 0.304064), Class: 0.478075, Obj: 0.001211, No Obj: 0.000583, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.164056, iou_loss = 1.091471, total_loss = 2.255527 \n",
" total_bbox = 33776, rewritten_bbox = 0.275343 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.239830, GIOU: 0.129651), Class: 0.565175, Obj: 0.012707, No Obj: 0.002290, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.035296, iou_loss = 0.004887, total_loss = 1.040183 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.436367, GIOU: 0.386405), Class: 0.503897, Obj: 0.003762, No Obj: 0.000614, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.729589, iou_loss = 0.277774, total_loss = 1.007363 \n",
" total_bbox = 33781, rewritten_bbox = 0.275303 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345412, GIOU: 0.297758), Class: 0.620579, Obj: 0.066269, No Obj: 0.003146, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.053927, iou_loss = 0.013768, total_loss = 1.067696 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.155390, GIOU: -0.046244), Class: 0.352928, Obj: 0.003242, No Obj: 0.000605, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.229554, iou_loss = 3.074447, total_loss = 4.304002 \n",
" total_bbox = 33787, rewritten_bbox = 0.275254 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.333194, GIOU: 0.333041), Class: 0.586809, Obj: 0.024878, No Obj: 0.002467, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.290125, iou_loss = 0.011409, total_loss = 1.301534 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000608, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001488, iou_loss = 0.000000, total_loss = 0.001488 \n",
" total_bbox = 33792, rewritten_bbox = 0.278172 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382739, GIOU: 0.361995), Class: 0.454460, Obj: 0.022747, No Obj: 0.002306, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.612408, iou_loss = 0.043674, total_loss = 1.656083 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000561, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001290, iou_loss = 0.000000, total_loss = 0.001290 \n",
" total_bbox = 33796, rewritten_bbox = 0.278139 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370136, GIOU: 0.242444), Class: 0.662819, Obj: 0.053307, No Obj: 0.002545, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.145937, iou_loss = 0.078100, total_loss = 1.224037 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.391613, GIOU: 0.211481), Class: 0.468546, Obj: 0.001493, No Obj: 0.000648, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.592755, iou_loss = 1.170506, total_loss = 2.763260 \n",
" total_bbox = 33804, rewritten_bbox = 0.278074 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.401834, GIOU: 0.392546), Class: 0.421288, Obj: 0.015453, No Obj: 0.002079, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.977928, iou_loss = 0.124702, total_loss = 2.102630 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.362512, GIOU: 0.206518), Class: 0.493214, Obj: 0.002193, No Obj: 0.000569, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.207615, iou_loss = 0.638410, total_loss = 1.846025 \n",
" total_bbox = 33812, rewritten_bbox = 0.278008 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.290879, GIOU: 0.138185), Class: 0.517877, Obj: 0.046677, No Obj: 0.002484, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.446591, iou_loss = 0.015044, total_loss = 1.461635 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000574, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001223, iou_loss = 0.000000, total_loss = 0.001223 \n",
" total_bbox = 33816, rewritten_bbox = 0.277975 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.414549, GIOU: 0.341714), Class: 0.487008, Obj: 0.042055, No Obj: 0.002655, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.132157, iou_loss = 0.100784, total_loss = 1.232941 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.510503, GIOU: 0.405618), Class: 0.635719, Obj: 0.001514, No Obj: 0.000610, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.320444, iou_loss = 0.131768, total_loss = 0.452212 \n",
" total_bbox = 33820, rewritten_bbox = 0.277942 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.496837, GIOU: 0.436271), Class: 0.387205, Obj: 0.047140, No Obj: 0.002497, .5R: 0.400000, .75R: 0.400000, count: 5, class_loss = 1.917870, iou_loss = 0.068087, total_loss = 1.985957 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000570, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001209, iou_loss = 0.000000, total_loss = 0.001209 \n",
" total_bbox = 33825, rewritten_bbox = 0.277901 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308157, GIOU: 0.162782), Class: 0.680061, Obj: 0.014250, No Obj: 0.002383, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.712079, iou_loss = 0.013672, total_loss = 1.725751 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000571, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001265, iou_loss = 0.000000, total_loss = 0.001265 \n",
" total_bbox = 33830, rewritten_bbox = 0.277860 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 423: 1.036793, 0.921163 avg loss, 0.000084 rate, 0.708526 seconds, 27072 images, 0.720908 hours left\n",
"Loaded: 0.000061 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390450, GIOU: 0.320279), Class: 0.646752, Obj: 0.078730, No Obj: 0.002741, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.241347, iou_loss = 0.015595, total_loss = 1.256942 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000566, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001302, iou_loss = 0.000000, total_loss = 0.001302 \n",
" total_bbox = 33834, rewritten_bbox = 0.277827 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.228781, GIOU: 0.155193), Class: 0.535306, Obj: 0.009501, No Obj: 0.002285, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.443385, iou_loss = 0.004351, total_loss = 1.447736 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000599, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001356, iou_loss = 0.000000, total_loss = 0.001356 \n",
" total_bbox = 33838, rewritten_bbox = 0.277794 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436390, GIOU: 0.360950), Class: 0.582317, Obj: 0.047037, No Obj: 0.002357, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.135185, iou_loss = 0.213354, total_loss = 2.348540 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000614, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002810, iou_loss = 0.000000, total_loss = 0.002810 \n",
" total_bbox = 33844, rewritten_bbox = 0.277745 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352709, GIOU: 0.252176), Class: 0.615413, Obj: 0.031047, No Obj: 0.003021, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.048725, iou_loss = 0.086621, total_loss = 2.135346 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000593, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001369, iou_loss = 0.000000, total_loss = 0.001369 \n",
" total_bbox = 33850, rewritten_bbox = 0.277696 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.543079, GIOU: 0.504035), Class: 0.627453, Obj: 0.020446, No Obj: 0.002682, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.375877, iou_loss = 0.028217, total_loss = 1.404094 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.270057, GIOU: 0.258974), Class: 0.565315, Obj: 0.007693, No Obj: 0.000557, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.683772, iou_loss = 0.114037, total_loss = 0.797809 \n",
" total_bbox = 33856, rewritten_bbox = 0.277647 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.311675, GIOU: 0.241807), Class: 0.614975, Obj: 0.008927, No Obj: 0.002104, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.739927, iou_loss = 0.131160, total_loss = 1.871087 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000549, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001354, iou_loss = 0.000000, total_loss = 0.001354 \n",
" total_bbox = 33861, rewritten_bbox = 0.277606 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322441, GIOU: 0.283940), Class: 0.598339, Obj: 0.025788, No Obj: 0.002331, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.416138, iou_loss = 0.043404, total_loss = 1.459542 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000592, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001493, iou_loss = 0.000000, total_loss = 0.001493 \n",
" total_bbox = 33865, rewritten_bbox = 0.277573 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.469950, GIOU: 0.410313), Class: 0.697782, Obj: 0.045785, No Obj: 0.002326, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.016687, iou_loss = 0.049059, total_loss = 1.065745 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.218119, GIOU: -0.175733), Class: 0.573009, Obj: 0.000660, No Obj: 0.000583, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.380837, iou_loss = 0.018503, total_loss = 0.399340 \n",
" total_bbox = 33869, rewritten_bbox = 0.277540 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415117, GIOU: 0.394341), Class: 0.607766, Obj: 0.025619, No Obj: 0.002501, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.346380, iou_loss = 0.114322, total_loss = 2.460702 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.491995, GIOU: 0.356397), Class: 0.487500, Obj: 0.008162, No Obj: 0.000648, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.597806, iou_loss = 0.692060, total_loss = 2.289866 \n",
" total_bbox = 33880, rewritten_bbox = 0.277450 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.621645, GIOU: 0.610895), Class: 0.520218, Obj: 0.064062, No Obj: 0.002548, .5R: 0.750000, .75R: 0.500000, count: 4, class_loss = 1.289346, iou_loss = 0.033185, total_loss = 1.322530 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000555, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001170, iou_loss = 0.000000, total_loss = 0.001170 \n",
" total_bbox = 33884, rewritten_bbox = 0.277417 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.480820, GIOU: 0.462152), Class: 0.623431, Obj: 0.018898, No Obj: 0.002588, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.716232, iou_loss = 0.036779, total_loss = 1.753011 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000570, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001300, iou_loss = 0.000000, total_loss = 0.001300 \n",
" total_bbox = 33889, rewritten_bbox = 0.277376 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.518555, GIOU: 0.505354), Class: 0.428346, Obj: 0.038554, No Obj: 0.002791, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.535700, iou_loss = 0.060996, total_loss = 1.596696 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000570, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001437, iou_loss = 0.000000, total_loss = 0.001437 \n",
" total_bbox = 33893, rewritten_bbox = 0.277343 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392704, GIOU: 0.369137), Class: 0.408230, Obj: 0.020663, No Obj: 0.002571, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.546009, iou_loss = 0.010665, total_loss = 1.556675 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000608, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001560, iou_loss = 0.000000, total_loss = 0.001560 \n",
" total_bbox = 33897, rewritten_bbox = 0.277311 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.305122, GIOU: 0.206689), Class: 0.614869, Obj: 0.103176, No Obj: 0.002480, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.185962, iou_loss = 0.135561, total_loss = 1.321523 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.316605, GIOU: 0.226460), Class: 0.355502, Obj: 0.004976, No Obj: 0.000573, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.380914, iou_loss = 0.066846, total_loss = 0.447761 \n",
" total_bbox = 33902, rewritten_bbox = 0.277270 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398774, GIOU: 0.298875), Class: 0.688268, Obj: 0.018224, No Obj: 0.002397, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.992433, iou_loss = 0.007292, total_loss = 0.999725 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000581, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001189, iou_loss = 0.000000, total_loss = 0.001189 \n",
" total_bbox = 33905, rewritten_bbox = 0.277245 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.362242, GIOU: 0.200724), Class: 0.494828, Obj: 0.019414, No Obj: 0.002918, .5R: 0.250000, .75R: 0.000000, count: 8, class_loss = 2.972554, iou_loss = 0.098683, total_loss = 3.071237 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000572, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001261, iou_loss = 0.000000, total_loss = 0.001261 \n",
" total_bbox = 33913, rewritten_bbox = 0.277180 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 424: 0.914859, 0.920533 avg loss, 0.000084 rate, 0.713326 seconds, 27136 images, 0.720740 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308720, GIOU: 0.282803), Class: 0.567359, Obj: 0.089176, No Obj: 0.002381, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.297325, iou_loss = 0.027365, total_loss = 1.324689 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000533, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001082, iou_loss = 0.000000, total_loss = 0.001082 \n",
" total_bbox = 33917, rewritten_bbox = 0.277147 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372434, GIOU: 0.302208), Class: 0.609020, Obj: 0.069213, No Obj: 0.002693, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 1.961579, iou_loss = 0.161822, total_loss = 2.123400 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.318178, GIOU: 0.059194), Class: 0.662644, Obj: 0.004451, No Obj: 0.000661, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.318092, iou_loss = 0.119951, total_loss = 0.438043 \n",
" total_bbox = 33924, rewritten_bbox = 0.277090 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.497506, GIOU: 0.467075), Class: 0.536914, Obj: 0.020280, No Obj: 0.002795, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.169338, iou_loss = 0.029029, total_loss = 1.198367 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000619, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001621, iou_loss = 0.000000, total_loss = 0.001621 \n",
" total_bbox = 33927, rewritten_bbox = 0.277065 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.454583, GIOU: 0.424591), Class: 0.407011, Obj: 0.031457, No Obj: 0.002779, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.917600, iou_loss = 0.089213, total_loss = 2.006813 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000575, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001414, iou_loss = 0.000000, total_loss = 0.001414 \n",
" total_bbox = 33932, rewritten_bbox = 0.277025 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.271436, GIOU: -0.019159), Class: 0.642615, Obj: 0.021423, No Obj: 0.002483, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.991409, iou_loss = 0.006010, total_loss = 0.997419 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.424763, GIOU: 0.263378), Class: 0.658236, Obj: 0.002416, No Obj: 0.000588, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.333089, iou_loss = 0.257424, total_loss = 0.590514 \n",
" total_bbox = 33936, rewritten_bbox = 0.276992 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.555171, GIOU: 0.506649), Class: 0.510191, Obj: 0.053549, No Obj: 0.002622, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.360900, iou_loss = 0.026161, total_loss = 1.387061 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000586, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002555, iou_loss = 0.000000, total_loss = 0.002555 \n",
" total_bbox = 33940, rewritten_bbox = 0.276959 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.386387, GIOU: 0.287011), Class: 0.602479, Obj: 0.026072, No Obj: 0.001957, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.687130, iou_loss = 0.012802, total_loss = 0.699932 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.297426, GIOU: 0.286947), Class: 0.693768, Obj: 0.015388, No Obj: 0.000614, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.670548, iou_loss = 0.224577, total_loss = 0.895125 \n",
" total_bbox = 33944, rewritten_bbox = 0.276927 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.324064, GIOU: 0.251443), Class: 0.542498, Obj: 0.036772, No Obj: 0.002713, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.500797, iou_loss = 0.073040, total_loss = 1.573836 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000593, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001318, iou_loss = 0.000000, total_loss = 0.001318 \n",
" total_bbox = 33948, rewritten_bbox = 0.276894 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.509907, GIOU: 0.398741), Class: 0.564291, Obj: 0.020987, No Obj: 0.002636, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.121603, iou_loss = 0.023649, total_loss = 1.145252 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.275285, GIOU: 0.220956), Class: 0.581816, Obj: 0.010682, No Obj: 0.000641, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.649908, iou_loss = 0.126382, total_loss = 0.776291 \n",
" total_bbox = 33953, rewritten_bbox = 0.276853 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.442302, GIOU: 0.347790), Class: 0.507314, Obj: 0.015730, No Obj: 0.002273, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.489919, iou_loss = 0.083093, total_loss = 1.573012 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000589, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001643, iou_loss = 0.000000, total_loss = 0.001643 \n",
" total_bbox = 33957, rewritten_bbox = 0.276821 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.454978, GIOU: 0.439194), Class: 0.450614, Obj: 0.026630, No Obj: 0.002526, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.933031, iou_loss = 0.095463, total_loss = 2.028494 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000582, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001261, iou_loss = 0.000000, total_loss = 0.001261 \n",
" total_bbox = 33962, rewritten_bbox = 0.276780 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.313785, GIOU: 0.141186), Class: 0.338856, Obj: 0.029380, No Obj: 0.002455, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.336304, iou_loss = 0.007247, total_loss = 1.343551 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.071524, GIOU: -0.627241), Class: 0.217403, Obj: 0.001791, No Obj: 0.000655, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.471242, iou_loss = 0.002007, total_loss = 0.473249 \n",
" total_bbox = 33966, rewritten_bbox = 0.276747 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.434475, GIOU: 0.375181), Class: 0.582258, Obj: 0.021948, No Obj: 0.002626, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 1.986958, iou_loss = 0.183202, total_loss = 2.170160 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.524062, GIOU: 0.465496), Class: 0.329818, Obj: 0.001827, No Obj: 0.000621, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.512197, iou_loss = 0.365070, total_loss = 0.877266 \n",
" total_bbox = 33973, rewritten_bbox = 0.276690 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427714, GIOU: 0.425348), Class: 0.463420, Obj: 0.040533, No Obj: 0.002882, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.451904, iou_loss = 0.032735, total_loss = 1.484639 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000576, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001383, iou_loss = 0.000000, total_loss = 0.001383 \n",
" total_bbox = 33977, rewritten_bbox = 0.276658 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477077, GIOU: 0.412145), Class: 0.597264, Obj: 0.047398, No Obj: 0.002269, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.579668, iou_loss = 0.103629, total_loss = 1.683296 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000572, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001164, iou_loss = 0.000000, total_loss = 0.001164 \n",
" total_bbox = 33982, rewritten_bbox = 0.276617 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.314824, GIOU: 0.193849), Class: 0.621379, Obj: 0.009322, No Obj: 0.002847, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.480692, iou_loss = 0.030306, total_loss = 1.510998 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.161746, GIOU: -0.043222), Class: 0.483656, Obj: 0.001965, No Obj: 0.000639, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.542509, iou_loss = 2.521203, total_loss = 4.063712 \n",
" total_bbox = 33990, rewritten_bbox = 0.276552 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 425: 0.875902, 0.916070 avg loss, 0.000085 rate, 0.688588 seconds, 27200 images, 0.720619 hours left\n",
"Loaded: 0.000037 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392201, GIOU: 0.356895), Class: 0.524404, Obj: 0.073009, No Obj: 0.002903, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.469262, iou_loss = 0.037421, total_loss = 1.506683 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000610, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.008376, iou_loss = 0.000000, total_loss = 0.008376 \n",
" total_bbox = 33994, rewritten_bbox = 0.276519 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.461021, GIOU: 0.443890), Class: 0.535533, Obj: 0.043182, No Obj: 0.002791, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.835362, iou_loss = 0.161775, total_loss = 1.997138 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000581, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002439, iou_loss = 0.000000, total_loss = 0.002439 \n",
" total_bbox = 33999, rewritten_bbox = 0.276479 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.207393, GIOU: 0.075739), Class: 0.426399, Obj: 0.051049, No Obj: 0.003505, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.667252, iou_loss = 0.012556, total_loss = 1.679808 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.384037, GIOU: 0.246507), Class: 0.526446, Obj: 0.009551, No Obj: 0.000590, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.340666, iou_loss = 0.220396, total_loss = 0.561062 \n",
" total_bbox = 34004, rewritten_bbox = 0.276438 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.202685, GIOU: 0.067183), Class: 0.596909, Obj: 0.017492, No Obj: 0.002050, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.562018, iou_loss = 0.016446, total_loss = 1.578465 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000578, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001188, iou_loss = 0.000000, total_loss = 0.001188 \n",
" total_bbox = 34008, rewritten_bbox = 0.276406 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.430664, GIOU: 0.425334), Class: 0.453802, Obj: 0.112469, No Obj: 0.002467, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.058325, iou_loss = 0.096380, total_loss = 1.154704 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.277502, GIOU: 0.213883), Class: 0.558272, Obj: 0.007695, No Obj: 0.000598, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.679262, iou_loss = 0.740748, total_loss = 1.420010 \n",
" total_bbox = 34013, rewritten_bbox = 0.276365 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.425543, GIOU: 0.365024), Class: 0.482180, Obj: 0.064598, No Obj: 0.003523, .5R: 0.428571, .75R: 0.142857, count: 7, class_loss = 2.487886, iou_loss = 0.131746, total_loss = 2.619632 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000573, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001768, iou_loss = 0.000000, total_loss = 0.001768 \n",
" total_bbox = 34020, rewritten_bbox = 0.276308 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427740, GIOU: 0.414319), Class: 0.447725, Obj: 0.041008, No Obj: 0.002885, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.596977, iou_loss = 0.056868, total_loss = 1.653845 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.553590, GIOU: 0.545782), Class: 0.630936, Obj: 0.000733, No Obj: 0.000606, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.335374, iou_loss = 1.000292, total_loss = 1.335666 \n",
" total_bbox = 34025, rewritten_bbox = 0.276267 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307250, GIOU: 0.249539), Class: 0.518718, Obj: 0.028418, No Obj: 0.002259, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.129545, iou_loss = 0.007799, total_loss = 1.137343 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.255872, GIOU: 0.164137), Class: 0.374985, Obj: 0.001456, No Obj: 0.000572, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.281410, iou_loss = 2.461295, total_loss = 3.742705 \n",
" total_bbox = 34032, rewritten_bbox = 0.279149 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.390083, GIOU: 0.229594), Class: 0.410931, Obj: 0.036308, No Obj: 0.002482, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.783410, iou_loss = 0.019873, total_loss = 0.803283 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.597390, GIOU: 0.591842), Class: 0.482609, Obj: 0.003448, No Obj: 0.000657, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.422386, iou_loss = 0.246225, total_loss = 0.668611 \n",
" total_bbox = 34035, rewritten_bbox = 0.279124 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.441128, GIOU: 0.419937), Class: 0.523783, Obj: 0.066035, No Obj: 0.003267, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.890080, iou_loss = 0.034384, total_loss = 1.924464 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.288897, GIOU: 0.093108), Class: 0.611097, Obj: 0.000585, No Obj: 0.000551, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.309809, iou_loss = 0.045240, total_loss = 0.355050 \n",
" total_bbox = 34041, rewritten_bbox = 0.279075 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.248988, GIOU: 0.195564), Class: 0.535073, Obj: 0.069732, No Obj: 0.002351, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.653247, iou_loss = 0.010636, total_loss = 0.663883 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.364555, GIOU: 0.348718), Class: 0.527126, Obj: 0.012150, No Obj: 0.000555, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.771111, iou_loss = 0.422783, total_loss = 1.193894 \n",
" total_bbox = 34045, rewritten_bbox = 0.279042 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.327211, GIOU: 0.299218), Class: 0.466798, Obj: 0.024603, No Obj: 0.002575, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.883320, iou_loss = 0.026603, total_loss = 1.909923 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000586, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001332, iou_loss = 0.000000, total_loss = 0.001332 \n",
" total_bbox = 34050, rewritten_bbox = 0.279001 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402827, GIOU: 0.343081), Class: 0.482601, Obj: 0.034786, No Obj: 0.003004, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.892732, iou_loss = 0.050917, total_loss = 1.943648 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000582, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001560, iou_loss = 0.000000, total_loss = 0.001560 \n",
" total_bbox = 34055, rewritten_bbox = 0.278960 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352992, GIOU: 0.297224), Class: 0.553290, Obj: 0.113647, No Obj: 0.002361, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.223522, iou_loss = 0.045247, total_loss = 1.268769 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000609, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004728, iou_loss = 0.000000, total_loss = 0.004728 \n",
" total_bbox = 34059, rewritten_bbox = 0.278928 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477742, GIOU: 0.477742), Class: 0.283729, Obj: 0.016489, No Obj: 0.002412, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.464741, iou_loss = 0.003531, total_loss = 0.468272 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.218232, GIOU: -0.074546), Class: 0.477427, Obj: 0.001075, No Obj: 0.000605, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.194476, iou_loss = 0.190099, total_loss = 1.384575 \n",
" total_bbox = 34063, rewritten_bbox = 0.278895 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.378020, GIOU: 0.355393), Class: 0.535925, Obj: 0.029897, No Obj: 0.002568, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.404416, iou_loss = 0.056885, total_loss = 1.461301 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000567, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001866, iou_loss = 0.000000, total_loss = 0.001866 \n",
" total_bbox = 34067, rewritten_bbox = 0.278862 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 426: 0.894550, 0.913918 avg loss, 0.000086 rate, 0.731823 seconds, 27264 images, 0.720251 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.393707, GIOU: 0.196391), Class: 0.601279, Obj: 0.030478, No Obj: 0.002646, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.397872, iou_loss = 0.015178, total_loss = 1.413050 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.349595, GIOU: 0.149060), Class: 0.278666, Obj: 0.004354, No Obj: 0.000573, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.477126, iou_loss = 0.042318, total_loss = 0.519444 \n",
" total_bbox = 34072, rewritten_bbox = 0.278821 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.283102, GIOU: 0.214466), Class: 0.550983, Obj: 0.021405, No Obj: 0.002135, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.414251, iou_loss = 0.041798, total_loss = 1.456049 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.543042, GIOU: 0.533184), Class: 0.443858, Obj: 0.019305, No Obj: 0.000621, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.791715, iou_loss = 2.135986, total_loss = 2.927701 \n",
" total_bbox = 34078, rewritten_bbox = 0.278772 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422901, GIOU: 0.338813), Class: 0.501992, Obj: 0.036967, No Obj: 0.003051, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.373684, iou_loss = 0.047596, total_loss = 1.421280 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.574063, GIOU: 0.507293), Class: 0.471391, Obj: 0.020809, No Obj: 0.000588, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.383839, iou_loss = 0.191494, total_loss = 0.575333 \n",
" total_bbox = 34083, rewritten_bbox = 0.278731 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.277695, GIOU: 0.097304), Class: 0.521690, Obj: 0.011254, No Obj: 0.002401, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.841493, iou_loss = 0.008196, total_loss = 1.849689 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.408347, GIOU: 0.347784), Class: 0.473238, Obj: 0.010783, No Obj: 0.000591, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.145185, iou_loss = 0.587002, total_loss = 1.732186 \n",
" total_bbox = 34091, rewritten_bbox = 0.278666 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319550, GIOU: 0.098757), Class: 0.731243, Obj: 0.048738, No Obj: 0.002836, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.644415, iou_loss = 0.008144, total_loss = 0.652559 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.309474, GIOU: 0.251788), Class: 0.501639, Obj: 0.002306, No Obj: 0.000566, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.175412, iou_loss = 0.347000, total_loss = 1.522412 \n",
" total_bbox = 34096, rewritten_bbox = 0.278625 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.516133, GIOU: 0.448891), Class: 0.434356, Obj: 0.031133, No Obj: 0.002586, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.607640, iou_loss = 0.050418, total_loss = 1.658058 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.085815, GIOU: -0.527241), Class: 0.654491, Obj: 0.000524, No Obj: 0.000556, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.363069, iou_loss = 0.006915, total_loss = 0.369984 \n",
" total_bbox = 34101, rewritten_bbox = 0.278584 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.419500, GIOU: 0.283248), Class: 0.538130, Obj: 0.020902, No Obj: 0.002153, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.709121, iou_loss = 0.204845, total_loss = 1.913966 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000609, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001646, iou_loss = 0.000000, total_loss = 0.001646 \n",
" total_bbox = 34106, rewritten_bbox = 0.278543 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.341177, GIOU: 0.303164), Class: 0.454071, Obj: 0.023649, No Obj: 0.002714, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.629701, iou_loss = 0.119155, total_loss = 1.748856 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.256557, GIOU: 0.010610), Class: 0.419407, Obj: 0.000708, No Obj: 0.000615, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.432944, iou_loss = 0.090198, total_loss = 0.523142 \n",
" total_bbox = 34111, rewritten_bbox = 0.278503 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.349766, GIOU: 0.321142), Class: 0.633529, Obj: 0.026905, No Obj: 0.002713, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.310549, iou_loss = 0.074571, total_loss = 1.385119 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.131651, GIOU: -0.457128), Class: 0.393077, Obj: 0.000689, No Obj: 0.000552, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.371571, iou_loss = 0.004849, total_loss = 0.376420 \n",
" total_bbox = 34116, rewritten_bbox = 0.278462 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319603, GIOU: 0.159915), Class: 0.464746, Obj: 0.027103, No Obj: 0.002319, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.224234, iou_loss = 0.016034, total_loss = 1.240268 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.353552, GIOU: 0.353552), Class: 0.567497, Obj: 0.000944, No Obj: 0.000553, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.710306, iou_loss = 0.573297, total_loss = 1.283603 \n",
" total_bbox = 34121, rewritten_bbox = 0.278421 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.308302, GIOU: 0.137081), Class: 0.539379, Obj: 0.029974, No Obj: 0.002051, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.466685, iou_loss = 0.076637, total_loss = 1.543321 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.467355, GIOU: 0.408620), Class: 0.486702, Obj: 0.014520, No Obj: 0.000638, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.481870, iou_loss = 1.135525, total_loss = 2.617396 \n",
" total_bbox = 34129, rewritten_bbox = 0.278356 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.330891, GIOU: 0.152298), Class: 0.493220, Obj: 0.028747, No Obj: 0.002104, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.163554, iou_loss = 0.077931, total_loss = 2.241486 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000586, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001506, iou_loss = 0.000000, total_loss = 0.001506 \n",
" total_bbox = 34135, rewritten_bbox = 0.278307 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.313804, GIOU: 0.313804), Class: 0.693745, Obj: 0.068074, No Obj: 0.002777, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.190110, iou_loss = 0.008846, total_loss = 1.198956 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000621, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003680, iou_loss = 0.000000, total_loss = 0.003680 \n",
" total_bbox = 34139, rewritten_bbox = 0.278274 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422168, GIOU: 0.338282), Class: 0.568402, Obj: 0.018742, No Obj: 0.002804, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.677587, iou_loss = 0.068080, total_loss = 1.745667 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000699, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001867, iou_loss = 0.000000, total_loss = 0.001867 \n",
" total_bbox = 34144, rewritten_bbox = 0.278233 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.208718, GIOU: 0.054193), Class: 0.534277, Obj: 0.012158, No Obj: 0.002434, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.896798, iou_loss = 0.019318, total_loss = 1.916116 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000618, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002313, iou_loss = 0.000000, total_loss = 0.002313 \n",
" total_bbox = 34149, rewritten_bbox = 0.278193 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.558017, GIOU: 0.533108), Class: 0.479417, Obj: 0.051859, No Obj: 0.002559, .5R: 0.800000, .75R: 0.000000, count: 5, class_loss = 1.714470, iou_loss = 0.159707, total_loss = 1.874177 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000595, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001885, iou_loss = 0.000000, total_loss = 0.001885 \n",
" total_bbox = 34154, rewritten_bbox = 0.278152 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 427: 0.996905, 0.922216 avg loss, 0.000087 rate, 0.710928 seconds, 27328 images, 0.720315 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.474641, GIOU: 0.426445), Class: 0.627135, Obj: 0.098010, No Obj: 0.002969, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.230213, iou_loss = 0.084448, total_loss = 1.314661 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.370389, GIOU: 0.348476), Class: 0.427558, Obj: 0.006048, No Obj: 0.000599, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.818616, iou_loss = 0.999405, total_loss = 1.818020 \n",
" total_bbox = 34160, rewritten_bbox = 0.278103 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.484992, GIOU: 0.457537), Class: 0.583598, Obj: 0.140246, No Obj: 0.002828, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.248266, iou_loss = 0.128796, total_loss = 1.377061 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000576, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001638, iou_loss = 0.000000, total_loss = 0.001638 \n",
" total_bbox = 34164, rewritten_bbox = 0.278070 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.272762, GIOU: 0.241818), Class: 0.611509, Obj: 0.151968, No Obj: 0.003113, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.880446, iou_loss = 0.023867, total_loss = 0.904314 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.587403, GIOU: 0.533593), Class: 0.637986, Obj: 0.000694, No Obj: 0.000556, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.406405, iou_loss = 0.632880, total_loss = 1.039285 \n",
" total_bbox = 34168, rewritten_bbox = 0.278038 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.366629, GIOU: 0.222470), Class: 0.504151, Obj: 0.070611, No Obj: 0.002711, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.628601, iou_loss = 0.067964, total_loss = 1.696565 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.238540, GIOU: 0.104389), Class: 0.580638, Obj: 0.003052, No Obj: 0.000565, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.308660, iou_loss = 0.173896, total_loss = 0.482556 \n",
" total_bbox = 34174, rewritten_bbox = 0.277989 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.175627, GIOU: 0.175627), Class: 0.487485, Obj: 0.025151, No Obj: 0.002200, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.006314, iou_loss = 0.002474, total_loss = 1.008788 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000550, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001185, iou_loss = 0.000000, total_loss = 0.001185 \n",
" total_bbox = 34177, rewritten_bbox = 0.277965 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.463447, GIOU: 0.422605), Class: 0.422402, Obj: 0.034848, No Obj: 0.002526, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.908806, iou_loss = 0.036530, total_loss = 1.945337 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000536, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001112, iou_loss = 0.000000, total_loss = 0.001112 \n",
" total_bbox = 34182, rewritten_bbox = 0.277924 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398247, GIOU: 0.384499), Class: 0.465287, Obj: 0.006732, No Obj: 0.001973, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.818830, iou_loss = 0.003956, total_loss = 0.822786 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.272702, GIOU: 0.127666), Class: 0.678264, Obj: 0.011718, No Obj: 0.000530, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.750154, iou_loss = 0.284735, total_loss = 1.034889 \n",
" total_bbox = 34186, rewritten_bbox = 0.277892 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.365079, GIOU: 0.229867), Class: 0.557530, Obj: 0.053725, No Obj: 0.002729, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.737773, iou_loss = 0.031794, total_loss = 1.769567 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000566, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001347, iou_loss = 0.000000, total_loss = 0.001347 \n",
" total_bbox = 34191, rewritten_bbox = 0.277851 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322103, GIOU: 0.293431), Class: 0.472500, Obj: 0.025402, No Obj: 0.003118, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.193967, iou_loss = 0.154281, total_loss = 2.348248 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.348441, GIOU: 0.348440), Class: 0.405298, Obj: 0.007955, No Obj: 0.000622, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.360563, iou_loss = 0.106085, total_loss = 0.466647 \n",
" total_bbox = 34198, rewritten_bbox = 0.277794 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.492885, GIOU: 0.364638), Class: 0.540221, Obj: 0.064430, No Obj: 0.003082, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.614474, iou_loss = 0.083004, total_loss = 1.697478 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.103607, GIOU: -0.518868), Class: 0.617430, Obj: 0.001057, No Obj: 0.000604, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.705603, iou_loss = 0.408600, total_loss = 1.114203 \n",
" total_bbox = 34205, rewritten_bbox = 0.277737 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421076, GIOU: 0.367111), Class: 0.421388, Obj: 0.068928, No Obj: 0.002894, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.530692, iou_loss = 0.035146, total_loss = 1.565838 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000604, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001290, iou_loss = 0.000000, total_loss = 0.001290 \n",
" total_bbox = 34209, rewritten_bbox = 0.277705 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392310, GIOU: 0.392310), Class: 0.655599, Obj: 0.026627, No Obj: 0.002213, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.979032, iou_loss = 0.013120, total_loss = 0.992152 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.653152, GIOU: 0.622473), Class: 0.420946, Obj: 0.007425, No Obj: 0.000536, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.379665, iou_loss = 0.342871, total_loss = 0.722536 \n",
" total_bbox = 34213, rewritten_bbox = 0.277672 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292372, GIOU: 0.129232), Class: 0.539470, Obj: 0.036201, No Obj: 0.002514, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.372587, iou_loss = 0.008113, total_loss = 1.380700 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000553, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001235, iou_loss = 0.000000, total_loss = 0.001235 \n",
" total_bbox = 34217, rewritten_bbox = 0.277640 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.497554, GIOU: 0.470783), Class: 0.362141, Obj: 0.033948, No Obj: 0.002618, .5R: 0.800000, .75R: 0.000000, count: 5, class_loss = 2.053787, iou_loss = 0.097149, total_loss = 2.150936 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.456689, GIOU: 0.367932), Class: 0.426671, Obj: 0.003058, No Obj: 0.000557, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.772638, iou_loss = 3.417850, total_loss = 4.190488 \n",
" total_bbox = 34224, rewritten_bbox = 0.277583 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.573256, GIOU: 0.556868), Class: 0.532260, Obj: 0.033216, No Obj: 0.002803, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.706469, iou_loss = 0.008437, total_loss = 0.714907 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.215620, GIOU: -0.196922), Class: 0.643981, Obj: 0.000281, No Obj: 0.000649, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.356095, iou_loss = 0.050385, total_loss = 0.406480 \n",
" total_bbox = 34227, rewritten_bbox = 0.277559 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.387253, GIOU: 0.285602), Class: 0.555491, Obj: 0.101382, No Obj: 0.002889, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.246284, iou_loss = 0.036036, total_loss = 1.282320 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.293940, GIOU: -0.003299), Class: 0.338995, Obj: 0.002407, No Obj: 0.000550, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.404388, iou_loss = 0.102015, total_loss = 0.506403 \n",
" total_bbox = 34232, rewritten_bbox = 0.277518 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 428: 0.865891, 0.916584 avg loss, 0.000088 rate, 0.703664 seconds, 27392 images, 0.720168 hours left\n",
"Loaded: 0.000043 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.389296, GIOU: 0.358072), Class: 0.509864, Obj: 0.040526, No Obj: 0.002575, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.105804, iou_loss = 0.053673, total_loss = 1.159476 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.293311, GIOU: 0.198855), Class: 0.711122, Obj: 0.006110, No Obj: 0.000599, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.664913, iou_loss = 0.105982, total_loss = 0.770895 \n",
" total_bbox = 34237, rewritten_bbox = 0.277478 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.431372, GIOU: 0.327999), Class: 0.526968, Obj: 0.045485, No Obj: 0.002561, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.799304, iou_loss = 0.099619, total_loss = 1.898922 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000580, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002598, iou_loss = 0.000000, total_loss = 0.002598 \n",
" total_bbox = 34242, rewritten_bbox = 0.277437 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.365239, GIOU: 0.320524), Class: 0.466420, Obj: 0.023920, No Obj: 0.002630, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.929192, iou_loss = 0.015415, total_loss = 1.944607 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000534, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001042, iou_loss = 0.000000, total_loss = 0.001042 \n",
" total_bbox = 34247, rewritten_bbox = 0.277397 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.226089, GIOU: 0.216062), Class: 0.491401, Obj: 0.030764, No Obj: 0.002909, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.850494, iou_loss = 0.036953, total_loss = 1.887448 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000597, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001280, iou_loss = 0.000000, total_loss = 0.001280 \n",
" total_bbox = 34252, rewritten_bbox = 0.277356 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.571885, GIOU: 0.562524), Class: 0.505392, Obj: 0.098841, No Obj: 0.003272, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.346854, iou_loss = 0.047411, total_loss = 1.394265 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.283412, GIOU: 0.283412), Class: 0.687942, Obj: 0.002970, No Obj: 0.000535, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354475, iou_loss = 0.070079, total_loss = 0.424554 \n",
" total_bbox = 34257, rewritten_bbox = 0.277316 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.331606, GIOU: 0.331606), Class: 0.580145, Obj: 0.040100, No Obj: 0.003005, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.074762, iou_loss = 0.008736, total_loss = 1.083497 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.514717, GIOU: 0.514717), Class: 0.372836, Obj: 0.042971, No Obj: 0.000602, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.794935, iou_loss = 1.251460, total_loss = 2.046395 \n",
" total_bbox = 34262, rewritten_bbox = 0.277275 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398139, GIOU: 0.364918), Class: 0.605309, Obj: 0.037662, No Obj: 0.002051, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.400155, iou_loss = 0.067294, total_loss = 1.467449 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000554, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001204, iou_loss = 0.000000, total_loss = 0.001204 \n",
" total_bbox = 34266, rewritten_bbox = 0.277243 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.336346, GIOU: 0.201981), Class: 0.673876, Obj: 0.012678, No Obj: 0.002371, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.935606, iou_loss = 0.006784, total_loss = 0.942390 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000540, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001103, iou_loss = 0.000000, total_loss = 0.001103 \n",
" total_bbox = 34269, rewritten_bbox = 0.277218 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.314216, GIOU: 0.170285), Class: 0.420494, Obj: 0.045209, No Obj: 0.002469, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.733021, iou_loss = 0.017193, total_loss = 0.750214 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.358782, GIOU: 0.355037), Class: 0.427819, Obj: 0.011988, No Obj: 0.000559, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.804070, iou_loss = 0.242821, total_loss = 1.046890 \n",
" total_bbox = 34273, rewritten_bbox = 0.277186 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.282104, GIOU: 0.226743), Class: 0.641141, Obj: 0.033022, No Obj: 0.003374, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.483194, iou_loss = 0.051111, total_loss = 1.534305 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000603, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002870, iou_loss = 0.000000, total_loss = 0.002870 \n",
" total_bbox = 34277, rewritten_bbox = 0.277154 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.389559, GIOU: 0.310774), Class: 0.464810, Obj: 0.016701, No Obj: 0.002077, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.626969, iou_loss = 0.173589, total_loss = 1.800559 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.143636, GIOU: -0.137606), Class: 0.565539, Obj: 0.000353, No Obj: 0.000562, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.759530, iou_loss = 1.016544, total_loss = 1.776074 \n",
" total_bbox = 34283, rewritten_bbox = 0.277105 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.376091, GIOU: 0.329256), Class: 0.587016, Obj: 0.072927, No Obj: 0.002820, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.516906, iou_loss = 0.069687, total_loss = 1.586593 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000544, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001294, iou_loss = 0.000000, total_loss = 0.001294 \n",
" total_bbox = 34288, rewritten_bbox = 0.277065 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.496195, GIOU: 0.402529), Class: 0.507900, Obj: 0.056672, No Obj: 0.002707, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 1.689834, iou_loss = 0.102166, total_loss = 1.792001 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.698838, GIOU: 0.678660), Class: 0.435639, Obj: 0.003345, No Obj: 0.000550, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.385098, iou_loss = 1.390806, total_loss = 1.775904 \n",
" total_bbox = 34294, rewritten_bbox = 0.277016 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.340088, GIOU: 0.254118), Class: 0.532015, Obj: 0.023791, No Obj: 0.002186, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.064608, iou_loss = 0.041669, total_loss = 1.106277 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.217622, GIOU: 0.217622), Class: 0.553885, Obj: 0.002796, No Obj: 0.000594, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.339939, iou_loss = 0.041185, total_loss = 0.381123 \n",
" total_bbox = 34298, rewritten_bbox = 0.276984 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.254933, GIOU: 0.159443), Class: 0.513375, Obj: 0.015623, No Obj: 0.002351, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.854170, iou_loss = 0.021269, total_loss = 1.875439 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.256112, GIOU: 0.173286), Class: 0.772478, Obj: 0.004161, No Obj: 0.000600, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.322172, iou_loss = 0.095031, total_loss = 0.417203 \n",
" total_bbox = 34304, rewritten_bbox = 0.276936 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429277, GIOU: 0.415726), Class: 0.525263, Obj: 0.030360, No Obj: 0.002471, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.431621, iou_loss = 0.076267, total_loss = 1.507888 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000577, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002481, iou_loss = 0.000000, total_loss = 0.002481 \n",
" total_bbox = 34308, rewritten_bbox = 0.276903 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 429: 0.860915, 0.911017 avg loss, 0.000088 rate, 0.727848 seconds, 27456 images, 0.719949 hours left\n",
"Loaded: 0.000042 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.468941, GIOU: 0.389488), Class: 0.478710, Obj: 0.025897, No Obj: 0.002468, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.936448, iou_loss = 0.102136, total_loss = 2.038584 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.406311, GIOU: 0.363248), Class: 0.575111, Obj: 0.009332, No Obj: 0.000603, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.020493, iou_loss = 0.521006, total_loss = 1.541499 \n",
" total_bbox = 34316, rewritten_bbox = 0.276839 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.474192, GIOU: 0.474192), Class: 0.476685, Obj: 0.061088, No Obj: 0.002164, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.727339, iou_loss = 0.007050, total_loss = 0.734389 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.369416, GIOU: 0.307969), Class: 0.319347, Obj: 0.000964, No Obj: 0.000552, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.153432, iou_loss = 7.783303, total_loss = 8.936735 \n",
" total_bbox = 34321, rewritten_bbox = 0.276798 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.366997, GIOU: 0.317222), Class: 0.586776, Obj: 0.139923, No Obj: 0.003819, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.953338, iou_loss = 0.035380, total_loss = 0.988718 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.392863, GIOU: 0.218530), Class: 0.606458, Obj: 0.000983, No Obj: 0.000551, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.408326, iou_loss = 0.073240, total_loss = 0.481566 \n",
" total_bbox = 34325, rewritten_bbox = 0.276766 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.448993, GIOU: 0.313160), Class: 0.546897, Obj: 0.040386, No Obj: 0.002189, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.178073, iou_loss = 0.037961, total_loss = 1.216035 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.446221, GIOU: 0.365504), Class: 0.545318, Obj: 0.013879, No Obj: 0.000604, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.357547, iou_loss = 0.332059, total_loss = 0.689606 \n",
" total_bbox = 34329, rewritten_bbox = 0.276734 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.287426, GIOU: 0.214838), Class: 0.552809, Obj: 0.024555, No Obj: 0.002291, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.340701, iou_loss = 0.007342, total_loss = 1.348043 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000537, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001101, iou_loss = 0.000000, total_loss = 0.001101 \n",
" total_bbox = 34333, rewritten_bbox = 0.276702 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370245, GIOU: 0.262174), Class: 0.598091, Obj: 0.131931, No Obj: 0.002633, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.261795, iou_loss = 0.037045, total_loss = 1.298840 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.215158, GIOU: -0.196059), Class: 0.536000, Obj: 0.024825, No Obj: 0.000616, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.335302, iou_loss = 0.013857, total_loss = 0.349158 \n",
" total_bbox = 34338, rewritten_bbox = 0.276661 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.452113, GIOU: 0.427477), Class: 0.580368, Obj: 0.053866, No Obj: 0.002634, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.282181, iou_loss = 0.036638, total_loss = 1.318820 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.185385, GIOU: 0.185385), Class: 0.513808, Obj: 0.001716, No Obj: 0.000560, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.357098, iou_loss = 0.029952, total_loss = 0.387049 \n",
" total_bbox = 34343, rewritten_bbox = 0.276621 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.236068, GIOU: 0.087350), Class: 0.588084, Obj: 0.055563, No Obj: 0.002499, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.345716, iou_loss = 0.033162, total_loss = 1.378878 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.440689, GIOU: 0.323665), Class: 0.561395, Obj: 0.012000, No Obj: 0.000586, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.319539, iou_loss = 0.076470, total_loss = 0.396009 \n",
" total_bbox = 34348, rewritten_bbox = 0.276581 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.389771, GIOU: 0.352158), Class: 0.392140, Obj: 0.040964, No Obj: 0.002519, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.957969, iou_loss = 0.042959, total_loss = 2.000928 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000518, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001007, iou_loss = 0.000000, total_loss = 0.001007 \n",
" total_bbox = 34353, rewritten_bbox = 0.276541 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.263033, GIOU: 0.156173), Class: 0.438930, Obj: 0.018510, No Obj: 0.002687, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.342340, iou_loss = 0.024706, total_loss = 2.367046 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000544, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001297, iou_loss = 0.000000, total_loss = 0.001297 \n",
" total_bbox = 34359, rewritten_bbox = 0.276492 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.545557, GIOU: 0.535651), Class: 0.533870, Obj: 0.018903, No Obj: 0.002721, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.385329, iou_loss = 0.019420, total_loss = 1.404749 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.536478, GIOU: 0.536478), Class: 0.602889, Obj: 0.001442, No Obj: 0.000559, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.652421, iou_loss = 0.671822, total_loss = 1.324243 \n",
" total_bbox = 34365, rewritten_bbox = 0.276444 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.336195, GIOU: 0.185960), Class: 0.535481, Obj: 0.036047, No Obj: 0.002588, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.408380, iou_loss = 0.043699, total_loss = 1.452079 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000591, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004657, iou_loss = 0.000000, total_loss = 0.004657 \n",
" total_bbox = 34370, rewritten_bbox = 0.279313 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.519413, GIOU: 0.448045), Class: 0.585378, Obj: 0.039290, No Obj: 0.002485, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.323665, iou_loss = 0.039852, total_loss = 1.363517 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.296447, GIOU: 0.269717), Class: 0.324502, Obj: 0.001904, No Obj: 0.000558, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.415082, iou_loss = 0.170369, total_loss = 0.585452 \n",
" total_bbox = 34375, rewritten_bbox = 0.279273 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438965, GIOU: 0.377675), Class: 0.445320, Obj: 0.045922, No Obj: 0.002469, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.541848, iou_loss = 0.038942, total_loss = 1.580790 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000544, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001213, iou_loss = 0.000000, total_loss = 0.001213 \n",
" total_bbox = 34379, rewritten_bbox = 0.279240 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.455566, GIOU: 0.391129), Class: 0.537071, Obj: 0.054188, No Obj: 0.002935, .5R: 0.428571, .75R: 0.142857, count: 7, class_loss = 2.350549, iou_loss = 0.198717, total_loss = 2.549266 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.429458, GIOU: 0.371427), Class: 0.677124, Obj: 0.021696, No Obj: 0.000604, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.354979, iou_loss = 0.275388, total_loss = 0.630367 \n",
" total_bbox = 34387, rewritten_bbox = 0.279175 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.378386, GIOU: 0.295523), Class: 0.654872, Obj: 0.101977, No Obj: 0.003034, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.281007, iou_loss = 0.028460, total_loss = 1.309466 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.130385, GIOU: 0.130385), Class: 0.409634, Obj: 0.002489, No Obj: 0.000565, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.416873, iou_loss = 0.015383, total_loss = 0.432257 \n",
" total_bbox = 34392, rewritten_bbox = 0.279135 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 430: 0.928426, 0.912758 avg loss, 0.000089 rate, 0.715509 seconds, 27520 images, 0.719969 hours left\n",
"Loaded: 0.000039 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359234, GIOU: 0.336862), Class: 0.506835, Obj: 0.111944, No Obj: 0.002729, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.336818, iou_loss = 0.057017, total_loss = 1.393835 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000541, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001684, iou_loss = 0.000000, total_loss = 0.001684 \n",
" total_bbox = 34396, rewritten_bbox = 0.279102 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.376836, GIOU: 0.232633), Class: 0.503364, Obj: 0.030701, No Obj: 0.002518, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.814458, iou_loss = 0.045216, total_loss = 1.859674 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000598, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001176, iou_loss = 0.000000, total_loss = 0.001176 \n",
" total_bbox = 34401, rewritten_bbox = 0.279062 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292318, GIOU: 0.223734), Class: 0.439488, Obj: 0.015766, No Obj: 0.002198, .5R: 0.166667, .75R: 0.166667, count: 6, class_loss = 1.906401, iou_loss = 0.013055, total_loss = 1.919456 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.398488, GIOU: 0.235188), Class: 0.348579, Obj: 0.008445, No Obj: 0.000543, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.404518, iou_loss = 0.078880, total_loss = 0.483398 \n",
" total_bbox = 34408, rewritten_bbox = 0.281911 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.145273, GIOU: -0.064542), Class: 0.638530, Obj: 0.015614, No Obj: 0.002198, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.938481, iou_loss = 0.016003, total_loss = 0.954484 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.238840, GIOU: 0.238840), Class: 0.337729, Obj: 0.008065, No Obj: 0.000680, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.508904, iou_loss = 0.050038, total_loss = 0.558942 \n",
" total_bbox = 34412, rewritten_bbox = 0.281878 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.245656, GIOU: 0.073227), Class: 0.615956, Obj: 0.075601, No Obj: 0.002797, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.084113, iou_loss = 0.016210, total_loss = 1.100323 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000570, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001665, iou_loss = 0.000000, total_loss = 0.001665 \n",
" total_bbox = 34415, rewritten_bbox = 0.281854 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.469273, GIOU: 0.467453), Class: 0.397724, Obj: 0.013051, No Obj: 0.002252, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.820480, iou_loss = 0.012879, total_loss = 0.833358 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.294416, GIOU: 0.179597), Class: 0.498357, Obj: 0.009530, No Obj: 0.000552, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 1.860100, iou_loss = 1.763449, total_loss = 3.623549 \n",
" total_bbox = 34423, rewritten_bbox = 0.284693 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.613221, GIOU: 0.599750), Class: 0.461070, Obj: 0.026918, No Obj: 0.002416, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.219874, iou_loss = 0.033458, total_loss = 1.253332 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.256127, GIOU: 0.256127), Class: 0.514693, Obj: 0.002683, No Obj: 0.000530, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.407959, iou_loss = 0.057397, total_loss = 0.465356 \n",
" total_bbox = 34428, rewritten_bbox = 0.287557 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.348053, GIOU: 0.209808), Class: 0.527738, Obj: 0.040549, No Obj: 0.002552, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.493500, iou_loss = 0.030890, total_loss = 1.524391 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.588305, GIOU: 0.540650), Class: 0.583486, Obj: 0.001687, No Obj: 0.000528, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.357169, iou_loss = 0.265918, total_loss = 0.623087 \n",
" total_bbox = 34433, rewritten_bbox = 0.287515 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.361660, GIOU: 0.309415), Class: 0.590798, Obj: 0.110576, No Obj: 0.002736, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.236012, iou_loss = 0.015109, total_loss = 1.251122 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.525168, GIOU: 0.525168), Class: 0.507371, Obj: 0.002368, No Obj: 0.000531, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.353377, iou_loss = 0.240496, total_loss = 0.593873 \n",
" total_bbox = 34438, rewritten_bbox = 0.287473 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438132, GIOU: 0.396408), Class: 0.560081, Obj: 0.053237, No Obj: 0.002757, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.075236, iou_loss = 0.008213, total_loss = 1.083449 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.493514, GIOU: 0.456652), Class: 0.560966, Obj: 0.004851, No Obj: 0.000555, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.052463, iou_loss = 2.099449, total_loss = 3.151912 \n",
" total_bbox = 34444, rewritten_bbox = 0.287423 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.466187, GIOU: 0.439178), Class: 0.489699, Obj: 0.077820, No Obj: 0.002563, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.433444, iou_loss = 0.054120, total_loss = 1.487564 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.277513, GIOU: 0.237862), Class: 0.405239, Obj: 0.002401, No Obj: 0.000585, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.878173, iou_loss = 0.180936, total_loss = 1.059109 \n",
" total_bbox = 34450, rewritten_bbox = 0.287373 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.374141, GIOU: 0.280094), Class: 0.326709, Obj: 0.029492, No Obj: 0.002400, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.266846, iou_loss = 0.022102, total_loss = 1.288948 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.153545, GIOU: 0.153545), Class: 0.543877, Obj: 0.026996, No Obj: 0.000583, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.649586, iou_loss = 0.042089, total_loss = 0.691674 \n",
" total_bbox = 34455, rewritten_bbox = 0.287331 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.510310, GIOU: 0.483379), Class: 0.622592, Obj: 0.029447, No Obj: 0.002665, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.764460, iou_loss = 0.161231, total_loss = 1.925691 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.478463, GIOU: 0.261713), Class: 0.529005, Obj: 0.030379, No Obj: 0.000582, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.702873, iou_loss = 0.726831, total_loss = 1.429704 \n",
" total_bbox = 34462, rewritten_bbox = 0.287273 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.257814, GIOU: 0.241272), Class: 0.413667, Obj: 0.114293, No Obj: 0.003005, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.834087, iou_loss = 0.012652, total_loss = 1.846740 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.195662, GIOU: -0.116228), Class: 0.479857, Obj: 0.022222, No Obj: 0.000567, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.807026, iou_loss = 0.887625, total_loss = 1.694651 \n",
" total_bbox = 34469, rewritten_bbox = 0.287215 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.336796, GIOU: 0.251361), Class: 0.627986, Obj: 0.042497, No Obj: 0.002693, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.238761, iou_loss = 0.086859, total_loss = 1.325620 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.309513, GIOU: 0.184163), Class: 0.543305, Obj: 0.007389, No Obj: 0.000545, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.348188, iou_loss = 0.054738, total_loss = 0.402926 \n",
" total_bbox = 34474, rewritten_bbox = 0.287173 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320040, GIOU: 0.242336), Class: 0.550514, Obj: 0.022523, No Obj: 0.002738, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.177240, iou_loss = 0.054984, total_loss = 2.232224 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000527, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001044, iou_loss = 0.000000, total_loss = 0.001044 \n",
" total_bbox = 34480, rewritten_bbox = 0.287123 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 431: 0.977779, 0.919260 avg loss, 0.000090 rate, 0.679123 seconds, 27584 images, 0.719866 hours left\n",
"Loaded: 0.000051 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.411310, GIOU: 0.409168), Class: 0.522308, Obj: 0.092991, No Obj: 0.003177, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.715959, iou_loss = 0.072434, total_loss = 1.788393 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000519, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001097, iou_loss = 0.000000, total_loss = 0.001097 \n",
" total_bbox = 34485, rewritten_bbox = 0.287081 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382269, GIOU: 0.308250), Class: 0.520587, Obj: 0.042574, No Obj: 0.002748, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.155165, iou_loss = 0.085348, total_loss = 2.240513 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000540, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001063, iou_loss = 0.000000, total_loss = 0.001063 \n",
" total_bbox = 34491, rewritten_bbox = 0.287031 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.489992, GIOU: 0.451677), Class: 0.524417, Obj: 0.040463, No Obj: 0.002642, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.455478, iou_loss = 0.031583, total_loss = 1.487061 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000534, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001315, iou_loss = 0.000000, total_loss = 0.001315 \n",
" total_bbox = 34495, rewritten_bbox = 0.286998 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.505827, GIOU: 0.505502), Class: 0.571649, Obj: 0.060209, No Obj: 0.002858, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.036712, iou_loss = 0.084942, total_loss = 1.121655 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.371921, GIOU: 0.270647), Class: 0.545175, Obj: 0.004588, No Obj: 0.000539, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.779106, iou_loss = 0.712843, total_loss = 1.491948 \n",
" total_bbox = 34500, rewritten_bbox = 0.286957 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.402314, GIOU: 0.370181), Class: 0.551902, Obj: 0.054710, No Obj: 0.002741, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.334917, iou_loss = 0.064072, total_loss = 1.398989 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000595, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001996, iou_loss = 0.000000, total_loss = 0.001996 \n",
" total_bbox = 34504, rewritten_bbox = 0.286923 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.623358, GIOU: 0.592140), Class: 0.516931, Obj: 0.022743, No Obj: 0.002099, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.534535, iou_loss = 0.058243, total_loss = 1.592777 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000530, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001064, iou_loss = 0.000000, total_loss = 0.001064 \n",
" total_bbox = 34508, rewritten_bbox = 0.286890 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422354, GIOU: 0.383291), Class: 0.465756, Obj: 0.019895, No Obj: 0.002455, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.406274, iou_loss = 0.013385, total_loss = 1.419659 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000547, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001060, iou_loss = 0.000000, total_loss = 0.001060 \n",
" total_bbox = 34512, rewritten_bbox = 0.286857 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344646, GIOU: 0.290683), Class: 0.521044, Obj: 0.032874, No Obj: 0.002086, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.394857, iou_loss = 0.036954, total_loss = 1.431812 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000541, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001261, iou_loss = 0.000000, total_loss = 0.001261 \n",
" total_bbox = 34516, rewritten_bbox = 0.286824 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415818, GIOU: 0.349618), Class: 0.375939, Obj: 0.062251, No Obj: 0.002312, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.743596, iou_loss = 0.010673, total_loss = 0.754269 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.410222, GIOU: 0.328394), Class: 0.551396, Obj: 0.004639, No Obj: 0.000550, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.710911, iou_loss = 0.171598, total_loss = 0.882510 \n",
" total_bbox = 34520, rewritten_bbox = 0.286790 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.318597, GIOU: 0.269831), Class: 0.350416, Obj: 0.101516, No Obj: 0.002672, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.121989, iou_loss = 0.009840, total_loss = 1.131829 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.248967, GIOU: 0.248967), Class: 0.360119, Obj: 0.005206, No Obj: 0.000547, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.430255, iou_loss = 0.055441, total_loss = 0.485695 \n",
" total_bbox = 34524, rewritten_bbox = 0.286757 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.394783, GIOU: 0.252124), Class: 0.475281, Obj: 0.089298, No Obj: 0.002966, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.067170, iou_loss = 0.048749, total_loss = 2.115920 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000519, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001149, iou_loss = 0.000000, total_loss = 0.001149 \n",
" total_bbox = 34530, rewritten_bbox = 0.286707 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300828, GIOU: 0.173770), Class: 0.460498, Obj: 0.026539, No Obj: 0.002235, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.047700, iou_loss = 0.027331, total_loss = 1.075031 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.406701, GIOU: 0.400223), Class: 0.358558, Obj: 0.002244, No Obj: 0.000530, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.412204, iou_loss = 0.507706, total_loss = 0.919910 \n",
" total_bbox = 34534, rewritten_bbox = 0.286674 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.358851, GIOU: 0.304356), Class: 0.503440, Obj: 0.046825, No Obj: 0.002486, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.370499, iou_loss = 0.056281, total_loss = 1.426780 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.403900, GIOU: 0.285404), Class: 0.466161, Obj: 0.034795, No Obj: 0.000619, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.816547, iou_loss = 1.879328, total_loss = 2.695874 \n",
" total_bbox = 34540, rewritten_bbox = 0.286624 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.374033, GIOU: 0.370632), Class: 0.428911, Obj: 0.029659, No Obj: 0.002347, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.437384, iou_loss = 0.008819, total_loss = 1.446203 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.355097, GIOU: 0.210171), Class: 0.491280, Obj: 0.000595, No Obj: 0.000534, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.416903, iou_loss = 0.264243, total_loss = 0.681146 \n",
" total_bbox = 34545, rewritten_bbox = 0.286583 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370160, GIOU: 0.242027), Class: 0.449545, Obj: 0.025077, No Obj: 0.002357, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.537387, iou_loss = 0.167362, total_loss = 1.704749 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.358871, GIOU: 0.258152), Class: 0.398597, Obj: 0.002486, No Obj: 0.000594, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.402005, iou_loss = 0.312137, total_loss = 0.714142 \n",
" total_bbox = 34550, rewritten_bbox = 0.286541 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.443817, GIOU: 0.272764), Class: 0.639096, Obj: 0.068364, No Obj: 0.002206, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.925862, iou_loss = 0.055996, total_loss = 0.981858 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.292034, GIOU: 0.193707), Class: 0.660570, Obj: 0.005617, No Obj: 0.000569, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.034260, iou_loss = 4.003753, total_loss = 5.038013 \n",
" total_bbox = 34556, rewritten_bbox = 0.286491 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 432: 0.861033, 0.913437 avg loss, 0.000091 rate, 0.688521 seconds, 27648 images, 0.719400 hours left\n",
"Loaded: 0.000035 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.380552, GIOU: 0.291227), Class: 0.663304, Obj: 0.042896, No Obj: 0.002601, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.200861, iou_loss = 0.054809, total_loss = 1.255670 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000527, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001086, iou_loss = 0.000000, total_loss = 0.001086 \n",
" total_bbox = 34560, rewritten_bbox = 0.286458 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.327578, GIOU: 0.220246), Class: 0.649377, Obj: 0.017245, No Obj: 0.002446, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.656200, iou_loss = 0.023460, total_loss = 1.679660 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.778126, GIOU: 0.774602), Class: 0.256476, Obj: 0.002758, No Obj: 0.000532, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 0.425356, iou_loss = 0.418580, total_loss = 0.843936 \n",
" total_bbox = 34566, rewritten_bbox = 0.286409 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.501457, GIOU: 0.495918), Class: 0.408385, Obj: 0.073466, No Obj: 0.002988, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.566970, iou_loss = 0.040563, total_loss = 1.607533 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000514, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001199, iou_loss = 0.000000, total_loss = 0.001199 \n",
" total_bbox = 34570, rewritten_bbox = 0.286375 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.442194, GIOU: 0.379639), Class: 0.572029, Obj: 0.041811, No Obj: 0.002470, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 2.341100, iou_loss = 0.146662, total_loss = 2.487762 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000561, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002359, iou_loss = 0.000000, total_loss = 0.002359 \n",
" total_bbox = 34577, rewritten_bbox = 0.286317 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.354211, GIOU: 0.274922), Class: 0.526610, Obj: 0.154709, No Obj: 0.002356, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.179064, iou_loss = 0.093502, total_loss = 1.272566 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000609, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.011152, iou_loss = 0.000000, total_loss = 0.011152 \n",
" total_bbox = 34581, rewritten_bbox = 0.286284 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.525102, GIOU: 0.374114), Class: 0.564109, Obj: 0.079521, No Obj: 0.002769, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 0.936070, iou_loss = 0.174162, total_loss = 1.110232 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.354911, GIOU: 0.354911), Class: 0.498200, Obj: 0.001265, No Obj: 0.000543, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.353367, iou_loss = 0.109821, total_loss = 0.463188 \n",
" total_bbox = 34585, rewritten_bbox = 0.286251 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.455774, GIOU: 0.402351), Class: 0.507048, Obj: 0.114162, No Obj: 0.002626, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.359537, iou_loss = 0.069574, total_loss = 1.429111 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000605, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003058, iou_loss = 0.000000, total_loss = 0.003058 \n",
" total_bbox = 34589, rewritten_bbox = 0.286218 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.393464, GIOU: 0.294251), Class: 0.569764, Obj: 0.040844, No Obj: 0.002401, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.029440, iou_loss = 0.027788, total_loss = 1.057227 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.695652), Class: 0.330632, Obj: 0.001119, No Obj: 0.000577, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.400807, iou_loss = 0.008163, total_loss = 0.408969 \n",
" total_bbox = 34593, rewritten_bbox = 0.286185 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.482175, GIOU: 0.441171), Class: 0.434814, Obj: 0.054053, No Obj: 0.001993, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.184673, iou_loss = 0.154116, total_loss = 1.338789 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.369424, GIOU: 0.249226), Class: 0.485036, Obj: 0.002978, No Obj: 0.000565, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.760909, iou_loss = 4.848166, total_loss = 5.609075 \n",
" total_bbox = 34598, rewritten_bbox = 0.286144 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.329545, GIOU: 0.156849), Class: 0.541467, Obj: 0.061472, No Obj: 0.002810, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.726083, iou_loss = 0.012418, total_loss = 1.738501 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.178197, GIOU: -0.138683), Class: 0.615994, Obj: 0.005486, No Obj: 0.000589, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.733661, iou_loss = 0.048505, total_loss = 0.782166 \n",
" total_bbox = 34605, rewritten_bbox = 0.286086 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.406289, GIOU: 0.253010), Class: 0.554147, Obj: 0.099516, No Obj: 0.003054, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.041743, iou_loss = 0.039241, total_loss = 1.080984 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000592, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002199, iou_loss = 0.000000, total_loss = 0.002199 \n",
" total_bbox = 34608, rewritten_bbox = 0.286061 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.497205, GIOU: 0.435256), Class: 0.564104, Obj: 0.083520, No Obj: 0.002691, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.399712, iou_loss = 0.050146, total_loss = 1.449858 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.275825, GIOU: -0.044070), Class: 0.409735, Obj: 0.004013, No Obj: 0.000612, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.386697, iou_loss = 0.088018, total_loss = 0.474716 \n",
" total_bbox = 34613, rewritten_bbox = 0.286020 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.325660, GIOU: 0.250896), Class: 0.633600, Obj: 0.113591, No Obj: 0.002986, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.230238, iou_loss = 0.025359, total_loss = 1.255596 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.183635, GIOU: 0.183633), Class: 0.504113, Obj: 0.018157, No Obj: 0.000550, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.343464, iou_loss = 0.103212, total_loss = 0.446676 \n",
" total_bbox = 34618, rewritten_bbox = 0.285978 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.484800, GIOU: 0.459496), Class: 0.415605, Obj: 0.077759, No Obj: 0.002693, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.090090, iou_loss = 0.028680, total_loss = 1.118770 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.469343, GIOU: 0.409375), Class: 0.705637, Obj: 0.018531, No Obj: 0.000550, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.688131, iou_loss = 0.296683, total_loss = 0.984814 \n",
" total_bbox = 34623, rewritten_bbox = 0.285937 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.279384, GIOU: 0.153083), Class: 0.551681, Obj: 0.060695, No Obj: 0.003098, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.429981, iou_loss = 0.013110, total_loss = 1.443091 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000555, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001974, iou_loss = 0.000000, total_loss = 0.001974 \n",
" total_bbox = 34627, rewritten_bbox = 0.285904 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.558083, GIOU: 0.541227), Class: 0.458745, Obj: 0.055222, No Obj: 0.002898, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.170408, iou_loss = 0.056747, total_loss = 1.227155 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.543073, GIOU: 0.484833), Class: 0.533088, Obj: 0.002574, No Obj: 0.000634, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.688248, iou_loss = 1.789692, total_loss = 2.477940 \n",
" total_bbox = 34632, rewritten_bbox = 0.285863 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 433: 0.831568, 0.905250 avg loss, 0.000092 rate, 0.710551 seconds, 27712 images, 0.719031 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.464609, GIOU: 0.404787), Class: 0.493867, Obj: 0.067855, No Obj: 0.003462, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 2.555328, iou_loss = 0.206610, total_loss = 2.761938 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000576, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002868, iou_loss = 0.000000, total_loss = 0.002868 \n",
" total_bbox = 34639, rewritten_bbox = 0.285805 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399442, GIOU: 0.355947), Class: 0.508660, Obj: 0.009512, No Obj: 0.002215, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.442886, iou_loss = 0.054908, total_loss = 1.497793 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000527, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001505, iou_loss = 0.000000, total_loss = 0.001505 \n",
" total_bbox = 34643, rewritten_bbox = 0.285772 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.601884, GIOU: 0.580053), Class: 0.464395, Obj: 0.047366, No Obj: 0.002291, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.464263, iou_loss = 0.061683, total_loss = 1.525946 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000511, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001099, iou_loss = 0.000000, total_loss = 0.001099 \n",
" total_bbox = 34647, rewritten_bbox = 0.285739 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.298963, GIOU: 0.070971), Class: 0.487500, Obj: 0.020410, No Obj: 0.002435, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.127267, iou_loss = 0.061061, total_loss = 2.188328 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.417122, GIOU: 0.261237), Class: 0.557948, Obj: 0.010309, No Obj: 0.000583, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.320725, iou_loss = 0.085399, total_loss = 0.406124 \n",
" total_bbox = 34654, rewritten_bbox = 0.285681 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.587730, GIOU: 0.560854), Class: 0.509374, Obj: 0.041230, No Obj: 0.002414, .5R: 1.000000, .75R: 0.000000, count: 3, class_loss = 1.106982, iou_loss = 0.075568, total_loss = 1.182549 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.100609, GIOU: 0.100609), Class: 0.438777, Obj: 0.193271, No Obj: 0.000645, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.351468, iou_loss = 0.008555, total_loss = 0.360023 \n",
" total_bbox = 34658, rewritten_bbox = 0.285648 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.239633, GIOU: 0.184279), Class: 0.554646, Obj: 0.032917, No Obj: 0.002386, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.507187, iou_loss = 0.005540, total_loss = 1.512727 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000528, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001332, iou_loss = 0.000000, total_loss = 0.001332 \n",
" total_bbox = 34662, rewritten_bbox = 0.285615 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.446902, GIOU: 0.298248), Class: 0.513425, Obj: 0.055122, No Obj: 0.002394, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.031292, iou_loss = 0.017759, total_loss = 1.049051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.478408, GIOU: 0.369118), Class: 0.516208, Obj: 0.027303, No Obj: 0.000604, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.348347, iou_loss = 0.098822, total_loss = 0.447169 \n",
" total_bbox = 34666, rewritten_bbox = 0.285582 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.280629, GIOU: 0.254167), Class: 0.570422, Obj: 0.044251, No Obj: 0.003159, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.793032, iou_loss = 0.042743, total_loss = 1.835775 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.233634, GIOU: -0.114289), Class: 0.422399, Obj: 0.007981, No Obj: 0.000542, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.425299, iou_loss = 0.022956, total_loss = 0.448256 \n",
" total_bbox = 34672, rewritten_bbox = 0.285533 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.463198, GIOU: 0.397958), Class: 0.459512, Obj: 0.112757, No Obj: 0.003510, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.540144, iou_loss = 0.078219, total_loss = 1.618362 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.379832, GIOU: 0.347891), Class: 0.588951, Obj: 0.003910, No Obj: 0.000511, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.959302, iou_loss = 0.885552, total_loss = 1.844854 \n",
" total_bbox = 34679, rewritten_bbox = 0.285475 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.198474, GIOU: -0.022419), Class: 0.471732, Obj: 0.115236, No Obj: 0.003282, .5R: 0.000000, .75R: 0.000000, count: 7, class_loss = 2.365074, iou_loss = 0.012873, total_loss = 2.377947 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000555, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001490, iou_loss = 0.000000, total_loss = 0.001490 \n",
" total_bbox = 34686, rewritten_bbox = 0.285418 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.265422, GIOU: 0.126955), Class: 0.540172, Obj: 0.025881, No Obj: 0.002091, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.737696, iou_loss = 0.027595, total_loss = 1.765290 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000518, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001579, iou_loss = 0.000000, total_loss = 0.001579 \n",
" total_bbox = 34691, rewritten_bbox = 0.285377 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.231400, GIOU: 0.231400), Class: 0.444113, Obj: 0.046857, No Obj: 0.002603, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.090908, iou_loss = 0.002630, total_loss = 1.093538 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000563, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001796, iou_loss = 0.000000, total_loss = 0.001796 \n",
" total_bbox = 34694, rewritten_bbox = 0.285352 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.377210, GIOU: 0.288617), Class: 0.402192, Obj: 0.058643, No Obj: 0.002313, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.922837, iou_loss = 0.024719, total_loss = 1.947557 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.624921, GIOU: 0.624921), Class: 0.645321, Obj: 0.001086, No Obj: 0.000518, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.318532, iou_loss = 0.340359, total_loss = 0.658891 \n",
" total_bbox = 34700, rewritten_bbox = 0.285303 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.355768, GIOU: 0.222886), Class: 0.622749, Obj: 0.039647, No Obj: 0.002265, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.257197, iou_loss = 0.078287, total_loss = 1.335483 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000539, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001587, iou_loss = 0.000000, total_loss = 0.001587 \n",
" total_bbox = 34704, rewritten_bbox = 0.285270 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.349706, GIOU: 0.264417), Class: 0.523443, Obj: 0.017987, No Obj: 0.001902, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.511175, iou_loss = 0.044044, total_loss = 1.555219 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.321132, GIOU: 0.321131), Class: 0.489300, Obj: 0.061253, No Obj: 0.000535, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.357209, iou_loss = 0.091730, total_loss = 0.448938 \n",
" total_bbox = 34709, rewritten_bbox = 0.285229 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.309176, GIOU: 0.250244), Class: 0.548598, Obj: 0.017427, No Obj: 0.002411, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 2.405971, iou_loss = 0.208462, total_loss = 2.614433 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000510, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001164, iou_loss = 0.000000, total_loss = 0.001164 \n",
" total_bbox = 34716, rewritten_bbox = 0.285171 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 434: 0.944050, 0.909130 avg loss, 0.000093 rate, 0.740551 seconds, 27776 images, 0.718881 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300884, GIOU: 0.172122), Class: 0.402751, Obj: 0.025651, No Obj: 0.003049, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.169882, iou_loss = 0.007443, total_loss = 1.177325 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.411174, GIOU: 0.400467), Class: 0.491933, Obj: 0.004264, No Obj: 0.000526, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.379664, iou_loss = 0.376242, total_loss = 0.755907 \n",
" total_bbox = 34720, rewritten_bbox = 0.285138 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.397100, GIOU: 0.397100), Class: 0.388483, Obj: 0.024640, No Obj: 0.001950, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.655544, iou_loss = 0.010619, total_loss = 1.666163 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000527, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000969, iou_loss = 0.000000, total_loss = 0.000969 \n",
" total_bbox = 34724, rewritten_bbox = 0.285105 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.236827, GIOU: 0.015157), Class: 0.596194, Obj: 0.032096, No Obj: 0.002762, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.390042, iou_loss = 0.012639, total_loss = 1.402680 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000529, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001151, iou_loss = 0.000000, total_loss = 0.001151 \n",
" total_bbox = 34728, rewritten_bbox = 0.285073 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.393521, GIOU: 0.311221), Class: 0.438992, Obj: 0.079542, No Obj: 0.002840, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.523655, iou_loss = 0.038558, total_loss = 1.562213 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000506, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001749, iou_loss = 0.000000, total_loss = 0.001749 \n",
" total_bbox = 34732, rewritten_bbox = 0.285040 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.265092, GIOU: -0.039737), Class: 0.558414, Obj: 0.078700, No Obj: 0.002714, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.119792, iou_loss = 0.019814, total_loss = 2.139607 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.409550, GIOU: 0.401091), Class: 0.396207, Obj: 0.011347, No Obj: 0.000536, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.878288, iou_loss = 0.880562, total_loss = 1.758851 \n",
" total_bbox = 34740, rewritten_bbox = 0.284974 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.381167, GIOU: 0.311878), Class: 0.602744, Obj: 0.049649, No Obj: 0.002444, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.378758, iou_loss = 0.020896, total_loss = 1.399654 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000504, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001015, iou_loss = 0.000000, total_loss = 0.001015 \n",
" total_bbox = 34744, rewritten_bbox = 0.284941 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.473732, GIOU: 0.445953), Class: 0.574525, Obj: 0.031107, No Obj: 0.002189, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.358537, iou_loss = 0.065128, total_loss = 1.423665 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.353770, GIOU: 0.258358), Class: 0.764345, Obj: 0.006441, No Obj: 0.000537, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.350959, iou_loss = 2.009640, total_loss = 2.360599 \n",
" total_bbox = 34749, rewritten_bbox = 0.284900 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.274694, GIOU: 0.177197), Class: 0.512319, Obj: 0.064645, No Obj: 0.002970, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.477194, iou_loss = 0.006483, total_loss = 1.483677 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.387254, GIOU: 0.193713), Class: 0.576278, Obj: 0.003405, No Obj: 0.000510, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.368532, iou_loss = 0.204119, total_loss = 0.572651 \n",
" total_bbox = 34754, rewritten_bbox = 0.284859 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.349144, GIOU: 0.341065), Class: 0.624481, Obj: 0.085030, No Obj: 0.003118, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.243207, iou_loss = 0.032811, total_loss = 1.276018 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000529, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001930, iou_loss = 0.000000, total_loss = 0.001930 \n",
" total_bbox = 34758, rewritten_bbox = 0.284827 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345753, GIOU: 0.309245), Class: 0.517744, Obj: 0.062778, No Obj: 0.002669, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.510050, iou_loss = 0.048446, total_loss = 1.558496 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000499, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000890, iou_loss = 0.000000, total_loss = 0.000890 \n",
" total_bbox = 34762, rewritten_bbox = 0.284794 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.398233, GIOU: 0.333066), Class: 0.564034, Obj: 0.044006, No Obj: 0.002528, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.350920, iou_loss = 0.028773, total_loss = 1.379693 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.398571, GIOU: 0.360326), Class: 0.528093, Obj: 0.000510, No Obj: 0.000520, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.411936, iou_loss = 0.312430, total_loss = 0.724366 \n",
" total_bbox = 34767, rewritten_bbox = 0.284753 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.334000, GIOU: 0.334000), Class: 0.490969, Obj: 0.015818, No Obj: 0.001840, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.192641, iou_loss = 0.017125, total_loss = 1.209766 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.716989, GIOU: 0.707048), Class: 0.403398, Obj: 0.061509, No Obj: 0.000556, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.439224, iou_loss = 0.298350, total_loss = 0.737574 \n",
" total_bbox = 34771, rewritten_bbox = 0.284720 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.427084, GIOU: 0.332637), Class: 0.667728, Obj: 0.062301, No Obj: 0.002751, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.499495, iou_loss = 0.177618, total_loss = 1.677113 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.442701, GIOU: 0.410803), Class: 0.369727, Obj: 0.002046, No Obj: 0.000638, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.482051, iou_loss = 0.411379, total_loss = 0.893430 \n",
" total_bbox = 34777, rewritten_bbox = 0.284671 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.242764, GIOU: 0.045970), Class: 0.523511, Obj: 0.024781, No Obj: 0.002457, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.793031, iou_loss = 0.048200, total_loss = 1.841231 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000562, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002195, iou_loss = 0.000000, total_loss = 0.002195 \n",
" total_bbox = 34782, rewritten_bbox = 0.284630 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352979, GIOU: 0.242484), Class: 0.605390, Obj: 0.017877, No Obj: 0.001852, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.837978, iou_loss = 0.052998, total_loss = 1.890976 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000500, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001033, iou_loss = 0.000000, total_loss = 0.001033 \n",
" total_bbox = 34787, rewritten_bbox = 0.284589 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369414, GIOU: 0.358237), Class: 0.414922, Obj: 0.065266, No Obj: 0.002738, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.202810, iou_loss = 0.026045, total_loss = 1.228854 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.353505, GIOU: 0.286423), Class: 0.424335, Obj: 0.001067, No Obj: 0.000557, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.374519, iou_loss = 1.336079, total_loss = 1.710598 \n",
" total_bbox = 34791, rewritten_bbox = 0.284556 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 435: 0.864433, 0.904661 avg loss, 0.000093 rate, 0.692929 seconds, 27840 images, 0.719029 hours left\n",
"Loaded: 0.000037 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395098, GIOU: 0.391340), Class: 0.517151, Obj: 0.046883, No Obj: 0.001932, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.448303, iou_loss = 0.102820, total_loss = 1.551123 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000584, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004364, iou_loss = 0.000000, total_loss = 0.004364 \n",
" total_bbox = 34795, rewritten_bbox = 0.284524 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.483647, GIOU: 0.466639), Class: 0.461890, Obj: 0.065283, No Obj: 0.002469, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.129112, iou_loss = 0.029631, total_loss = 1.158744 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.680681), Class: 0.541994, Obj: 0.001563, No Obj: 0.000505, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.362877, iou_loss = 0.010286, total_loss = 0.373163 \n",
" total_bbox = 34799, rewritten_bbox = 0.284491 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.538517, GIOU: 0.506176), Class: 0.635419, Obj: 0.033144, No Obj: 0.002574, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.699921, iou_loss = 0.219660, total_loss = 1.919581 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.301170, GIOU: 0.256614), Class: 0.502965, Obj: 0.000823, No Obj: 0.000564, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.394468, iou_loss = 0.068973, total_loss = 0.463441 \n",
" total_bbox = 34805, rewritten_bbox = 0.284442 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.298085, GIOU: 0.131290), Class: 0.442661, Obj: 0.021246, No Obj: 0.002307, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.577872, iou_loss = 0.068292, total_loss = 1.646164 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000529, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001299, iou_loss = 0.000000, total_loss = 0.001299 \n",
" total_bbox = 34809, rewritten_bbox = 0.284409 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.278127, GIOU: 0.076057), Class: 0.591128, Obj: 0.076503, No Obj: 0.001866, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.329985, iou_loss = 0.053955, total_loss = 1.383940 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.592825, GIOU: 0.586415), Class: 0.551007, Obj: 0.001348, No Obj: 0.000535, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.326040, iou_loss = 0.369775, total_loss = 0.695815 \n",
" total_bbox = 34814, rewritten_bbox = 0.284368 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344091, GIOU: 0.223298), Class: 0.581919, Obj: 0.027261, No Obj: 0.002828, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.143082, iou_loss = 0.015797, total_loss = 1.158880 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000563, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004561, iou_loss = 0.000000, total_loss = 0.004561 \n",
" total_bbox = 34817, rewritten_bbox = 0.284344 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.298753, GIOU: 0.203589), Class: 0.540633, Obj: 0.050896, No Obj: 0.002442, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.123890, iou_loss = 0.020640, total_loss = 1.144530 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.350605, GIOU: 0.211727), Class: 0.512432, Obj: 0.030072, No Obj: 0.000610, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.768717, iou_loss = 0.524726, total_loss = 1.293443 \n",
" total_bbox = 34822, rewritten_bbox = 0.284303 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.541869, GIOU: 0.508295), Class: 0.415311, Obj: 0.045427, No Obj: 0.002540, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.424788, iou_loss = 0.016647, total_loss = 1.441435 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.410139, GIOU: 0.410139), Class: 0.651863, Obj: 0.003069, No Obj: 0.000488, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.331555, iou_loss = 0.146620, total_loss = 0.478175 \n",
" total_bbox = 34827, rewritten_bbox = 0.284262 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.292735, GIOU: 0.193332), Class: 0.492166, Obj: 0.041083, No Obj: 0.002176, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.087450, iou_loss = 0.010761, total_loss = 1.098211 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.387783, GIOU: 0.275368), Class: 0.540595, Obj: 0.000520, No Obj: 0.000534, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.738636, iou_loss = 0.230866, total_loss = 0.969502 \n",
" total_bbox = 34832, rewritten_bbox = 0.284221 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.502572, GIOU: 0.492704), Class: 0.625981, Obj: 0.167029, No Obj: 0.002863, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.157017, iou_loss = 0.147457, total_loss = 1.304473 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000527, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001211, iou_loss = 0.000000, total_loss = 0.001211 \n",
" total_bbox = 34836, rewritten_bbox = 0.284189 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.340280, GIOU: 0.339982), Class: 0.527283, Obj: 0.021979, No Obj: 0.002274, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.411674, iou_loss = 0.010564, total_loss = 1.422238 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000516, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001038, iou_loss = 0.000000, total_loss = 0.001038 \n",
" total_bbox = 34840, rewritten_bbox = 0.284156 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.507130, GIOU: 0.446532), Class: 0.534497, Obj: 0.106591, No Obj: 0.002414, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.608528, iou_loss = 0.039908, total_loss = 0.648436 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.386753, GIOU: 0.295516), Class: 0.430981, Obj: 0.008673, No Obj: 0.000562, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.180309, iou_loss = 0.626529, total_loss = 1.806839 \n",
" total_bbox = 34845, rewritten_bbox = 0.284115 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.468016, GIOU: 0.464260), Class: 0.444769, Obj: 0.089482, No Obj: 0.002935, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.019926, iou_loss = 0.043784, total_loss = 1.063710 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.382617, GIOU: 0.321089), Class: 0.418449, Obj: 0.001749, No Obj: 0.000507, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.775476, iou_loss = 0.579661, total_loss = 1.355138 \n",
" total_bbox = 34850, rewritten_bbox = 0.284075 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.227217, GIOU: 0.209595), Class: 0.500954, Obj: 0.026148, No Obj: 0.002658, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.050477, iou_loss = 0.018054, total_loss = 1.068531 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.601896, GIOU: 0.572727), Class: 0.587727, Obj: 0.003359, No Obj: 0.000573, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.734278, iou_loss = 1.028406, total_loss = 1.762684 \n",
" total_bbox = 34855, rewritten_bbox = 0.284034 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415824, GIOU: 0.390127), Class: 0.541724, Obj: 0.022021, No Obj: 0.002506, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.815845, iou_loss = 0.056754, total_loss = 1.872600 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.520550, GIOU: 0.461853), Class: 0.561134, Obj: 0.001316, No Obj: 0.000493, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.473134, iou_loss = 4.633908, total_loss = 6.107041 \n",
" total_bbox = 34864, rewritten_bbox = 0.283961 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.400487, GIOU: 0.265258), Class: 0.559457, Obj: 0.025553, No Obj: 0.002166, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.829479, iou_loss = 0.099946, total_loss = 1.929425 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000502, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001272, iou_loss = 0.000000, total_loss = 0.001272 \n",
" total_bbox = 34869, rewritten_bbox = 0.283920 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 436: 0.882247, 0.902419 avg loss, 0.000094 rate, 0.729443 seconds, 27904 images, 0.718701 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.288348, GIOU: 0.288348), Class: 0.445418, Obj: 0.028713, No Obj: 0.002756, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.380577, iou_loss = 0.008641, total_loss = 1.389218 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000528, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001087, iou_loss = 0.000000, total_loss = 0.001087 \n",
" total_bbox = 34873, rewritten_bbox = 0.283887 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.276120, GIOU: 0.123994), Class: 0.665871, Obj: 0.014434, No Obj: 0.002574, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.310112, iou_loss = 0.010954, total_loss = 1.321066 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.155309, GIOU: -0.310423), Class: 0.566012, Obj: 0.000112, No Obj: 0.000519, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.370652, iou_loss = 0.028245, total_loss = 0.398897 \n",
" total_bbox = 34878, rewritten_bbox = 0.283847 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.261025, GIOU: 0.145617), Class: 0.543421, Obj: 0.044374, No Obj: 0.002501, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.642193, iou_loss = 0.022365, total_loss = 1.664558 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000534, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002416, iou_loss = 0.000000, total_loss = 0.002416 \n",
" total_bbox = 34883, rewritten_bbox = 0.283806 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421604, GIOU: 0.363361), Class: 0.593043, Obj: 0.050241, No Obj: 0.003251, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.480828, iou_loss = 0.025462, total_loss = 1.506290 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000533, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001599, iou_loss = 0.000000, total_loss = 0.001599 \n",
" total_bbox = 34887, rewritten_bbox = 0.283773 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392627, GIOU: 0.358737), Class: 0.355715, Obj: 0.030518, No Obj: 0.002454, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.750383, iou_loss = 0.041926, total_loss = 1.792309 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.388637, GIOU: 0.265971), Class: 0.567728, Obj: 0.000946, No Obj: 0.000496, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.346108, iou_loss = 0.083908, total_loss = 0.430016 \n",
" total_bbox = 34892, rewritten_bbox = 0.283733 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.220465, GIOU: 0.000971), Class: 0.475792, Obj: 0.136956, No Obj: 0.002809, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.931728, iou_loss = 0.009351, total_loss = 0.941079 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.282450, GIOU: -0.002762), Class: 0.524241, Obj: 0.007295, No Obj: 0.000531, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.758508, iou_loss = 0.215858, total_loss = 0.974366 \n",
" total_bbox = 34897, rewritten_bbox = 0.283692 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.543417, GIOU: 0.512779), Class: 0.436768, Obj: 0.034594, No Obj: 0.002404, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.158169, iou_loss = 0.016255, total_loss = 1.174424 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000497, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000979, iou_loss = 0.000000, total_loss = 0.000979 \n",
" total_bbox = 34900, rewritten_bbox = 0.283668 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.253723, GIOU: 0.241412), Class: 0.381366, Obj: 0.009622, No Obj: 0.003112, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.218327, iou_loss = 0.078642, total_loss = 1.296969 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000571, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001450, iou_loss = 0.000000, total_loss = 0.001450 \n",
" total_bbox = 34903, rewritten_bbox = 0.283643 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.440450, GIOU: 0.314881), Class: 0.502272, Obj: 0.059869, No Obj: 0.002406, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.877967, iou_loss = 0.073843, total_loss = 1.951810 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.359716, GIOU: 0.252861), Class: 0.560414, Obj: 0.002340, No Obj: 0.000533, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.751906, iou_loss = 1.424896, total_loss = 2.176802 \n",
" total_bbox = 34910, rewritten_bbox = 0.283586 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.394473, GIOU: 0.309158), Class: 0.571609, Obj: 0.044506, No Obj: 0.002993, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.678604, iou_loss = 0.073880, total_loss = 1.752484 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000547, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001583, iou_loss = 0.000000, total_loss = 0.001583 \n",
" total_bbox = 34915, rewritten_bbox = 0.283546 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.500122, GIOU: 0.407251), Class: 0.481751, Obj: 0.036425, No Obj: 0.002492, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.032758, iou_loss = 0.016980, total_loss = 1.049739 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000499, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000998, iou_loss = 0.000000, total_loss = 0.000998 \n",
" total_bbox = 34918, rewritten_bbox = 0.283521 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.431253, GIOU: 0.338932), Class: 0.488788, Obj: 0.027207, No Obj: 0.002444, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 2.206287, iou_loss = 0.175516, total_loss = 2.381803 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000523, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002145, iou_loss = 0.000000, total_loss = 0.002145 \n",
" total_bbox = 34925, rewritten_bbox = 0.286328 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320870, GIOU: 0.231334), Class: 0.561636, Obj: 0.022594, No Obj: 0.002175, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.336275, iou_loss = 0.057593, total_loss = 1.393868 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.208130, GIOU: -0.147016), Class: 0.475490, Obj: 0.002824, No Obj: 0.000530, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.385063, iou_loss = 0.020051, total_loss = 0.405115 \n",
" total_bbox = 34930, rewritten_bbox = 0.286287 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.381632, GIOU: 0.358123), Class: 0.656372, Obj: 0.026792, No Obj: 0.002876, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.031688, iou_loss = 0.029036, total_loss = 1.060724 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000578, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002216, iou_loss = 0.000000, total_loss = 0.002216 \n",
" total_bbox = 34933, rewritten_bbox = 0.286262 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436694, GIOU: 0.410904), Class: 0.635004, Obj: 0.055523, No Obj: 0.002392, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.968975, iou_loss = 0.069982, total_loss = 1.038957 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000497, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001202, iou_loss = 0.000000, total_loss = 0.001202 \n",
" total_bbox = 34936, rewritten_bbox = 0.286238 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.371156, GIOU: 0.243911), Class: 0.633504, Obj: 0.069135, No Obj: 0.002391, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.286578, iou_loss = 0.043723, total_loss = 1.330301 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000539, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002915, iou_loss = 0.000000, total_loss = 0.002915 \n",
" total_bbox = 34940, rewritten_bbox = 0.286205 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 437: 0.786298, 0.890807 avg loss, 0.000095 rate, 0.712001 seconds, 27968 images, 0.718736 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.287049, GIOU: 0.287049), Class: 0.479141, Obj: 0.025585, No Obj: 0.002270, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.119675, iou_loss = 0.055078, total_loss = 1.174753 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.383162, GIOU: 0.383162), Class: 0.519253, Obj: 0.016355, No Obj: 0.000505, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.374695, iou_loss = 0.127817, total_loss = 0.502512 \n",
" total_bbox = 34944, rewritten_bbox = 0.286172 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.250578, GIOU: 0.196646), Class: 0.420059, Obj: 0.064618, No Obj: 0.002728, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.176493, iou_loss = 0.012604, total_loss = 1.189097 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.313909, GIOU: 0.160096), Class: 0.406646, Obj: 0.003651, No Obj: 0.000471, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.966866, iou_loss = 3.494254, total_loss = 5.461120 \n",
" total_bbox = 34952, rewritten_bbox = 0.286107 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403725, GIOU: 0.213716), Class: 0.611908, Obj: 0.029997, No Obj: 0.002606, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.012371, iou_loss = 0.010828, total_loss = 1.023199 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.447313, GIOU: 0.397288), Class: 0.485856, Obj: 0.002124, No Obj: 0.000518, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.389960, iou_loss = 0.210063, total_loss = 0.600022 \n",
" total_bbox = 34956, rewritten_bbox = 0.286074 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403452, GIOU: 0.399511), Class: 0.650601, Obj: 0.120914, No Obj: 0.004189, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.215209, iou_loss = 0.082000, total_loss = 1.297208 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.239037, GIOU: 0.065125), Class: 0.549373, Obj: 0.001625, No Obj: 0.000549, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.766786, iou_loss = 0.632401, total_loss = 1.399187 \n",
" total_bbox = 34961, rewritten_bbox = 0.286033 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.367594, GIOU: 0.198858), Class: 0.464350, Obj: 0.030985, No Obj: 0.002601, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.972257, iou_loss = 0.022710, total_loss = 1.994967 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.359660, GIOU: 0.359660), Class: 0.505737, Obj: 0.001141, No Obj: 0.000503, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.346019, iou_loss = 0.112761, total_loss = 0.458780 \n",
" total_bbox = 34967, rewritten_bbox = 0.285984 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.457899, GIOU: 0.406436), Class: 0.674184, Obj: 0.018341, No Obj: 0.002371, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.998114, iou_loss = 0.043754, total_loss = 1.041868 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.730574, GIOU: 0.717027), Class: 0.551466, Obj: 0.002065, No Obj: 0.000541, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.339641, iou_loss = 0.453897, total_loss = 0.793539 \n",
" total_bbox = 34971, rewritten_bbox = 0.285951 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.509765, GIOU: 0.509444), Class: 0.479160, Obj: 0.031049, No Obj: 0.002296, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.141890, iou_loss = 0.129164, total_loss = 1.271054 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.289089, GIOU: 0.195637), Class: 0.502441, Obj: 0.159703, No Obj: 0.000592, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.360906, iou_loss = 0.040119, total_loss = 0.401025 \n",
" total_bbox = 34975, rewritten_bbox = 0.285919 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.312947, GIOU: 0.312947), Class: 0.441015, Obj: 0.030935, No Obj: 0.002477, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.532548, iou_loss = 0.010458, total_loss = 1.543007 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000561, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001336, iou_loss = 0.000000, total_loss = 0.001336 \n",
" total_bbox = 34979, rewritten_bbox = 0.285886 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.313103, GIOU: 0.244574), Class: 0.457208, Obj: 0.017260, No Obj: 0.002352, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.212134, iou_loss = 0.158129, total_loss = 2.370262 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.304772, GIOU: 0.123167), Class: 0.502441, Obj: 0.002445, No Obj: 0.000548, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.367610, iou_loss = 1.472839, total_loss = 3.840449 \n",
" total_bbox = 34991, rewritten_bbox = 0.285788 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.451657, GIOU: 0.433829), Class: 0.460030, Obj: 0.014285, No Obj: 0.002944, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.119383, iou_loss = 0.008286, total_loss = 1.127668 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000511, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001106, iou_loss = 0.000000, total_loss = 0.001106 \n",
" total_bbox = 34994, rewritten_bbox = 0.285763 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.351991, GIOU: 0.330054), Class: 0.546096, Obj: 0.087754, No Obj: 0.002630, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.616432, iou_loss = 0.116167, total_loss = 1.732598 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.434966, GIOU: 0.309613), Class: 0.491844, Obj: 0.001728, No Obj: 0.000504, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.071609, iou_loss = 0.511104, total_loss = 1.582713 \n",
" total_bbox = 35002, rewritten_bbox = 0.285698 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421104, GIOU: 0.302685), Class: 0.621679, Obj: 0.086940, No Obj: 0.002334, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.826400, iou_loss = 0.015611, total_loss = 0.842011 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.486258, GIOU: 0.366908), Class: 0.705589, Obj: 0.028213, No Obj: 0.000537, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.330267, iou_loss = 0.115508, total_loss = 0.445775 \n",
" total_bbox = 35005, rewritten_bbox = 0.285673 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.593396, GIOU: 0.543914), Class: 0.452756, Obj: 0.063301, No Obj: 0.002585, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.368219, iou_loss = 0.125917, total_loss = 1.494135 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000502, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001534, iou_loss = 0.000000, total_loss = 0.001534 \n",
" total_bbox = 35009, rewritten_bbox = 0.285641 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.335233, GIOU: 0.319139), Class: 0.531226, Obj: 0.037748, No Obj: 0.002713, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.379926, iou_loss = 0.034994, total_loss = 1.414920 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000514, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004470, iou_loss = 0.000000, total_loss = 0.004470 \n",
" total_bbox = 35013, rewritten_bbox = 0.285608 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399889, GIOU: 0.308716), Class: 0.544067, Obj: 0.078748, No Obj: 0.002684, .5R: 0.166667, .75R: 0.166667, count: 6, class_loss = 2.011610, iou_loss = 0.065434, total_loss = 2.077044 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.318717, GIOU: 0.109609), Class: 0.576992, Obj: 0.012892, No Obj: 0.000508, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.352050, iou_loss = 0.121658, total_loss = 0.473708 \n",
" total_bbox = 35020, rewritten_bbox = 0.285551 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399895, GIOU: 0.305868), Class: 0.618926, Obj: 0.143377, No Obj: 0.003172, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.134844, iou_loss = 0.071163, total_loss = 1.206006 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000600, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005579, iou_loss = 0.000000, total_loss = 0.005579 \n",
" total_bbox = 35024, rewritten_bbox = 0.285518 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 438: 0.962673, 0.897994 avg loss, 0.000096 rate, 0.693733 seconds, 28032 images, 0.718596 hours left\n",
"Loaded: 0.000041 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.406351, GIOU: 0.300475), Class: 0.646689, Obj: 0.073340, No Obj: 0.002962, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.293385, iou_loss = 0.012260, total_loss = 1.305645 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000533, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001685, iou_loss = 0.000000, total_loss = 0.001685 \n",
" total_bbox = 35028, rewritten_bbox = 0.285486 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.446603, GIOU: 0.324478), Class: 0.521803, Obj: 0.029854, No Obj: 0.002303, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.444398, iou_loss = 0.097514, total_loss = 1.541912 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.408666, GIOU: 0.345361), Class: 0.716095, Obj: 0.012996, No Obj: 0.000498, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.285048, iou_loss = 0.156375, total_loss = 0.441424 \n",
" total_bbox = 35033, rewritten_bbox = 0.285445 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357669, GIOU: 0.303575), Class: 0.490517, Obj: 0.060857, No Obj: 0.002560, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.461569, iou_loss = 0.057864, total_loss = 1.519433 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000537, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.006850, iou_loss = 0.000000, total_loss = 0.006850 \n",
" total_bbox = 35037, rewritten_bbox = 0.285413 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.351765, GIOU: 0.196768), Class: 0.494622, Obj: 0.170468, No Obj: 0.003201, .5R: 0.125000, .75R: 0.000000, count: 8, class_loss = 1.996078, iou_loss = 0.059323, total_loss = 2.055401 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.271888, GIOU: 0.096452), Class: 0.589681, Obj: 0.002013, No Obj: 0.000505, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.708365, iou_loss = 0.690907, total_loss = 1.399272 \n",
" total_bbox = 35047, rewritten_bbox = 0.291038 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.237767, GIOU: 0.237767), Class: 0.514964, Obj: 0.060193, No Obj: 0.002352, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.305581, iou_loss = 0.006140, total_loss = 1.311722 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000481, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000976, iou_loss = 0.000000, total_loss = 0.000976 \n",
" total_bbox = 35051, rewritten_bbox = 0.291005 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.448723, GIOU: 0.380277), Class: 0.524922, Obj: 0.046617, No Obj: 0.002358, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.477968, iou_loss = 0.030214, total_loss = 1.508182 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.547559, GIOU: 0.490008), Class: 0.597691, Obj: 0.068844, No Obj: 0.000512, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.609958, iou_loss = 0.482287, total_loss = 1.092245 \n",
" total_bbox = 35057, rewritten_bbox = 0.290955 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.306080, GIOU: 0.243963), Class: 0.465894, Obj: 0.060920, No Obj: 0.003019, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.450704, iou_loss = 0.012517, total_loss = 1.463222 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000497, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001088, iou_loss = 0.000000, total_loss = 0.001088 \n",
" total_bbox = 35061, rewritten_bbox = 0.290922 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.275869, GIOU: 0.159264), Class: 0.523804, Obj: 0.164124, No Obj: 0.002709, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.661162, iou_loss = 0.007799, total_loss = 0.668960 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.303448, GIOU: 0.142448), Class: 0.418857, Obj: 0.002260, No Obj: 0.000510, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.787262, iou_loss = 0.465446, total_loss = 1.252708 \n",
" total_bbox = 35065, rewritten_bbox = 0.290888 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.358919, GIOU: 0.207375), Class: 0.694665, Obj: 0.041569, No Obj: 0.002421, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.243788, iou_loss = 0.021856, total_loss = 1.265644 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.132854, GIOU: -0.035779), Class: 0.786430, Obj: 0.001174, No Obj: 0.000536, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.298041, iou_loss = 0.171391, total_loss = 0.469432 \n",
" total_bbox = 35070, rewritten_bbox = 0.290847 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399625, GIOU: 0.391467), Class: 0.557782, Obj: 0.071622, No Obj: 0.002357, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.024573, iou_loss = 0.043100, total_loss = 1.067672 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.088988, GIOU: -0.094999), Class: 0.591187, Obj: 0.000558, No Obj: 0.000491, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347708, iou_loss = 0.014326, total_loss = 0.362034 \n",
" total_bbox = 35074, rewritten_bbox = 0.290814 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.440001, GIOU: 0.330945), Class: 0.521652, Obj: 0.054976, No Obj: 0.002140, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.723583, iou_loss = 0.088055, total_loss = 1.811638 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000522, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001729, iou_loss = 0.000000, total_loss = 0.001729 \n",
" total_bbox = 35079, rewritten_bbox = 0.290772 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.332947, GIOU: 0.252688), Class: 0.585546, Obj: 0.072000, No Obj: 0.002584, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.251190, iou_loss = 0.025860, total_loss = 1.277050 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.692507, GIOU: 0.662767), Class: 0.573706, Obj: 0.003432, No Obj: 0.000512, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.344939, iou_loss = 2.275038, total_loss = 2.619977 \n",
" total_bbox = 35084, rewritten_bbox = 0.290731 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.397764, GIOU: 0.397765), Class: 0.434128, Obj: 0.022180, No Obj: 0.002745, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.572428, iou_loss = 0.093139, total_loss = 1.665567 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.599367, GIOU: 0.559812), Class: 0.579681, Obj: 0.021897, No Obj: 0.000532, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.341560, iou_loss = 0.165740, total_loss = 0.507300 \n",
" total_bbox = 35089, rewritten_bbox = 0.290689 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.376781, GIOU: 0.376539), Class: 0.491011, Obj: 0.118966, No Obj: 0.003220, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.309678, iou_loss = 0.018540, total_loss = 1.328218 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000479, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000899, iou_loss = 0.000000, total_loss = 0.000899 \n",
" total_bbox = 35093, rewritten_bbox = 0.290656 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.251900, GIOU: 0.214231), Class: 0.526686, Obj: 0.085466, No Obj: 0.002583, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.365949, iou_loss = 0.003377, total_loss = 1.369326 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000481, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000960, iou_loss = 0.000000, total_loss = 0.000960 \n",
" total_bbox = 35097, rewritten_bbox = 0.290623 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.697870, GIOU: 0.665272), Class: 0.649041, Obj: 0.084250, No Obj: 0.002327, .5R: 1.000000, .75R: 0.333333, count: 3, class_loss = 0.948239, iou_loss = 0.362295, total_loss = 1.310534 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.334925, GIOU: 0.294049), Class: 0.439386, Obj: 0.002307, No Obj: 0.000517, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.410257, iou_loss = 0.074064, total_loss = 0.484321 \n",
" total_bbox = 35101, rewritten_bbox = 0.290590 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 439: 0.810920, 0.889286 avg loss, 0.000097 rate, 0.674987 seconds, 28096 images, 0.718274 hours left\n",
"Loaded: 0.000062 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319770, GIOU: 0.236612), Class: 0.518391, Obj: 0.122588, No Obj: 0.002897, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.356645, iou_loss = 0.024985, total_loss = 1.381629 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000512, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001001, iou_loss = 0.000000, total_loss = 0.001001 \n",
" total_bbox = 35105, rewritten_bbox = 0.290557 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.396013, GIOU: 0.306198), Class: 0.550348, Obj: 0.020926, No Obj: 0.002295, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.545589, iou_loss = 0.049595, total_loss = 1.595184 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000542, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001800, iou_loss = 0.000000, total_loss = 0.001800 \n",
" total_bbox = 35109, rewritten_bbox = 0.290524 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.558889, GIOU: 0.483074), Class: 0.592151, Obj: 0.052408, No Obj: 0.002177, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.411119, iou_loss = 0.085302, total_loss = 1.496420 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.411159, GIOU: 0.312755), Class: 0.494000, Obj: 0.001622, No Obj: 0.000566, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.396434, iou_loss = 2.568237, total_loss = 2.964671 \n",
" total_bbox = 35114, rewritten_bbox = 0.290482 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.388959, GIOU: 0.268448), Class: 0.537868, Obj: 0.038786, No Obj: 0.001880, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.427263, iou_loss = 0.035585, total_loss = 1.462848 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000553, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003561, iou_loss = 0.000000, total_loss = 0.003561 \n",
" total_bbox = 35118, rewritten_bbox = 0.290449 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.551718, GIOU: 0.497085), Class: 0.350258, Obj: 0.053671, No Obj: 0.002245, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.683370, iou_loss = 0.046168, total_loss = 1.729538 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000474, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000997, iou_loss = 0.000000, total_loss = 0.000997 \n",
" total_bbox = 35122, rewritten_bbox = 0.290416 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.363259, GIOU: 0.319418), Class: 0.478655, Obj: 0.064389, No Obj: 0.003637, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 2.005719, iou_loss = 0.072160, total_loss = 2.077879 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000578, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001568, iou_loss = 0.000000, total_loss = 0.001568 \n",
" total_bbox = 35127, rewritten_bbox = 0.290375 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353999, GIOU: 0.210679), Class: 0.568285, Obj: 0.023392, No Obj: 0.001927, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.670007, iou_loss = 0.023307, total_loss = 1.693314 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000509, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002926, iou_loss = 0.000000, total_loss = 0.002926 \n",
" total_bbox = 35132, rewritten_bbox = 0.290334 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.462387, GIOU: 0.441814), Class: 0.563323, Obj: 0.180016, No Obj: 0.002802, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.274148, iou_loss = 0.193865, total_loss = 1.468013 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.249699, GIOU: -0.040151), Class: 0.787008, Obj: 0.005043, No Obj: 0.000528, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.350500, iou_loss = 0.029219, total_loss = 0.379719 \n",
" total_bbox = 35137, rewritten_bbox = 0.290292 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.234252, GIOU: 0.094828), Class: 0.450177, Obj: 0.047413, No Obj: 0.002362, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.482536, iou_loss = 0.004628, total_loss = 1.487164 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.094382, GIOU: -0.401956), Class: 0.720928, Obj: 0.001422, No Obj: 0.000489, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.290604, iou_loss = 0.004511, total_loss = 0.295115 \n",
" total_bbox = 35142, rewritten_bbox = 0.290251 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.281336, GIOU: 0.197551), Class: 0.473653, Obj: 0.011412, No Obj: 0.001879, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.503837, iou_loss = 0.067214, total_loss = 1.571051 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000518, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000989, iou_loss = 0.000000, total_loss = 0.000989 \n",
" total_bbox = 35146, rewritten_bbox = 0.290218 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.502700, GIOU: 0.456019), Class: 0.584083, Obj: 0.037934, No Obj: 0.002016, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.656258, iou_loss = 0.146397, total_loss = 1.802655 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.332735, GIOU: 0.332735), Class: 0.543866, Obj: 0.002250, No Obj: 0.000539, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.115069, iou_loss = 0.344582, total_loss = 1.459651 \n",
" total_bbox = 35154, rewritten_bbox = 0.290152 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.556451, GIOU: 0.531809), Class: 0.599562, Obj: 0.097037, No Obj: 0.002828, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.245438, iou_loss = 0.036695, total_loss = 1.282133 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000550, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002741, iou_loss = 0.000000, total_loss = 0.002741 \n",
" total_bbox = 35158, rewritten_bbox = 0.290119 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.450500, GIOU: 0.384168), Class: 0.630373, Obj: 0.150046, No Obj: 0.003595, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.966641, iou_loss = 0.048773, total_loss = 1.015414 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000502, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001327, iou_loss = 0.000000, total_loss = 0.001327 \n",
" total_bbox = 35161, rewritten_bbox = 0.290094 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421544, GIOU: 0.323489), Class: 0.473669, Obj: 0.022686, No Obj: 0.002318, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.349113, iou_loss = 0.198305, total_loss = 2.547418 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000507, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001724, iou_loss = 0.000000, total_loss = 0.001724 \n",
" total_bbox = 35167, rewritten_bbox = 0.290045 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477478, GIOU: 0.375055), Class: 0.509880, Obj: 0.073444, No Obj: 0.002436, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.308708, iou_loss = 0.088962, total_loss = 1.397670 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.497033, GIOU: 0.444041), Class: 0.405690, Obj: 0.000758, No Obj: 0.000509, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.381794, iou_loss = 0.225610, total_loss = 0.607404 \n",
" total_bbox = 35173, rewritten_bbox = 0.292838 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429665, GIOU: 0.403123), Class: 0.582720, Obj: 0.105663, No Obj: 0.002629, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.971742, iou_loss = 0.061015, total_loss = 1.032757 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.446257, GIOU: 0.313956), Class: 0.461886, Obj: 0.027262, No Obj: 0.000522, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.426080, iou_loss = 0.102381, total_loss = 0.528461 \n",
" total_bbox = 35177, rewritten_bbox = 0.292805 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 440: 0.845894, 0.884947 avg loss, 0.000098 rate, 0.717721 seconds, 28160 images, 0.717769 hours left\n",
"Loaded: 0.000043 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.354101, GIOU: 0.327457), Class: 0.560726, Obj: 0.100312, No Obj: 0.002103, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.211494, iou_loss = 0.257318, total_loss = 1.468812 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.477911, GIOU: 0.437677), Class: 0.533433, Obj: 0.002628, No Obj: 0.000556, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.720206, iou_loss = 1.238159, total_loss = 1.958365 \n",
" total_bbox = 35183, rewritten_bbox = 0.292755 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300464, GIOU: 0.265512), Class: 0.490185, Obj: 0.085345, No Obj: 0.002566, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.346874, iou_loss = 0.033640, total_loss = 1.380514 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.300296, GIOU: 0.300296), Class: 0.665470, Obj: 0.008986, No Obj: 0.000516, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.324708, iou_loss = 0.079497, total_loss = 0.404205 \n",
" total_bbox = 35188, rewritten_bbox = 0.292713 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.416635, GIOU: 0.367660), Class: 0.553513, Obj: 0.033905, No Obj: 0.001964, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.842770, iou_loss = 0.052507, total_loss = 1.895277 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000504, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000974, iou_loss = 0.000000, total_loss = 0.000974 \n",
" total_bbox = 35193, rewritten_bbox = 0.292672 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.332076, GIOU: 0.204662), Class: 0.617024, Obj: 0.079335, No Obj: 0.002574, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.295691, iou_loss = 0.007823, total_loss = 1.303513 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.681203), Class: 0.492666, Obj: 0.000504, No Obj: 0.000504, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.406032, iou_loss = 0.077545, total_loss = 0.483577 \n",
" total_bbox = 35198, rewritten_bbox = 0.292630 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.386940, GIOU: 0.359869), Class: 0.500083, Obj: 0.045945, No Obj: 0.002925, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.384060, iou_loss = 0.015316, total_loss = 1.399377 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.219591, GIOU: 0.158090), Class: 0.474391, Obj: 0.001963, No Obj: 0.000515, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.428729, iou_loss = 0.802779, total_loss = 1.231509 \n",
" total_bbox = 35203, rewritten_bbox = 0.292589 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.570993, GIOU: 0.566089), Class: 0.560434, Obj: 0.019114, No Obj: 0.002212, .5R: 0.500000, .75R: 0.500000, count: 2, class_loss = 0.679664, iou_loss = 0.009914, total_loss = 0.689578 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.469377, GIOU: 0.345973), Class: 0.466676, Obj: 0.003375, No Obj: 0.000499, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.741512, iou_loss = 0.375638, total_loss = 1.117151 \n",
" total_bbox = 35207, rewritten_bbox = 0.292555 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421514, GIOU: 0.316394), Class: 0.502415, Obj: 0.048705, No Obj: 0.002536, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.112374, iou_loss = 0.038095, total_loss = 1.150468 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.357746, GIOU: 0.318103), Class: 0.530536, Obj: 0.002288, No Obj: 0.000488, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.655885, iou_loss = 0.191061, total_loss = 0.846946 \n",
" total_bbox = 35212, rewritten_bbox = 0.292514 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.575349, GIOU: 0.520647), Class: 0.552175, Obj: 0.034517, No Obj: 0.002627, .5R: 0.750000, .75R: 0.250000, count: 4, class_loss = 1.508889, iou_loss = 0.033536, total_loss = 1.542425 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000502, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000987, iou_loss = 0.000000, total_loss = 0.000987 \n",
" total_bbox = 35216, rewritten_bbox = 0.292481 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.259250, GIOU: 0.104470), Class: 0.562925, Obj: 0.039919, No Obj: 0.002409, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.129167, iou_loss = 0.085682, total_loss = 2.214848 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.418307, GIOU: 0.334474), Class: 0.437684, Obj: 0.022376, No Obj: 0.000540, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.415898, iou_loss = 0.119504, total_loss = 0.535402 \n",
" total_bbox = 35223, rewritten_bbox = 0.292423 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.394355, GIOU: 0.219399), Class: 0.488865, Obj: 0.036039, No Obj: 0.002107, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.358288, iou_loss = 0.010189, total_loss = 1.368477 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000506, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001398, iou_loss = 0.000000, total_loss = 0.001398 \n",
" total_bbox = 35227, rewritten_bbox = 0.292389 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.436413, GIOU: 0.316890), Class: 0.520569, Obj: 0.024034, No Obj: 0.002235, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.602203, iou_loss = 0.014925, total_loss = 1.617128 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.378568, GIOU: 0.316553), Class: 0.308067, Obj: 0.008445, No Obj: 0.000525, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.424304, iou_loss = 0.785296, total_loss = 1.209600 \n",
" total_bbox = 35232, rewritten_bbox = 0.292348 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345948, GIOU: 0.171049), Class: 0.525255, Obj: 0.040015, No Obj: 0.002387, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.436824, iou_loss = 0.016216, total_loss = 1.453040 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000503, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002332, iou_loss = 0.000000, total_loss = 0.002332 \n",
" total_bbox = 35236, rewritten_bbox = 0.292315 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.370953, GIOU: 0.370953), Class: 0.452460, Obj: 0.038480, No Obj: 0.002581, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.404104, iou_loss = 0.010938, total_loss = 1.415042 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000476, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001099, iou_loss = 0.000000, total_loss = 0.001099 \n",
" total_bbox = 35240, rewritten_bbox = 0.292282 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.277309, GIOU: 0.137991), Class: 0.428957, Obj: 0.051388, No Obj: 0.002284, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.520514, iou_loss = 0.009501, total_loss = 1.530014 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000476, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001278, iou_loss = 0.000000, total_loss = 0.001278 \n",
" total_bbox = 35244, rewritten_bbox = 0.292248 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.301301, GIOU: 0.132281), Class: 0.455660, Obj: 0.037295, No Obj: 0.002933, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.870244, iou_loss = 0.018215, total_loss = 1.888459 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000559, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001362, iou_loss = 0.000000, total_loss = 0.001362 \n",
" total_bbox = 35249, rewritten_bbox = 0.292207 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.303928, GIOU: 0.034603), Class: 0.387837, Obj: 0.046282, No Obj: 0.001988, .5R: 0.000000, .75R: 0.000000, count: 7, class_loss = 3.003402, iou_loss = 0.053455, total_loss = 3.056857 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.348348, GIOU: 0.178476), Class: 0.567100, Obj: 0.008283, No Obj: 0.000501, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.714499, iou_loss = 0.127294, total_loss = 0.841793 \n",
" total_bbox = 35258, rewritten_bbox = 0.292132 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 441: 0.932108, 0.889663 avg loss, 0.000099 rate, 0.721203 seconds, 28224 images, 0.717689 hours left\n",
"Loaded: 0.000029 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.317228, GIOU: 0.249184), Class: 0.458952, Obj: 0.101300, No Obj: 0.002732, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.840194, iou_loss = 0.071191, total_loss = 1.911385 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000526, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002532, iou_loss = 0.000000, total_loss = 0.002532 \n",
" total_bbox = 35263, rewritten_bbox = 0.292091 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.490077, GIOU: 0.459842), Class: 0.557450, Obj: 0.041605, No Obj: 0.003003, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.042825, iou_loss = 0.030274, total_loss = 1.073099 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.539855, GIOU: 0.490829), Class: 0.362517, Obj: 0.007781, No Obj: 0.000496, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.821316, iou_loss = 0.403837, total_loss = 1.225153 \n",
" total_bbox = 35268, rewritten_bbox = 0.292049 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.241919, GIOU: 0.001091), Class: 0.498829, Obj: 0.028704, No Obj: 0.002339, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.462622, iou_loss = 0.008162, total_loss = 1.470784 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.165139, GIOU: 0.126293), Class: 0.480337, Obj: 0.000976, No Obj: 0.000502, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.406238, iou_loss = 0.039695, total_loss = 0.445933 \n",
" total_bbox = 35273, rewritten_bbox = 0.292008 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.519390, GIOU: 0.519389), Class: 0.610546, Obj: 0.054714, No Obj: 0.002332, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.609237, iou_loss = 0.040843, total_loss = 0.650081 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.501157, GIOU: 0.422227), Class: 0.498442, Obj: 0.002183, No Obj: 0.000509, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.175946, iou_loss = 1.897387, total_loss = 3.073333 \n",
" total_bbox = 35278, rewritten_bbox = 0.291967 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.396518, GIOU: 0.226392), Class: 0.631320, Obj: 0.069835, No Obj: 0.002038, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.234833, iou_loss = 0.136738, total_loss = 1.371571 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000484, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001976, iou_loss = 0.000000, total_loss = 0.001976 \n",
" total_bbox = 35282, rewritten_bbox = 0.291934 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.321192, GIOU: 0.208624), Class: 0.487444, Obj: 0.040322, No Obj: 0.002758, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.521332, iou_loss = 0.018889, total_loss = 1.540221 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000490, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001048, iou_loss = 0.000000, total_loss = 0.001048 \n",
" total_bbox = 35286, rewritten_bbox = 0.291900 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.473235, GIOU: 0.322970), Class: 0.666121, Obj: 0.025118, No Obj: 0.002786, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.689961, iou_loss = 0.005223, total_loss = 0.695183 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.334189, GIOU: 0.298155), Class: 0.490662, Obj: 0.005094, No Obj: 0.000585, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.717786, iou_loss = 0.439088, total_loss = 1.156874 \n",
" total_bbox = 35290, rewritten_bbox = 0.291867 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.454680, GIOU: 0.331608), Class: 0.590839, Obj: 0.061496, No Obj: 0.003391, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.692561, iou_loss = 0.052464, total_loss = 1.745025 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000535, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001736, iou_loss = 0.000000, total_loss = 0.001736 \n",
" total_bbox = 35295, rewritten_bbox = 0.291826 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.357961, GIOU: 0.309829), Class: 0.467774, Obj: 0.030216, No Obj: 0.002331, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.094372, iou_loss = 0.073524, total_loss = 1.167896 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.385864, GIOU: 0.189945), Class: 0.421450, Obj: 0.002386, No Obj: 0.000491, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.434854, iou_loss = 0.063611, total_loss = 0.498465 \n",
" total_bbox = 35299, rewritten_bbox = 0.291793 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.368775, GIOU: 0.350144), Class: 0.444275, Obj: 0.059002, No Obj: 0.002468, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.444849, iou_loss = 0.097917, total_loss = 1.542766 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.512054, GIOU: 0.468933), Class: 0.511404, Obj: 0.002025, No Obj: 0.000504, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.699250, iou_loss = 0.343543, total_loss = 1.042794 \n",
" total_bbox = 35305, rewritten_bbox = 0.291743 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.424417, GIOU: 0.322863), Class: 0.564387, Obj: 0.061714, No Obj: 0.002410, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.294325, iou_loss = 0.073297, total_loss = 1.367622 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000481, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001057, iou_loss = 0.000000, total_loss = 0.001057 \n",
" total_bbox = 35309, rewritten_bbox = 0.291710 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.252942, GIOU: 0.147767), Class: 0.503028, Obj: 0.076406, No Obj: 0.003362, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.802007, iou_loss = 0.015545, total_loss = 1.817552 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.466390, GIOU: 0.466390), Class: 0.544592, Obj: 0.001287, No Obj: 0.000480, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.330286, iou_loss = 0.592186, total_loss = 0.922472 \n",
" total_bbox = 35315, rewritten_bbox = 0.291661 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.437986, GIOU: 0.396125), Class: 0.461022, Obj: 0.176927, No Obj: 0.003493, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.475647, iou_loss = 0.076196, total_loss = 1.551843 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.346841, GIOU: 0.346841), Class: 0.662012, Obj: 0.006439, No Obj: 0.000509, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.296964, iou_loss = 0.323059, total_loss = 0.620023 \n",
" total_bbox = 35320, rewritten_bbox = 0.291619 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421127, GIOU: 0.339059), Class: 0.503215, Obj: 0.100181, No Obj: 0.002909, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.410638, iou_loss = 0.012618, total_loss = 1.423255 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000478, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001340, iou_loss = 0.000000, total_loss = 0.001340 \n",
" total_bbox = 35324, rewritten_bbox = 0.291586 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369374, GIOU: 0.369374), Class: 0.601301, Obj: 0.069667, No Obj: 0.003019, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.818694, iou_loss = 0.004239, total_loss = 0.822933 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.298890, GIOU: 0.187809), Class: 0.653125, Obj: 0.001264, No Obj: 0.000546, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.359035, iou_loss = 0.769152, total_loss = 1.128188 \n",
" total_bbox = 35327, rewritten_bbox = 0.291562 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.455452, GIOU: 0.364742), Class: 0.444030, Obj: 0.022097, No Obj: 0.002821, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.443728, iou_loss = 0.010654, total_loss = 1.454382 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000487, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000969, iou_loss = 0.000000, total_loss = 0.000969 \n",
" total_bbox = 35331, rewritten_bbox = 0.291529 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 442: 0.824851, 0.883182 avg loss, 0.000100 rate, 0.701101 seconds, 28288 images, 0.717643 hours left\n",
"Loaded: 0.000036 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.125482, GIOU: 0.002885), Class: 0.619904, Obj: 0.100850, No Obj: 0.002148, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.588518, iou_loss = 0.000767, total_loss = 0.589285 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.169216, GIOU: -0.372758), Class: 0.666051, Obj: 0.000666, No Obj: 0.000513, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.680895, iou_loss = 0.034288, total_loss = 0.715183 \n",
" total_bbox = 35335, rewritten_bbox = 0.291496 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.485483, GIOU: 0.428896), Class: 0.539995, Obj: 0.013234, No Obj: 0.002359, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.159017, iou_loss = 0.183519, total_loss = 1.342536 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000525, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002024, iou_loss = 0.000000, total_loss = 0.002024 \n",
" total_bbox = 35338, rewritten_bbox = 0.291471 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.485308, GIOU: 0.447064), Class: 0.661195, Obj: 0.141482, No Obj: 0.003854, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.152935, iou_loss = 0.112564, total_loss = 1.265498 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.599587, GIOU: 0.593268), Class: 0.451194, Obj: 0.032674, No Obj: 0.000502, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.382957, iou_loss = 0.193255, total_loss = 0.576212 \n",
" total_bbox = 35343, rewritten_bbox = 0.291430 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.459613, GIOU: 0.365536), Class: 0.606233, Obj: 0.071750, No Obj: 0.002372, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.384760, iou_loss = 0.116389, total_loss = 1.501149 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000544, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001979, iou_loss = 0.000000, total_loss = 0.001979 \n",
" total_bbox = 35347, rewritten_bbox = 0.291397 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.484951, GIOU: 0.401188), Class: 0.471611, Obj: 0.069379, No Obj: 0.002260, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.381137, iou_loss = 0.051297, total_loss = 1.432435 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000470, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000859, iou_loss = 0.000000, total_loss = 0.000859 \n",
" total_bbox = 35351, rewritten_bbox = 0.291364 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372392, GIOU: 0.174879), Class: 0.594593, Obj: 0.144696, No Obj: 0.002882, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.830078, iou_loss = 0.016014, total_loss = 0.846093 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.520332, GIOU: 0.405133), Class: 0.509980, Obj: 0.003429, No Obj: 0.000481, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.727921, iou_loss = 0.664168, total_loss = 1.392089 \n",
" total_bbox = 35356, rewritten_bbox = 0.291323 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.348448, GIOU: 0.214422), Class: 0.535982, Obj: 0.033434, No Obj: 0.002852, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.500800, iou_loss = 0.053224, total_loss = 1.554024 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000499, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001681, iou_loss = 0.000000, total_loss = 0.001681 \n",
" total_bbox = 35360, rewritten_bbox = 0.291290 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.269790, GIOU: 0.160414), Class: 0.694458, Obj: 0.128404, No Obj: 0.003110, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.137929, iou_loss = 0.020046, total_loss = 1.157975 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.178547, GIOU: 0.035702), Class: 0.556079, Obj: 0.005713, No Obj: 0.000499, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.962163, iou_loss = 0.547859, total_loss = 1.510023 \n",
" total_bbox = 35367, rewritten_bbox = 0.291232 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.252132, GIOU: 0.252132), Class: 0.377658, Obj: 0.048092, No Obj: 0.003546, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.449161, iou_loss = 0.004827, total_loss = 0.453987 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.402359, GIOU: 0.392859), Class: 0.508155, Obj: 0.010712, No Obj: 0.000589, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.085373, iou_loss = 1.107185, total_loss = 2.192558 \n",
" total_bbox = 35371, rewritten_bbox = 0.291199 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413607, GIOU: 0.351993), Class: 0.540249, Obj: 0.031824, No Obj: 0.002253, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.444115, iou_loss = 0.055217, total_loss = 1.499331 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000462, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000914, iou_loss = 0.000000, total_loss = 0.000914 \n",
" total_bbox = 35375, rewritten_bbox = 0.291166 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.355974, GIOU: 0.258389), Class: 0.556130, Obj: 0.029296, No Obj: 0.003312, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.500560, iou_loss = 0.022134, total_loss = 1.522694 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.758372, GIOU: 0.745917), Class: 0.583817, Obj: 0.065160, No Obj: 0.000624, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 0.303711, iou_loss = 0.460493, total_loss = 0.764204 \n",
" total_bbox = 35380, rewritten_bbox = 0.291125 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.280861, GIOU: 0.220199), Class: 0.513065, Obj: 0.007297, No Obj: 0.002347, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.121643, iou_loss = 0.059058, total_loss = 2.180701 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000513, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001802, iou_loss = 0.000000, total_loss = 0.001802 \n",
" total_bbox = 35386, rewritten_bbox = 0.291076 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.355945, GIOU: 0.278542), Class: 0.561883, Obj: 0.024209, No Obj: 0.001931, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.282943, iou_loss = 0.013090, total_loss = 1.296032 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000466, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000906, iou_loss = 0.000000, total_loss = 0.000906 \n",
" total_bbox = 35390, rewritten_bbox = 0.291043 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.265889, GIOU: 0.186476), Class: 0.482150, Obj: 0.045773, No Obj: 0.002665, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.444586, iou_loss = 0.044173, total_loss = 1.488759 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.577170, GIOU: 0.554449), Class: 0.459557, Obj: 0.003218, No Obj: 0.000502, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.109850, iou_loss = 1.924970, total_loss = 3.034820 \n",
" total_bbox = 35397, rewritten_bbox = 0.290985 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.323427, GIOU: 0.278906), Class: 0.552916, Obj: 0.035762, No Obj: 0.002304, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.766873, iou_loss = 0.010035, total_loss = 1.776908 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000460, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001153, iou_loss = 0.000000, total_loss = 0.001153 \n",
" total_bbox = 35402, rewritten_bbox = 0.290944 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.494468, GIOU: 0.463050), Class: 0.512629, Obj: 0.010858, No Obj: 0.002351, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 1.825946, iou_loss = 0.084739, total_loss = 1.910685 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.181188, GIOU: -0.267659), Class: 0.603327, Obj: 0.000935, No Obj: 0.000504, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.359229, iou_loss = 0.008651, total_loss = 0.367880 \n",
" total_bbox = 35408, rewritten_bbox = 0.290895 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 443: 0.839191, 0.878783 avg loss, 0.000101 rate, 0.694104 seconds, 28352 images, 0.717396 hours left\n",
"Loaded: 0.000041 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320162, GIOU: 0.164478), Class: 0.544312, Obj: 0.040473, No Obj: 0.002421, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.539119, iou_loss = 0.039768, total_loss = 1.578886 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000466, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001001, iou_loss = 0.000000, total_loss = 0.001001 \n",
" total_bbox = 35412, rewritten_bbox = 0.290862 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.293464, GIOU: 0.264318), Class: 0.552529, Obj: 0.029976, No Obj: 0.002962, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.395337, iou_loss = 0.026135, total_loss = 1.421471 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.253407, GIOU: 0.227113), Class: 0.548165, Obj: 0.001560, No Obj: 0.000504, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.758348, iou_loss = 0.444130, total_loss = 1.202478 \n",
" total_bbox = 35418, rewritten_bbox = 0.290813 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.418944, GIOU: 0.393616), Class: 0.484804, Obj: 0.066947, No Obj: 0.002670, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 2.109465, iou_loss = 0.103169, total_loss = 2.212635 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.187185, GIOU: -0.238906), Class: 0.445211, Obj: 0.000272, No Obj: 0.000482, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.817491, iou_loss = 0.076481, total_loss = 0.893973 \n",
" total_bbox = 35426, rewritten_bbox = 0.290747 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.204437, GIOU: 0.097983), Class: 0.510447, Obj: 0.127247, No Obj: 0.002399, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.702936, iou_loss = 0.053575, total_loss = 1.756511 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.524715, GIOU: 0.513985), Class: 0.515876, Obj: 0.001266, No Obj: 0.000466, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.777979, iou_loss = 1.475771, total_loss = 2.253750 \n",
" total_bbox = 35433, rewritten_bbox = 0.290689 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.289415, GIOU: 0.205919), Class: 0.540565, Obj: 0.057597, No Obj: 0.002567, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.360137, iou_loss = 0.024215, total_loss = 1.384352 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000476, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001022, iou_loss = 0.000000, total_loss = 0.001022 \n",
" total_bbox = 35437, rewritten_bbox = 0.290657 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.591423, GIOU: 0.543568), Class: 0.520676, Obj: 0.024861, No Obj: 0.002355, .5R: 0.833333, .75R: 0.000000, count: 6, class_loss = 2.134851, iou_loss = 0.274573, total_loss = 2.409424 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000531, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004554, iou_loss = 0.000000, total_loss = 0.004554 \n",
" total_bbox = 35443, rewritten_bbox = 0.290607 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410554, GIOU: 0.261964), Class: 0.554681, Obj: 0.169260, No Obj: 0.002317, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.983539, iou_loss = 0.114583, total_loss = 1.098122 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000539, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002958, iou_loss = 0.000000, total_loss = 0.002958 \n",
" total_bbox = 35446, rewritten_bbox = 0.290583 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.384455, GIOU: 0.365037), Class: 0.633947, Obj: 0.094836, No Obj: 0.002543, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.859094, iou_loss = 0.061143, total_loss = 0.920238 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.252820, GIOU: 0.144491), Class: 0.528268, Obj: 0.011142, No Obj: 0.000537, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.887280, iou_loss = 0.065050, total_loss = 0.952329 \n",
" total_bbox = 35451, rewritten_bbox = 0.290542 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.410138, GIOU: 0.403689), Class: 0.603657, Obj: 0.047786, No Obj: 0.002856, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.107202, iou_loss = 0.111019, total_loss = 1.218221 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.343751, GIOU: 0.148854), Class: 0.465127, Obj: 0.000942, No Obj: 0.000497, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.224966, iou_loss = 4.134154, total_loss = 6.359121 \n",
" total_bbox = 35461, rewritten_bbox = 0.293280 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.363382, GIOU: 0.297846), Class: 0.542515, Obj: 0.021972, No Obj: 0.002723, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.430493, iou_loss = 0.061389, total_loss = 1.491881 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000464, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001023, iou_loss = 0.000000, total_loss = 0.001023 \n",
" total_bbox = 35465, rewritten_bbox = 0.293247 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.512804, GIOU: 0.413057), Class: 0.465109, Obj: 0.038705, No Obj: 0.002160, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 1.860902, iou_loss = 0.118820, total_loss = 1.979722 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000492, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002387, iou_loss = 0.000000, total_loss = 0.002387 \n",
" total_bbox = 35470, rewritten_bbox = 0.293206 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.318453, GIOU: 0.166421), Class: 0.607138, Obj: 0.048172, No Obj: 0.002754, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 1.970624, iou_loss = 0.087773, total_loss = 2.058397 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.190069, GIOU: 0.190070), Class: 0.782441, Obj: 0.000452, No Obj: 0.000530, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.350787, iou_loss = 0.031600, total_loss = 0.382387 \n",
" total_bbox = 35477, rewritten_bbox = 0.293148 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.386835, GIOU: 0.267834), Class: 0.530561, Obj: 0.037375, No Obj: 0.002443, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.753983, iou_loss = 0.030780, total_loss = 1.784763 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000442, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000755, iou_loss = 0.000000, total_loss = 0.000755 \n",
" total_bbox = 35482, rewritten_bbox = 0.293106 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.423419, GIOU: 0.374689), Class: 0.596072, Obj: 0.062189, No Obj: 0.002345, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.385585, iou_loss = 0.028120, total_loss = 1.413706 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000446, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000754, iou_loss = 0.000000, total_loss = 0.000754 \n",
" total_bbox = 35486, rewritten_bbox = 0.293073 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429080, GIOU: 0.411185), Class: 0.474822, Obj: 0.024133, No Obj: 0.002220, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.445986, iou_loss = 0.032542, total_loss = 1.478528 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.359312, GIOU: 0.163523), Class: 0.462789, Obj: 0.002307, No Obj: 0.000479, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.764045, iou_loss = 0.655277, total_loss = 1.419322 \n",
" total_bbox = 35492, rewritten_bbox = 0.293024 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.213716, GIOU: 0.213716), Class: 0.481717, Obj: 0.071899, No Obj: 0.002382, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.418787, iou_loss = 0.005995, total_loss = 1.424782 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000489, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002172, iou_loss = 0.000000, total_loss = 0.002172 \n",
" total_bbox = 35496, rewritten_bbox = 0.292991 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 444: 0.978645, 0.888769 avg loss, 0.000101 rate, 0.703904 seconds, 28416 images, 0.717081 hours left\n",
"Loaded: 0.000055 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.275214, GIOU: 0.234027), Class: 0.591970, Obj: 0.037480, No Obj: 0.001989, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.099950, iou_loss = 0.063673, total_loss = 1.163623 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.109033, GIOU: -0.313332), Class: 0.493331, Obj: 0.000852, No Obj: 0.000485, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.394297, iou_loss = 0.016195, total_loss = 0.410492 \n",
" total_bbox = 35500, rewritten_bbox = 0.292958 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.259324, GIOU: 0.220126), Class: 0.458075, Obj: 0.031626, No Obj: 0.002202, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.577165, iou_loss = 0.015750, total_loss = 1.592914 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000520, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001208, iou_loss = 0.000000, total_loss = 0.001208 \n",
" total_bbox = 35504, rewritten_bbox = 0.292925 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.249574, GIOU: 0.166077), Class: 0.477097, Obj: 0.041251, No Obj: 0.001909, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.501539, iou_loss = 0.011482, total_loss = 1.513021 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000466, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000859, iou_loss = 0.000000, total_loss = 0.000859 \n",
" total_bbox = 35508, rewritten_bbox = 0.292892 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.489552, GIOU: 0.482184), Class: 0.565361, Obj: 0.034847, No Obj: 0.002471, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.239063, iou_loss = 0.018610, total_loss = 1.257673 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000489, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001122, iou_loss = 0.000000, total_loss = 0.001122 \n",
" total_bbox = 35512, rewritten_bbox = 0.292859 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399754, GIOU: 0.321749), Class: 0.523508, Obj: 0.088371, No Obj: 0.002400, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.415173, iou_loss = 0.029891, total_loss = 1.445063 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000468, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001024, iou_loss = 0.000000, total_loss = 0.001024 \n",
" total_bbox = 35516, rewritten_bbox = 0.292826 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.724941, GIOU: 0.705716), Class: 0.701909, Obj: 0.031065, No Obj: 0.002579, .5R: 1.000000, .75R: 0.333333, count: 3, class_loss = 0.873140, iou_loss = 0.056096, total_loss = 0.929236 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.391053, GIOU: 0.221744), Class: 0.643669, Obj: 0.001707, No Obj: 0.000458, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.662037, iou_loss = 0.164706, total_loss = 0.826743 \n",
" total_bbox = 35521, rewritten_bbox = 0.292785 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.316938, GIOU: 0.280765), Class: 0.448984, Obj: 0.029665, No Obj: 0.002701, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.505313, iou_loss = 0.053583, total_loss = 1.558896 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000526, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001736, iou_loss = 0.000000, total_loss = 0.001736 \n",
" total_bbox = 35525, rewritten_bbox = 0.292752 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.392178, GIOU: 0.357085), Class: 0.487602, Obj: 0.099111, No Obj: 0.002549, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.942236, iou_loss = 0.081219, total_loss = 1.023456 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.308673, GIOU: 0.308673), Class: 0.365804, Obj: 0.012556, No Obj: 0.000588, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.376842, iou_loss = 0.082333, total_loss = 0.459176 \n",
" total_bbox = 35529, rewritten_bbox = 0.292719 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409195, GIOU: 0.360650), Class: 0.559134, Obj: 0.028891, No Obj: 0.003092, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 1.992170, iou_loss = 0.081698, total_loss = 2.073868 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000499, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001372, iou_loss = 0.000000, total_loss = 0.001372 \n",
" total_bbox = 35535, rewritten_bbox = 0.292669 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.364603, GIOU: 0.295210), Class: 0.391208, Obj: 0.020443, No Obj: 0.002493, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.182227, iou_loss = 0.057168, total_loss = 1.239396 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000445, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001228, iou_loss = 0.000000, total_loss = 0.001228 \n",
" total_bbox = 35538, rewritten_bbox = 0.292645 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346098, GIOU: 0.201397), Class: 0.393241, Obj: 0.009255, No Obj: 0.002566, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.748454, iou_loss = 0.088457, total_loss = 1.836911 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.368002, GIOU: 0.316283), Class: 0.490229, Obj: 0.000498, No Obj: 0.000484, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.384778, iou_loss = 0.045470, total_loss = 0.430247 \n",
" total_bbox = 35543, rewritten_bbox = 0.292603 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.414845, GIOU: 0.396195), Class: 0.645999, Obj: 0.096832, No Obj: 0.003148, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.468522, iou_loss = 0.057763, total_loss = 1.526286 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000520, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001956, iou_loss = 0.000000, total_loss = 0.001956 \n",
" total_bbox = 35548, rewritten_bbox = 0.292562 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.527589, GIOU: 0.450752), Class: 0.562164, Obj: 0.032584, No Obj: 0.002906, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.463161, iou_loss = 0.048277, total_loss = 1.511438 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.510236, GIOU: 0.429041), Class: 0.528274, Obj: 0.026030, No Obj: 0.000485, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.683871, iou_loss = 0.372797, total_loss = 1.056668 \n",
" total_bbox = 35554, rewritten_bbox = 0.292513 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.523961, GIOU: 0.472988), Class: 0.537543, Obj: 0.032927, No Obj: 0.002879, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.494601, iou_loss = 0.018675, total_loss = 1.513275 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000439, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000754, iou_loss = 0.000000, total_loss = 0.000754 \n",
" total_bbox = 35558, rewritten_bbox = 0.292480 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.352214, GIOU: 0.321876), Class: 0.535989, Obj: 0.014218, No Obj: 0.002400, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.404963, iou_loss = 0.024980, total_loss = 1.429943 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.483993, GIOU: 0.413283), Class: 0.566801, Obj: 0.006284, No Obj: 0.000469, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.020560, iou_loss = 0.369453, total_loss = 1.390013 \n",
" total_bbox = 35565, rewritten_bbox = 0.292422 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.446683, GIOU: 0.385802), Class: 0.502692, Obj: 0.031844, No Obj: 0.002381, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.086873, iou_loss = 0.019357, total_loss = 1.106230 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.326573, GIOU: 0.326573), Class: 0.318377, Obj: 0.004919, No Obj: 0.000522, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.873934, iou_loss = 0.222634, total_loss = 1.096568 \n",
" total_bbox = 35570, rewritten_bbox = 0.292381 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 445: 0.832728, 0.883165 avg loss, 0.000102 rate, 0.675946 seconds, 28480 images, 0.716863 hours left\n",
"Loaded: 0.000080 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.201985, GIOU: 0.150884), Class: 0.568367, Obj: 0.040288, No Obj: 0.002253, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.329805, iou_loss = 0.007170, total_loss = 1.336976 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000498, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001237, iou_loss = 0.000000, total_loss = 0.001237 \n",
" total_bbox = 35574, rewritten_bbox = 0.292348 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.477035, GIOU: 0.372881), Class: 0.484412, Obj: 0.078095, No Obj: 0.002680, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.407298, iou_loss = 0.138480, total_loss = 1.545777 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000485, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001311, iou_loss = 0.000000, total_loss = 0.001311 \n",
" total_bbox = 35578, rewritten_bbox = 0.292315 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.320667, GIOU: 0.132787), Class: 0.606977, Obj: 0.032911, No Obj: 0.002231, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.737423, iou_loss = 0.010996, total_loss = 1.748419 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000460, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001105, iou_loss = 0.000000, total_loss = 0.001105 \n",
" total_bbox = 35583, rewritten_bbox = 0.292274 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.437407, GIOU: 0.376104), Class: 0.474791, Obj: 0.029578, No Obj: 0.002620, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.946399, iou_loss = 0.014828, total_loss = 1.961227 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.486369, GIOU: 0.367135), Class: 0.515577, Obj: 0.016862, No Obj: 0.000470, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.339805, iou_loss = 0.112235, total_loss = 0.452041 \n",
" total_bbox = 35589, rewritten_bbox = 0.292225 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.407669, GIOU: 0.247717), Class: 0.374497, Obj: 0.019176, No Obj: 0.002309, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 2.004803, iou_loss = 0.026028, total_loss = 2.030831 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.363086, GIOU: 0.297388), Class: 0.367530, Obj: 0.001646, No Obj: 0.000505, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.637845, iou_loss = 1.608364, total_loss = 3.246209 \n",
" total_bbox = 35598, rewritten_bbox = 0.292151 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322866, GIOU: 0.165139), Class: 0.370609, Obj: 0.017371, No Obj: 0.001802, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.589171, iou_loss = 0.057799, total_loss = 1.646970 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000476, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001624, iou_loss = 0.000000, total_loss = 0.001624 \n",
" total_bbox = 35602, rewritten_bbox = 0.292118 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.342021, GIOU: 0.270875), Class: 0.408354, Obj: 0.046303, No Obj: 0.002696, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 2.362829, iou_loss = 0.096609, total_loss = 2.459438 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.414136, GIOU: 0.241358), Class: 0.574615, Obj: 0.035084, No Obj: 0.000554, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.742066, iou_loss = 0.364490, total_loss = 1.106556 \n",
" total_bbox = 35610, rewritten_bbox = 0.292053 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.557539, GIOU: 0.540790), Class: 0.565520, Obj: 0.106946, No Obj: 0.002943, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.221957, iou_loss = 0.050876, total_loss = 1.272833 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000511, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004236, iou_loss = 0.000000, total_loss = 0.004236 \n",
" total_bbox = 35614, rewritten_bbox = 0.292020 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.421339, GIOU: 0.363724), Class: 0.503469, Obj: 0.112867, No Obj: 0.002778, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.342226, iou_loss = 0.028751, total_loss = 1.370978 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.587004, GIOU: 0.587006), Class: 0.499306, Obj: 0.001214, No Obj: 0.000523, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.370977, iou_loss = 0.949624, total_loss = 1.320601 \n",
" total_bbox = 35619, rewritten_bbox = 0.291979 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.210303, GIOU: 0.121454), Class: 0.611820, Obj: 0.108334, No Obj: 0.002718, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.174622, iou_loss = 0.023591, total_loss = 1.198212 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.439237, GIOU: 0.299597), Class: 0.648220, Obj: 0.003063, No Obj: 0.000548, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.427789, iou_loss = 1.558501, total_loss = 2.986290 \n",
" total_bbox = 35627, rewritten_bbox = 0.291913 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.572191, GIOU: 0.557728), Class: 0.540375, Obj: 0.021387, No Obj: 0.002466, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.083723, iou_loss = 0.057433, total_loss = 1.141156 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.488902, GIOU: 0.424905), Class: 0.379205, Obj: 0.037033, No Obj: 0.000471, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.395269, iou_loss = 0.121621, total_loss = 0.516891 \n",
" total_bbox = 35631, rewritten_bbox = 0.291881 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.314806, GIOU: 0.266174), Class: 0.529230, Obj: 0.024939, No Obj: 0.002902, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.799798, iou_loss = 0.025724, total_loss = 1.825522 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000466, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000952, iou_loss = 0.000000, total_loss = 0.000952 \n",
" total_bbox = 35636, rewritten_bbox = 0.291840 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.228353, GIOU: 0.153342), Class: 0.589644, Obj: 0.044355, No Obj: 0.002075, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.915980, iou_loss = 0.032535, total_loss = 0.948515 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.256223, GIOU: -0.091212), Class: 0.574822, Obj: 0.000199, No Obj: 0.000472, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.365316, iou_loss = 0.070743, total_loss = 0.436058 \n",
" total_bbox = 35640, rewritten_bbox = 0.291807 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.260914, GIOU: 0.260914), Class: 0.471487, Obj: 0.045864, No Obj: 0.002883, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.657446, iou_loss = 0.002213, total_loss = 0.659659 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.154925, GIOU: -0.058861), Class: 0.445353, Obj: 0.001495, No Obj: 0.000519, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.083575, iou_loss = 2.429381, total_loss = 3.512956 \n",
" total_bbox = 35646, rewritten_bbox = 0.294563 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.296309, GIOU: 0.270504), Class: 0.611334, Obj: 0.115437, No Obj: 0.002786, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.240422, iou_loss = 0.020191, total_loss = 1.260613 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000445, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000763, iou_loss = 0.000000, total_loss = 0.000763 \n",
" total_bbox = 35650, rewritten_bbox = 0.294530 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.349744, GIOU: 0.239640), Class: 0.578945, Obj: 0.088687, No Obj: 0.002189, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.679409, iou_loss = 0.048919, total_loss = 1.728328 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.323978, GIOU: 0.116811), Class: 0.404252, Obj: 0.024487, No Obj: 0.000552, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.830618, iou_loss = 0.572172, total_loss = 1.402789 \n",
" total_bbox = 35657, rewritten_bbox = 0.294472 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 446: 0.967927, 0.891641 avg loss, 0.000103 rate, 0.690634 seconds, 28544 images, 0.716370 hours left\n",
"Loaded: 0.000070 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.384098, GIOU: 0.373458), Class: 0.604730, Obj: 0.139302, No Obj: 0.002969, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.218374, iou_loss = 0.017238, total_loss = 1.235612 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000460, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000869, iou_loss = 0.000000, total_loss = 0.000869 \n",
" total_bbox = 35661, rewritten_bbox = 0.294439 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319157, GIOU: 0.282366), Class: 0.566371, Obj: 0.075290, No Obj: 0.002053, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.345738, iou_loss = 0.106869, total_loss = 1.452607 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.366902, GIOU: 0.366902), Class: 0.421734, Obj: 0.006520, No Obj: 0.000580, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.372075, iou_loss = 0.116038, total_loss = 0.488113 \n",
" total_bbox = 35666, rewritten_bbox = 0.294398 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.555477, GIOU: 0.492854), Class: 0.515190, Obj: 0.024626, No Obj: 0.002079, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.433399, iou_loss = 0.083142, total_loss = 1.516541 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000460, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001880, iou_loss = 0.000000, total_loss = 0.001880 \n",
" total_bbox = 35670, rewritten_bbox = 0.294365 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.362125, GIOU: 0.226002), Class: 0.425111, Obj: 0.033141, No Obj: 0.002816, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.551560, iou_loss = 0.011057, total_loss = 1.562616 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000473, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001186, iou_loss = 0.000000, total_loss = 0.001186 \n",
" total_bbox = 35674, rewritten_bbox = 0.294332 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.449669, GIOU: 0.368279), Class: 0.519048, Obj: 0.018447, No Obj: 0.002316, .5R: 0.500000, .75R: 0.000000, count: 6, class_loss = 1.982440, iou_loss = 0.094487, total_loss = 2.076927 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.245631, GIOU: -0.006605), Class: 0.467307, Obj: 0.000333, No Obj: 0.000459, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.340446, iou_loss = 0.067140, total_loss = 0.407586 \n",
" total_bbox = 35681, rewritten_bbox = 0.294274 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344957, GIOU: 0.313638), Class: 0.448008, Obj: 0.028033, No Obj: 0.003068, .5R: 0.285714, .75R: 0.000000, count: 7, class_loss = 3.034264, iou_loss = 0.098680, total_loss = 3.132944 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.217309, GIOU: 0.134626), Class: 0.446920, Obj: 0.001491, No Obj: 0.000507, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.372290, iou_loss = 0.032313, total_loss = 0.404603 \n",
" total_bbox = 35689, rewritten_bbox = 0.294208 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.394972, GIOU: 0.394972), Class: 0.483663, Obj: 0.042948, No Obj: 0.002470, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.453223, iou_loss = 0.039502, total_loss = 1.492725 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000452, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000928, iou_loss = 0.000000, total_loss = 0.000928 \n",
" total_bbox = 35693, rewritten_bbox = 0.294175 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.375823, GIOU: 0.317352), Class: 0.525668, Obj: 0.058834, No Obj: 0.002026, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.642208, iou_loss = 0.207834, total_loss = 1.850042 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.513378, GIOU: 0.315976), Class: 0.524579, Obj: 0.007518, No Obj: 0.000542, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.110833, iou_loss = 2.172134, total_loss = 3.282967 \n",
" total_bbox = 35701, rewritten_bbox = 0.294109 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.227942, GIOU: 0.206692), Class: 0.493377, Obj: 0.027046, No Obj: 0.002267, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 1.888538, iou_loss = 0.030562, total_loss = 1.919100 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.285823, GIOU: 0.285823), Class: 0.369411, Obj: 0.002673, No Obj: 0.000504, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.765647, iou_loss = 0.142916, total_loss = 0.908563 \n",
" total_bbox = 35709, rewritten_bbox = 0.296844 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.302324, GIOU: 0.155734), Class: 0.572841, Obj: 0.014543, No Obj: 0.002068, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.993031, iou_loss = 0.014336, total_loss = 1.007368 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000470, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001788, iou_loss = 0.000000, total_loss = 0.001788 \n",
" total_bbox = 35712, rewritten_bbox = 0.296819 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.269967, GIOU: 0.147653), Class: 0.538150, Obj: 0.038919, No Obj: 0.002275, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.126833, iou_loss = 0.043768, total_loss = 2.170601 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000527, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001566, iou_loss = 0.000000, total_loss = 0.001566 \n",
" total_bbox = 35718, rewritten_bbox = 0.296769 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.466935, GIOU: 0.404530), Class: 0.504240, Obj: 0.045700, No Obj: 0.002669, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 2.774477, iou_loss = 0.117219, total_loss = 2.891696 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000473, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001325, iou_loss = 0.000000, total_loss = 0.001325 \n",
" total_bbox = 35725, rewritten_bbox = 0.296711 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.415167, GIOU: 0.314704), Class: 0.592823, Obj: 0.039419, No Obj: 0.002614, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.718988, iou_loss = 0.026893, total_loss = 1.745881 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000465, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001034, iou_loss = 0.000000, total_loss = 0.001034 \n",
" total_bbox = 35730, rewritten_bbox = 0.296669 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.336954, GIOU: 0.317213), Class: 0.459425, Obj: 0.078956, No Obj: 0.002777, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.364841, iou_loss = 0.017145, total_loss = 1.381986 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.542783, GIOU: 0.534624), Class: 0.342228, Obj: 0.001321, No Obj: 0.000525, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.863947, iou_loss = 1.581746, total_loss = 2.445693 \n",
" total_bbox = 35736, rewritten_bbox = 0.296620 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.330632, GIOU: 0.173091), Class: 0.567127, Obj: 0.103982, No Obj: 0.002319, .5R: 0.166667, .75R: 0.000000, count: 6, class_loss = 1.889061, iou_loss = 0.101801, total_loss = 1.990862 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.376708, GIOU: 0.347246), Class: 0.343700, Obj: 0.001641, No Obj: 0.000538, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.467818, iou_loss = 0.537498, total_loss = 1.005316 \n",
" total_bbox = 35743, rewritten_bbox = 0.296562 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.527572, GIOU: 0.406543), Class: 0.538668, Obj: 0.057608, No Obj: 0.002854, .5R: 0.500000, .75R: 0.166667, count: 6, class_loss = 1.943425, iou_loss = 0.367993, total_loss = 2.311418 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000537, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.010234, iou_loss = 0.000000, total_loss = 0.010234 \n",
" total_bbox = 35749, rewritten_bbox = 0.296512 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 447: 1.028939, 0.905371 avg loss, 0.000104 rate, 0.721285 seconds, 28608 images, 0.716025 hours left\n",
"Loaded: 0.000064 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.339356, GIOU: 0.177758), Class: 0.537393, Obj: 0.015808, No Obj: 0.002275, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.345162, iou_loss = 0.107920, total_loss = 2.453082 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000537, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003290, iou_loss = 0.000000, total_loss = 0.003290 \n",
" total_bbox = 35755, rewritten_bbox = 0.296462 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.407014, GIOU: 0.263471), Class: 0.615663, Obj: 0.045739, No Obj: 0.002480, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.576049, iou_loss = 0.082449, total_loss = 1.658498 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.461439, GIOU: 0.380633), Class: 0.714864, Obj: 0.003709, No Obj: 0.000460, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.656755, iou_loss = 0.642593, total_loss = 1.299348 \n",
" total_bbox = 35762, rewritten_bbox = 0.296404 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429944, GIOU: 0.378430), Class: 0.487990, Obj: 0.060028, No Obj: 0.002228, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.055753, iou_loss = 0.036867, total_loss = 1.092620 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.236300, GIOU: 0.171988), Class: 0.532701, Obj: 0.012151, No Obj: 0.000483, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.349438, iou_loss = 0.439851, total_loss = 0.789288 \n",
" total_bbox = 35766, rewritten_bbox = 0.296371 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346726, GIOU: 0.244311), Class: 0.580266, Obj: 0.102629, No Obj: 0.002848, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.161277, iou_loss = 0.043555, total_loss = 1.204831 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.817116, GIOU: 0.812417), Class: 0.638182, Obj: 0.001544, No Obj: 0.000463, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 0.315427, iou_loss = 0.622454, total_loss = 0.937881 \n",
" total_bbox = 35771, rewritten_bbox = 0.296329 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359427, GIOU: 0.317230), Class: 0.491210, Obj: 0.063514, No Obj: 0.003251, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.731784, iou_loss = 0.020795, total_loss = 1.752579 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.591081, GIOU: 0.525345), Class: 0.266293, Obj: 0.027182, No Obj: 0.000496, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.487674, iou_loss = 0.195839, total_loss = 0.683512 \n",
" total_bbox = 35777, rewritten_bbox = 0.296280 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413069, GIOU: 0.323249), Class: 0.521819, Obj: 0.067777, No Obj: 0.002086, .5R: 0.250000, .75R: 0.250000, count: 4, class_loss = 1.372285, iou_loss = 0.042211, total_loss = 1.414495 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.292719, GIOU: 0.102313), Class: 0.640781, Obj: 0.000607, No Obj: 0.000477, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.356132, iou_loss = 0.122091, total_loss = 0.478223 \n",
" total_bbox = 35782, rewritten_bbox = 0.296238 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.601038, GIOU: 0.600221), Class: 0.621201, Obj: 0.071016, No Obj: 0.002788, .5R: 1.000000, .75R: 0.000000, count: 3, class_loss = 0.981964, iou_loss = 0.125867, total_loss = 1.107831 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.249033, GIOU: 0.039099), Class: 0.709569, Obj: 0.017811, No Obj: 0.000506, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.309883, iou_loss = 0.033089, total_loss = 0.342972 \n",
" total_bbox = 35786, rewritten_bbox = 0.296205 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353673, GIOU: 0.327854), Class: 0.623469, Obj: 0.115155, No Obj: 0.002373, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.105618, iou_loss = 0.044234, total_loss = 1.149853 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000489, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002164, iou_loss = 0.000000, total_loss = 0.002164 \n",
" total_bbox = 35790, rewritten_bbox = 0.296172 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.252574, GIOU: 0.218389), Class: 0.521341, Obj: 0.067706, No Obj: 0.002297, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.917487, iou_loss = 0.019019, total_loss = 1.936506 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.234398, GIOU: -0.088537), Class: 0.721163, Obj: 0.004557, No Obj: 0.000443, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.347227, iou_loss = 0.015437, total_loss = 0.362664 \n",
" total_bbox = 35796, rewritten_bbox = 0.296122 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.460625, GIOU: 0.422803), Class: 0.568081, Obj: 0.054288, No Obj: 0.002535, .5R: 0.333333, .75R: 0.166667, count: 6, class_loss = 2.020344, iou_loss = 0.217906, total_loss = 2.238250 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000510, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.004689, iou_loss = 0.000000, total_loss = 0.004689 \n",
" total_bbox = 35802, rewritten_bbox = 0.296073 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.529763, GIOU: 0.450499), Class: 0.738167, Obj: 0.046408, No Obj: 0.002575, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.214283, iou_loss = 0.036823, total_loss = 1.251106 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000475, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001559, iou_loss = 0.000000, total_loss = 0.001559 \n",
" total_bbox = 35806, rewritten_bbox = 0.296040 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.433372, GIOU: 0.383858), Class: 0.546791, Obj: 0.082653, No Obj: 0.002226, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.570388, iou_loss = 0.120949, total_loss = 1.691337 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.554570, GIOU: 0.511243), Class: 0.581758, Obj: 0.000925, No Obj: 0.000450, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.315277, iou_loss = 0.400827, total_loss = 0.716104 \n",
" total_bbox = 35812, rewritten_bbox = 0.295990 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.321209, GIOU: 0.078080), Class: 0.513005, Obj: 0.023522, No Obj: 0.002604, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.766207, iou_loss = 0.009482, total_loss = 1.775689 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000479, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001418, iou_loss = 0.000000, total_loss = 0.001418 \n",
" total_bbox = 35817, rewritten_bbox = 0.295949 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.420996, GIOU: 0.398230), Class: 0.651622, Obj: 0.033332, No Obj: 0.002563, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 0.934841, iou_loss = 0.016708, total_loss = 0.951548 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.467796, GIOU: 0.417372), Class: 0.553815, Obj: 0.016004, No Obj: 0.000525, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.113709, iou_loss = 0.711747, total_loss = 1.825457 \n",
" total_bbox = 35823, rewritten_bbox = 0.295899 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.205395, GIOU: 0.071865), Class: 0.629745, Obj: 0.126859, No Obj: 0.002435, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.771227, iou_loss = 0.002835, total_loss = 0.774062 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000488, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001034, iou_loss = 0.000000, total_loss = 0.001034 \n",
" total_bbox = 35826, rewritten_bbox = 0.295875 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.524705, GIOU: 0.478172), Class: 0.500219, Obj: 0.054450, No Obj: 0.002424, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.403611, iou_loss = 0.062634, total_loss = 1.466244 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000461, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001353, iou_loss = 0.000000, total_loss = 0.001353 \n",
" total_bbox = 35830, rewritten_bbox = 0.295841 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 448: 0.857891, 0.900623 avg loss, 0.000105 rate, 0.678296 seconds, 28672 images, 0.715985 hours left\n",
"Loaded: 0.000037 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307620, GIOU: 0.206237), Class: 0.663852, Obj: 0.050643, No Obj: 0.002699, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.293022, iou_loss = 0.017624, total_loss = 1.310646 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000462, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001449, iou_loss = 0.000000, total_loss = 0.001449 \n",
" total_bbox = 35834, rewritten_bbox = 0.295808 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438490, GIOU: 0.419531), Class: 0.552631, Obj: 0.154201, No Obj: 0.002763, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.632937, iou_loss = 0.013476, total_loss = 0.646413 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.362075, GIOU: 0.256839), Class: 0.555517, Obj: 0.003078, No Obj: 0.000456, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.356880, iou_loss = 0.251169, total_loss = 0.608048 \n",
" total_bbox = 35837, rewritten_bbox = 0.295784 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.496530, GIOU: 0.421298), Class: 0.571016, Obj: 0.125208, No Obj: 0.002651, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.515749, iou_loss = 0.178984, total_loss = 1.694734 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.291509, GIOU: 0.146218), Class: 0.607983, Obj: 0.009130, No Obj: 0.000505, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.124957, iou_loss = 1.164176, total_loss = 2.289134 \n",
" total_bbox = 35845, rewritten_bbox = 0.295718 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.278510, GIOU: 0.253202), Class: 0.638671, Obj: 0.036314, No Obj: 0.002218, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.367929, iou_loss = 0.011837, total_loss = 1.379767 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000502, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003582, iou_loss = 0.000000, total_loss = 0.003582 \n",
" total_bbox = 35849, rewritten_bbox = 0.295685 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.411321, GIOU: 0.393012), Class: 0.453923, Obj: 0.005130, No Obj: 0.002172, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.596959, iou_loss = 0.076637, total_loss = 1.673595 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.275037, GIOU: 0.275037), Class: 0.652843, Obj: 0.003290, No Obj: 0.000444, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.334685, iou_loss = 0.070128, total_loss = 0.404813 \n",
" total_bbox = 35854, rewritten_bbox = 0.295643 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.511583, GIOU: 0.404849), Class: 0.548441, Obj: 0.059163, No Obj: 0.001901, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.397512, iou_loss = 0.145605, total_loss = 1.543117 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000499, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003128, iou_loss = 0.000000, total_loss = 0.003128 \n",
" total_bbox = 35858, rewritten_bbox = 0.295610 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444110, GIOU: 0.423404), Class: 0.543648, Obj: 0.031832, No Obj: 0.002275, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.365301, iou_loss = 0.044889, total_loss = 1.410190 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000486, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001140, iou_loss = 0.000000, total_loss = 0.001140 \n",
" total_bbox = 35862, rewritten_bbox = 0.295577 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444049, GIOU: 0.396993), Class: 0.540751, Obj: 0.042197, No Obj: 0.002058, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.475155, iou_loss = 0.013217, total_loss = 1.488372 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000420, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000748, iou_loss = 0.000000, total_loss = 0.000748 \n",
" total_bbox = 35866, rewritten_bbox = 0.295545 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.447430, GIOU: 0.401033), Class: 0.575903, Obj: 0.075844, No Obj: 0.002504, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.300390, iou_loss = 0.072603, total_loss = 1.372993 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.473687, GIOU: 0.401158), Class: 0.452731, Obj: 0.007167, No Obj: 0.000530, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.480475, iou_loss = 1.288225, total_loss = 2.768700 \n",
" total_bbox = 35874, rewritten_bbox = 0.295479 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.503040, GIOU: 0.373851), Class: 0.423530, Obj: 0.067530, No Obj: 0.003245, .5R: 0.600000, .75R: 0.200000, count: 5, class_loss = 1.945954, iou_loss = 0.087133, total_loss = 2.033086 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.371470, GIOU: 0.216695), Class: 0.469774, Obj: 0.068105, No Obj: 0.000486, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.356247, iou_loss = 0.124478, total_loss = 0.480724 \n",
" total_bbox = 35880, rewritten_bbox = 0.295429 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.473566, GIOU: 0.469398), Class: 0.504861, Obj: 0.069641, No Obj: 0.002594, .5R: 0.428571, .75R: 0.000000, count: 7, class_loss = 2.305790, iou_loss = 0.098941, total_loss = 2.404731 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000438, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000824, iou_loss = 0.000000, total_loss = 0.000824 \n",
" total_bbox = 35887, rewritten_bbox = 0.295372 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.337607, GIOU: 0.330377), Class: 0.403052, Obj: 0.024829, No Obj: 0.002095, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.567784, iou_loss = 0.146170, total_loss = 1.713954 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.340897, GIOU: 0.296435), Class: 0.478473, Obj: 0.017074, No Obj: 0.000477, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.125316, iou_loss = 1.600895, total_loss = 2.726210 \n",
" total_bbox = 35894, rewritten_bbox = 0.295314 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413941, GIOU: 0.283747), Class: 0.492252, Obj: 0.021518, No Obj: 0.002184, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.478397, iou_loss = 0.016901, total_loss = 1.495299 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000437, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000783, iou_loss = 0.000000, total_loss = 0.000783 \n",
" total_bbox = 35898, rewritten_bbox = 0.295281 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.459322, GIOU: 0.398849), Class: 0.535906, Obj: 0.033160, No Obj: 0.002497, .5R: 0.428571, .75R: 0.142857, count: 7, class_loss = 2.477019, iou_loss = 0.149243, total_loss = 2.626262 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.488886, GIOU: 0.431417), Class: 0.513247, Obj: 0.003114, No Obj: 0.000484, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.402803, iou_loss = 0.507297, total_loss = 0.910100 \n",
" total_bbox = 35906, rewritten_bbox = 0.295215 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.451169, GIOU: 0.409174), Class: 0.568840, Obj: 0.051116, No Obj: 0.001836, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.325276, iou_loss = 0.032974, total_loss = 1.358251 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.476433, GIOU: 0.454471), Class: 0.756144, Obj: 0.003174, No Obj: 0.000515, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.358404, iou_loss = 0.213933, total_loss = 0.572337 \n",
" total_bbox = 35911, rewritten_bbox = 0.295174 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.462016, GIOU: 0.308834), Class: 0.519751, Obj: 0.043600, No Obj: 0.002087, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.450858, iou_loss = 0.016179, total_loss = 1.467037 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000468, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000880, iou_loss = 0.000000, total_loss = 0.000880 \n",
" total_bbox = 35915, rewritten_bbox = 0.295141 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 449: 0.946688, 0.905229 avg loss, 0.000106 rate, 0.735156 seconds, 28736 images, 0.715518 hours left\n",
"Loaded: 0.000038 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.442476, GIOU: 0.423805), Class: 0.520260, Obj: 0.034946, No Obj: 0.001894, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 1.054076, iou_loss = 0.025102, total_loss = 1.079178 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.623250, GIOU: 0.612770), Class: 0.263999, Obj: 0.001297, No Obj: 0.000451, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.937284, iou_loss = 0.660769, total_loss = 1.598054 \n",
" total_bbox = 35920, rewritten_bbox = 0.295100 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.458814, GIOU: 0.326820), Class: 0.535205, Obj: 0.080314, No Obj: 0.003326, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.302021, iou_loss = 0.024906, total_loss = 1.326927 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.066676, GIOU: -0.694491), Class: 0.516545, Obj: 0.000629, No Obj: 0.000535, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.367953, iou_loss = 0.003632, total_loss = 0.371585 \n",
" total_bbox = 35925, rewritten_bbox = 0.295059 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.409093, GIOU: 0.309065), Class: 0.487147, Obj: 0.016357, No Obj: 0.001721, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.144895, iou_loss = 0.061506, total_loss = 1.206401 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.286509, GIOU: 0.286509), Class: 0.546930, Obj: 0.002557, No Obj: 0.000528, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.355014, iou_loss = 0.071546, total_loss = 0.426560 \n",
" total_bbox = 35929, rewritten_bbox = 0.295026 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.504805, GIOU: 0.419894), Class: 0.631270, Obj: 0.086934, No Obj: 0.003004, .5R: 0.600000, .75R: 0.000000, count: 5, class_loss = 1.559438, iou_loss = 0.106307, total_loss = 1.665745 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.603574, GIOU: 0.603575), Class: 0.571157, Obj: 0.004724, No Obj: 0.000474, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.353048, iou_loss = 0.318004, total_loss = 0.671052 \n",
" total_bbox = 35935, rewritten_bbox = 0.294977 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.231164, GIOU: 0.214538), Class: 0.581448, Obj: 0.031941, No Obj: 0.002434, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.282744, iou_loss = 0.026906, total_loss = 1.309651 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.361398, GIOU: 0.236772), Class: 0.525275, Obj: 0.001381, No Obj: 0.000457, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.427927, iou_loss = 0.073360, total_loss = 0.501286 \n",
" total_bbox = 35940, rewritten_bbox = 0.294936 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.212045, GIOU: 0.105859), Class: 0.534115, Obj: 0.049596, No Obj: 0.001778, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.010617, iou_loss = 0.014849, total_loss = 1.025466 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.620387, GIOU: 0.581212), Class: 0.496725, Obj: 0.001197, No Obj: 0.000459, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.392466, iou_loss = 0.311632, total_loss = 0.704098 \n",
" total_bbox = 35944, rewritten_bbox = 0.294903 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438062, GIOU: 0.339271), Class: 0.589396, Obj: 0.027063, No Obj: 0.002975, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.641509, iou_loss = 0.067717, total_loss = 1.709226 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000575, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.003279, iou_loss = 0.000000, total_loss = 0.003279 \n",
" total_bbox = 35949, rewritten_bbox = 0.294862 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.294495, GIOU: 0.144746), Class: 0.523795, Obj: 0.046846, No Obj: 0.002652, .5R: 0.142857, .75R: 0.000000, count: 7, class_loss = 2.429158, iou_loss = 0.047591, total_loss = 2.476749 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.272553, GIOU: 0.144265), Class: 0.604907, Obj: 0.002071, No Obj: 0.000461, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.343945, iou_loss = 1.195717, total_loss = 1.539662 \n",
" total_bbox = 35957, rewritten_bbox = 0.294797 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.449088, GIOU: 0.348008), Class: 0.650970, Obj: 0.103273, No Obj: 0.002588, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.896553, iou_loss = 0.031788, total_loss = 0.928341 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.803040, GIOU: 0.791536), Class: 0.395743, Obj: 0.035495, No Obj: 0.000453, .5R: 1.000000, .75R: 1.000000, count: 1, class_loss = 0.416039, iou_loss = 0.448945, total_loss = 0.864984 \n",
" total_bbox = 35961, rewritten_bbox = 0.294764 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.316098, GIOU: 0.281384), Class: 0.617439, Obj: 0.030967, No Obj: 0.003328, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.306713, iou_loss = 0.022347, total_loss = 1.329060 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.376247, GIOU: 0.254159), Class: 0.507024, Obj: 0.000271, No Obj: 0.000520, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.420211, iou_loss = 0.219922, total_loss = 0.640133 \n",
" total_bbox = 35966, rewritten_bbox = 0.294723 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.385855, GIOU: 0.341869), Class: 0.372456, Obj: 0.051002, No Obj: 0.002416, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.577612, iou_loss = 0.012403, total_loss = 1.590015 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.552738, GIOU: 0.540567), Class: 0.477232, Obj: 0.004414, No Obj: 0.000451, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.394655, iou_loss = 0.619973, total_loss = 1.014628 \n",
" total_bbox = 35971, rewritten_bbox = 0.294682 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413223, GIOU: 0.336836), Class: 0.446549, Obj: 0.112022, No Obj: 0.003412, .5R: 0.333333, .75R: 0.166667, count: 6, class_loss = 2.310311, iou_loss = 0.167001, total_loss = 2.477312 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.282854, GIOU: 0.282854), Class: 0.268321, Obj: 0.000690, No Obj: 0.000506, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.452755, iou_loss = 0.069754, total_loss = 0.522510 \n",
" total_bbox = 35978, rewritten_bbox = 0.294625 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.187734, GIOU: 0.187734), Class: 0.486796, Obj: 0.043896, No Obj: 0.002254, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.060593, iou_loss = 0.007137, total_loss = 1.067729 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000490, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001038, iou_loss = 0.000000, total_loss = 0.001038 \n",
" total_bbox = 35981, rewritten_bbox = 0.294600 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.328103, GIOU: 0.320920), Class: 0.570549, Obj: 0.142074, No Obj: 0.002859, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.316420, iou_loss = 0.022454, total_loss = 1.338874 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.161260, GIOU: 0.027720), Class: 0.558229, Obj: 0.001453, No Obj: 0.000452, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.357978, iou_loss = 0.040848, total_loss = 0.398826 \n",
" total_bbox = 35986, rewritten_bbox = 0.294559 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.364477, GIOU: 0.359010), Class: 0.499234, Obj: 0.031826, No Obj: 0.002690, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.579038, iou_loss = 0.078189, total_loss = 1.657227 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.528001, GIOU: 0.522240), Class: 0.226413, Obj: 0.002252, No Obj: 0.000488, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.498048, iou_loss = 0.434586, total_loss = 0.932634 \n",
" total_bbox = 35991, rewritten_bbox = 0.294518 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300755, GIOU: 0.184045), Class: 0.575016, Obj: 0.020727, No Obj: 0.002005, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.934593, iou_loss = 0.034250, total_loss = 1.968843 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.300755, GIOU: 0.298581), Class: 0.459274, Obj: 0.007689, No Obj: 0.000421, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.407208, iou_loss = 0.067486, total_loss = 0.474695 \n",
" total_bbox = 35997, rewritten_bbox = 0.294469 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 450: 0.933049, 0.908011 avg loss, 0.000107 rate, 0.753847 seconds, 28800 images, 0.715615 hours left\n",
"Loaded: 0.000067 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.380392, GIOU: 0.377676), Class: 0.582772, Obj: 0.038686, No Obj: 0.002672, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.026706, iou_loss = 0.011877, total_loss = 1.038583 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.134382, GIOU: -0.362258), Class: 0.574011, Obj: 0.000308, No Obj: 0.000529, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.350435, iou_loss = 0.125087, total_loss = 0.475522 \n",
" total_bbox = 36001, rewritten_bbox = 0.294436 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322192, GIOU: 0.322192), Class: 0.438661, Obj: 0.036920, No Obj: 0.002639, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.517338, iou_loss = 0.015879, total_loss = 1.533217 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000437, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002321, iou_loss = 0.000000, total_loss = 0.002321 \n",
" total_bbox = 36005, rewritten_bbox = 0.294404 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.431777, GIOU: 0.349441), Class: 0.548229, Obj: 0.052985, No Obj: 0.002819, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.383320, iou_loss = 0.049073, total_loss = 1.432392 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.210818, GIOU: 0.210818), Class: 0.509128, Obj: 0.004459, No Obj: 0.000432, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.349463, iou_loss = 0.038289, total_loss = 0.387751 \n",
" total_bbox = 36010, rewritten_bbox = 0.294363 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.307465, GIOU: 0.290495), Class: 0.471503, Obj: 0.028544, No Obj: 0.002324, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.555158, iou_loss = 0.004966, total_loss = 1.560123 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.226106, GIOU: 0.226105), Class: 0.476228, Obj: 0.001106, No Obj: 0.000466, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.361397, iou_loss = 0.044418, total_loss = 0.405814 \n",
" total_bbox = 36015, rewritten_bbox = 0.294322 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.508617, GIOU: 0.456296), Class: 0.482625, Obj: 0.162101, No Obj: 0.002457, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.252613, iou_loss = 0.199321, total_loss = 1.451934 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000505, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002427, iou_loss = 0.000000, total_loss = 0.002427 \n",
" total_bbox = 36019, rewritten_bbox = 0.294289 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.319083, GIOU: 0.055412), Class: 0.603951, Obj: 0.148618, No Obj: 0.002958, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.138465, iou_loss = 0.016732, total_loss = 1.155198 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.310383, GIOU: 0.287324), Class: 0.434784, Obj: 0.071976, No Obj: 0.000477, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.736098, iou_loss = 0.850252, total_loss = 1.586350 \n",
" total_bbox = 36025, rewritten_bbox = 0.294240 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.332667, GIOU: 0.194780), Class: 0.536333, Obj: 0.029836, No Obj: 0.002595, .5R: 0.125000, .75R: 0.000000, count: 8, class_loss = 2.751849, iou_loss = 0.090811, total_loss = 2.842660 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000481, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001777, iou_loss = 0.000000, total_loss = 0.001777 \n",
" total_bbox = 36033, rewritten_bbox = 0.294175 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.374535, GIOU: 0.314567), Class: 0.460975, Obj: 0.094862, No Obj: 0.002826, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.411426, iou_loss = 0.079578, total_loss = 1.491004 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.637767, GIOU: 0.628015), Class: 0.457573, Obj: 0.024424, No Obj: 0.000511, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.331756, iou_loss = 0.359324, total_loss = 0.691080 \n",
" total_bbox = 36038, rewritten_bbox = 0.294134 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.455245, GIOU: 0.411613), Class: 0.603313, Obj: 0.015871, No Obj: 0.001924, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.753079, iou_loss = 0.022716, total_loss = 1.775795 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.438853, GIOU: 0.286919), Class: 0.443930, Obj: 0.003049, No Obj: 0.000467, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.137256, iou_loss = 1.065397, total_loss = 2.202652 \n",
" total_bbox = 36046, rewritten_bbox = 0.294069 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.333181, GIOU: 0.217035), Class: 0.600474, Obj: 0.052415, No Obj: 0.002212, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.971320, iou_loss = 0.025579, total_loss = 0.996900 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.266423, GIOU: 0.083724), Class: 0.794356, Obj: 0.008020, No Obj: 0.000497, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.332960, iou_loss = 0.039079, total_loss = 0.372038 \n",
" total_bbox = 36050, rewritten_bbox = 0.294036 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.259373, GIOU: 0.118826), Class: 0.434238, Obj: 0.064326, No Obj: 0.002832, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.481672, iou_loss = 0.022840, total_loss = 1.504512 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000439, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000781, iou_loss = 0.000000, total_loss = 0.000781 \n",
" total_bbox = 36054, rewritten_bbox = 0.294003 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.346714, GIOU: 0.299134), Class: 0.555035, Obj: 0.046615, No Obj: 0.002676, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.046288, iou_loss = 0.022630, total_loss = 1.068918 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000467, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001234, iou_loss = 0.000000, total_loss = 0.001234 \n",
" total_bbox = 36057, rewritten_bbox = 0.293979 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.231412, GIOU: 0.139378), Class: 0.514165, Obj: 0.018429, No Obj: 0.001839, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.489078, iou_loss = 0.069167, total_loss = 1.558244 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000456, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000906, iou_loss = 0.000000, total_loss = 0.000906 \n",
" total_bbox = 36061, rewritten_bbox = 0.293946 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.341888, GIOU: 0.155996), Class: 0.535077, Obj: 0.042707, No Obj: 0.002669, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.469380, iou_loss = 0.056029, total_loss = 1.525409 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000545, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002361, iou_loss = 0.000000, total_loss = 0.002361 \n",
" total_bbox = 36065, rewritten_bbox = 0.293914 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.475232, GIOU: 0.460913), Class: 0.603740, Obj: 0.183523, No Obj: 0.002299, .5R: 0.666667, .75R: 0.000000, count: 3, class_loss = 0.857190, iou_loss = 0.105402, total_loss = 0.962592 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.329875, GIOU: 0.262535), Class: 0.380115, Obj: 0.052828, No Obj: 0.000578, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.439617, iou_loss = 0.123625, total_loss = 0.563242 \n",
" total_bbox = 36069, rewritten_bbox = 0.293881 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.254430, GIOU: 0.034878), Class: 0.490698, Obj: 0.071807, No Obj: 0.002285, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.117200, iou_loss = 0.198380, total_loss = 2.315580 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000486, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001892, iou_loss = 0.000000, total_loss = 0.001892 \n",
" total_bbox = 36075, rewritten_bbox = 0.293832 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 451: 0.860961, 0.903306 avg loss, 0.000108 rate, 0.727972 seconds, 28864 images, 0.715893 hours left\n",
"Loaded: 0.000040 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.389569, GIOU: 0.279456), Class: 0.565400, Obj: 0.087663, No Obj: 0.002909, .5R: 0.200000, .75R: 0.200000, count: 5, class_loss = 1.848254, iou_loss = 0.042082, total_loss = 1.890336 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.423172, GIOU: 0.291734), Class: 0.568555, Obj: 0.013259, No Obj: 0.000467, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.113461, iou_loss = 0.407348, total_loss = 1.520809 \n",
" total_bbox = 36083, rewritten_bbox = 0.293767 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.304031, GIOU: 0.304031), Class: 0.534936, Obj: 0.015253, No Obj: 0.002237, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.181672, iou_loss = 0.020586, total_loss = 1.202257 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.366344, GIOU: 0.313580), Class: 0.524095, Obj: 0.000469, No Obj: 0.000460, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.329631, iou_loss = 0.455276, total_loss = 0.784907 \n",
" total_bbox = 36087, rewritten_bbox = 0.293735 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.266178, GIOU: 0.266178), Class: 0.468665, Obj: 0.011441, No Obj: 0.001653, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.133464, iou_loss = 0.011352, total_loss = 2.144816 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.644167, GIOU: 0.644167), Class: 0.588182, Obj: 0.001228, No Obj: 0.000422, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.366514, iou_loss = 1.138504, total_loss = 1.505018 \n",
" total_bbox = 36094, rewritten_bbox = 0.293678 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472397, GIOU: 0.414116), Class: 0.529562, Obj: 0.105003, No Obj: 0.002545, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.478017, iou_loss = 0.060007, total_loss = 1.538024 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.210275, GIOU: 0.210275), Class: 0.498104, Obj: 0.006145, No Obj: 0.000468, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.361200, iou_loss = 0.118757, total_loss = 0.479957 \n",
" total_bbox = 36099, rewritten_bbox = 0.293637 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.403364, GIOU: 0.225516), Class: 0.671729, Obj: 0.015278, No Obj: 0.002314, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.655041, iou_loss = 0.002783, total_loss = 0.657824 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.429209, GIOU: 0.331935), Class: 0.483353, Obj: 0.015600, No Obj: 0.000450, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.694812, iou_loss = 0.454183, total_loss = 1.148995 \n",
" total_bbox = 36103, rewritten_bbox = 0.293604 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.287657, GIOU: 0.287657), Class: 0.591777, Obj: 0.053353, No Obj: 0.002684, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.038510, iou_loss = 0.019227, total_loss = 1.057737 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.513648, GIOU: 0.457759), Class: 0.500803, Obj: 0.041450, No Obj: 0.000526, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.732237, iou_loss = 0.371302, total_loss = 1.103539 \n",
" total_bbox = 36108, rewritten_bbox = 0.293564 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444380, GIOU: 0.392885), Class: 0.691443, Obj: 0.045782, No Obj: 0.002664, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.196832, iou_loss = 0.076327, total_loss = 1.273160 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000435, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000824, iou_loss = 0.000000, total_loss = 0.000824 \n",
" total_bbox = 36112, rewritten_bbox = 0.293531 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.433966, GIOU: 0.261903), Class: 0.513971, Obj: 0.012412, No Obj: 0.002008, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.059206, iou_loss = 0.140363, total_loss = 1.199569 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.473157, GIOU: 0.383068), Class: 0.587788, Obj: 0.004580, No Obj: 0.000424, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.672355, iou_loss = 0.869151, total_loss = 1.541506 \n",
" total_bbox = 36117, rewritten_bbox = 0.293491 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.417536, GIOU: 0.317432), Class: 0.561339, Obj: 0.066486, No Obj: 0.002768, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.644768, iou_loss = 0.074602, total_loss = 1.719370 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000519, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001893, iou_loss = 0.000000, total_loss = 0.001893 \n",
" total_bbox = 36122, rewritten_bbox = 0.293450 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.413012, GIOU: 0.255567), Class: 0.509107, Obj: 0.024106, No Obj: 0.002495, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.058106, iou_loss = 0.013161, total_loss = 1.071267 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.447765, GIOU: 0.285229), Class: 0.459092, Obj: 0.001611, No Obj: 0.000467, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.739716, iou_loss = 0.702893, total_loss = 1.442609 \n",
" total_bbox = 36127, rewritten_bbox = 0.293409 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.359968, GIOU: 0.180228), Class: 0.618807, Obj: 0.077736, No Obj: 0.002031, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.002107, iou_loss = 0.034000, total_loss = 1.036107 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.217308, GIOU: 0.217308), Class: 0.491035, Obj: 0.005469, No Obj: 0.000510, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.375602, iou_loss = 0.134469, total_loss = 0.510071 \n",
" total_bbox = 36131, rewritten_bbox = 0.293377 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.418419, GIOU: 0.376913), Class: 0.525019, Obj: 0.304408, No Obj: 0.002972, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.890038, iou_loss = 0.058528, total_loss = 0.948566 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.506828, GIOU: 0.488164), Class: 0.644573, Obj: 0.006968, No Obj: 0.000528, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.689673, iou_loss = 2.253315, total_loss = 3.942988 \n",
" total_bbox = 36139, rewritten_bbox = 0.293312 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.471364, GIOU: 0.421690), Class: 0.564102, Obj: 0.065395, No Obj: 0.002632, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.374217, iou_loss = 0.033824, total_loss = 1.408042 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000493, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001715, iou_loss = 0.000000, total_loss = 0.001715 \n",
" total_bbox = 36143, rewritten_bbox = 0.293279 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.553676, GIOU: 0.535143), Class: 0.505121, Obj: 0.024085, No Obj: 0.002191, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.687133, iou_loss = 0.017690, total_loss = 0.704823 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.399500, GIOU: 0.328599), Class: 0.318041, Obj: 0.001807, No Obj: 0.000445, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.464459, iou_loss = 0.365994, total_loss = 0.830453 \n",
" total_bbox = 36146, rewritten_bbox = 0.293255 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.472238, GIOU: 0.340020), Class: 0.509802, Obj: 0.022782, No Obj: 0.001969, .5R: 0.400000, .75R: 0.200000, count: 5, class_loss = 1.747368, iou_loss = 0.046608, total_loss = 1.793977 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.386894, GIOU: 0.205350), Class: 0.654217, Obj: 0.001233, No Obj: 0.000425, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.312961, iou_loss = 0.057032, total_loss = 0.369993 \n",
" total_bbox = 36152, rewritten_bbox = 0.293206 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.510026, GIOU: 0.497010), Class: 0.641008, Obj: 0.017701, No Obj: 0.002410, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.352594, iou_loss = 0.131387, total_loss = 1.483981 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.066611, GIOU: -0.475269), Class: 0.811893, Obj: 0.002203, No Obj: 0.000439, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.328422, iou_loss = 0.001023, total_loss = 0.329445 \n",
" total_bbox = 36157, rewritten_bbox = 0.293166 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 452: 0.901109, 0.903087 avg loss, 0.000109 rate, 0.724741 seconds, 28928 images, 0.715911 hours left\n",
"Loaded: 0.000036 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.522521, GIOU: 0.500932), Class: 0.458866, Obj: 0.064406, No Obj: 0.002811, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.079924, iou_loss = 0.020182, total_loss = 1.100105 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000422, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001473, iou_loss = 0.000000, total_loss = 0.001473 \n",
" total_bbox = 36160, rewritten_bbox = 0.293142 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.438583, GIOU: 0.359364), Class: 0.561305, Obj: 0.042595, No Obj: 0.002122, .5R: 0.333333, .75R: 0.000000, count: 6, class_loss = 2.240197, iou_loss = 0.094477, total_loss = 2.334674 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.451523, GIOU: 0.360873), Class: 0.407195, Obj: 0.001939, No Obj: 0.000490, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.314774, iou_loss = 0.774867, total_loss = 2.089641 \n",
" total_bbox = 36169, rewritten_bbox = 0.293069 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395597, GIOU: 0.310898), Class: 0.501981, Obj: 0.023670, No Obj: 0.002751, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.308259, iou_loss = 0.033760, total_loss = 2.342020 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000454, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000939, iou_loss = 0.000000, total_loss = 0.000939 \n",
" total_bbox = 36175, rewritten_bbox = 0.293020 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382433, GIOU: 0.300814), Class: 0.588871, Obj: 0.085323, No Obj: 0.002555, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.497672, iou_loss = 0.060971, total_loss = 1.558643 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.270526, GIOU: -0.057677), Class: 0.685906, Obj: 0.001616, No Obj: 0.000478, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.296116, iou_loss = 0.024852, total_loss = 0.320968 \n",
" total_bbox = 36181, rewritten_bbox = 0.292971 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.332474, GIOU: 0.232253), Class: 0.457720, Obj: 0.057025, No Obj: 0.002373, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.146908, iou_loss = 0.007914, total_loss = 1.154822 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.373004, GIOU: 0.183243), Class: 0.410904, Obj: 0.017861, No Obj: 0.000422, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.372048, iou_loss = 0.200453, total_loss = 0.572501 \n",
" total_bbox = 36185, rewritten_bbox = 0.292939 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.369650, GIOU: 0.227134), Class: 0.547816, Obj: 0.052926, No Obj: 0.002396, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.352122, iou_loss = 0.059620, total_loss = 1.411742 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.215203, GIOU: 0.209562), Class: 0.473852, Obj: 0.014561, No Obj: 0.000512, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.064816, iou_loss = 0.160536, total_loss = 1.225353 \n",
" total_bbox = 36192, rewritten_bbox = 0.292882 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.355345, GIOU: 0.320447), Class: 0.486466, Obj: 0.081217, No Obj: 0.002517, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.704733, iou_loss = 0.058628, total_loss = 1.763362 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000453, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002226, iou_loss = 0.000000, total_loss = 0.002226 \n",
" total_bbox = 36197, rewritten_bbox = 0.292842 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.509567, GIOU: 0.461262), Class: 0.504420, Obj: 0.021927, No Obj: 0.001955, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.517599, iou_loss = 0.013080, total_loss = 1.530679 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000449, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001064, iou_loss = 0.000000, total_loss = 0.001064 \n",
" total_bbox = 36201, rewritten_bbox = 0.292810 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.443180, GIOU: 0.264514), Class: 0.489578, Obj: 0.037181, No Obj: 0.002331, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.372573, iou_loss = 0.228748, total_loss = 1.601321 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.578843, GIOU: 0.515332), Class: 0.545949, Obj: 0.005800, No Obj: 0.000517, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.344143, iou_loss = 0.631544, total_loss = 0.975687 \n",
" total_bbox = 36206, rewritten_bbox = 0.292769 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.287275, GIOU: 0.102942), Class: 0.440937, Obj: 0.021113, No Obj: 0.002349, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.781141, iou_loss = 0.005421, total_loss = 1.786562 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.155339, GIOU: 0.155338), Class: 0.680853, Obj: 0.000414, No Obj: 0.000444, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.343386, iou_loss = 0.065555, total_loss = 0.408941 \n",
" total_bbox = 36211, rewritten_bbox = 0.292729 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.337590, GIOU: 0.315896), Class: 0.569372, Obj: 0.029126, No Obj: 0.002076, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.252424, iou_loss = 0.036830, total_loss = 1.289255 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000419, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000747, iou_loss = 0.000000, total_loss = 0.000747 \n",
" total_bbox = 36215, rewritten_bbox = 0.292696 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444332, GIOU: 0.349243), Class: 0.600200, Obj: 0.060133, No Obj: 0.002525, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.363276, iou_loss = 0.020920, total_loss = 1.384196 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.264520, GIOU: 0.133337), Class: 0.462205, Obj: 0.001757, No Obj: 0.000459, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.351162, iou_loss = 1.039091, total_loss = 1.390252 \n",
" total_bbox = 36220, rewritten_bbox = 0.292656 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.458327, GIOU: 0.384924), Class: 0.505207, Obj: 0.016637, No Obj: 0.002444, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 1.085904, iou_loss = 0.009238, total_loss = 1.095142 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.215857, GIOU: -0.121667), Class: 0.653933, Obj: 0.001769, No Obj: 0.000428, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.667067, iou_loss = 0.321654, total_loss = 0.988721 \n",
" total_bbox = 36225, rewritten_bbox = 0.292616 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.351325, GIOU: 0.206450), Class: 0.533905, Obj: 0.016179, No Obj: 0.001821, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.062077, iou_loss = 0.055382, total_loss = 1.117459 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.340839, GIOU: 0.326939), Class: 0.391521, Obj: 0.089397, No Obj: 0.000485, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.350088, iou_loss = 0.050751, total_loss = 0.400839 \n",
" total_bbox = 36229, rewritten_bbox = 0.292583 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.374013, GIOU: 0.281499), Class: 0.659621, Obj: 0.097109, No Obj: 0.002003, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.580116, iou_loss = 0.004606, total_loss = 0.584722 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.632040, GIOU: 0.610254), Class: 0.463471, Obj: 0.026096, No Obj: 0.000653, .5R: 0.600000, .75R: 0.400000, count: 5, class_loss = 1.955971, iou_loss = 3.395414, total_loss = 5.351385 \n",
" total_bbox = 36236, rewritten_bbox = 0.292527 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.424966, GIOU: 0.393216), Class: 0.643227, Obj: 0.111918, No Obj: 0.002513, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.265278, iou_loss = 0.187296, total_loss = 1.452574 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.455419, GIOU: 0.455419), Class: 0.528265, Obj: 0.010369, No Obj: 0.000497, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.332970, iou_loss = 0.179870, total_loss = 0.512840 \n",
" total_bbox = 36241, rewritten_bbox = 0.292486 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 453: 0.946876, 0.907466 avg loss, 0.000110 rate, 0.715680 seconds, 28992 images, 0.715895 hours left\n",
"Loaded: 0.000062 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.289833, GIOU: 0.127101), Class: 0.667556, Obj: 0.035781, No Obj: 0.002308, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.458630, iou_loss = 0.010009, total_loss = 1.468639 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.495032, GIOU: 0.484771), Class: 0.532905, Obj: 0.010641, No Obj: 0.000549, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.703788, iou_loss = 2.346734, total_loss = 3.050522 \n",
" total_bbox = 36247, rewritten_bbox = 0.292438 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.528593, GIOU: 0.450442), Class: 0.474627, Obj: 0.037786, No Obj: 0.002472, .5R: 0.666667, .75R: 0.333333, count: 3, class_loss = 1.120071, iou_loss = 0.013114, total_loss = 1.133185 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.263181, GIOU: 0.091712), Class: 0.555264, Obj: 0.002491, No Obj: 0.000411, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.694350, iou_loss = 0.087202, total_loss = 0.781552 \n",
" total_bbox = 36252, rewritten_bbox = 0.292398 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.277509, GIOU: 0.268566), Class: 0.591414, Obj: 0.058801, No Obj: 0.003071, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.802802, iou_loss = 0.029466, total_loss = 1.832268 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.451126, GIOU: 0.310454), Class: 0.570011, Obj: 0.021146, No Obj: 0.000498, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.643133, iou_loss = 0.403717, total_loss = 1.046850 \n",
" total_bbox = 36259, rewritten_bbox = 0.292341 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.367988, GIOU: 0.267982), Class: 0.460056, Obj: 0.036098, No Obj: 0.001873, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.155301, iou_loss = 0.027051, total_loss = 1.182352 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.640657, GIOU: 0.617425), Class: 0.580296, Obj: 0.009123, No Obj: 0.000462, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.356825, iou_loss = 0.623598, total_loss = 0.980423 \n",
" total_bbox = 36263, rewritten_bbox = 0.292309 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.383882, GIOU: 0.374550), Class: 0.561272, Obj: 0.074540, No Obj: 0.002411, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.379456, iou_loss = 0.061760, total_loss = 1.441217 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.609881, GIOU: 0.578782), Class: 0.437419, Obj: 0.008379, No Obj: 0.000415, .5R: 1.000000, .75R: 0.000000, count: 2, class_loss = 0.797495, iou_loss = 0.508486, total_loss = 1.305981 \n",
" total_bbox = 36269, rewritten_bbox = 0.292261 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.393602, GIOU: 0.266467), Class: 0.526488, Obj: 0.017690, No Obj: 0.001992, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.096697, iou_loss = 0.021967, total_loss = 1.118664 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.454904, GIOU: 0.454905), Class: 0.618680, Obj: 0.003262, No Obj: 0.000484, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.355315, iou_loss = 0.571973, total_loss = 0.927288 \n",
" total_bbox = 36273, rewritten_bbox = 0.292228 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.422892, GIOU: 0.399418), Class: 0.527588, Obj: 0.076684, No Obj: 0.001541, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.723416, iou_loss = 0.024762, total_loss = 0.748178 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.415221, GIOU: 0.253559), Class: 0.461226, Obj: 0.003602, No Obj: 0.000490, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.539332, iou_loss = 0.693483, total_loss = 2.232814 \n",
" total_bbox = 36279, rewritten_bbox = 0.292180 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.457357, GIOU: 0.365958), Class: 0.367430, Obj: 0.025518, No Obj: 0.002683, .5R: 0.500000, .75R: 0.250000, count: 4, class_loss = 1.635654, iou_loss = 0.059100, total_loss = 1.694754 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000455, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000983, iou_loss = 0.000000, total_loss = 0.000983 \n",
" total_bbox = 36283, rewritten_bbox = 0.292148 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.433278, GIOU: 0.370551), Class: 0.594682, Obj: 0.070945, No Obj: 0.002631, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.307994, iou_loss = 0.074149, total_loss = 1.382142 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.515649, GIOU: 0.421830), Class: 0.551133, Obj: 0.007013, No Obj: 0.000461, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.367154, iou_loss = 0.123947, total_loss = 0.491101 \n",
" total_bbox = 36288, rewritten_bbox = 0.292108 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.270546, GIOU: 0.152314), Class: 0.658472, Obj: 0.007415, No Obj: 0.002508, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.089508, iou_loss = 0.023159, total_loss = 1.112667 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.492197, GIOU: 0.416592), Class: 0.399389, Obj: 0.031233, No Obj: 0.000558, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.863064, iou_loss = 0.359884, total_loss = 1.222948 \n",
" total_bbox = 36293, rewritten_bbox = 0.292067 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.219190, GIOU: 0.129430), Class: 0.460560, Obj: 0.025110, No Obj: 0.002189, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.568045, iou_loss = 0.039929, total_loss = 1.607974 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000531, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001320, iou_loss = 0.000000, total_loss = 0.001320 \n",
" total_bbox = 36297, rewritten_bbox = 0.292035 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.435650, GIOU: 0.428986), Class: 0.671380, Obj: 0.060441, No Obj: 0.002198, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 0.884293, iou_loss = 0.009860, total_loss = 0.894153 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.258220, GIOU: -0.084363), Class: 0.540751, Obj: 0.045046, No Obj: 0.000420, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.343209, iou_loss = 0.021818, total_loss = 0.365027 \n",
" total_bbox = 36301, rewritten_bbox = 0.292003 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.585413, GIOU: 0.555746), Class: 0.478425, Obj: 0.034068, No Obj: 0.002471, .5R: 0.750000, .75R: 0.000000, count: 4, class_loss = 1.633808, iou_loss = 0.056191, total_loss = 1.689999 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.157975, GIOU: -0.370413), Class: 0.680963, Obj: 0.002273, No Obj: 0.000441, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.330489, iou_loss = 0.007175, total_loss = 0.337664 \n",
" total_bbox = 36306, rewritten_bbox = 0.291963 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.334743, GIOU: 0.334743), Class: 0.548219, Obj: 0.059239, No Obj: 0.003679, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.382134, iou_loss = 0.007638, total_loss = 1.389772 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.291067, GIOU: 0.012592), Class: 0.717418, Obj: 0.000883, No Obj: 0.000486, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.324621, iou_loss = 0.036524, total_loss = 0.361145 \n",
" total_bbox = 36311, rewritten_bbox = 0.291923 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.282962, GIOU: 0.215558), Class: 0.439021, Obj: 0.023862, No Obj: 0.002359, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.612148, iou_loss = 0.005362, total_loss = 1.617510 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000475, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.008426, iou_loss = 0.000000, total_loss = 0.008426 \n",
" total_bbox = 36315, rewritten_bbox = 0.291890 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.463748, GIOU: 0.373708), Class: 0.553783, Obj: 0.124841, No Obj: 0.002250, .5R: 0.333333, .75R: 0.000000, count: 3, class_loss = 1.045016, iou_loss = 0.043236, total_loss = 1.088253 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.231300, GIOU: 0.220132), Class: 0.604254, Obj: 0.010911, No Obj: 0.000528, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.958210, iou_loss = 0.328695, total_loss = 1.286904 \n",
" total_bbox = 36321, rewritten_bbox = 0.291842 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 454: 0.921571, 0.908876 avg loss, 0.000111 rate, 0.688772 seconds, 29056 images, 0.715788 hours left\n",
"Loaded: 0.000054 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.368034, GIOU: 0.341874), Class: 0.621911, Obj: 0.047143, No Obj: 0.002213, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.982983, iou_loss = 0.025694, total_loss = 1.008677 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.235757, GIOU: 0.116397), Class: 0.438533, Obj: 0.001629, No Obj: 0.000486, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 2.023506, iou_loss = 4.263137, total_loss = 6.286643 \n",
" total_bbox = 36329, rewritten_bbox = 0.291778 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.218292, GIOU: 0.014839), Class: 0.555885, Obj: 0.055707, No Obj: 0.002531, .5R: 0.000000, .75R: 0.000000, count: 6, class_loss = 2.060397, iou_loss = 0.018384, total_loss = 2.078781 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000442, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001566, iou_loss = 0.000000, total_loss = 0.001566 \n",
" total_bbox = 36335, rewritten_bbox = 0.291730 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.399860, GIOU: 0.283544), Class: 0.594411, Obj: 0.075524, No Obj: 0.002229, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.279843, iou_loss = 0.042715, total_loss = 1.322557 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000461, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001013, iou_loss = 0.000000, total_loss = 0.001013 \n",
" total_bbox = 36339, rewritten_bbox = 0.291698 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.361668, GIOU: 0.165717), Class: 0.489560, Obj: 0.010104, No Obj: 0.002368, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 2.054306, iou_loss = 0.114921, total_loss = 2.169227 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000450, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001963, iou_loss = 0.000000, total_loss = 0.001963 \n",
" total_bbox = 36344, rewritten_bbox = 0.291658 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.395978, GIOU: 0.291087), Class: 0.607750, Obj: 0.041498, No Obj: 0.001765, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.301261, iou_loss = 0.027376, total_loss = 1.328637 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.242756, GIOU: -0.005808), Class: 0.523381, Obj: 0.003267, No Obj: 0.000447, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.723351, iou_loss = 2.130354, total_loss = 2.853704 \n",
" total_bbox = 36350, rewritten_bbox = 0.291609 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.372024, GIOU: 0.280715), Class: 0.511337, Obj: 0.041736, No Obj: 0.002360, .5R: 0.200000, .75R: 0.000000, count: 5, class_loss = 1.813828, iou_loss = 0.069048, total_loss = 1.882876 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000463, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002995, iou_loss = 0.000000, total_loss = 0.002995 \n",
" total_bbox = 36355, rewritten_bbox = 0.291569 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.345608, GIOU: 0.316262), Class: 0.603260, Obj: 0.199942, No Obj: 0.003525, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.160330, iou_loss = 0.020293, total_loss = 1.180624 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.122953, GIOU: -0.213046), Class: 0.528035, Obj: 0.001757, No Obj: 0.000434, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.338049, iou_loss = 0.008306, total_loss = 0.346355 \n",
" total_bbox = 36360, rewritten_bbox = 0.291529 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.471903, GIOU: 0.420505), Class: 0.541244, Obj: 0.025439, No Obj: 0.002440, .5R: 0.500000, .75R: 0.000000, count: 4, class_loss = 1.434654, iou_loss = 0.048081, total_loss = 1.482735 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.483115, GIOU: 0.360018), Class: 0.492274, Obj: 0.000740, No Obj: 0.000466, .5R: 0.500000, .75R: 0.000000, count: 2, class_loss = 0.779385, iou_loss = 0.908603, total_loss = 1.687988 \n",
" total_bbox = 36366, rewritten_bbox = 0.291481 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.382534, GIOU: 0.302253), Class: 0.508301, Obj: 0.003378, No Obj: 0.002440, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.071649, iou_loss = 0.031812, total_loss = 1.103461 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.593136, GIOU: 0.588403), Class: 0.499791, Obj: 0.025869, No Obj: 0.000508, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.412906, iou_loss = 0.338397, total_loss = 0.751303 \n",
" total_bbox = 36370, rewritten_bbox = 0.291449 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344455, GIOU: 0.330460), Class: 0.571433, Obj: 0.025207, No Obj: 0.002144, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.986080, iou_loss = 0.006291, total_loss = 0.992371 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.540162, GIOU: 0.496634), Class: 0.537301, Obj: 0.010378, No Obj: 0.000523, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.335697, iou_loss = 0.222809, total_loss = 0.558506 \n",
" total_bbox = 36374, rewritten_bbox = 0.291417 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.397223, GIOU: 0.289772), Class: 0.399419, Obj: 0.040572, No Obj: 0.002379, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.563848, iou_loss = 0.011784, total_loss = 1.575632 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000423, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000925, iou_loss = 0.000000, total_loss = 0.000925 \n",
" total_bbox = 36378, rewritten_bbox = 0.291385 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.300858, GIOU: 0.179163), Class: 0.543743, Obj: 0.159066, No Obj: 0.003117, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.193154, iou_loss = 0.036266, total_loss = 1.229421 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.392181, GIOU: 0.277425), Class: 0.514434, Obj: 0.046487, No Obj: 0.000556, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.342696, iou_loss = 0.680588, total_loss = 1.023284 \n",
" total_bbox = 36383, rewritten_bbox = 0.291345 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.338635, GIOU: 0.248469), Class: 0.502284, Obj: 0.053260, No Obj: 0.002169, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.698027, iou_loss = 0.030760, total_loss = 0.728787 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.594382, GIOU: 0.589674), Class: 0.212996, Obj: 0.008311, No Obj: 0.000449, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.436911, iou_loss = 0.397503, total_loss = 0.834414 \n",
" total_bbox = 36386, rewritten_bbox = 0.291321 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.258682, GIOU: 0.251614), Class: 0.532966, Obj: 0.011176, No Obj: 0.002075, .5R: 0.000000, .75R: 0.000000, count: 5, class_loss = 1.875561, iou_loss = 0.058536, total_loss = 1.934097 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000498, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.006318, iou_loss = 0.000000, total_loss = 0.006318 \n",
" total_bbox = 36391, rewritten_bbox = 0.291281 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.464082, GIOU: 0.412935), Class: 0.629698, Obj: 0.104599, No Obj: 0.002530, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.217059, iou_loss = 0.050054, total_loss = 1.267113 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.336116, GIOU: 0.191672), Class: 0.522531, Obj: 0.009946, No Obj: 0.000451, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.675467, iou_loss = 0.410606, total_loss = 1.086073 \n",
" total_bbox = 36397, rewritten_bbox = 0.291233 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.522834, GIOU: 0.515396), Class: 0.458655, Obj: 0.039531, No Obj: 0.002521, .5R: 0.400000, .75R: 0.400000, count: 5, class_loss = 1.904527, iou_loss = 0.072904, total_loss = 1.977430 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.536120, GIOU: 0.461716), Class: 0.670009, Obj: 0.017874, No Obj: 0.000494, .5R: 1.000000, .75R: 0.000000, count: 1, class_loss = 0.349535, iou_loss = 0.166923, total_loss = 0.516458 \n",
" total_bbox = 36403, rewritten_bbox = 0.291185 % \n",
"\n",
" Tensor Cores are disabled until the first 3000 iterations are reached.\n",
"\n",
" 455: 0.915920, 0.909581 avg loss, 0.000112 rate, 0.688398 seconds, 29120 images, 0.715416 hours left\n",
"Loaded: 0.000062 seconds\n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.322509, GIOU: 0.275437), Class: 0.402646, Obj: 0.084087, No Obj: 0.002768, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.618708, iou_loss = 0.039608, total_loss = 1.658317 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000508, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.005096, iou_loss = 0.000000, total_loss = 0.005096 \n",
" total_bbox = 36407, rewritten_bbox = 0.291153 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.429379, GIOU: 0.346886), Class: 0.504240, Obj: 0.062664, No Obj: 0.002534, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.746793, iou_loss = 0.167186, total_loss = 1.913979 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000515, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.007796, iou_loss = 0.000000, total_loss = 0.007796 \n",
" total_bbox = 36412, rewritten_bbox = 0.291113 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.458246, GIOU: 0.434323), Class: 0.574990, Obj: 0.073394, No Obj: 0.002298, .5R: 0.333333, .75R: 0.333333, count: 3, class_loss = 0.917068, iou_loss = 0.009532, total_loss = 0.926600 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.086277, GIOU: 0.086276), Class: 0.411742, Obj: 0.000588, No Obj: 0.000451, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.407644, iou_loss = 0.020000, total_loss = 0.427644 \n",
" total_bbox = 36416, rewritten_bbox = 0.291081 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.336905, GIOU: 0.310073), Class: 0.584888, Obj: 0.031557, No Obj: 0.002449, .5R: 0.250000, .75R: 0.000000, count: 4, class_loss = 1.415574, iou_loss = 0.032817, total_loss = 1.448392 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000444, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.002337, iou_loss = 0.000000, total_loss = 0.002337 \n",
" total_bbox = 36420, rewritten_bbox = 0.291049 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.444312, GIOU: 0.411455), Class: 0.668506, Obj: 0.085538, No Obj: 0.002456, .5R: 0.400000, .75R: 0.000000, count: 5, class_loss = 1.501477, iou_loss = 0.082991, total_loss = 1.584467 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000432, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.000807, iou_loss = 0.000000, total_loss = 0.000807 \n",
" total_bbox = 36425, rewritten_bbox = 0.291009 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.353780, GIOU: 0.281061), Class: 0.426866, Obj: 0.179680, No Obj: 0.002848, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.646065, iou_loss = 0.008807, total_loss = 0.654872 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.348564, GIOU: 0.314536), Class: 0.559629, Obj: 0.001693, No Obj: 0.000458, .5R: 0.000000, .75R: 0.000000, count: 2, class_loss = 0.715602, iou_loss = 0.983099, total_loss = 1.698701 \n",
" total_bbox = 36429, rewritten_bbox = 0.290977 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.289066, GIOU: 0.126059), Class: 0.546020, Obj: 0.028603, No Obj: 0.002059, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 1.088302, iou_loss = 0.011871, total_loss = 1.100173 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.271435, GIOU: 0.154148), Class: 0.462103, Obj: 0.000427, No Obj: 0.000384, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.519102, iou_loss = 0.449516, total_loss = 1.968619 \n",
" total_bbox = 36436, rewritten_bbox = 0.290921 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.245482, GIOU: 0.154609), Class: 0.563875, Obj: 0.096483, No Obj: 0.002956, .5R: 0.000000, .75R: 0.000000, count: 3, class_loss = 0.956289, iou_loss = 0.005552, total_loss = 0.961841 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: -0.408296), Class: 0.544232, Obj: 0.000343, No Obj: 0.000425, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.418112, iou_loss = 0.350844, total_loss = 0.768956 \n",
" total_bbox = 36440, rewritten_bbox = 0.290889 % \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 30 Avg (IOU: 0.344600, GIOU: 0.262897), Class: 0.396470, Obj: 0.042405, No Obj: 0.002378, .5R: 0.000000, .75R: 0.000000, count: 4, class_loss = 1.502419, iou_loss = 0.019614, total_loss = 1.522033 \n",
"v3 (iou loss, Normalizer: (iou: 0.07, cls: 1.00) Region 37 Avg (IOU: 0.000000, GIOU: 0.000000), Class: 0.000000, Obj: 0.000000, No Obj: 0.000473, .5R: 0.000000, .75R: 0.000000, count: 1, class_loss = 0.001401, iou_loss = 0.000000, total_loss = 0.001401 \n",
" total_bbox = 36444, rewritten_bbox = 0.290857 % \n"
],
"name": "stdout"
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "rFfReDt3g4DY",
"outputId": "72e989ab-24fd-49b5-834c-1fae45678628",
"colab": {
"base_uri": "https://localhost:8080/",
"height": 1000
}
},
"source": [
"from torch_snippets import Glob, stem, show, read\n",
"# upload your own images to a folder\n",
"image_paths = Glob('images-of-trucks-and-busses')\n",
"for f in image_paths:\n",
" !./darknet detector test data/obj.data cfg/yolov4-tiny-bus-trucks.cfg\\\n",
" backup/yolov4-tiny-bus-trucks_4000.weights {f}\n",
" !mv predictions.jpg {stem(f)}_pred.jpg\n",
"\n",
"for i in Glob('*_pred.jpg', silent=True):\n",
" show(read(i, 1), sz=20)"
],
"execution_count": 5,
"outputs": [
{
"output_type": "stream",
"text": [
"2020-11-09 03:24:37.260 | INFO | torch_snippets.loader:Glob:181 - 3 files found at pred_*.png\n"
],
"name": "stderr"
},
{
"output_type": "display_data",
"data": {
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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
},
{
"output_type": "display_data",
"data": {
"image/png": 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\n",
"text/plain": [
""
]
},
"metadata": {
"tags": [],
"needs_background": "light"
}
}
]
},
{
"cell_type": "code",
"metadata": {
"id": "MqcAHhskBF9p"
},
"source": [
""
],
"execution_count": null,
"outputs": []
}
]
}