commit 7221ce886e8e40649b14f1a2c4a86d320d02573e Author: s444519 Date: Wed Feb 1 22:17:53 2023 +0100 project diff --git a/detection.ipynb b/detection.ipynb new file mode 100644 index 0000000..6102d65 --- /dev/null +++ b/detection.ipynb @@ -0,0 +1 @@ +{"cells":[{"cell_type":"code","execution_count":1,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":22480,"status":"ok","timestamp":1675269585564,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"},"user_tz":-60},"id":"O-wrMg3ahVxy","outputId":"7f8d45e0-361c-4e5e-f6d9-4edb6a2b505f"},"outputs":[{"name":"stdout","output_type":"stream","text":["Mounted at /content/gdrive\n"]}],"source":["from google.colab import drive\n","drive.mount('/content/gdrive', force_remount=True)"]},{"cell_type":"code","execution_count":2,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":27,"status":"ok","timestamp":1675269585568,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"},"user_tz":-60},"id":"TwwrZqPohp_p","outputId":"02bbfe66-a4a9-44fc-a9bf-f129791b6cd4"},"outputs":[{"name":"stdout","output_type":"stream","text":["/content/gdrive/MyDrive/WKO_PROJECT_NEW\n"]}],"source":["cd gdrive/MyDrive/WKO_PROJECT_NEW"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"background_save":true,"base_uri":"https://localhost:8080/"},"id":"V5t8Et8adgGW"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5/runs/train/exp16/weights/last.pt'], source=input.mp4, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n","\u001b[31m\u001b[1mrequirements:\u001b[0m YOLOv5 requirements \"gitpython\" \"ipython\" not found, attempting AutoUpdate...\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Collecting gitpython\n"," Downloading GitPython-3.1.30-py3-none-any.whl (184 kB)\n"," ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 184.0/184.0 KB 4.9 MB/s eta 0:00:00\n","Requirement already satisfied: ipython in /usr/local/lib/python3.8/dist-packages (7.9.0)\n","Collecting gitdb\u003c5,\u003e=4.0.1\n"," Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n"," ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.7/62.7 KB 7.2 MB/s eta 0:00:00\n","Requirement already satisfied: pickleshare in /usr/local/lib/python3.8/dist-packages (from ipython) (0.7.5)\n","Requirement already satisfied: pygments in /usr/local/lib/python3.8/dist-packages (from ipython) (2.6.1)\n","Requirement already satisfied: prompt-toolkit\u003c2.1.0,\u003e=2.0.0 in /usr/local/lib/python3.8/dist-packages (from ipython) (2.0.10)\n","Requirement already satisfied: decorator in /usr/local/lib/python3.8/dist-packages (from ipython) (4.4.2)\n","Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from ipython) (0.2.0)\n","Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from ipython) (4.8.0)\n","Collecting jedi\u003e=0.10\n"," Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n"," ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 31.9 MB/s eta 0:00:00\n","Requirement already satisfied: setuptools\u003e=18.5 in /usr/local/lib/python3.8/dist-packages (from ipython) (57.4.0)\n","Requirement already satisfied: traitlets\u003e=4.2 in /usr/local/lib/python3.8/dist-packages (from ipython) (5.7.1)\n","Collecting smmap\u003c6,\u003e=3.0.1\n"," Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n","Requirement already satisfied: parso\u003c0.9.0,\u003e=0.8.0 in /usr/local/lib/python3.8/dist-packages (from jedi\u003e=0.10-\u003eipython) (0.8.3)\n","Requirement already satisfied: six\u003e=1.9.0 in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit\u003c2.1.0,\u003e=2.0.0-\u003eipython) (1.15.0)\n","Requirement already satisfied: wcwidth in /usr/local/lib/python3.8/dist-packages (from prompt-toolkit\u003c2.1.0,\u003e=2.0.0-\u003eipython) (0.2.5)\n","Requirement already satisfied: ptyprocess\u003e=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect-\u003eipython) (0.7.0)\n","Installing collected packages: smmap, jedi, gitdb, gitpython\n","Successfully installed gitdb-4.0.10 gitpython-3.1.30 jedi-0.18.2 smmap-5.0.0\n","\n","\u001b[31m\u001b[1mrequirements:\u001b[0m 2 packages updated per /content/gdrive/MyDrive/WKO_PROJECT_NEW/yolov5/requirements.txt\n","\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n","\n","YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 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/content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 7 masks, 229.2ms\n","video 1/1 (429/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 6 masks, 237.6ms\n","video 1/1 (430/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 6 masks, 245.6ms\n","video 1/1 (431/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 6 masks, 227.9ms\n","video 1/1 (432/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 7 masks, 226.1ms\n","video 1/1 (433/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 4 masks, 225.0ms\n","video 1/1 (434/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 4 masks, 234.9ms\n","video 1/1 (435/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 4 masks, 220.4ms\n","video 1/1 (436/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 6 masks, 234.0ms\n","video 1/1 (437/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 5 masks, 224.6ms\n","video 1/1 (438/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 5 masks, 233.5ms\n","video 1/1 (439/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 7 masks, 225.0ms\n","video 1/1 (440/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 7 masks, 237.1ms\n","video 1/1 (441/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 1 no_mask, 8 masks, 229.4ms\n","video 1/1 (442/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 2 no_masks, 7 masks, 223.0ms\n","video 1/1 (443/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 3 no_masks, 7 masks, 222.0ms\n","video 1/1 (444/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 2 no_masks, 8 masks, 235.3ms\n","video 1/1 (445/452) /content/gdrive/MyDrive/WKO_PROJECT_NEW/input.mp4: 384x640 2 no_masks, 7 masks, 228.1ms\n","Speed: 0.9ms pre-process, 232.7ms inference, 0.8ms NMS per image at shape (1, 3, 640, 640)\n","Results saved to \u001b[1myolov5/runs/detect/exp5\u001b[0m\n"]}],"source":["! python3 yolov5/detect.py --source input.mp4 --weights yolov5/runs/train/exp16/weights/last.pt"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"1HWeIZG32VSh"},"outputs":[],"source":["! python3 yolov5/detect.py --source oxf.mp4 --weights yolov5/runs/train/exp16/weights/last.pt"]},{"cell_type":"code","execution_count":null,"metadata":{"id":"N_Ahmnn52XlM"},"outputs":[],"source":["! python3 yolov5/detect.py --source oxf2.mp4 --weights yolov5/runs/train/exp16/weights/last.pt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":8059,"status":"ok","timestamp":1675114361886,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"},"user_tz":-60},"id":"NE-eA9FohsAK","outputId":"b5a8e9a5-b9a0-4120-df06-d0dafcbd1783"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5/runs/train/exp8/weights/last.pt'], source=mask_dataset/images/maksssksksss788.png, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n","YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CPU\n","\n","Fusing layers... \n","YOLOv5s summary: 157 layers, 7018216 parameters, 0 gradients\n","image 1/1 /content/gdrive/MyDrive/WKO_PROJECT_NEW/mask_dataset/images/maksssksksss788.png: 448x640 5 1s, 500.2ms\n","Speed: 1.1ms pre-process, 500.2ms inference, 1.7ms NMS per image at shape (1, 3, 640, 640)\n","Results saved to \u001b[1myolov5/runs/detect/exp2\u001b[0m\n"]}],"source":["! python3 yolov5/detect.py --source mask_dataset/images/maksssksksss788.png --weights yolov5/runs/train/exp8/weights/last.pt"]},{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"executionInfo":{"elapsed":6974,"status":"ok","timestamp":1675114931890,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"},"user_tz":-60},"id":"kePati08oR6J","outputId":"8b0c5856-4794-4051-d895-f7d88a2cf129"},"outputs":[{"name":"stdout","output_type":"stream","text":["\u001b[34m\u001b[1mdetect: \u001b[0mweights=['yolov5/runs/train/exp8/weights/best.pt'], source=mask_dataset/images/maksssksksss787.png, data=yolov5/data/coco128.yaml, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=yolov5/runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False, dnn=False, vid_stride=1\n","YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CPU\n","\n","Fusing layers... \n","YOLOv5s summary: 157 layers, 7018216 parameters, 0 gradients\n","image 1/1 /content/gdrive/MyDrive/WKO_PROJECT_NEW/mask_dataset/images/maksssksksss787.png: 480x640 3 1s, 300.6ms\n","Speed: 1.1ms pre-process, 300.6ms inference, 1.3ms NMS per image at shape (1, 3, 640, 640)\n","Results saved to \u001b[1myolov5/runs/detect/exp3\u001b[0m\n"]}],"source":["! python3 yolov5/detect.py --source mask_dataset/images/maksssksksss787.png --weights yolov5/runs/train/exp8/weights/best.pt"]}],"metadata":{"colab":{"authorship_tag":"ABX9TyOlJGT9fwPaEIvolcpQ7hPL","name":"","version":""},"kernelspec":{"display_name":"Python 3","name":"python3"},"language_info":{"name":"python"}},"nbformat":4,"nbformat_minor":0} \ No newline at end of file diff --git a/input.mp4 b/input.mp4 new file mode 100644 index 0000000..0963d0c Binary files /dev/null and b/input.mp4 differ diff --git a/mask_realtime.py b/mask_realtime.py new file mode 100644 index 0000000..4e84921 --- /dev/null +++ b/mask_realtime.py @@ -0,0 +1,29 @@ +import cv2 +from importlib.resources import path +from time import time +import torch +from matplotlib import pyplot as plt +import numpy as np + + + +model_name='last.pt' +model = torch.hub.load('C:/Users/Lenovo/Documents/Downloads/yolov5/yolov5', 'custom', source='local', path ='C:/Users/Lenovo/Documents/Downloads/yolov5/yolov5/runs/train/exp16/weights/best.pt') +cap= cv2.VideoCapture(0) + +while cap.isOpened(): + start= time() + ret, frame = cap.read() + result= model(frame) + cv2.imshow('My Face', np.squeeze(result.render())) + if cv2.waitKey(10) & 0xff == ord('x'): + break + if cv2.getWindowProperty('My Face', cv2.WND_PROP_VISIBLE) < 1: + break + + end = time() + fps = 1/(end - start) + + +cap.release() +cv2.destroyAllWindows() \ No newline at end of file diff --git a/oxf.mp4 b/oxf.mp4 new file mode 100644 index 0000000..d9676e2 Binary files /dev/null and b/oxf.mp4 differ diff --git a/oxf2.mp4 b/oxf2.mp4 new file mode 100644 index 0000000..8bdbd96 Binary files /dev/null and b/oxf2.mp4 differ diff --git a/process_data b/process_data new file mode 100644 index 0000000..90c6db0 --- /dev/null +++ b/process_data @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyM4q90TKm68+fCgyse7d3la"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":1,"metadata":{"id":"YgcHkg_E9b9U","executionInfo":{"status":"ok","timestamp":1675102221170,"user_tz":-60,"elapsed":14,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}}},"outputs":[],"source":["import os\n","import cv2\n","from xml.etree.ElementTree import ElementTree\n","import re\n","import random\n","import numpy as np\n","from os.path import join\n","\n"]},{"cell_type":"code","source":["!fusermount -u drive\n","!google-drive-ocamlfuse drive"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"hQ_r-pBSMknR","executionInfo":{"status":"ok","timestamp":1675090876283,"user_tz":-60,"elapsed":464,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"0d2877c8-d320-4d0b-e9f3-43f52fe51eae"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["shell-init: error retrieving current directory: getcwd: cannot access parent directories: Transport endpoint is not connected\n","fusermount: bad mount point drive: Transport endpoint is not connected\n","shell-init: error retrieving current directory: getcwd: cannot access parent directories: Transport endpoint is not connected\n","/bin/bash: google-drive-ocamlfuse: command not found\n"]}]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive', force_remount=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"6Mqg8_uIEau4","executionInfo":{"status":"ok","timestamp":1675102254260,"user_tz":-60,"elapsed":33102,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"d04814c8-39f1-4b53-e031-837f255d86f4"},"execution_count":2,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["cd gdrive/MyDrive/WKO_PROJECT_NEW"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"yF5SmBNFEdjJ","executionInfo":{"status":"ok","timestamp":1675102254261,"user_tz":-60,"elapsed":13,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"c078f765-06c8-417d-d4c9-32b83f74c610"},"execution_count":3,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/gdrive/MyDrive/WKO_PROJECT_NEW\n"]}]},{"cell_type":"code","source":["# convert voc annotation format to darknet format\n","def xml_to_darknet(path):\n"," root = ElementTree().parse(path)\n","\n"," img_path = root.find('filename').text.replace('png','txt')\n"," img_size = root.find('size')\n"," width = int(img_size.find('width').text)\n"," height = int(img_size.find('height').text)\n"," with open('mask_dataset/labels/' + img_path, 'w') as f:\n"," lines = []\n"," \n"," for node in root.findall('object'):\n"," object_ = dict(class_=None, x=None, y=None, width=None, height=None)\n"," \n"," # class\n"," class_name = node.find('name').text\n","\n"," if(class_name == 'without_mask'):\n"," object_['class_'] = '0'\n"," elif(class_name == 'with_mask'):\n"," object_['class_'] = '1'\n"," else:\n"," object_['class_'] = '2'\n"," \n"," # bounding box\n"," bnd_box = node.find(\"bndbox\")\n"," x_min = float(bnd_box[0].text)\n"," y_min = float(bnd_box[1].text)\n"," x_max = float(bnd_box[2].text)\n"," y_max = float(bnd_box[3].text)\n","\n"," dw = float(1/width)\n"," dh = float(1/height)\n","\n"," w = float(x_max - x_min)\n"," h = float(y_max - y_min)\n","\n"," x = float((x_min + x_max)/2 -1)\n"," y = float((y_min + y_max)/2 -1)\n","\n"," w = float(w * dw)\n"," h = float(h * dh)\n"," x = float(x * dw)\n"," y = float(y * dh)\n"," \n"," object_['x'] = str(x)\n"," object_['y'] = str(y)\n"," object_['width'] = str(w)\n"," object_['height'] = str(h)\n","\n"," line = object_['class_'] + ' ' + object_['x'] + ' ' + object_['y'] + ' ' + object_['width'] + ' ' + object_['height']\n"," \n"," lines.append(line)\n"," lines.append('\\n')\n","\n"," for line in lines[:-1]:\n"," f.write(line) \n"," f.close"],"metadata":{"id":"wkrL0mpX9ljT"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["def process_data():\n","\n"," # get the paths of all the images available\n"," img_paths = []\n"," \n"," for dirname, _, filenames in os.walk('mask_dataset/images'):\n"," for filename in filenames:\n"," \n"," img_paths.append(os.path.join('/content/gdrive/MyDrive/WKO_PROJECT_NEW/mask_dataset/images', filename)) # google colab\n","\n"," \n"," # shuffle data\n"," random.shuffle(img_paths)\n","\n"," # split\n"," train, validate, test = np.split(img_paths, [int(len(img_paths)*0.8), int(len(img_paths)*0.9)])\n"," \n"," # training images set = 80% of all images\n"," # validating images set = 10% of all images\n"," # testing images set = 10% of all images\n","\n"," # write train.txt\n"," with open('mask_dataset/train.txt', 'w') as f:\n"," lines = list('\\n'.join(train))\n"," f.writelines(lines)\n"," f.close\n","\n"," # write validate.txt\n"," with open('mask_dataset/validate.txt', 'w') as f:\n"," lines = list('\\n'.join(validate))\n"," f.writelines(lines)\n"," f.close\n","\n"," # write test.txt\n"," with open('mask_dataset/test.txt', 'w') as f:\n"," lines = list('\\n'.join(test))\n"," f.writelines(lines)\n"," f.close\n","\n"," # process annotations\n"," for dirname, _, filenames in os.walk('mask_dataset/annotations'):\n"," for filename in filenames:\n"," annotation_path = (os.path.join(dirname, filename))\n"," print(annotation_path)\n"," xml_to_darknet(annotation_path)\n"," \n"," \n","\n","\n"," \n","\n"," \n","process_data()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"JXSpim7hD0VN","outputId":"126139a9-c749-4047-d562-a81007e736aa"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["mask_dataset/annotations/maksssksksss120.xml\n","mask_dataset/annotations/maksssksksss121.xml\n","mask_dataset/annotations/maksssksksss12.xml\n","mask_dataset/annotations/maksssksksss11.xml\n","mask_dataset/annotations/maksssksksss112.xml\n","mask_dataset/annotations/maksssksksss110.xml\n","mask_dataset/annotations/maksssksksss116.xml\n","mask_dataset/annotations/maksssksksss119.xml\n","mask_dataset/annotations/maksssksksss107.xml\n","mask_dataset/annotations/maksssksksss1.xml\n","mask_dataset/annotations/maksssksksss128.xml\n","mask_dataset/annotations/maksssksksss102.xml\n","mask_dataset/annotations/maksssksksss111.xml\n","mask_dataset/annotations/maksssksksss0.xml\n","mask_dataset/annotations/maksssksksss125.xml\n","mask_dataset/annotations/maksssksksss129.xml\n","mask_dataset/annotations/maksssksksss109.xml\n","mask_dataset/annotations/maksssksksss118.xml\n","mask_dataset/annotations/maksssksksss103.xml\n","mask_dataset/annotations/maksssksksss124.xml\n","mask_dataset/annotations/maksssksksss122.xml\n","mask_dataset/annotations/maksssksksss13.xml\n","mask_dataset/annotations/maksssksksss113.xml\n","mask_dataset/annotations/maksssksksss115.xml\n","mask_dataset/annotations/maksssksksss10.xml\n","mask_dataset/annotations/maksssksksss106.xml\n","mask_dataset/annotations/maksssksksss105.xml\n","mask_dataset/annotations/maksssksksss114.xml\n","mask_dataset/annotations/maksssksksss104.xml\n","mask_dataset/annotations/maksssksksss108.xml\n","mask_dataset/annotations/maksssksksss101.xml\n","mask_dataset/annotations/maksssksksss123.xml\n","mask_dataset/annotations/maksssksksss100.xml\n","mask_dataset/annotations/maksssksksss127.xml\n","mask_dataset/annotations/maksssksksss126.xml\n","mask_dataset/annotations/maksssksksss117.xml\n","mask_dataset/annotations/maksssksksss150.xml\n","mask_dataset/annotations/maksssksksss149.xml\n","mask_dataset/annotations/maksssksksss161.xml\n","mask_dataset/annotations/maksssksksss143.xml\n","mask_dataset/annotations/maksssksksss156.xml\n","mask_dataset/annotations/maksssksksss169.xml\n","mask_dataset/annotations/maksssksksss135.xml\n","mask_dataset/annotations/maksssksksss166.xml\n","mask_dataset/annotations/maksssksksss130.xml\n","mask_dataset/annotations/maksssksksss171.xml\n","mask_dataset/annotations/maksssksksss131.xml\n","mask_dataset/annotations/maksssksksss154.xml\n","mask_dataset/annotations/maksssksksss164.xml\n","mask_dataset/annotations/maksssksksss14.xml\n","mask_dataset/annotations/maksssksksss134.xml\n","mask_dataset/annotations/maksssksksss159.xml\n","mask_dataset/annotations/maksssksksss17.xml\n","mask_dataset/annotations/maksssksksss133.xml\n","mask_dataset/annotations/maksssksksss160.xml\n","mask_dataset/annotations/maksssksksss147.xml\n","mask_dataset/annotations/maksssksksss15.xml\n","mask_dataset/annotations/maksssksksss152.xml\n","mask_dataset/annotations/maksssksksss141.xml\n","mask_dataset/annotations/maksssksksss168.xml\n","mask_dataset/annotations/maksssksksss157.xml\n","mask_dataset/annotations/maksssksksss165.xml\n","mask_dataset/annotations/maksssksksss145.xml\n","mask_dataset/annotations/maksssksksss158.xml\n","mask_dataset/annotations/maksssksksss144.xml\n","mask_dataset/annotations/maksssksksss162.xml\n","mask_dataset/annotations/maksssksksss16.xml\n","mask_dataset/annotations/maksssksksss142.xml\n","mask_dataset/annotations/maksssksksss140.xml\n","mask_dataset/annotations/maksssksksss170.xml\n","mask_dataset/annotations/maksssksksss146.xml\n","mask_dataset/annotations/maksssksksss167.xml\n","mask_dataset/annotations/maksssksksss151.xml\n","mask_dataset/annotations/maksssksksss137.xml\n","mask_dataset/annotations/maksssksksss138.xml\n","mask_dataset/annotations/maksssksksss163.xml\n","mask_dataset/annotations/maksssksksss153.xml\n","mask_dataset/annotations/maksssksksss132.xml\n","mask_dataset/annotations/maksssksksss136.xml\n","mask_dataset/annotations/maksssksksss148.xml\n","mask_dataset/annotations/maksssksksss155.xml\n","mask_dataset/annotations/maksssksksss139.xml\n","mask_dataset/annotations/maksssksksss194.xml\n","mask_dataset/annotations/maksssksksss19.xml\n","mask_dataset/annotations/maksssksksss172.xml\n","mask_dataset/annotations/maksssksksss180.xml\n","mask_dataset/annotations/maksssksksss183.xml\n","mask_dataset/annotations/maksssksksss191.xml\n","mask_dataset/annotations/maksssksksss176.xml\n","mask_dataset/annotations/maksssksksss182.xml\n","mask_dataset/annotations/maksssksksss185.xml\n","mask_dataset/annotations/maksssksksss192.xml\n","mask_dataset/annotations/maksssksksss173.xml\n","mask_dataset/annotations/maksssksksss193.xml\n","mask_dataset/annotations/maksssksksss197.xml\n","mask_dataset/annotations/maksssksksss188.xml\n","mask_dataset/annotations/maksssksksss190.xml\n","mask_dataset/annotations/maksssksksss195.xml\n","mask_dataset/annotations/maksssksksss179.xml\n","mask_dataset/annotations/maksssksksss198.xml\n","mask_dataset/annotations/maksssksksss184.xml\n","mask_dataset/annotations/maksssksksss178.xml\n","mask_dataset/annotations/maksssksksss181.xml\n","mask_dataset/annotations/maksssksksss175.xml\n","mask_dataset/annotations/maksssksksss186.xml\n","mask_dataset/annotations/maksssksksss196.xml\n","mask_dataset/annotations/maksssksksss174.xml\n","mask_dataset/annotations/maksssksksss189.xml\n","mask_dataset/annotations/maksssksksss177.xml\n","mask_dataset/annotations/maksssksksss187.xml\n","mask_dataset/annotations/maksssksksss18.xml\n","mask_dataset/annotations/maksssksksss232.xml\n","mask_dataset/annotations/maksssksksss200.xml\n","mask_dataset/annotations/maksssksksss21.xml\n","mask_dataset/annotations/maksssksksss220.xml\n","mask_dataset/annotations/maksssksksss225.xml\n","mask_dataset/annotations/maksssksksss204.xml\n","mask_dataset/annotations/maksssksksss199.xml\n","mask_dataset/annotations/maksssksksss23.xml\n","mask_dataset/annotations/maksssksksss209.xml\n","mask_dataset/annotations/maksssksksss205.xml\n","mask_dataset/annotations/maksssksksss211.xml\n","mask_dataset/annotations/maksssksksss231.xml\n","mask_dataset/annotations/maksssksksss230.xml\n","mask_dataset/annotations/maksssksksss215.xml\n","mask_dataset/annotations/maksssksksss203.xml\n","mask_dataset/annotations/maksssksksss208.xml\n","mask_dataset/annotations/maksssksksss217.xml\n","mask_dataset/annotations/maksssksksss22.xml\n","mask_dataset/annotations/maksssksksss218.xml\n"]}]},{"cell_type":"markdown","source":["# Nowa sekcja"],"metadata":{"id":"RyBEbWZlHMJg"}},{"cell_type":"code","source":["import os\n","\n","_, _, files = next(os.walk(\"mask_dataset/annotations\"))\n","file_count = len(files)"],"metadata":{"id":"Y1TjRzjcKxaU","executionInfo":{"status":"ok","timestamp":1675102291297,"user_tz":-60,"elapsed":532,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}}},"execution_count":8,"outputs":[]},{"cell_type":"code","source":["file_count"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OHCRkozLK4PR","executionInfo":{"status":"ok","timestamp":1675102292520,"user_tz":-60,"elapsed":7,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"d4218f85-2b68-4b41-b6c4-c67b2a8c07f8"},"execution_count":9,"outputs":[{"output_type":"execute_result","data":{"text/plain":["853"]},"metadata":{},"execution_count":9}]},{"cell_type":"code","source":["_, _, files2 = next(os.walk(\"mask_dataset/labels\"))\n","file_count = len(files2)"],"metadata":{"id":"3oSH07p1K6Td","executionInfo":{"status":"ok","timestamp":1675102281750,"user_tz":-60,"elapsed":6013,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}}},"execution_count":4,"outputs":[]},{"cell_type":"code","source":["file_count"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"29xMvMMSK_JW","executionInfo":{"status":"ok","timestamp":1675102281751,"user_tz":-60,"elapsed":8,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"827b366b-11f2-40dd-9d21-d58f008732d9"},"execution_count":5,"outputs":[{"output_type":"execute_result","data":{"text/plain":["853"]},"metadata":{},"execution_count":5}]},{"cell_type":"code","source":["_, _, files = next(os.walk(\"mask_dataset/images\"))\n","file_count = len(files)"],"metadata":{"id":"T850UYjhLFIC","executionInfo":{"status":"ok","timestamp":1675102285517,"user_tz":-60,"elapsed":820,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}}},"execution_count":6,"outputs":[]},{"cell_type":"code","source":["file_count"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"To0EfxMILHrT","executionInfo":{"status":"ok","timestamp":1675102287768,"user_tz":-60,"elapsed":5,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"17e46df3-1298-4c73-9ac1-7b19f88f5354"},"execution_count":7,"outputs":[{"output_type":"execute_result","data":{"text/plain":["853"]},"metadata":{},"execution_count":7}]}]} \ No newline at end of file diff --git a/real_time_test/My Face 2023-02-01 19-53-32_Trim.mp4 b/real_time_test/My Face 2023-02-01 19-53-32_Trim.mp4 new file mode 100644 index 0000000..7d88d23 Binary files /dev/null and b/real_time_test/My Face 2023-02-01 19-53-32_Trim.mp4 differ diff --git a/training.ipynb b/training.ipynb new file mode 100644 index 0000000..05adee0 --- /dev/null +++ b/training.ipynb @@ -0,0 +1 @@ +{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyMsr6zQ2XcUQEqV8i87Sh9v"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"},"accelerator":"GPU","gpuClass":"premium"},"cells":[{"cell_type":"code","execution_count":null,"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"OEySyEO08SU3","executionInfo":{"status":"ok","timestamp":1675254185686,"user_tz":-60,"elapsed":1124,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"8263f62d-cc5c-4fd8-c363-afe4594da5a8"},"outputs":[{"output_type":"stream","name":"stdout","text":["Wed Feb 1 12:23:04 2023 \n","+-----------------------------------------------------------------------------+\n","| NVIDIA-SMI 510.47.03 Driver Version: 510.47.03 CUDA Version: 11.6 |\n","|-------------------------------+----------------------+----------------------+\n","| GPU Name Persistence-M| Bus-Id Disp.A | Volatile Uncorr. ECC |\n","| Fan Temp Perf Pwr:Usage/Cap| Memory-Usage | GPU-Util Compute M. |\n","| | | MIG M. |\n","|===============================+======================+======================|\n","| 0 NVIDIA A100-SXM... Off | 00000000:00:04.0 Off | 0 |\n","| N/A 30C P0 50W / 400W | 0MiB / 40960MiB | 0% Default |\n","| | | Disabled |\n","+-------------------------------+----------------------+----------------------+\n"," \n","+-----------------------------------------------------------------------------+\n","| Processes: |\n","| GPU GI CI PID Type Process name GPU Memory |\n","| ID ID Usage |\n","|=============================================================================|\n","| No running processes found |\n","+-----------------------------------------------------------------------------+\n"]}],"source":["!nvidia-smi"]},{"cell_type":"code","source":["from google.colab import drive\n","drive.mount('/content/gdrive', force_remount=True)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"9PsGmo0h8vBe","executionInfo":{"status":"ok","timestamp":1675254208492,"user_tz":-60,"elapsed":18056,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"81adc7fb-0b26-4541-ef82-d1b0008b30b3"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Mounted at /content/gdrive\n"]}]},{"cell_type":"code","source":["cd gdrive/MyDrive/WKO_PROJECT_NEW"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"61nw5yn9_rId","executionInfo":{"status":"ok","timestamp":1675254211441,"user_tz":-60,"elapsed":206,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"e594178c-4088-4881-b830-745741baf884"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/gdrive/MyDrive/WKO_PROJECT_NEW\n"]}]},{"cell_type":"code","source":["!git clone https://github.com/ultralytics/yolov5"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"PCV1rHaY8gLr","executionInfo":{"status":"ok","timestamp":1675071106760,"user_tz":-60,"elapsed":4182,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"8c7f5bed-04c5-4fff-8fba-ae6e7bc5bc6a"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["Cloning into 'yolov5'...\n","remote: Enumerating objects: 15002, done.\u001b[K\n","remote: Total 15002 (delta 0), reused 0 (delta 0), pack-reused 15002\u001b[K\n","Receiving objects: 100% (15002/15002), 14.02 MiB | 11.68 MiB/s, done.\n","Resolving deltas: 100% (10291/10291), done.\n"]}]},{"cell_type":"code","source":["%cd yolov5\n","!pip install -U -r requirements.txt\n","import torch\n","from IPython.display import Image"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"adYlb7-18lBJ","executionInfo":{"status":"ok","timestamp":1675071133115,"user_tz":-60,"elapsed":13098,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}},"outputId":"a7080eb9-c7a9-429f-8955-223a5a3f13f5"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["/content/gdrive/MyDrive/WKO_PROJECT_NEW/yolov5\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: gitpython in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 5)) (3.1.30)\n","Requirement already satisfied: ipython in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 6)) (8.9.0)\n","Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 7)) (3.6.3)\n","Requirement already satisfied: numpy>=1.18.5 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 8)) (1.24.1)\n","Requirement already satisfied: opencv-python>=4.1.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 9)) (4.7.0.68)\n","Requirement already satisfied: Pillow>=7.1.2 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 10)) (9.4.0)\n","Requirement already satisfied: psutil in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 11)) (5.9.4)\n","Requirement already satisfied: PyYAML>=5.3.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 12)) (6.0)\n","Requirement already satisfied: requests>=2.23.0 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 13)) (2.28.2)\n","Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 14)) (1.10.0)\n","Requirement already satisfied: thop>=0.1.1 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 15)) (0.1.1.post2209072238)\n","Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.8/dist-packages (from -r requirements.txt (line 16)) (1.13.1+cu116)\n","Requirement already 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executing>=1.2.0 in /usr/local/lib/python3.8/dist-packages (from stack-data->ipython->-r requirements.txt (line 6)) (1.2.0)\n","Requirement already satisfied: asttokens>=2.1.0 in /usr/local/lib/python3.8/dist-packages (from stack-data->ipython->-r requirements.txt (line 6)) (2.2.1)\n","Requirement already satisfied: pure-eval in /usr/local/lib/python3.8/dist-packages (from stack-data->ipython->-r requirements.txt (line 6)) (0.2.2)\n","Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.8/dist-packages (from importlib-metadata>=4.4->markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 22)) (3.11.0)\n","Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.8/dist-packages (from pyasn1-modules>=0.2.1->google-auth<3,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 22)) (0.4.8)\n","Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.8/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 22)) (3.2.2)\n"]}]},{"cell_type":"code","source":["!python3 yolov5/train.py --batch 1 --epochs 100 --data mask_dataset/data.yaml --cfg yolov5/models/yolov5s.yaml --weights '' --device 0"],"metadata":{"id":"RXR731Z39NsD","colab":{"base_uri":"https://localhost:8080/"},"outputId":"a23d8c49-39c0-4e1a-8312-24bb5d3bab87","executionInfo":{"status":"ok","timestamp":1675249360254,"user_tz":-60,"elapsed":748014,"user":{"displayName":"marti Xooo","userId":"17000102553335328898"}}},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[34m\u001b[1mtrain: \u001b[0mweights=, cfg=yolov5/models/yolov5s.yaml, data=mask_dataset/data.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=1, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n","\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n","YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n","\n","\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n","\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n","\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/\n","\n"," from n params module arguments \n"," 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n"," 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n"," 2 -1 1 18816 models.common.C3 [64, 64, 1] \n"," 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n"," 4 -1 2 115712 models.common.C3 [128, 128, 2] \n"," 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n"," 6 -1 3 625152 models.common.C3 [256, 256, 3] \n"," 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n"," 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n"," 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n"," 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n"," 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 12 [-1, 6] 1 0 models.common.Concat [1] \n"," 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n"," 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n"," 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 16 [-1, 4] 1 0 models.common.Concat [1] \n"," 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n"," 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n"," 19 [-1, 14] 1 0 models.common.Concat [1] \n"," 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n"," 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n"," 22 [-1, 10] 1 0 models.common.Concat [1] \n"," 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n"," 24 [17, 20, 23] 1 21576 models.yolo.Detect [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n","YOLOv5s summary: 214 layers, 7027720 parameters, 7027720 gradients, 16.0 GFLOPs\n","\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/gdrive/MyDrive/WKO_PROJECT_NEW/mask_dataset/train.cache... 682 images, 0 backgrounds, 0 corrupt: 100% 682/682 [00:00\n"," main(opt)\n"," File \"yolov5/train.py\", line 528, in main\n"," train(opt.hyp, opt, device, callbacks)\n"," File \"yolov5/train.py\", line 283, in train\n"," for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------\n"," File \"/usr/local/lib/python3.8/dist-packages/tqdm/std.py\", line 1195, in __iter__\n"," for obj in iterable:\n"," File \"/content/gdrive/MyDrive/WKO_PROJECT_NEW/yolov5/utils/dataloaders.py\", line 172, in __iter__\n"," yield next(self.iterator)\n"," File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\", line 628, in __next__\n"," data = self._next_data()\n"," File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/dataloader.py\", line 671, in _next_data\n"," data = self._dataset_fetcher.fetch(index) # may raise StopIteration\n"," File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\", line 58, in fetch\n"," data = [self.dataset[idx] for idx in possibly_batched_index]\n"," File \"/usr/local/lib/python3.8/dist-packages/torch/utils/data/_utils/fetch.py\", line 58, in \n"," data = [self.dataset[idx] for idx in possibly_batched_index]\n"," File \"/content/gdrive/MyDrive/WKO_PROJECT_NEW/yolov5/utils/dataloaders.py\", line 660, in __getitem__\n"," img, labels = self.load_mosaic(index)\n"," File \"/content/gdrive/MyDrive/WKO_PROJECT_NEW/yolov5/utils/dataloaders.py\", line 763, in load_mosaic\n"," img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles\n"," File \"/usr/local/lib/python3.8/dist-packages/numpy/core/numeric.py\", line 344, in full\n"," multiarray.copyto(a, fill_value, casting='unsafe')\n"," File \"<__array_function__ internals>\", line 5, in copyto\n","KeyboardInterrupt\n","^C\n"]}]},{"cell_type":"code","source":["!python3 yolov5/train.py --batch 1 --epochs 100 --data mask_dataset/mask_an.yaml --cfg yolov5/models/yolov5s.yaml --weights '' --device 0"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"MqlvNLa5oTro","outputId":"87dd0c9a-32fa-4d78-e77a-a5d88c3d70a6"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["\u001b[34m\u001b[1mtrain: \u001b[0mweights=, cfg=yolov5/models/yolov5s.yaml, data=mask_dataset/mask_an.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=1, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n","\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n","YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (Tesla T4, 15110MiB)\n","\n","\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n","\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n","\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/\n","\n"," from n params module arguments \n"," 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n"," 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n"," 2 -1 1 18816 models.common.C3 [64, 64, 1] \n"," 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n"," 4 -1 2 115712 models.common.C3 [128, 128, 2] \n"," 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n"," 6 -1 3 625152 models.common.C3 [256, 256, 3] \n"," 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n"," 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n"," 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n"," 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n"," 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 12 [-1, 6] 1 0 models.common.Concat [1] \n"," 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n"," 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n"," 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 16 [-1, 4] 1 0 models.common.Concat [1] \n"," 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n"," 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n"," 19 [-1, 14] 1 0 models.common.Concat [1] \n"," 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n"," 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n"," 22 [-1, 10] 1 0 models.common.Concat [1] \n"," 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n"," 24 [17, 20, 23] 1 21576 models.yolo.Detect [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n","YOLOv5s summary: 214 layers, 7027720 parameters, 7027720 gradients, 16.0 GFLOPs\n","\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/gdrive/MyDrive/WKO_PROJECT_NEW/mask_dataset/train.cache... 682 images, 0 backgrounds, 0 corrupt: 100% 682/682 [00:00=4.0.1\n"," Downloading gitdb-4.0.10-py3-none-any.whl (62 kB)\n"," ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 62.7/62.7 KB 9.3 MB/s eta 0:00:00\n","Collecting smmap<6,>=3.0.1\n"," Downloading smmap-5.0.0-py3-none-any.whl (24 kB)\n","Installing collected packages: smmap, gitdb, gitpython\n","Successfully installed gitdb-4.0.10 gitpython-3.1.30 smmap-5.0.0\n","\n","\u001b[31m\u001b[1mrequirements:\u001b[0m 1 package updated per ['gitpython']\n","\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n","\n","\u001b[34m\u001b[1mtrain: \u001b[0mweights=, cfg=yolov5/models/yolov5s.yaml, data=mask_dataset/mask_an.yaml, hyp=yolov5/data/hyps/hyp.scratch-low.yaml, epochs=100, batch_size=1, imgsz=640, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, noplots=False, evolve=None, bucket=, cache=None, image_weights=False, device=0, multi_scale=False, single_cls=False, optimizer=SGD, sync_bn=False, workers=8, project=yolov5/runs/train, name=exp, exist_ok=False, quad=False, cos_lr=False, label_smoothing=0.0, patience=100, freeze=[0], save_period=-1, seed=0, local_rank=-1, entity=None, upload_dataset=False, bbox_interval=-1, artifact_alias=latest\n","\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n","\u001b[31m\u001b[1mrequirements:\u001b[0m YOLOv5 requirements \"ipython\" \"thop>=0.1.1\" not found, attempting AutoUpdate...\n","Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n","Requirement already satisfied: ipython in /usr/local/lib/python3.8/dist-packages (7.9.0)\n","Collecting thop>=0.1.1\n"," Downloading thop-0.1.1.post2209072238-py3-none-any.whl (15 kB)\n","Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.8/dist-packages (from ipython) (57.4.0)\n","Requirement already satisfied: pexpect in /usr/local/lib/python3.8/dist-packages (from ipython) (4.8.0)\n","Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.8/dist-packages (from ipython) (5.7.1)\n","Requirement already satisfied: backcall in /usr/local/lib/python3.8/dist-packages (from ipython) (0.2.0)\n","Collecting jedi>=0.10\n"," Downloading jedi-0.18.2-py2.py3-none-any.whl (1.6 MB)\n"," ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━ 1.6/1.6 MB 22.4 MB/s eta 0:00:00\n","Requirement already satisfied: 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ptyprocess>=0.5 in /usr/local/lib/python3.8/dist-packages (from pexpect->ipython) (0.7.0)\n","Requirement already satisfied: typing-extensions in /usr/local/lib/python3.8/dist-packages (from torch->thop>=0.1.1) (4.4.0)\n","Installing collected packages: jedi, thop\n","Successfully installed jedi-0.18.2 thop-0.1.1.post2209072238\n","\n","\u001b[31m\u001b[1mrequirements:\u001b[0m 2 packages updated per /content/gdrive/MyDrive/WKO_PROJECT_NEW/yolov5/requirements.txt\n","\u001b[31m\u001b[1mrequirements:\u001b[0m ⚠️ \u001b[1mRestart runtime or rerun command for updates to take effect\u001b[0m\n","\n","YOLOv5 🚀 v7.0-72-g064365d Python-3.8.10 torch-1.13.1+cu116 CUDA:0 (NVIDIA A100-SXM4-40GB, 40536MiB)\n","\n","\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.01, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n","\u001b[34m\u001b[1mClearML: \u001b[0mrun 'pip install clearml' to automatically track, visualize and remotely train YOLOv5 🚀 in ClearML\n","\u001b[34m\u001b[1mComet: \u001b[0mrun 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet\n","\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir yolov5/runs/train', view at http://localhost:6006/\n","2023-02-01 12:24:09.162063: I tensorflow/core/util/util.cc:169] oneDNN custom operations are on. You may see slightly different numerical results due to floating-point round-off errors from different computation orders. To turn them off, set the environment variable `TF_ENABLE_ONEDNN_OPTS=0`.\n","Downloading https://ultralytics.com/assets/Arial.ttf to /root/.config/Ultralytics/Arial.ttf...\n","100% 755k/755k [00:00<00:00, 15.7MB/s]\n","\n"," from n params module arguments \n"," 0 -1 1 3520 models.common.Conv [3, 32, 6, 2, 2] \n"," 1 -1 1 18560 models.common.Conv [32, 64, 3, 2] \n"," 2 -1 1 18816 models.common.C3 [64, 64, 1] \n"," 3 -1 1 73984 models.common.Conv [64, 128, 3, 2] \n"," 4 -1 2 115712 models.common.C3 [128, 128, 2] \n"," 5 -1 1 295424 models.common.Conv [128, 256, 3, 2] \n"," 6 -1 3 625152 models.common.C3 [256, 256, 3] \n"," 7 -1 1 1180672 models.common.Conv [256, 512, 3, 2] \n"," 8 -1 1 1182720 models.common.C3 [512, 512, 1] \n"," 9 -1 1 656896 models.common.SPPF [512, 512, 5] \n"," 10 -1 1 131584 models.common.Conv [512, 256, 1, 1] \n"," 11 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 12 [-1, 6] 1 0 models.common.Concat [1] \n"," 13 -1 1 361984 models.common.C3 [512, 256, 1, False] \n"," 14 -1 1 33024 models.common.Conv [256, 128, 1, 1] \n"," 15 -1 1 0 torch.nn.modules.upsampling.Upsample [None, 2, 'nearest'] \n"," 16 [-1, 4] 1 0 models.common.Concat [1] \n"," 17 -1 1 90880 models.common.C3 [256, 128, 1, False] \n"," 18 -1 1 147712 models.common.Conv [128, 128, 3, 2] \n"," 19 [-1, 14] 1 0 models.common.Concat [1] \n"," 20 -1 1 296448 models.common.C3 [256, 256, 1, False] \n"," 21 -1 1 590336 models.common.Conv [256, 256, 3, 2] \n"," 22 [-1, 10] 1 0 models.common.Concat [1] \n"," 23 -1 1 1182720 models.common.C3 [512, 512, 1, False] \n"," 24 [17, 20, 23] 1 21576 models.yolo.Detect [3, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n","YOLOv5s summary: 214 layers, 7027720 parameters, 7027720 gradients\n","\n","\u001b[34m\u001b[1mAMP: \u001b[0mchecks passed ✅\n","\u001b[34m\u001b[1moptimizer:\u001b[0m SGD(lr=0.01) with parameter groups 57 weight(decay=0.0), 60 weight(decay=0.0005), 60 bias\n","\u001b[34m\u001b[1malbumentations: \u001b[0mBlur(p=0.01, blur_limit=(3, 7)), MedianBlur(p=0.01, blur_limit=(3, 7)), ToGray(p=0.01), CLAHE(p=0.01, clip_limit=(1, 4.0), tile_grid_size=(8, 8))\n","\u001b[34m\u001b[1mtrain: \u001b[0mScanning /content/gdrive/MyDrive/WKO_PROJECT_NEW/mask_dataset/train.cache... 682 images, 0 backgrounds, 0 corrupt: 100% 682/682 [00:00