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wko-projekt/yolo.ipynb

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add yolov3 and ocr to detect license plate numbers
2023-01-22 21:00:32 +01:00
{
"cells": [
{
"cell_type": "markdown",
"id": "4b1d82bd",
"metadata": {},
"source": [
"# LICENSE PLATE DETECTION"
]
},
{
"cell_type": "markdown",
"id": "9b54ab67",
"metadata": {
"deletable": false,
"nbgrader": {
"cell_type": "code",
"checksum": "775443e2f8e6d780f7310e57d00701e7",
"grade": true,
"grade_id": "cell-53e7c09d33eaba20",
"locked": false,
"points": 6,
"schema_version": 3,
"solution": true,
"task": false
}
},
"source": [
"### YOLO V3"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "61ac9526",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Cloning into 'keras-yolo3'...\n",
"remote: Enumerating objects: 169, done.\u001b[K\n",
"remote: Total 169 (delta 0), reused 0 (delta 0), pack-reused 169\u001b[K\n",
"Receiving objects: 100% (169/169), 172.74 KiB | 625.00 KiB/s, done.\n",
"Resolving deltas: 100% (80/80), done.\n"
]
}
],
"source": [
"!git clone https://github.com/roboflow-ai/keras-yolo3"
]
},
{
"cell_type": "code",
"execution_count": 2,
"id": "5d7010e7",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" % Total % Received % Xferd Average Speed Time Time Time Current\n",
" Dload Upload Total Spent Left Speed\n",
"100 897 100 897 0 0 269 0 0:00:03 0:00:03 --:--:-- 269 0\n",
"100 2120k 100 2120k 0 0 515k 0 0:00:04 0:00:04 --:--:-- 23.5M\n",
"Archive: roboflow.zip\n",
" extracting: README.dataset.txt \n",
" extracting: README.roboflow.txt \n",
" creating: test/\n",
" extracting: test/2images1_png.rf.5de47b3b58bc776388f9547915f46edf.jpg \n",
" extracting: test/2images41_png.rf.2f711be90f9f9e796139a02cb45fe9ba.jpg \n",
" extracting: test/2images45_png.rf.cbcc994c49d1a2ca5e7bc52cb9b2a1a3.jpg \n",
" extracting: test/3images22_png.rf.b139cdb6065c658e0c7acc2124854383.jpg \n",
" extracting: test/3images34_png.rf.9a25c14870c5acae15ee0f159a9707b4.jpg \n",
" extracting: test/3images4_png.rf.a3d6d0b11320142ada8e8347c918dc30.jpg \n",
" extracting: test/6images3_png.rf.8b1268f1823ea224077f537939c2ccba.jpg \n",
" extracting: test/7images0_png.rf.fb9d6e1e739e19321bdc7050f4a95798.jpg \n",
" extracting: test/_annotations.txt \n",
" extracting: test/_classes.txt \n",
" extracting: test/images6_png.rf.56641c848717baa02774239ac0039bd6.jpg \n",
" extracting: test/img105_png.rf.d69f400c7410b1e265136d01b1a2cc5e.jpg \n",
" extracting: test/img149_png.rf.c487d9bc6be853e23cc7a12359178b40.jpg \n",
" extracting: test/img14_png.rf.1a47d3748ad1566280dc8199d96430de.jpg \n",
" extracting: test/img35_png.rf.16e367a1ce2db4dc0b0b1491814e8c95.jpg \n",
" extracting: test/img89_png.rf.f0f546c24ed5d6a16a2cbf9389065678.jpg \n",
" creating: train/\n",
" extracting: train/20img2_png.rf.015a51172ce51d61531b54af5a144183.jpg \n",
" extracting: train/21img3_png.rf.c1601abdfd96ebfc6f13205c638364bc.jpg \n",
" extracting: train/22img34_png.rf.02ddffee2d6e8dc6ef169f89f622a933.jpg \n",
" extracting: train/23img46_png.rf.fd5a109b78b90ed3582888880b743303.jpg \n",
" extracting: train/24img50_png.rf.1f28fdcb1632f237fb0bf7be7d877351.jpg \n",
" extracting: train/25img73_png.rf.25d9c97db5c2c466bbe2692f9f69c869.jpg \n",
" extracting: train/26img74_png.rf.861f6c881709f3bb65637c7ea3871dca.jpg \n",
" extracting: train/2images0_png.rf.b8c8f0d2594f6bfaf8be2dca50416bb6.jpg \n",
" extracting: train/2images18_png.rf.951b35372d913193f0899fda6877cbee.jpg \n",
" extracting: train/2images22_png.rf.bb299b6d237016c2714b68aead8266d7.jpg \n",
" extracting: train/2images23_png.rf.cea092359f78eb1c22db6b50627790d6.jpg \n",
" extracting: train/2images29_png.rf.91d8be50c5d0f82577d74268153ac5fc.jpg \n",
" extracting: train/2images2_png.rf.62684ca2757500eaeac877d48e04c92f.jpg \n",
" extracting: train/2images34_png.rf.ffff2284b01426e5cd22ca8053450348.jpg \n",
" extracting: train/2images3_png.rf.c7b635e1dc54f5bb10aa338d78969c22.jpg \n",
" extracting: train/2images46_png.rf.d4143a5946da0d1bc8e540c239a648b8.jpg \n",
" extracting: train/2images4_png.rf.64541674b6b6df83b15534c2d8bf0030.jpg \n",
" extracting: train/30img11_png.rf.1a236b6935fd926336da07248a867a36.jpg \n",
" extracting: train/3images0_png.rf.ff30aaf2256dde95d2dc4893b7074098.jpg \n",
" extracting: train/3images11_png.rf.8a11e1eae3b52a369681843c7d7116d1.jpg \n",
" extracting: train/3images18_png.rf.0673ed9396fa1ae5a43ff44f10422ff5.jpg \n",
" extracting: train/3images29_png.rf.a96af5fe85f477adc0fce370e788f76c.jpg \n",
" extracting: train/3images2_png.rf.d7de4c45de845226a8391e8f332352d9.jpg \n",
" extracting: train/3images30_png.rf.dd0080eda6b7d8ff2e188c7e5590e7c6.jpg \n",
" extracting: train/3images33_png.rf.3abc75a93214fc0a497dd54cabd690a0.jpg \n",
" extracting: train/3images3_png.rf.e9771234c266dba02be2fd6f204aa66b.jpg \n",
" extracting: train/3images42_png.rf.b3b45a46d57ac11c2d546831ad52cceb.jpg \n",
" extracting: train/3images43_png.rf.0603c0f1b7a15be7449b6d46c621e7af.jpg \n",
" extracting: train/3images5_png.rf.6a53d28cdfade27885d25f8208f3028a.jpg \n",
" extracting: train/6images0_png.rf.1e11dd3d7f4e5a79ce207c7770185b0c.jpg \n",
" extracting: train/6images12_png.rf.d0d6b3319c39fdb6a9356047f5ddb8ee.jpg \n",
" extracting: train/6images1_png.rf.8c65b6bfe8d5b01a2a1545337de6c390.jpg \n",
" extracting: train/6images4_png.rf.2c77da3c85f4cb57ebe5d90ab8ed5e0c.jpg \n",
" extracting: train/6images5_png.rf.7033ded0e4684504365b5b0345529c5c.jpg \n",
" extracting: train/7images12_png.rf.c46a44810aea7edafc53b6b561c6cf6a.jpg \n",
" extracting: train/7images17_png.rf.ff8fc5bb0f84483dd914f5f2de524933.jpg \n",
" extracting: train/7images1_png.rf.cf5406f149f35ab24eda2c621f9298ed.jpg \n",
" extracting: train/7images2_png.rf.f84de676f7fb3de9d7789e1dafab8fa3.jpg \n",
" extracting: train/7images3_png.rf.14c5f2588d07e7234659792e20bd7fd8.jpg \n",
" extracting: train/7images4_png.rf.5e455f9a5c94b0a3b56043ef05d06854.jpg \n",
" extracting: train/_annotations.txt \n",
" extracting: train/_classes.txt \n",
" extracting: train/images0_png.rf.d1f446cd89662b7ccf994dc77f63ff56.jpg \n",
" extracting: train/images10_png.rf.bc421baf20b7cbf6af4ea822f259fcab.jpg \n",
" extracting: train/images13_png.rf.dff8711d203b47a3f8709c4cee5d6927.jpg \n",
" extracting: train/images15_png.rf.e1b904b94d5539da79117c3613ae5765.jpg \n",
" extracting: train/images1_png.rf.9c2cb373d7f4613a2735410f1fdb3043.jpg \n",
" extracting: train/images3_png.rf.e7cf0078d44c2571ebc5d607ffaacbc8.jpg \n",
" extracting: train/images4_png.rf.97f8f01f67adf77de50c99fd6ed7f879.jpg \n",
" extracting: train/images5_png.rf.d16b8c87a8a593e5971124648ba63736.jpg \n",
" extracting: train/img0_png.rf.fa065b68c3d51d65399f883f8713ccf2.jpg \n",
" extracting: train/img102_png.rf.3da7ec4deedfb6f15834e9a42aee4e7c.jpg \n",
" extracting: train/img103_png.rf.67216b08a719a9a9dba68f83c5460a74.jpg \n",
" extracting: train/img104_png.rf.db759d639a6b1ace6dc8e7442c86ba9a.jpg \n",
" extracting: train/img106_png.rf.d882268d61ac720e54c35110fb8bc4b0.jpg \n",
" extracting: train/img107_png.rf.a62231fc47913091ec76468e536d6f28.jpg \n",
" extracting: train/img10_png.rf.e7bba8322d47d623f71903aa50f48730.jpg \n",
" extracting: train/img113_png.rf.cb3afcbea4e7177a2ed703b4b1d94887.jpg \n",
" extracting: train/img116_png.rf.1f7034a069e5a888b00da9496e0df5ae.jpg \n",
" extracting: train/img118_png.rf.9e21a52ffda3719b2cc6deb0309efd7d.jpg \n",
" extracting: train/img11_png.rf.a9584dc2d254fd84ca6a30cc9b821bd5.jpg \n",
" extracting: train/img125_png.rf.4a0a9a2f74bd5127343124c4fb4d0670.jpg \n",
" extracting: train/img126_png.rf.0bad29364a3846287498838f6791cae8.jpg \n",
" extracting: train/img133_png.rf.e66c88015d6fb51921b20ad8008fc981.jpg \n",
" extracting: train/img146_png.rf.9811cc9a676e18c4cf2bce86398feb9d.jpg \n",
" extracting: train/img170_png.rf.1d04d991430ba0d672fabff684817dc6.jpg \n",
" extracting: train/img174_png.rf.4d01b9ebbdc8c1b434c61c945794a79e.jpg \n",
" extracting: train/img178_png.rf.b0e5b6547069d86483e91fc99356e5d9.jpg \n",
" extracting: train/img181_png.rf.363074a89b0325055d28f3794083e479.jpg \n",
" extracting: train/img189_png.rf.07aedf508ccbfc3e0244bd54bd76cbf8.jpg \n",
" extracting: train/img197_png.rf.36119ab11e392cfeded10c61aa97eac6.jpg \n",
" extracting: train/img1_png.rf.bf5b1060d3cb9959dc94b75d4fc78334.jpg \n",
" extracting: train/img202_png.rf.f6520c22d6c95c8e5a105b6ee48b8da1.jpg \n",
" extracting: train/img205_png.rf.98d121af5e0548a1402eb3e93560465d.jpg \n",
" extracting: train/img215_png.rf.6d29cfcf38f6a4b2165ba5ba110454d2.jpg \n",
" extracting: train/img270_png.rf.52541958250f2b45297faa1440d55d56.jpg \n",
" extracting: train/img278_png.rf.82173849dfde92f2a2ab2761e5679891.jpg \n",
" extracting: train/img283_png.rf.809b4e6edbe803fbcab887a40e59f526.jpg \n",
" extracting: train/img2_png.rf.23b2d7fe287627739888976776de8437.jpg \n",
" extracting: train/img306_png.rf.642a9812ecebfd9784d9eb593b78dcf2.jpg \n",
" extracting: train/img34_png.rf.f98b7fa7325ddb9ca373121c5c120f55.jpg \n",
" extracting: train/img38_png.rf.cd97a110e34ad869a4b79d8237d92a36.jpg \n",
" extracting: train/img39_png.rf.e9b1634ca400418b29839bad544e8634.jpg \n",
" extracting: train/img3_png.rf.3f382680461124ba2e19c1df51d895e7.jpg \n",
" extracting: train/img45_png.rf.870b550082c3da2c42e40017442c115b.jpg \n",
" extracting: train/img46_png.rf.2c1d961d3e61d1389c825f2aba32ab39.jpg \n",
" extracting: train/img4_png.rf.4f0ce3c02167bf3f8ae2454471c9c4fd.jpg \n",
" extracting: train/img57_png.rf.dc254e143fec0667ac462e303290e301.jpg \n",
" extracting: train/img58_png.rf.1a6e09bda52588bb7f3890768f0db5f2.jpg \n",
" extracting: train/img5_png.rf.542fe1bdd2a910b20f27ce55cf8689ff.jpg \n",
" extracting: train/img66_png.rf.534ec186146ae4409f8c875cf28dcb84.jpg \n",
" extracting: train/img6_png.rf.7aceac81d4a22f02ab0460ee5bd2227f.jpg \n",
" extracting: train/img77_png.rf.8f8e23567322fd7de129380c6a54bd01.jpg \n",
" extracting: train/img78_png.rf.eb48e94d48c04b3077d049cb8cd920bb.jpg \n",
" extracting: train/img7_png.rf.2dd95d826f13ab03805de7f7b842eb40.jpg \n",
" extracting: train/img85_png.rf.f7a4ae3bb16a8c3fe7f164e35f11ea65.jpg \n",
" extracting: train/img86_png.rf.3addc2b6c62b8d5098feba035bd6014d.jpg \n",
" extracting: train/img92_png.rf.5b79211320122e08554541c15fc041dd.jpg \n",
" extracting: train/img93_png.rf.7fbe9b0dcab1f063b154796d00ae669b.jpg \n",
" extracting: train/img95_png.rf.c97bb901c22e4f1519bac037ffbdbbf7.jpg \n",
" extracting: train/img97_png.rf.2e3f7205a9d122aa07906ebe643f1c04.jpg \n",
" extracting: train/img98_png.rf.c6da81320ec0c22868d84c2291b416f5.jpg \n",
" creating: valid/\n",
" extracting: valid/27img121_png.rf.6b1bbeee06ff52963c7b12c7bfb2aacc.jpg \n",
" extracting: valid/2images12_png.rf.ba715b76693ae62d01e142ba9859ffc9.jpg \n",
" extracting: valid/2images35_png.rf.81e0cc483a896440e148a5df5550d243.jpg \n",
" extracting: valid/2images40_png.rf.45e16e4d96b21eeb7b0e06556ca12291.jpg \n",
" extracting: valid/3images19_png.rf.aec1de41eff03d6e343427691b2a3029.jpg \n",
" extracting: valid/3images1_png.rf.f293d93f952977825a07613f23a55f70.jpg \n",
" extracting: valid/6images11_png.rf.a467d473bfa546de8e2c5ef4ef894802.jpg \n",
" extracting: valid/6images2_png.rf.386c9a11cef823c522619aefd9c7ca9d.jpg \n",
" extracting: valid/_annotations.txt \n",
" extracting: valid/_classes.txt \n",
" extracting: valid/images14_png.rf.f0a78b8df38e6394e9cc3d56d7677c87.jpg \n",
" extracting: valid/images2_png.rf.1f566a50352095712ec385ffc17b14c5.jpg \n",
" extracting: valid/img101_png.rf.aca3e688b7798ee456467954274733de.jpg \n",
" extracting: valid/img111_png.rf.4bc2a8d175d8bbe2a289ba9e0ed4c717.jpg \n",
" extracting: valid/img112_png.rf.aaadc30802c92e3c1196a96b859c8ebb.jpg \n",
" extracting: valid/img117_png.rf.76d5b2f35f4974cca3750f258af86101.jpg \n",
" extracting: valid/img121_png.rf.a11051677709f708036ca072d0725099.jpg \n",
" extracting: valid/img122_png.rf.f6c62a3f0290eae81ffc5c457f546adf.jpg \n",
" extracting: valid/img141_png.rf.9d9ff6b78c2940546bf364e662b1c813.jpg \n",
" extracting: valid/img165_png.rf.6bb45f3455f0340e377ec61e662d7846.jpg \n",
" extracting: valid/img177_png.rf.fd279311108df43a7d9225cc26c2542f.jpg \n",
" extracting: valid/img262_png.rf.cd066cf49feb976bf8cd8eca32dcf729.jpg \n",
" extracting: valid/img27_png.rf.09745a24cc36301e1eca5c3a9bab3853.jpg \n",
" extracting: valid/img304_png.rf.f91aa4dfe963c390a521fd748f1ab9f5.jpg \n",
" extracting: valid/img313_png.rf.8ea5815425e82f42c06715e0b98342f2.jpg \n",
" extracting: valid/img31_png.rf.3b72bf618de466d70ab487fe5e20ff70.jpg \n",
" extracting: valid/img40_png.rf.8389bb867a237cad805b4819dc788a98.jpg \n",
" extracting: valid/img41_png.rf.4f6f5b9dcbe9eb80f9913e223f321f66.jpg \n",
" extracting: valid/img69_png.rf.52cb5ea0d37bc73a2fcc1ee19de2b124.jpg \n",
" extracting: valid/img84_png.rf.c9700ee5dee2697886b497a2e17f1573.jpg \n"
]
}
],
"source": [
"!curl -L \"https://app.roboflow.com/ds/hTj8Pr7g7U?key=q9kdROYojM\" > roboflow.zip; unzip roboflow.zip; rm roboflow.zip"
]
},
{
"cell_type": "code",
"execution_count": 5,
"id": "6989cf92",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"--2023-01-18 12:01:19-- https://pjreddie.com/media/files/yolov3.weights\n",
"Translacja pjreddie.com (pjreddie.com)... 128.208.4.108\n",
"Łączenie się z pjreddie.com (pjreddie.com)|128.208.4.108|:443... połączono.\n",
"Żądanie HTTP wysłano, oczekiwanie na odpowiedź... 200 OK\n",
"Długość: 248007048 (237M) [application/octet-stream]\n",
"Zapis do: `yolov3.weights'\n",
"\n",
"yolov3.weights 100%[===================>] 236,52M 17,0MB/s w 15s \n",
"\n",
"2023-01-18 12:01:35 (15,4 MB/s) - zapisano `yolov3.weights' [248007048/248007048]\n",
"\n"
]
}
],
"source": [
"!wget https://pjreddie.com/media/files/yolov3.weights"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "e981c95d",
"metadata": {},
"outputs": [],
"source": [
"from keras.layers import ELU, PReLU, LeakyReLU"
]
},
{
"cell_type": "code",
"execution_count": 10,
"id": "d8924aad",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Loading weights.\n",
"Weights Header: 0 2 0 [32013312]\n",
"Parsing Darknet config.\n",
"Creating Keras model.\n",
"Parsing section net_0\n",
"Parsing section convolutional_0\n",
"conv2d bn leaky (3, 3, 3, 32)\n",
"Metal device set to: Apple M1\n",
"\n",
"systemMemory: 8.00 GB\n",
"maxCacheSize: 2.67 GB\n",
"\n",
"2023-01-18 12:03:25.001841: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:306] Could not identify NUMA node of platform GPU ID 0, defaulting to 0. Your kernel may not have been built with NUMA support.\n",
"2023-01-18 12:03:25.002402: I tensorflow/core/common_runtime/pluggable_device/pluggable_device_factory.cc:272] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 0 MB memory) -> physical PluggableDevice (device: 0, name: METAL, pci bus id: <undefined>)\n",
"Parsing section convolutional_1\n",
"conv2d bn leaky (3, 3, 32, 64)\n",
"Parsing section convolutional_2\n",
"conv2d bn leaky (1, 1, 64, 32)\n",
"Parsing section convolutional_3\n",
"conv2d bn leaky (3, 3, 32, 64)\n",
"Parsing section shortcut_0\n",
"Parsing section convolutional_4\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section convolutional_5\n",
"conv2d bn leaky (1, 1, 128, 64)\n",
"Parsing section convolutional_6\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section shortcut_1\n",
"Parsing section convolutional_7\n",
"conv2d bn leaky (1, 1, 128, 64)\n",
"Parsing section convolutional_8\n",
"conv2d bn leaky (3, 3, 64, 128)\n",
"Parsing section shortcut_2\n",
"Parsing section convolutional_9\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_10\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_11\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_3\n",
"Parsing section convolutional_12\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_13\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_4\n",
"Parsing section convolutional_14\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_15\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_5\n",
"Parsing section convolutional_16\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_17\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_6\n",
"Parsing section convolutional_18\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_19\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_7\n",
"Parsing section convolutional_20\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_21\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_8\n",
"Parsing section convolutional_22\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_23\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_9\n",
"Parsing section convolutional_24\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_25\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section shortcut_10\n",
"Parsing section convolutional_26\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_27\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_28\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_11\n",
"Parsing section convolutional_29\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_30\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_12\n",
"Parsing section convolutional_31\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_32\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_13\n",
"Parsing section convolutional_33\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_34\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_14\n",
"Parsing section convolutional_35\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_36\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_15\n",
"Parsing section convolutional_37\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_38\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_16\n",
"Parsing section convolutional_39\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_40\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_17\n",
"Parsing section convolutional_41\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_42\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section shortcut_18\n",
"Parsing section convolutional_43\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_44\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_45\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_19\n",
"Parsing section convolutional_46\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_47\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_20\n",
"Parsing section convolutional_48\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_49\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_21\n",
"Parsing section convolutional_50\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_51\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section shortcut_22\n",
"Parsing section convolutional_52\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_53\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_54\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_55\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_56\n",
"conv2d bn leaky (1, 1, 1024, 512)\n",
"Parsing section convolutional_57\n",
"conv2d bn leaky (3, 3, 512, 1024)\n",
"Parsing section convolutional_58\n",
"conv2d linear (1, 1, 1024, 255)\n",
"Parsing section yolo_0\n",
"Parsing section route_0\n",
"Parsing section convolutional_59\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section upsample_0\n",
"Parsing section route_1\n",
"Concatenating route layers: [<KerasTensor: shape=(None, None, None, 256) dtype=float32 (created by layer 'up_sampling2d')>, <KerasTensor: shape=(None, None, None, 512) dtype=float32 (created by layer 'add_18')>]\n",
"Parsing section convolutional_60\n",
"conv2d bn leaky (1, 1, 768, 256)\n",
"Parsing section convolutional_61\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_62\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_63\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_64\n",
"conv2d bn leaky (1, 1, 512, 256)\n",
"Parsing section convolutional_65\n",
"conv2d bn leaky (3, 3, 256, 512)\n",
"Parsing section convolutional_66\n",
"conv2d linear (1, 1, 512, 255)\n",
"Parsing section yolo_1\n",
"Parsing section route_2\n",
"Parsing section convolutional_67\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section upsample_1\n",
"Parsing section route_3\n",
"Concatenating route layers: [<KerasTensor: shape=(None, None, None, 128) dtype=float32 (created by layer 'up_sampling2d_1')>, <KerasTensor: shape=(None, None, None, 256) dtype=float32 (created by layer 'add_10')>]\n",
"Parsing section convolutional_68\n",
"conv2d bn leaky (1, 1, 384, 128)\n",
"Parsing section convolutional_69\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_70\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_71\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_72\n",
"conv2d bn leaky (1, 1, 256, 128)\n",
"Parsing section convolutional_73\n",
"conv2d bn leaky (3, 3, 128, 256)\n",
"Parsing section convolutional_74\n",
"conv2d linear (1, 1, 256, 255)\n",
"Parsing section yolo_2\n",
"Model: \"model\"\n",
"__________________________________________________________________________________________________\n",
" Layer (type) Output Shape Param # Connected to \n",
"==================================================================================================\n",
" input_1 (InputLayer) [(None, None, None, 0 [] \n",
" 3)] \n",
" \n",
" conv2d (Conv2D) (None, None, None, 864 ['input_1[0][0]'] \n",
" 32) \n",
" \n",
" batch_normalization (BatchNorm (None, None, None, 128 ['conv2d[0][0]'] \n",
" alization) 32) \n",
" \n",
" leaky_re_lu (LeakyReLU) (None, None, None, 0 ['batch_normalization[0][0]'] \n",
" 32) \n",
" \n",
" zero_padding2d (ZeroPadding2D) (None, None, None, 0 ['leaky_re_lu[0][0]'] \n",
" 32) \n",
" \n",
" conv2d_1 (Conv2D) (None, None, None, 18432 ['zero_padding2d[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_1 (BatchNo (None, None, None, 256 ['conv2d_1[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_1 (LeakyReLU) (None, None, None, 0 ['batch_normalization_1[0][0]'] \n",
" 64) \n",
" \n",
" conv2d_2 (Conv2D) (None, None, None, 2048 ['leaky_re_lu_1[0][0]'] \n",
" 32) \n",
" \n",
" batch_normalization_2 (BatchNo (None, None, None, 128 ['conv2d_2[0][0]'] \n",
" rmalization) 32) \n",
" \n",
" leaky_re_lu_2 (LeakyReLU) (None, None, None, 0 ['batch_normalization_2[0][0]'] \n",
" 32) \n",
" \n",
" conv2d_3 (Conv2D) (None, None, None, 18432 ['leaky_re_lu_2[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_3 (BatchNo (None, None, None, 256 ['conv2d_3[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_3 (LeakyReLU) (None, None, None, 0 ['batch_normalization_3[0][0]'] \n",
" 64) \n",
" \n",
" add (Add) (None, None, None, 0 ['leaky_re_lu_1[0][0]', \n",
" 64) 'leaky_re_lu_3[0][0]'] \n",
" \n",
" zero_padding2d_1 (ZeroPadding2 (None, None, None, 0 ['add[0][0]'] \n",
" D) 64) \n",
" \n",
" conv2d_4 (Conv2D) (None, None, None, 73728 ['zero_padding2d_1[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_4 (BatchNo (None, None, None, 512 ['conv2d_4[0][0]'] \n",
" rmalization) 128) \n",
" \n",
" leaky_re_lu_4 (LeakyReLU) (None, None, None, 0 ['batch_normalization_4[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_5 (Conv2D) (None, None, None, 8192 ['leaky_re_lu_4[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_5 (BatchNo (None, None, None, 256 ['conv2d_5[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_5 (LeakyReLU) (None, None, None, 0 ['batch_normalization_5[0][0]'] \n",
" 64) \n",
" \n",
" conv2d_6 (Conv2D) (None, None, None, 73728 ['leaky_re_lu_5[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_6 (BatchNo (None, None, None, 512 ['conv2d_6[0][0]'] \n",
" rmalization) 128) \n",
" \n",
" leaky_re_lu_6 (LeakyReLU) (None, None, None, 0 ['batch_normalization_6[0][0]'] \n",
" 128) \n",
" \n",
" add_1 (Add) (None, None, None, 0 ['leaky_re_lu_4[0][0]', \n",
" 128) 'leaky_re_lu_6[0][0]'] \n",
" \n",
" conv2d_7 (Conv2D) (None, None, None, 8192 ['add_1[0][0]'] \n",
" 64) \n",
" \n",
" batch_normalization_7 (BatchNo (None, None, None, 256 ['conv2d_7[0][0]'] \n",
" rmalization) 64) \n",
" \n",
" leaky_re_lu_7 (LeakyReLU) (None, None, None, 0 ['batch_normalization_7[0][0]'] \n",
" 64) \n",
" \n",
" conv2d_8 (Conv2D) (None, None, None, 73728 ['leaky_re_lu_7[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_8 (BatchNo (None, None, None, 512 ['conv2d_8[0][0]'] \n",
" rmalization) 128) \n",
" \n",
" leaky_re_lu_8 (LeakyReLU) (None, None, None, 0 ['batch_normalization_8[0][0]'] \n",
" 128) \n",
" \n",
" add_2 (Add) (None, None, None, 0 ['add_1[0][0]', \n",
" 128) 'leaky_re_lu_8[0][0]'] \n",
" \n",
" zero_padding2d_2 (ZeroPadding2 (None, None, None, 0 ['add_2[0][0]'] \n",
" D) 128) \n",
" \n",
" conv2d_9 (Conv2D) (None, None, None, 294912 ['zero_padding2d_2[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_9 (BatchNo (None, None, None, 1024 ['conv2d_9[0][0]'] \n",
" rmalization) 256) \n",
" \n",
" leaky_re_lu_9 (LeakyReLU) (None, None, None, 0 ['batch_normalization_9[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_10 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_9[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_10 (BatchN (None, None, None, 512 ['conv2d_10[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_10 (LeakyReLU) (None, None, None, 0 ['batch_normalization_10[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_11 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_10[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_11 (BatchN (None, None, None, 1024 ['conv2d_11[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_11 (LeakyReLU) (None, None, None, 0 ['batch_normalization_11[0][0]'] \n",
" 256) \n",
" \n",
" add_3 (Add) (None, None, None, 0 ['leaky_re_lu_9[0][0]', \n",
" 256) 'leaky_re_lu_11[0][0]'] \n",
" \n",
" conv2d_12 (Conv2D) (None, None, None, 32768 ['add_3[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_12 (BatchN (None, None, None, 512 ['conv2d_12[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_12 (LeakyReLU) (None, None, None, 0 ['batch_normalization_12[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_13 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_12[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_13 (BatchN (None, None, None, 1024 ['conv2d_13[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_13 (LeakyReLU) (None, None, None, 0 ['batch_normalization_13[0][0]'] \n",
" 256) \n",
" \n",
" add_4 (Add) (None, None, None, 0 ['add_3[0][0]', \n",
" 256) 'leaky_re_lu_13[0][0]'] \n",
" \n",
" conv2d_14 (Conv2D) (None, None, None, 32768 ['add_4[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_14 (BatchN (None, None, None, 512 ['conv2d_14[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_14 (LeakyReLU) (None, None, None, 0 ['batch_normalization_14[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_15 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_14[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_15 (BatchN (None, None, None, 1024 ['conv2d_15[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_15 (LeakyReLU) (None, None, None, 0 ['batch_normalization_15[0][0]'] \n",
" 256) \n",
" \n",
" add_5 (Add) (None, None, None, 0 ['add_4[0][0]', \n",
" 256) 'leaky_re_lu_15[0][0]'] \n",
" \n",
" conv2d_16 (Conv2D) (None, None, None, 32768 ['add_5[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_16 (BatchN (None, None, None, 512 ['conv2d_16[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_16 (LeakyReLU) (None, None, None, 0 ['batch_normalization_16[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_17 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_16[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_17 (BatchN (None, None, None, 1024 ['conv2d_17[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_17 (LeakyReLU) (None, None, None, 0 ['batch_normalization_17[0][0]'] \n",
" 256) \n",
" \n",
" add_6 (Add) (None, None, None, 0 ['add_5[0][0]', \n",
" 256) 'leaky_re_lu_17[0][0]'] \n",
" \n",
" conv2d_18 (Conv2D) (None, None, None, 32768 ['add_6[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_18 (BatchN (None, None, None, 512 ['conv2d_18[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_18 (LeakyReLU) (None, None, None, 0 ['batch_normalization_18[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_19 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_18[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_19 (BatchN (None, None, None, 1024 ['conv2d_19[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_19 (LeakyReLU) (None, None, None, 0 ['batch_normalization_19[0][0]'] \n",
" 256) \n",
" \n",
" add_7 (Add) (None, None, None, 0 ['add_6[0][0]', \n",
" 256) 'leaky_re_lu_19[0][0]'] \n",
" \n",
" conv2d_20 (Conv2D) (None, None, None, 32768 ['add_7[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_20 (BatchN (None, None, None, 512 ['conv2d_20[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_20 (LeakyReLU) (None, None, None, 0 ['batch_normalization_20[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_21 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_20[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_21 (BatchN (None, None, None, 1024 ['conv2d_21[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_21 (LeakyReLU) (None, None, None, 0 ['batch_normalization_21[0][0]'] \n",
" 256) \n",
" \n",
" add_8 (Add) (None, None, None, 0 ['add_7[0][0]', \n",
" 256) 'leaky_re_lu_21[0][0]'] \n",
" \n",
" conv2d_22 (Conv2D) (None, None, None, 32768 ['add_8[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_22 (BatchN (None, None, None, 512 ['conv2d_22[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_22 (LeakyReLU) (None, None, None, 0 ['batch_normalization_22[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_23 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_22[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_23 (BatchN (None, None, None, 1024 ['conv2d_23[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_23 (LeakyReLU) (None, None, None, 0 ['batch_normalization_23[0][0]'] \n",
" 256) \n",
" \n",
" add_9 (Add) (None, None, None, 0 ['add_8[0][0]', \n",
" 256) 'leaky_re_lu_23[0][0]'] \n",
" \n",
" conv2d_24 (Conv2D) (None, None, None, 32768 ['add_9[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_24 (BatchN (None, None, None, 512 ['conv2d_24[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_24 (LeakyReLU) (None, None, None, 0 ['batch_normalization_24[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_25 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_24[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_25 (BatchN (None, None, None, 1024 ['conv2d_25[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_25 (LeakyReLU) (None, None, None, 0 ['batch_normalization_25[0][0]'] \n",
" 256) \n",
" \n",
" add_10 (Add) (None, None, None, 0 ['add_9[0][0]', \n",
" 256) 'leaky_re_lu_25[0][0]'] \n",
" \n",
" zero_padding2d_3 (ZeroPadding2 (None, None, None, 0 ['add_10[0][0]'] \n",
" D) 256) \n",
" \n",
" conv2d_26 (Conv2D) (None, None, None, 1179648 ['zero_padding2d_3[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_26 (BatchN (None, None, None, 2048 ['conv2d_26[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_26 (LeakyReLU) (None, None, None, 0 ['batch_normalization_26[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_27 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_26[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_27 (BatchN (None, None, None, 1024 ['conv2d_27[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_27 (LeakyReLU) (None, None, None, 0 ['batch_normalization_27[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_28 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_27[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_28 (BatchN (None, None, None, 2048 ['conv2d_28[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_28 (LeakyReLU) (None, None, None, 0 ['batch_normalization_28[0][0]'] \n",
" 512) \n",
" \n",
" add_11 (Add) (None, None, None, 0 ['leaky_re_lu_26[0][0]', \n",
" 512) 'leaky_re_lu_28[0][0]'] \n",
" \n",
" conv2d_29 (Conv2D) (None, None, None, 131072 ['add_11[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_29 (BatchN (None, None, None, 1024 ['conv2d_29[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_29 (LeakyReLU) (None, None, None, 0 ['batch_normalization_29[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_30 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_29[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_30 (BatchN (None, None, None, 2048 ['conv2d_30[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_30 (LeakyReLU) (None, None, None, 0 ['batch_normalization_30[0][0]'] \n",
" 512) \n",
" \n",
" add_12 (Add) (None, None, None, 0 ['add_11[0][0]', \n",
" 512) 'leaky_re_lu_30[0][0]'] \n",
" \n",
" conv2d_31 (Conv2D) (None, None, None, 131072 ['add_12[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_31 (BatchN (None, None, None, 1024 ['conv2d_31[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_31 (LeakyReLU) (None, None, None, 0 ['batch_normalization_31[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_32 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_31[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_32 (BatchN (None, None, None, 2048 ['conv2d_32[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_32 (LeakyReLU) (None, None, None, 0 ['batch_normalization_32[0][0]'] \n",
" 512) \n",
" \n",
" add_13 (Add) (None, None, None, 0 ['add_12[0][0]', \n",
" 512) 'leaky_re_lu_32[0][0]'] \n",
" \n",
" conv2d_33 (Conv2D) (None, None, None, 131072 ['add_13[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_33 (BatchN (None, None, None, 1024 ['conv2d_33[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_33 (LeakyReLU) (None, None, None, 0 ['batch_normalization_33[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_34 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_33[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_34 (BatchN (None, None, None, 2048 ['conv2d_34[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_34 (LeakyReLU) (None, None, None, 0 ['batch_normalization_34[0][0]'] \n",
" 512) \n",
" \n",
" add_14 (Add) (None, None, None, 0 ['add_13[0][0]', \n",
" 512) 'leaky_re_lu_34[0][0]'] \n",
" \n",
" conv2d_35 (Conv2D) (None, None, None, 131072 ['add_14[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_35 (BatchN (None, None, None, 1024 ['conv2d_35[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_35 (LeakyReLU) (None, None, None, 0 ['batch_normalization_35[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_36 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_35[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_36 (BatchN (None, None, None, 2048 ['conv2d_36[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_36 (LeakyReLU) (None, None, None, 0 ['batch_normalization_36[0][0]'] \n",
" 512) \n",
" \n",
" add_15 (Add) (None, None, None, 0 ['add_14[0][0]', \n",
" 512) 'leaky_re_lu_36[0][0]'] \n",
" \n",
" conv2d_37 (Conv2D) (None, None, None, 131072 ['add_15[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_37 (BatchN (None, None, None, 1024 ['conv2d_37[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_37 (LeakyReLU) (None, None, None, 0 ['batch_normalization_37[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_38 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_37[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_38 (BatchN (None, None, None, 2048 ['conv2d_38[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_38 (LeakyReLU) (None, None, None, 0 ['batch_normalization_38[0][0]'] \n",
" 512) \n",
" \n",
" add_16 (Add) (None, None, None, 0 ['add_15[0][0]', \n",
" 512) 'leaky_re_lu_38[0][0]'] \n",
" \n",
" conv2d_39 (Conv2D) (None, None, None, 131072 ['add_16[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_39 (BatchN (None, None, None, 1024 ['conv2d_39[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_39 (LeakyReLU) (None, None, None, 0 ['batch_normalization_39[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_40 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_39[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_40 (BatchN (None, None, None, 2048 ['conv2d_40[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_40 (LeakyReLU) (None, None, None, 0 ['batch_normalization_40[0][0]'] \n",
" 512) \n",
" \n",
" add_17 (Add) (None, None, None, 0 ['add_16[0][0]', \n",
" 512) 'leaky_re_lu_40[0][0]'] \n",
" \n",
" conv2d_41 (Conv2D) (None, None, None, 131072 ['add_17[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_41 (BatchN (None, None, None, 1024 ['conv2d_41[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_41 (LeakyReLU) (None, None, None, 0 ['batch_normalization_41[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_42 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_41[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_42 (BatchN (None, None, None, 2048 ['conv2d_42[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_42 (LeakyReLU) (None, None, None, 0 ['batch_normalization_42[0][0]'] \n",
" 512) \n",
" \n",
" add_18 (Add) (None, None, None, 0 ['add_17[0][0]', \n",
" 512) 'leaky_re_lu_42[0][0]'] \n",
" \n",
" zero_padding2d_4 (ZeroPadding2 (None, None, None, 0 ['add_18[0][0]'] \n",
" D) 512) \n",
" \n",
" conv2d_43 (Conv2D) (None, None, None, 4718592 ['zero_padding2d_4[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_43 (BatchN (None, None, None, 4096 ['conv2d_43[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_43 (LeakyReLU) (None, None, None, 0 ['batch_normalization_43[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_44 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_43[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_44 (BatchN (None, None, None, 2048 ['conv2d_44[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_44 (LeakyReLU) (None, None, None, 0 ['batch_normalization_44[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_45 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_44[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_45 (BatchN (None, None, None, 4096 ['conv2d_45[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_45 (LeakyReLU) (None, None, None, 0 ['batch_normalization_45[0][0]'] \n",
" 1024) \n",
" \n",
" add_19 (Add) (None, None, None, 0 ['leaky_re_lu_43[0][0]', \n",
" 1024) 'leaky_re_lu_45[0][0]'] \n",
" \n",
" conv2d_46 (Conv2D) (None, None, None, 524288 ['add_19[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_46 (BatchN (None, None, None, 2048 ['conv2d_46[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_46 (LeakyReLU) (None, None, None, 0 ['batch_normalization_46[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_47 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_46[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_47 (BatchN (None, None, None, 4096 ['conv2d_47[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_47 (LeakyReLU) (None, None, None, 0 ['batch_normalization_47[0][0]'] \n",
" 1024) \n",
" \n",
" add_20 (Add) (None, None, None, 0 ['add_19[0][0]', \n",
" 1024) 'leaky_re_lu_47[0][0]'] \n",
" \n",
" conv2d_48 (Conv2D) (None, None, None, 524288 ['add_20[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_48 (BatchN (None, None, None, 2048 ['conv2d_48[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_48 (LeakyReLU) (None, None, None, 0 ['batch_normalization_48[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_49 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_48[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_49 (BatchN (None, None, None, 4096 ['conv2d_49[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_49 (LeakyReLU) (None, None, None, 0 ['batch_normalization_49[0][0]'] \n",
" 1024) \n",
" \n",
" add_21 (Add) (None, None, None, 0 ['add_20[0][0]', \n",
" 1024) 'leaky_re_lu_49[0][0]'] \n",
" \n",
" conv2d_50 (Conv2D) (None, None, None, 524288 ['add_21[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_50 (BatchN (None, None, None, 2048 ['conv2d_50[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_50 (LeakyReLU) (None, None, None, 0 ['batch_normalization_50[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_51 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_50[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_51 (BatchN (None, None, None, 4096 ['conv2d_51[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_51 (LeakyReLU) (None, None, None, 0 ['batch_normalization_51[0][0]'] \n",
" 1024) \n",
" \n",
" add_22 (Add) (None, None, None, 0 ['add_21[0][0]', \n",
" 1024) 'leaky_re_lu_51[0][0]'] \n",
" \n",
" conv2d_52 (Conv2D) (None, None, None, 524288 ['add_22[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_52 (BatchN (None, None, None, 2048 ['conv2d_52[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_52 (LeakyReLU) (None, None, None, 0 ['batch_normalization_52[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_53 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_52[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_53 (BatchN (None, None, None, 4096 ['conv2d_53[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_53 (LeakyReLU) (None, None, None, 0 ['batch_normalization_53[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_54 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_53[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_54 (BatchN (None, None, None, 2048 ['conv2d_54[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_54 (LeakyReLU) (None, None, None, 0 ['batch_normalization_54[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_55 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_54[0][0]'] \n",
" 1024) \n",
" \n",
" batch_normalization_55 (BatchN (None, None, None, 4096 ['conv2d_55[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" leaky_re_lu_55 (LeakyReLU) (None, None, None, 0 ['batch_normalization_55[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_56 (Conv2D) (None, None, None, 524288 ['leaky_re_lu_55[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_56 (BatchN (None, None, None, 2048 ['conv2d_56[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_56 (LeakyReLU) (None, None, None, 0 ['batch_normalization_56[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_59 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_56[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_58 (BatchN (None, None, None, 1024 ['conv2d_59[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_58 (LeakyReLU) (None, None, None, 0 ['batch_normalization_58[0][0]'] \n",
" 256) \n",
" \n",
" up_sampling2d (UpSampling2D) (None, None, None, 0 ['leaky_re_lu_58[0][0]'] \n",
" 256) \n",
" \n",
" concatenate (Concatenate) (None, None, None, 0 ['up_sampling2d[0][0]', \n",
" 768) 'add_18[0][0]'] \n",
" \n",
" conv2d_60 (Conv2D) (None, None, None, 196608 ['concatenate[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_59 (BatchN (None, None, None, 1024 ['conv2d_60[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_59 (LeakyReLU) (None, None, None, 0 ['batch_normalization_59[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_61 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_59[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_60 (BatchN (None, None, None, 2048 ['conv2d_61[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_60 (LeakyReLU) (None, None, None, 0 ['batch_normalization_60[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_62 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_60[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_61 (BatchN (None, None, None, 1024 ['conv2d_62[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_61 (LeakyReLU) (None, None, None, 0 ['batch_normalization_61[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_63 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_61[0][0]'] \n",
" 512) \n",
" \n",
" batch_normalization_62 (BatchN (None, None, None, 2048 ['conv2d_63[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" leaky_re_lu_62 (LeakyReLU) (None, None, None, 0 ['batch_normalization_62[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_64 (Conv2D) (None, None, None, 131072 ['leaky_re_lu_62[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_63 (BatchN (None, None, None, 1024 ['conv2d_64[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_63 (LeakyReLU) (None, None, None, 0 ['batch_normalization_63[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_67 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_63[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_65 (BatchN (None, None, None, 512 ['conv2d_67[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_65 (LeakyReLU) (None, None, None, 0 ['batch_normalization_65[0][0]'] \n",
" 128) \n",
" \n",
" up_sampling2d_1 (UpSampling2D) (None, None, None, 0 ['leaky_re_lu_65[0][0]'] \n",
" 128) \n",
" \n",
" concatenate_1 (Concatenate) (None, None, None, 0 ['up_sampling2d_1[0][0]', \n",
" 384) 'add_10[0][0]'] \n",
" \n",
" conv2d_68 (Conv2D) (None, None, None, 49152 ['concatenate_1[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_66 (BatchN (None, None, None, 512 ['conv2d_68[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_66 (LeakyReLU) (None, None, None, 0 ['batch_normalization_66[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_69 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_66[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_67 (BatchN (None, None, None, 1024 ['conv2d_69[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_67 (LeakyReLU) (None, None, None, 0 ['batch_normalization_67[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_70 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_67[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_68 (BatchN (None, None, None, 512 ['conv2d_70[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_68 (LeakyReLU) (None, None, None, 0 ['batch_normalization_68[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_71 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_68[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_69 (BatchN (None, None, None, 1024 ['conv2d_71[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_69 (LeakyReLU) (None, None, None, 0 ['batch_normalization_69[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_72 (Conv2D) (None, None, None, 32768 ['leaky_re_lu_69[0][0]'] \n",
" 128) \n",
" \n",
" batch_normalization_70 (BatchN (None, None, None, 512 ['conv2d_72[0][0]'] \n",
" ormalization) 128) \n",
" \n",
" leaky_re_lu_70 (LeakyReLU) (None, None, None, 0 ['batch_normalization_70[0][0]'] \n",
" 128) \n",
" \n",
" conv2d_57 (Conv2D) (None, None, None, 4718592 ['leaky_re_lu_56[0][0]'] \n",
" 1024) \n",
" \n",
" conv2d_65 (Conv2D) (None, None, None, 1179648 ['leaky_re_lu_63[0][0]'] \n",
" 512) \n",
" \n",
" conv2d_73 (Conv2D) (None, None, None, 294912 ['leaky_re_lu_70[0][0]'] \n",
" 256) \n",
" \n",
" batch_normalization_57 (BatchN (None, None, None, 4096 ['conv2d_57[0][0]'] \n",
" ormalization) 1024) \n",
" \n",
" batch_normalization_64 (BatchN (None, None, None, 2048 ['conv2d_65[0][0]'] \n",
" ormalization) 512) \n",
" \n",
" batch_normalization_71 (BatchN (None, None, None, 1024 ['conv2d_73[0][0]'] \n",
" ormalization) 256) \n",
" \n",
" leaky_re_lu_57 (LeakyReLU) (None, None, None, 0 ['batch_normalization_57[0][0]'] \n",
" 1024) \n",
" \n",
" leaky_re_lu_64 (LeakyReLU) (None, None, None, 0 ['batch_normalization_64[0][0]'] \n",
" 512) \n",
" \n",
" leaky_re_lu_71 (LeakyReLU) (None, None, None, 0 ['batch_normalization_71[0][0]'] \n",
" 256) \n",
" \n",
" conv2d_58 (Conv2D) (None, None, None, 261375 ['leaky_re_lu_57[0][0]'] \n",
" 255) \n",
" \n",
" conv2d_66 (Conv2D) (None, None, None, 130815 ['leaky_re_lu_64[0][0]'] \n",
" 255) \n",
" \n",
" conv2d_74 (Conv2D) (None, None, None, 65535 ['leaky_re_lu_71[0][0]'] \n",
" 255) \n",
" \n",
"==================================================================================================\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Total params: 62,001,757\n",
"Trainable params: 61,949,149\n",
"Non-trainable params: 52,608\n",
"__________________________________________________________________________________________________\n",
"None\n",
"WARNING:tensorflow:Compiled the loaded model, but the compiled metrics have yet to be built. `model.compile_metrics` will be empty until you train or evaluate the model.\n",
"Saved Keras model to model_data/yolo.h5\n",
"Read 62001757 of 62001757.0 from Darknet weights.\n"
]
}
],
"source": [
"!python keras-yolo3/convert.py keras-yolo3/yolov3.cfg yolov3.weights model_data/yolo.h5"
]
},
{
"cell_type": "code",
"execution_count": 6,
"id": "4038756b",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"-------------------CLASS NAMES-------------------\n",
"['licence']\n",
"-------------------CLASS NAMES-------------------\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-01-22 02:42:51.965089: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: SSE4.1 SSE4.2 AVX AVX2 FMA\n",
"To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Create YOLOv3 model with 9 anchors and 1 classes.\n",
"WARNING:tensorflow:Skipping loading weights for layer #249 (named conv2d_58) due to mismatch in shape for weight conv2d_58/kernel:0. Weight expects shape (1, 1, 1024, 18). Received saved weight with shape (255, 1024, 1, 1)\n",
"WARNING:tensorflow:Skipping loading weights for layer #249 (named conv2d_58) due to mismatch in shape for weight conv2d_58/bias:0. Weight expects shape (18,). Received saved weight with shape (255,)\n",
"WARNING:tensorflow:Skipping loading weights for layer #250 (named conv2d_66) due to mismatch in shape for weight conv2d_66/kernel:0. Weight expects shape (1, 1, 512, 18). Received saved weight with shape (255, 512, 1, 1)\n",
"WARNING:tensorflow:Skipping loading weights for layer #250 (named conv2d_66) due to mismatch in shape for weight conv2d_66/bias:0. Weight expects shape (18,). Received saved weight with shape (255,)\n",
"WARNING:tensorflow:Skipping loading weights for layer #251 (named conv2d_74) due to mismatch in shape for weight conv2d_74/kernel:0. Weight expects shape (1, 1, 256, 18). Received saved weight with shape (255, 256, 1, 1)\n",
"WARNING:tensorflow:Skipping loading weights for layer #251 (named conv2d_74) due to mismatch in shape for weight conv2d_74/bias:0. Weight expects shape (18,). Received saved weight with shape (255,)\n",
"Load weights ./model_data/yolo.h5.\n",
"Freeze the first 249 layers of total 252 layers.\n",
"WARNING:tensorflow:`period` argument is deprecated. Please use `save_freq` to specify the frequency in number of batches seen.\n",
"Train on 488 samples, val on 121 samples, with batch size 16.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/Users/aczajka/miniconda3/envs/yolov3/lib/python3.9/site-packages/keras/optimizers/optimizer_v2/adam.py:117: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.\n",
" super().__init__(name, **kwargs)\n",
"/var/folders/j_/grk4ythd0392dcw5z3gkgw5w0000gn/T/ipykernel_39692/4035785499.py:62: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/500\n",
"WARNING:tensorflow:From /Users/aczajka/miniconda3/envs/yolov3/lib/python3.9/site-packages/tensorflow/python/autograph/pyct/static_analysis/liveness.py:83: Analyzer.lamba_check (from tensorflow.python.autograph.pyct.static_analysis.liveness) is deprecated and will be removed after 2023-09-23.\n",
"Instructions for updating:\n",
"Lambda fuctions will be no more assumed to be used in the statement where they are used, or at least in the same block. https://github.com/tensorflow/tensorflow/issues/56089\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-01-22 02:43:01.274999: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"30/30 [==============================] - ETA: 0s - loss: 1092.7228"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-01-22 02:43:54.290859: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"30/30 [==============================] - 70s 2s/step - loss: 1092.7228 - val_loss: 216.0935\n",
"Epoch 2/500\n",
"30/30 [==============================] - 57s 2s/step - loss: 135.6953 - val_loss: 94.2604\n",
"Epoch 3/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 79.2672 - val_loss: 68.8617\n",
"Epoch 4/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 60.4469 - val_loss: 54.7572\n",
"Epoch 5/500\n",
"30/30 [==============================] - 50s 2s/step - loss: 50.0802 - val_loss: 47.2904\n",
"Epoch 6/500\n",
"30/30 [==============================] - 51s 2s/step - loss: 43.6335 - val_loss: 41.2742\n",
"Epoch 7/500\n",
"30/30 [==============================] - 51s 2s/step - loss: 39.3473 - val_loss: 38.5374\n",
"Epoch 8/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 36.2422 - val_loss: 35.2012\n",
"Epoch 9/500\n",
"30/30 [==============================] - 51s 2s/step - loss: 33.6743 - val_loss: 33.0579\n",
"Epoch 10/500\n",
"30/30 [==============================] - 49s 2s/step - loss: 32.0283 - val_loss: 30.6336\n",
"Epoch 11/500\n",
"30/30 [==============================] - 49s 2s/step - loss: 30.3864 - val_loss: 29.2345\n",
"Epoch 12/500\n",
"30/30 [==============================] - 51s 2s/step - loss: 29.6261 - val_loss: 28.6320\n",
"Epoch 13/500\n",
"30/30 [==============================] - 51s 2s/step - loss: 28.1432 - val_loss: 27.8887\n",
"Epoch 14/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 27.6032 - val_loss: 27.0226\n",
"Epoch 15/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 26.9148 - val_loss: 26.3452\n",
"Epoch 16/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 26.4210 - val_loss: 26.4830\n",
"Epoch 17/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 25.6399 - val_loss: 25.2511\n",
"Epoch 18/500\n",
"30/30 [==============================] - 60s 2s/step - loss: 25.5443 - val_loss: 24.6174\n",
"Epoch 19/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 25.2961 - val_loss: 24.7754\n",
"Epoch 20/500\n",
"30/30 [==============================] - 54s 2s/step - loss: 24.7307 - val_loss: 24.6782\n",
"Epoch 21/500\n",
"30/30 [==============================] - 58s 2s/step - loss: 24.2857 - val_loss: 24.3096\n",
"Epoch 22/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 24.2008 - val_loss: 24.3196\n",
"Epoch 23/500\n",
"30/30 [==============================] - 51s 2s/step - loss: 23.5739 - val_loss: 23.3351\n",
"Epoch 24/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 23.6946 - val_loss: 24.0281\n",
"Epoch 25/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 23.7198 - val_loss: 23.4021\n",
"Epoch 26/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 23.2751 - val_loss: 23.3185\n",
"Epoch 27/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 23.2101 - val_loss: 22.7601\n",
"Epoch 28/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 22.9937 - val_loss: 22.6282\n",
"Epoch 29/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 22.8363 - val_loss: 22.1787\n",
"Epoch 30/500\n",
"30/30 [==============================] - 55s 2s/step - loss: 22.6890 - val_loss: 22.1749\n",
"Epoch 31/500\n",
"30/30 [==============================] - 56s 2s/step - loss: 22.4564 - val_loss: 22.6868\n",
"Epoch 32/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 22.3397 - val_loss: 22.1918\n",
"Epoch 33/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 22.8438 - val_loss: 22.4380\n",
"Epoch 34/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 22.0734 - val_loss: 22.9481\n",
"Epoch 35/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 21.9711 - val_loss: 22.8436\n",
"Epoch 36/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 22.0127 - val_loss: 22.7770\n",
"Epoch 37/500\n",
"30/30 [==============================] - 53s 2s/step - loss: 22.6367 - val_loss: 21.8047\n",
"Epoch 38/500\n",
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"Epoch 39/500\n",
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"Epoch 40/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 21.8194 - val_loss: 21.5877\n",
"Epoch 41/500\n",
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"Epoch 42/500\n",
"30/30 [==============================] - 56s 2s/step - loss: 21.4056 - val_loss: 21.0999\n",
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"Epoch 44/500\n",
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"Epoch 80/500\n",
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"Epoch 81/500\n",
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"Epoch 82/500\n",
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"Epoch 83/500\n",
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"Epoch 84/500\n",
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"Epoch 86/500\n",
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"Epoch 87/500\n",
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"Epoch 88/500\n",
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"Epoch 89/500\n",
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"Epoch 90/500\n",
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"Epoch 91/500\n",
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"Epoch 92/500\n",
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"Epoch 93/500\n",
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"Epoch 94/500\n",
"30/30 [==============================] - 57s 2s/step - loss: 19.5174 - val_loss: 18.7876\n",
"Epoch 95/500\n",
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"Epoch 96/500\n",
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"Epoch 97/500\n",
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"Epoch 98/500\n",
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"Epoch 99/500\n",
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"Epoch 100/500\n",
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"Epoch 101/500\n",
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"Epoch 102/500\n",
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"Epoch 103/500\n",
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"Epoch 104/500\n",
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"Epoch 105/500\n",
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"Epoch 106/500\n",
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"Epoch 107/500\n",
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"Epoch 108/500\n",
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"Epoch 109/500\n",
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"Epoch 110/500\n",
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"Epoch 111/500\n",
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"Epoch 112/500\n",
"30/30 [==============================] - 64s 2s/step - loss: 19.1675 - val_loss: 18.6330\n",
"Epoch 113/500\n",
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"Epoch 115/500\n",
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"Epoch 116/500\n",
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"Epoch 117/500\n",
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"Epoch 118/500\n",
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"Epoch 119/500\n",
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"Epoch 126/500\n",
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"Epoch 127/500\n",
"30/30 [==============================] - 58s 2s/step - loss: 18.5290 - val_loss: 19.0881\n",
"Epoch 128/500\n",
"30/30 [==============================] - 58s 2s/step - loss: 18.6084 - val_loss: 18.3423\n",
"Epoch 129/500\n",
"30/30 [==============================] - 67s 2s/step - loss: 18.2576 - val_loss: 17.7641\n",
"Epoch 130/500\n",
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"Epoch 131/500\n",
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"Epoch 132/500\n",
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"Epoch 133/500\n",
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"Epoch 134/500\n",
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"Epoch 135/500\n",
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"Epoch 136/500\n",
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"Epoch 137/500\n",
"30/30 [==============================] - 61s 2s/step - loss: 18.2460 - val_loss: 18.6843\n",
"Epoch 138/500\n",
"30/30 [==============================] - 58s 2s/step - loss: 18.2683 - val_loss: 18.1989\n",
"Epoch 139/500\n",
"30/30 [==============================] - 59s 2s/step - loss: 18.4373 - val_loss: 18.2519\n",
"Epoch 140/500\n",
"30/30 [==============================] - 59s 2s/step - loss: 18.0950 - val_loss: 18.2093\n",
"Epoch 141/500\n",
"30/30 [==============================] - 59s 2s/step - loss: 18.6397 - val_loss: 17.5036\n",
"Epoch 142/500\n",
"30/30 [==============================] - 58s 2s/step - loss: 18.4368 - val_loss: 18.0884\n",
"Epoch 143/500\n",
"30/30 [==============================] - 58s 2s/step - loss: 18.2509 - val_loss: 18.0419\n",
"Epoch 144/500\n",
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"Epoch 145/500\n",
"30/30 [==============================] - 67s 2s/step - loss: 18.1403 - val_loss: 17.9708\n",
"Epoch 146/500\n",
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"Epoch 147/500\n",
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"Epoch 148/500\n",
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"Epoch 149/500\n",
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"Epoch 150/500\n",
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"Epoch 151/500\n",
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"Epoch 152/500\n",
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"Epoch 153/500\n",
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"Epoch 155/500\n",
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"Epoch 156/500\n",
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"Epoch 157/500\n",
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"Epoch 158/500\n",
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"Epoch 159/500\n",
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"Epoch 160/500\n",
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]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 161/500\n",
"30/30 [==============================] - 66s 2s/step - loss: 17.8075 - val_loss: 17.9411\n",
"Epoch 162/500\n",
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"Epoch 163/500\n",
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"Epoch 164/500\n",
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"Epoch 166/500\n",
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"Epoch 167/500\n",
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"Epoch 168/500\n",
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"Epoch 169/500\n",
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"Epoch 170/500\n",
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"Epoch 171/500\n",
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"Epoch 175/500\n",
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"Epoch 176/500\n",
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"Epoch 177/500\n",
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"Epoch 178/500\n",
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"Epoch 179/500\n",
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"Epoch 180/500\n",
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"Epoch 181/500\n",
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"Epoch 182/500\n",
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"Epoch 183/500\n",
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"Epoch 185/500\n",
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"Epoch 186/500\n",
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"Epoch 188/500\n",
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"Epoch 189/500\n",
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"Epoch 190/500\n",
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"Epoch 191/500\n",
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"Epoch 192/500\n",
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"Epoch 193/500\n",
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"Epoch 194/500\n",
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"Epoch 195/500\n",
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"Epoch 196/500\n",
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"Epoch 198/500\n",
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"Epoch 199/500\n",
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"Epoch 200/500\n",
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"Epoch 201/500\n",
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"Epoch 202/500\n",
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"Epoch 203/500\n",
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"Epoch 218/500\n",
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"Epoch 219/500\n",
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"Epoch 220/500\n",
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"Epoch 225/500\n",
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"Epoch 228/500\n",
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"Epoch 235/500\n",
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"Epoch 238/500\n",
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"Epoch 239/500\n",
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"Epoch 240/500\n",
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"Epoch 241/500\n",
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"Epoch 242/500\n",
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"Epoch 243/500\n",
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"Epoch 249/500\n",
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"Epoch 250/500\n",
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"Epoch 251/500\n",
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"Epoch 252/500\n",
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"Epoch 253/500\n",
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"Epoch 254/500\n",
"30/30 [==============================] - 119s 4s/step - loss: 16.6808 - val_loss: 16.1435\n",
"Epoch 255/500\n",
"30/30 [==============================] - 54s 2s/step - loss: 16.5841 - val_loss: 16.3919\n",
"Epoch 256/500\n",
"30/30 [==============================] - 78s 3s/step - loss: 16.5055 - val_loss: 16.5761\n",
"Epoch 257/500\n",
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"Epoch 258/500\n",
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"Epoch 260/500\n",
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"Epoch 264/500\n",
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"Epoch 265/500\n",
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"Epoch 266/500\n",
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"Epoch 268/500\n",
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"Epoch 269/500\n",
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"Epoch 270/500\n",
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"Epoch 273/500\n",
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"Epoch 274/500\n",
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"Epoch 275/500\n",
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"Epoch 276/500\n",
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"Epoch 277/500\n",
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"Epoch 278/500\n",
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"Epoch 279/500\n",
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"Epoch 281/500\n",
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"Epoch 284/500\n",
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"Epoch 286/500\n",
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"Epoch 287/500\n",
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"Epoch 288/500\n",
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"Epoch 290/500\n",
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"Epoch 292/500\n",
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"Epoch 299/500\n",
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"Epoch 300/500\n",
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"Epoch 301/500\n",
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"Epoch 303/500\n",
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"Epoch 304/500\n",
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"Epoch 305/500\n",
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"Epoch 306/500\n",
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"Epoch 308/500\n",
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"Epoch 309/500\n",
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"Epoch 310/500\n",
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"Epoch 311/500\n",
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"Epoch 312/500\n",
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"Epoch 313/500\n",
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"Epoch 314/500\n",
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"Epoch 315/500\n",
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"Epoch 316/500\n",
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"Epoch 317/500\n",
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"Epoch 318/500\n",
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"Epoch 319/500\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"30/30 [==============================] - 129s 4s/step - loss: 16.2571 - val_loss: 15.7867\n",
"Epoch 320/500\n",
"30/30 [==============================] - 125s 4s/step - loss: 16.5441 - val_loss: 15.8499\n",
"Epoch 321/500\n",
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"Epoch 322/500\n",
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"Epoch 337/500\n",
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"Epoch 350/500\n",
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"Epoch 352/500\n",
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"Epoch 359/500\n",
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"Epoch 360/500\n",
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"Epoch 361/500\n",
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"Epoch 363/500\n",
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"Epoch 365/500\n",
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"Epoch 386/500\n",
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"Epoch 392/500\n",
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"Epoch 395/500\n",
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"Epoch 396/500\n",
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"Epoch 398/500\n",
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"Epoch 399/500\n",
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"Epoch 400/500\n",
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"Epoch 401/500\n",
"30/30 [==============================] - 62s 2s/step - loss: 15.8997 - val_loss: 15.6500\n",
"Epoch 402/500\n",
"30/30 [==============================] - 83s 3s/step - loss: 16.2010 - val_loss: 15.8539\n",
"Epoch 403/500\n",
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"Epoch 404/500\n",
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"Epoch 407/500\n",
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"Epoch 408/500\n",
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"Epoch 409/500\n",
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"Epoch 410/500\n",
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"Epoch 411/500\n",
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"Epoch 412/500\n",
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"Epoch 413/500\n",
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"Epoch 414/500\n",
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"Epoch 415/500\n",
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"Epoch 416/500\n",
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"Epoch 417/500\n",
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"Epoch 418/500\n",
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"Epoch 419/500\n",
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"Epoch 420/500\n",
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"Epoch 421/500\n",
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"Epoch 422/500\n",
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"Epoch 423/500\n",
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"Epoch 424/500\n",
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"Epoch 425/500\n",
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"Epoch 426/500\n",
"30/30 [==============================] - 71s 2s/step - loss: 16.1241 - val_loss: 15.4657\n",
"Epoch 427/500\n",
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"Epoch 428/500\n",
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"Epoch 434/500\n",
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"Epoch 435/500\n",
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"Epoch 436/500\n",
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"Epoch 437/500\n",
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"Epoch 438/500\n",
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"Epoch 439/500\n",
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"Epoch 440/500\n",
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"Epoch 441/500\n",
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"Epoch 442/500\n",
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"Epoch 443/500\n",
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"Epoch 444/500\n",
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"Epoch 445/500\n",
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"Epoch 446/500\n",
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"Epoch 447/500\n",
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"Epoch 448/500\n",
"30/30 [==============================] - 83s 3s/step - loss: 15.9381 - val_loss: 15.3606\n",
"Epoch 449/500\n",
"30/30 [==============================] - 107s 4s/step - loss: 15.7734 - val_loss: 15.8835\n",
"Epoch 450/500\n",
"30/30 [==============================] - 118s 4s/step - loss: 16.2426 - val_loss: 16.0760\n",
"Epoch 451/500\n",
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"Epoch 452/500\n",
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"Epoch 453/500\n",
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"Epoch 454/500\n",
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"Epoch 455/500\n",
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"Epoch 456/500\n",
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"Epoch 457/500\n",
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"Epoch 458/500\n",
"30/30 [==============================] - 123s 4s/step - loss: 15.7483 - val_loss: 16.0871\n",
"Epoch 459/500\n",
"30/30 [==============================] - 125s 4s/step - loss: 15.9154 - val_loss: 15.7753\n",
"Epoch 460/500\n",
"30/30 [==============================] - 116s 4s/step - loss: 16.1634 - val_loss: 16.0291\n",
"Epoch 461/500\n",
"30/30 [==============================] - 52s 2s/step - loss: 16.0713 - val_loss: 15.6570\n",
"Epoch 462/500\n",
"30/30 [==============================] - 68s 2s/step - loss: 15.7077 - val_loss: 15.3641\n",
"Epoch 463/500\n",
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"Epoch 464/500\n",
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"Epoch 465/500\n",
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"Epoch 466/500\n",
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"Epoch 467/500\n",
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"Epoch 468/500\n",
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"Epoch 469/500\n",
"30/30 [==============================] - 64s 2s/step - loss: 15.9682 - val_loss: 15.9137\n",
"Epoch 470/500\n",
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"Epoch 471/500\n",
"30/30 [==============================] - 88s 3s/step - loss: 16.0309 - val_loss: 15.9491\n",
"Epoch 472/500\n",
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"Epoch 473/500\n",
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"Epoch 474/500\n",
"30/30 [==============================] - 122s 4s/step - loss: 15.8100 - val_loss: 16.0189\n",
"Epoch 475/500\n",
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"Epoch 476/500\n",
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"Epoch 477/500\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"30/30 [==============================] - 123s 4s/step - loss: 16.1264 - val_loss: 16.2662\n",
"Epoch 478/500\n",
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"Epoch 479/500\n",
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"Epoch 480/500\n",
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"Epoch 481/500\n",
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"Epoch 482/500\n",
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"Epoch 483/500\n",
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"Epoch 484/500\n",
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"Epoch 485/500\n",
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"Epoch 486/500\n",
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"Epoch 487/500\n",
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"Epoch 488/500\n",
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"Epoch 490/500\n",
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"Epoch 491/500\n",
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"Epoch 492/500\n",
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"Epoch 493/500\n",
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"Epoch 494/500\n",
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"Epoch 495/500\n",
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"Epoch 496/500\n",
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"Epoch 497/500\n",
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"Epoch 498/500\n",
"30/30 [==============================] - 123s 4s/step - loss: 15.8485 - val_loss: 16.0082\n",
"Epoch 499/500\n",
"30/30 [==============================] - 126s 4s/step - loss: 15.8425 - val_loss: 14.8089\n",
"Epoch 500/500\n",
"30/30 [==============================] - 101s 3s/step - loss: 16.0761 - val_loss: 15.9947\n",
"Unfreeze all of the layers.\n",
"Train on 488 samples, val on 121 samples, with batch size 16.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/var/folders/j_/grk4ythd0392dcw5z3gkgw5w0000gn/T/ipykernel_39692/4035785499.py:81: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 51/100\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-01-22 13:37:37.110606: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"30/30 [==============================] - ETA: 0s - loss: 16.2221 "
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"2023-01-22 13:43:00.326593: E tensorflow/core/grappler/optimizers/meta_optimizer.cc:954] layout failed: INVALID_ARGUMENT: Subshape must have computed start >= end since stride is negative, but is 0 and 2 (computed from start 0 and end 9223372036854775807 over shape with rank 2 and stride-1)\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"30/30 [==============================] - 350s 11s/step - loss: 16.2221 - val_loss: 15.0134 - lr: 1.0000e-04\n",
"Epoch 52/100\n",
"30/30 [==============================] - 346s 12s/step - loss: 14.9261 - val_loss: 14.5771 - lr: 1.0000e-04\n",
"Epoch 53/100\n",
"30/30 [==============================] - 243s 8s/step - loss: 14.5103 - val_loss: 14.5714 - lr: 1.0000e-04\n",
"Epoch 54/100\n",
"30/30 [==============================] - 231s 8s/step - loss: 14.2489 - val_loss: 13.8991 - lr: 1.0000e-04\n",
"Epoch 55/100\n",
"30/30 [==============================] - 276s 9s/step - loss: 14.1362 - val_loss: 14.2145 - lr: 1.0000e-04\n",
"Epoch 56/100\n",
"30/30 [==============================] - 334s 11s/step - loss: 13.6959 - val_loss: 13.6794 - lr: 1.0000e-04\n",
"Epoch 57/100\n",
"30/30 [==============================] - 340s 11s/step - loss: 13.5898 - val_loss: 13.1452 - lr: 1.0000e-04\n",
"Epoch 58/100\n",
"30/30 [==============================] - 336s 11s/step - loss: 13.4866 - val_loss: 13.5824 - lr: 1.0000e-04\n",
"Epoch 59/100\n",
"30/30 [==============================] - 259s 9s/step - loss: 13.4531 - val_loss: 13.1278 - lr: 1.0000e-04\n",
"Epoch 60/100\n",
"30/30 [==============================] - 219s 7s/step - loss: 13.3503 - val_loss: 13.0499 - lr: 1.0000e-04\n",
"Epoch 61/100\n",
"30/30 [==============================] - 254s 8s/step - loss: 13.2267 - val_loss: 13.0210 - lr: 1.0000e-04\n",
"Epoch 62/100\n",
"30/30 [==============================] - 414s 14s/step - loss: 13.2120 - val_loss: 14.0383 - lr: 1.0000e-04\n",
"Epoch 63/100\n",
"30/30 [==============================] - 472s 16s/step - loss: 12.9336 - val_loss: 13.2708 - lr: 1.0000e-04\n",
"Epoch 64/100\n",
"30/30 [==============================] - ETA: 0s - loss: 13.1477 \n",
"Epoch 64: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-06.\n",
"30/30 [==============================] - 470s 16s/step - loss: 13.1477 - val_loss: 13.6016 - lr: 1.0000e-04\n",
"Epoch 65/100\n",
"30/30 [==============================] - 272s 9s/step - loss: 13.2001 - val_loss: 12.9789 - lr: 1.0000e-05\n",
"Epoch 66/100\n",
"30/30 [==============================] - 256s 9s/step - loss: 12.8699 - val_loss: 12.7537 - lr: 1.0000e-05\n",
"Epoch 67/100\n",
"30/30 [==============================] - 460s 15s/step - loss: 12.8529 - val_loss: 12.5797 - lr: 1.0000e-05\n",
"Epoch 68/100\n",
"30/30 [==============================] - 520s 17s/step - loss: 12.8881 - val_loss: 12.8464 - lr: 1.0000e-05\n",
"Epoch 69/100\n",
"30/30 [==============================] - 316s 10s/step - loss: 12.8289 - val_loss: 13.0487 - lr: 1.0000e-05\n",
"Epoch 70/100\n",
"30/30 [==============================] - ETA: 0s - loss: 12.7765\n",
"Epoch 70: ReduceLROnPlateau reducing learning rate to 9.999999747378752e-07.\n",
"30/30 [==============================] - 236s 8s/step - loss: 12.7765 - val_loss: 12.7764 - lr: 1.0000e-05\n",
"Epoch 71/100\n",
"30/30 [==============================] - 357s 12s/step - loss: 12.7222 - val_loss: 12.6030 - lr: 1.0000e-06\n",
"Epoch 72/100\n",
"30/30 [==============================] - 471s 16s/step - loss: 12.9312 - val_loss: 12.7407 - lr: 1.0000e-06\n",
"Epoch 73/100\n",
"30/30 [==============================] - ETA: 0s - loss: 12.7563 \n",
"Epoch 73: ReduceLROnPlateau reducing learning rate to 9.999999974752428e-08.\n",
"30/30 [==============================] - 474s 16s/step - loss: 12.7563 - val_loss: 12.8981 - lr: 1.0000e-06\n",
"Epoch 74/100\n",
"30/30 [==============================] - 337s 11s/step - loss: 12.6372 - val_loss: 13.0085 - lr: 1.0000e-07\n",
"Epoch 75/100\n",
"30/30 [==============================] - 238s 8s/step - loss: 12.6892 - val_loss: 12.6015 - lr: 1.0000e-07\n",
"Epoch 76/100\n",
"30/30 [==============================] - ETA: 0s - loss: 12.7828\n",
"Epoch 76: ReduceLROnPlateau reducing learning rate to 1.0000000116860975e-08.\n",
"30/30 [==============================] - 308s 10s/step - loss: 12.7828 - val_loss: 13.2228 - lr: 1.0000e-07\n",
"Epoch 77/100\n",
"30/30 [==============================] - 336s 11s/step - loss: 12.7876 - val_loss: 12.4209 - lr: 1.0000e-08\n",
"Epoch 78/100\n",
"30/30 [==============================] - 337s 11s/step - loss: 12.5455 - val_loss: 12.7752 - lr: 1.0000e-08\n",
"Epoch 79/100\n",
"30/30 [==============================] - 258s 8s/step - loss: 12.7785 - val_loss: 12.7235 - lr: 1.0000e-08\n",
"Epoch 80/100\n",
"30/30 [==============================] - ETA: 0s - loss: 12.7194\n",
"Epoch 80: ReduceLROnPlateau reducing learning rate to 9.999999939225292e-10.\n",
"30/30 [==============================] - 222s 7s/step - loss: 12.7194 - val_loss: 12.8656 - lr: 1.0000e-08\n",
"Epoch 81/100\n",
"30/30 [==============================] - 255s 9s/step - loss: 13.0056 - val_loss: 12.7722 - lr: 1.0000e-09\n",
"Epoch 82/100\n",
"30/30 [==============================] - 275s 9s/step - loss: 12.6045 - val_loss: 12.7747 - lr: 1.0000e-09\n",
"Epoch 83/100\n",
"30/30 [==============================] - ETA: 0s - loss: 12.8213 \n",
"Epoch 83: ReduceLROnPlateau reducing learning rate to 9.999999717180686e-11.\n",
"30/30 [==============================] - 371s 12s/step - loss: 12.8213 - val_loss: 12.7278 - lr: 1.0000e-09\n",
"Epoch 84/100\n",
"30/30 [==============================] - 476s 16s/step - loss: 12.8018 - val_loss: 12.7182 - lr: 1.0000e-10\n",
"Epoch 85/100\n",
"30/30 [==============================] - 395s 13s/step - loss: 12.6051 - val_loss: 12.9475 - lr: 1.0000e-10\n",
"Epoch 86/100\n",
"30/30 [==============================] - ETA: 0s - loss: 12.7930\n",
"Epoch 86: ReduceLROnPlateau reducing learning rate to 9.99999943962493e-12.\n",
"30/30 [==============================] - 221s 7s/step - loss: 12.7930 - val_loss: 12.5336 - lr: 1.0000e-10\n",
"Epoch 87/100\n",
"30/30 [==============================] - 239s 8s/step - loss: 12.6282 - val_loss: 12.7567 - lr: 1.0000e-11\n",
"Epoch 87: early stopping\n"
]
}
],
"source": [
"\"\"\"\n",
"Self-contained Python script to train YOLOv3 on your own dataset\n",
"\"\"\"\n",
"\n",
"import numpy as np\n",
"import keras.backend as K\n",
"from keras.layers import Input, Lambda\n",
"from keras.models import Model\n",
"from keras.optimizers import Adam\n",
"from keras.callbacks import TensorBoard, ModelCheckpoint, ReduceLROnPlateau, EarlyStopping\n",
"\n",
"from yolo3.model import preprocess_true_boxes, yolo_body, tiny_yolo_body, yolo_loss\n",
"from yolo3.utils import get_random_data\n",
"\n",
"\n",
"def _main():\n",
" annotation_path = './train/_annotations.txt' # path to Roboflow data annotations\n",
" log_dir = './logs/000/' # where we're storing our logs\n",
" classes_path = './train/_classes.txt' # path to Roboflow class names\n",
" anchors_path = './model_data/yolo_anchors.txt'\n",
" class_names = get_classes(classes_path)\n",
" print(\"-------------------CLASS NAMES-------------------\")\n",
" print(class_names)\n",
" print(\"-------------------CLASS NAMES-------------------\")\n",
" num_classes = len(class_names)\n",
" anchors = get_anchors(anchors_path)\n",
"\n",
" input_shape = (256,256) # multiple of 32, hw default = (416,416)\n",
"\n",
" is_tiny_version = len(anchors)==6 # default setting\n",
" if is_tiny_version:\n",
" model = create_tiny_model(input_shape, anchors, num_classes,\n",
" freeze_body=2, weights_path='./model_data/tiny_yolo_weights.h5')\n",
" else:\n",
" model = create_model(input_shape, anchors, num_classes,\n",
" freeze_body=2, weights_path='./model_data/yolo.h5') # make sure you know what you freeze\n",
"\n",
" logging = TensorBoard(log_dir=log_dir)\n",
" checkpoint = ModelCheckpoint(log_dir + 'ep{epoch:03d}-loss{loss:.3f}-val_loss{val_loss:.3f}.h5',\n",
" monitor='val_loss', save_weights_only=True, save_best_only=True, period=3)\n",
" reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor=0.1, patience=3, verbose=1)\n",
" early_stopping = EarlyStopping(monitor='val_loss', min_delta=0, patience=10, verbose=1)\n",
"\n",
" val_split = 0.2 # set the size of the validation set\n",
" with open(annotation_path) as f:\n",
" lines = f.readlines()\n",
" np.random.seed(10101)\n",
" np.random.shuffle(lines)\n",
" np.random.seed(None)\n",
" num_val = int(len(lines)*val_split)\n",
" num_train = len(lines) - num_val\n",
"\n",
" # Train with frozen layers first, to get a stable loss.\n",
" # Adjust num epochs to your dataset. This step is enough to obtain a not bad model.\n",
" if True:\n",
" model.compile(optimizer=Adam(lr=1e-3), loss={\n",
" # use custom yolo_loss Lambda layer.\n",
" 'yolo_loss': lambda y_true, y_pred: y_pred})\n",
"\n",
" batch_size = 16\n",
" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
" steps_per_epoch=max(1, num_train//batch_size),\n",
" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
" validation_steps=max(1, num_val//batch_size),\n",
" epochs=500,\n",
" initial_epoch=0,\n",
" callbacks=[logging, checkpoint])\n",
" model.save_weights(log_dir + 'trained_weights_stage_1.h5')\n",
"\n",
" # Unfreeze and continue training, to fine-tune.\n",
" # Train longer if the result is not good.\n",
" if True:\n",
" for i in range(len(model.layers)):\n",
" model.layers[i].trainable = True\n",
" model.compile(optimizer=Adam(lr=1e-4), loss={'yolo_loss': lambda y_true, y_pred: y_pred}) # recompile to apply the change\n",
" print('Unfreeze all of the layers.')\n",
"\n",
" batch_size = 16 # note that more GPU memory is required after unfreezing the body\n",
" print('Train on {} samples, val on {} samples, with batch size {}.'.format(num_train, num_val, batch_size))\n",
" model.fit_generator(data_generator_wrapper(lines[:num_train], batch_size, input_shape, anchors, num_classes),\n",
" steps_per_epoch=max(1, num_train//batch_size),\n",
" validation_data=data_generator_wrapper(lines[num_train:], batch_size, input_shape, anchors, num_classes),\n",
" validation_steps=max(1, num_val//batch_size),\n",
" epochs=100,\n",
" initial_epoch=50,\n",
" callbacks=[logging, checkpoint, reduce_lr, early_stopping])\n",
" model.save_weights(log_dir + 'trained_weights_final.h5')\n",
"\n",
" # Further training if needed.\n",
"\n",
"\n",
"def get_classes(classes_path):\n",
" '''loads the classes'''\n",
" with open(classes_path) as f:\n",
" class_names = f.readlines()\n",
" class_names = [c.strip() for c in class_names]\n",
" return class_names\n",
"\n",
"def get_anchors(anchors_path):\n",
" '''loads the anchors from a file'''\n",
" with open(anchors_path) as f:\n",
" anchors = f.readline()\n",
" anchors = [float(x) for x in anchors.split(',')]\n",
" return np.array(anchors).reshape(-1, 2)\n",
"\n",
"\n",
"def create_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
" weights_path='./model_data/yolo.h5'):\n",
" '''create the training model'''\n",
" K.clear_session() # get a new session\n",
" image_input = Input(shape=(None, None, 3))\n",
" h, w = input_shape\n",
" num_anchors = len(anchors)\n",
"\n",
" y_true = [Input(shape=(h//{0:32, 1:16, 2:8}[l], w//{0:32, 1:16, 2:8}[l], \\\n",
" num_anchors//3, num_classes+5)) for l in range(3)]\n",
"\n",
" model_body = yolo_body(image_input, num_anchors//3, num_classes)\n",
" print('Create YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
"\n",
" if load_pretrained:\n",
" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
" print('Load weights {}.'.format(weights_path))\n",
" if freeze_body in [1, 2]:\n",
" # Freeze darknet53 body or freeze all but 3 output layers.\n",
" num = (185, len(model_body.layers)-3)[freeze_body-1]\n",
" for i in range(num): model_body.layers[i].trainable = False\n",
" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
"\n",
" model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n",
" arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.5})(\n",
" [*model_body.output, *y_true])\n",
" model = Model([model_body.input, *y_true], model_loss)\n",
"\n",
" return model\n",
"\n",
"def create_tiny_model(input_shape, anchors, num_classes, load_pretrained=True, freeze_body=2,\n",
" weights_path='./model_data/tiny_yolo_weights.h5'):\n",
" '''create the training model, for Tiny YOLOv3'''\n",
" K.clear_session() # get a new session\n",
" image_input = Input(shape=(None, None, 3))\n",
" h, w = input_shape\n",
" num_anchors = len(anchors)\n",
"\n",
" y_true = [Input(shape=(h//{0:32, 1:16}[l], w//{0:32, 1:16}[l], \\\n",
" num_anchors//2, num_classes+5)) for l in range(2)]\n",
"\n",
" model_body = tiny_yolo_body(image_input, num_anchors//2, num_classes)\n",
" print('Create Tiny YOLOv3 model with {} anchors and {} classes.'.format(num_anchors, num_classes))\n",
"\n",
" if load_pretrained:\n",
" model_body.load_weights(weights_path, by_name=True, skip_mismatch=True)\n",
" print('Load weights {}.'.format(weights_path))\n",
" if freeze_body in [1, 2]:\n",
" # Freeze the darknet body or freeze all but 2 output layers.\n",
" num = (20, len(model_body.layers)-2)[freeze_body-1]\n",
" for i in range(num): model_body.layers[i].trainable = False\n",
" print('Freeze the first {} layers of total {} layers.'.format(num, len(model_body.layers)))\n",
"\n",
" model_loss = Lambda(yolo_loss, output_shape=(1,), name='yolo_loss',\n",
" arguments={'anchors': anchors, 'num_classes': num_classes, 'ignore_thresh': 0.7})(\n",
" [*model_body.output, *y_true])\n",
" model = Model([model_body.input, *y_true], model_loss)\n",
"\n",
" return model\n",
"\n",
"def data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes):\n",
" '''data generator for fit_generator'''\n",
" n = len(annotation_lines)\n",
" i = 0\n",
" while True:\n",
" image_data = []\n",
" box_data = []\n",
" for b in range(batch_size):\n",
" if i==0:\n",
" np.random.shuffle(annotation_lines)\n",
" image, box = get_random_data(annotation_lines[i], input_shape, random=True)\n",
" image_data.append(image)\n",
" box_data.append(box)\n",
" i = (i+1) % n\n",
" image_data = np.array(image_data)\n",
" box_data = np.array(box_data)\n",
" y_true = preprocess_true_boxes(box_data, input_shape, anchors, num_classes)\n",
" yield [image_data, *y_true], np.zeros(batch_size)\n",
"\n",
"def data_generator_wrapper(annotation_lines, batch_size, input_shape, anchors, num_classes):\n",
" n = len(annotation_lines)\n",
" if n==0 or batch_size<=0: return None\n",
" return data_generator(annotation_lines, batch_size, input_shape, anchors, num_classes)\n",
"\n",
"if __name__ == '__main__':\n",
" _main()"
]
},
{
"cell_type": "markdown",
"id": "214e7684",
"metadata": {},
"source": [
"# Prepare image to ocr"
]
},
{
"cell_type": "code",
"execution_count": 4,
"id": "abe450c6",
"metadata": {},
"outputs": [],
"source": [
"import cv2 as cv\n",
"from matplotlib import pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 218,
"id": "f650aacf",
"metadata": {},
"outputs": [],
"source": [
"def grayscale(image):\n",
" return cv.cvtColor(image, cv.COLOR_BGR2GRAY)\n",
"\n",
"def noise_removal(image):\n",
" import numpy as np\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.morphologyEx(image, cv.MORPH_CLOSE, kernel)\n",
" image = cv.medianBlur(image, 3)\n",
" return (image)\n",
"\n",
"def thin_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)\n",
"\n",
"def thick_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)\n",
"\n",
"def remove_borders(image):\n",
" contours, heiarchy = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n",
" cntsSorted = sorted(contours, key=lambda x:cv.contourArea(x))\n",
" cnt = cntsSorted[-1]\n",
" x, y, w, h = cv.boundingRect(cnt)\n",
" crop = image[y:y+h, x:x+w]\n",
" return (crop)"
]
},
{
"cell_type": "code",
"execution_count": 231,
"id": "9df02404",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 643.75x287.5 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"image_file = './img/img00.png'\n",
"img = cv.imread(image_file)\n",
"gray_image = grayscale(img)\n",
"thresh, im_bw = cv.threshold(gray_image, 100, 150, cv.THRESH_BINARY)\n",
"no_noise = noise_removal(im_bw)\n",
"# eroded_image = thin_font(no_noise)\n",
"# dilated_image = thick_font(eroded_image)\n",
"no_borders = remove_borders(no_noise)\n",
"cv.imwrite(\"temp/no_borders.jpg\", no_borders)\n",
"display('temp/no_borders.jpg')"
]
},
{
"cell_type": "code",
"execution_count": 169,
"id": "68bb5c6b",
"metadata": {},
"outputs": [],
"source": [
"def display(im_path):\n",
" dpi = 80\n",
" im_data = plt.imread(im_path)\n",
"\n",
" height, width = im_data.shape[:2]\n",
" \n",
" # What size does the figure need to be in inches to fit the image?\n",
" figsize = width / float(dpi), height / float(dpi)\n",
"\n",
" # Create a figure of the right size with one axes that takes up the full figure\n",
" fig = plt.figure(figsize=figsize)\n",
" ax = fig.add_axes([0, 0, 1, 1])\n",
"\n",
" # Hide spines, ticks, etc.\n",
" ax.axis('off')\n",
"\n",
" # Display the image.\n",
" ax.imshow(im_data, cmap='gray')\n",
"\n",
" plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 170,
"id": "c57a2af5",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(image_file)"
]
},
{
"cell_type": "code",
"execution_count": 171,
"id": "d52ff256",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"inverted_image = cv.bitwise_not(img)\n",
"cv.imwrite(\"temp/inverted.jpg\", inverted_image)\n",
"display(\"temp/inverted.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 172,
"id": "e653440c",
"metadata": {},
"outputs": [],
"source": [
"def grayscale(image):\n",
" return cv.cvtColor(image, cv.COLOR_BGR2GRAY)"
]
},
{
"cell_type": "code",
"execution_count": 203,
"id": "0985148f",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"gray_image = grayscale(img)\n",
"cv.imwrite(\"temp/gray.jpg\", gray_image)"
]
},
{
"cell_type": "code",
"execution_count": 204,
"id": "b54b6db2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/gray.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 254,
"id": "90df13c4",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 254,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"thresh, im_bw = cv.threshold(gray_image, 170, 210, cv.THRESH_BINARY)\n",
"cv.imwrite(\"temp/bw_image.jpg\", im_bw)"
]
},
{
"cell_type": "code",
"execution_count": 255,
"id": "d6f7feb2",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/bw_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 207,
"id": "fe31b009",
"metadata": {},
"outputs": [],
"source": [
"def noise_removal(image):\n",
" import numpy as np\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" kernel = np.ones((1, 1), np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.morphologyEx(image, cv.MORPH_CLOSE, kernel)\n",
" image = cv.medianBlur(image, 3)\n",
" return (image)"
]
},
{
"cell_type": "code",
"execution_count": 208,
"id": "4e873cc8",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 208,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"no_noise = noise_removal(im_bw)\n",
"cv.imwrite(\"temp/no_noise.jpg\", no_noise)"
]
},
{
"cell_type": "code",
"execution_count": 209,
"id": "a993d061",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/no_noise.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 210,
"id": "e175dc1d",
"metadata": {},
"outputs": [],
"source": [
"def thin_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.erode(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)"
]
},
{
"cell_type": "code",
"execution_count": 211,
"id": "c77d5076",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 211,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"eroded_image = thin_font(no_noise)\n",
"cv.imwrite(\"temp/eroded_image.jpg\", eroded_image)"
]
},
{
"cell_type": "code",
"execution_count": 212,
"id": "0517c085",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "iVBORw0KGgoAAAANSUhEUgAAArUAAAGLCAYAAADZOGoBAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjYuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8o6BhiAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOx9d5xU5fX+c6fvLlVAmgh2isKC2CMKdowFRbG3mG+ssdE0mmJioqCixm6KCWpUxG6wBLHFREVcBMQCUgQhKNVld/r8/tjf8+65Z+7szsIuM7O8z+fDh52Z2+9bnvec55zjZDKZDCwsLCwsLCwsLCxKGL5CX4CFhYWFhYWFhYXF1sKSWgsLCwsLCwsLi5KHJbUWFhYWFhYWFhYlD0tqLSwsLCwsLCwsSh6W1FpYWFhYWFhYWJQ8LKm1sLCwsLCwsLAoeVhSa2FhYWFhYWFhUfKwpNbCwsLCwsLCwqLkYUmthYWFhYWFhYVFySOQ74aO47TkdVhsx3AcB7Kwnd/vRyqVyvm7hYWFxfYGn88Hx3HM2BgIBJBKpXDUUUfhueeeQzweh9/vh+M4SKfTSKVSCAaDqKysxOLFiwHYsdSitJFP27WWWouCI5PJwHEc+Hx1zTGdTsNxHPPPDsIWFhYWbiSTSTNm1tTUoKyszBiffD4fwuEwUqmUa/y0Y6lFa4cltRYFhfYAaBJrB2ELCwuLurGQBgC54HccB6FQCIlEAul02mwfjUbx+9//Hps2bTLkF4DrbwuL1oa85QcWFi0BDsxysJaf5TYWFhYW2ys4LkpSmslksHHjRsyfPx81NTXw+Xzw+/0AgB9++AGTJ09GIBBwGQ+slNCiNcPJ5MkWbEewaEmQ0EpLg9d3FhYWFtsrGlvg+3w+Q36B+vgEbTCwsChFWE2tRdEjFAoZywLJaygUMgOwJbQWFhYWdZCTOq2ytNx6kVaOn/J7a6CyaM2wllqLgkK3K7rXOBhba62FhcX2Dq9xMtd2krxyPLUWWovWgHzasNXUWhQUmsDSOstB3A7EFhYW2zvyGQdl4Jgkt3YMtdieYEmtRUHh9/uRTqex4447olevXkin0/D5fFi0aBE2btwIwA7KFhYWFhpewV+5SC2/I+yYatFaYUmtRUERj8fhOA5OOeUU3HbbbUilUohEIhg5ciRmzZpltguFQjmPkUqlXMUaLCwsLLYHSKkBDQI6FoFFGnTwmIVFa4QltRYFh8/nM4NxIBBAMpnMCmpYvHgxysvLAdQPyqlUCn6/H3/6058wceJEa32wsLAoeUgrq5Rn8Te/3+8aI4F6cusVe5BMJl2fLaG1aM2w2Q8sCo5UKmWsDLQocJDeeeed8f7776O8vBx+v9/kXAyFQohEIgiFQoYQ22BGCwuLUgIrKeqCCDJAltsw0wFTdLFsLmDlBBYWhLXUWhQFPvroI9xyyy0AgHHjxuHcc8/FoYceik6dOmHvvfc2pBeos0bIQdwO7BYWFqUI6ZHyylagpQJaQmAlBRYWbtiUXhYFh3S3BQIBrFixAhUVFeb3ZDKJQCAAv9+PTCbjGrwdx8FDDz2EsWPH2spjFhYWJQuSWQbPAnVkNZFIuLZzHMfoZGnJpSTBwqI1wxZfsCh6BAJuZ4HjOKitrUU8HkcikTCEFqgLKovH40ZqkMlkUFNTg9raWgAwRRwsLCwsSg30QJGcOo5jCK2WGiQSCddC3xJaC4s6WEutRcGhLayhUAjPP/88Dj30UGQyGQSDQcTjcQSDQRMglk6nUV5ejlGjRuGNN96wg7qFhUXJQUsNpLSKFlh6p0hsveRXgUAgy6JrYdHaYIsvWBQ9GN0rAyMSiQR+/vOfo02bNgBgMiL4/X6ccsopuOKKKxAOh5FIJBCNRpFMJq30wMLCouQgxy0GgElIuZUcJxkwRqJrF/UWFnWwpNaioJBZC2Sy8MWLF7t+lwnEN23aZDRlS5Ysce1rYWFhUSporHytDhyTn+X+dvyzsKiDlR9YFBxe1gkZDaxrmfPvhn63sLCwKDXIMaxDhw7YfffdPTMbaPlBMplEVVXVtrxUC4ttDis/sCh60OJKksrBuzGyyt8DgYBx0VlCa2FhUUrQC/VgMGh+O+SQQ/DEE08YiZZMaajL3m7YsAF77rmnS6pgJQkW2yOaZKmVbpBcVjG5qtTbSN3kFl+wxzEtmdk+wEFdEl5Jagk9Udj2YWFRWEgCJvOvynlla+YFACaQlMfRsqaG5g79t9zPa18Naltz5Yrl/ert5HNp164dli5d6kphmE6nTdEFBojJkrgS8XjcpACbOXMmzjrrLFfp3MbGxIYqmVlYFAPymcubLD9gB5OpRjTRJdGQnVB2qC2N1JTaSktsty/od5wr2bi0avh8PlcQWXMsqiwsLLYcrIyVSCSyyNaWEClmCJAensaOFQgEzNzEgCuv4+ZT4IXZCa655hr8+Mc/NscGgMmTJ+Pll192zYsycwH/Hzt2LE488UQEAgHss88+nudNp9MmtWEsFkMwGMwqk8t51e/3o7q6Gl999ZUpJT5lyhQ899xznvcnn5Xf77eBZxZFi2aXH8gOBiBrpcgT8nfZeeUAtjWkQkbL68HBovXCS34AIGtiZFuT5FW3E7sIsrAoDLzGf62N35JjbqkFVv7NhbDMD8uFcDgcxrXXXmsWygBc6bZGjhyJwYMHG4OP3+/Heeedh/Lycjz99NNZwV6RSATXXXcdAODYY49FZWWlOba2XFOSQEJMcivvh9Zfzo8VFRUYPHiw2e+ss85COBzG008/nXXfvHdJuK2l1qJU0SRLLTuxXNV55czLJUtoDhcT0zvZ0qgWWhID1Ls2AeR0BdoB28Ji28Krz2ld6Nb2SS+5gVzcSk2q1qdmMhnstttu6NKlCwAYUkvLZXl5OV544QVjDZUSilQqhWAwaOZDfk6n0/jnP/+JMWPGuObK9u3bo7KyEi+//HLWM4rH4wiFQkin0ya9YXl5OTKZ+gILwWDQpDiUz4xWWcYXBAIBBAIBxGIxAMDs2bMxYcIEzJ071xWDwHFUelZthTKLYkSzW2p1J5C6IA4S8js5oEj9VFMHLxJirxW2tbptPyBZ5eDrpaXVmm4J7UmwsLDYNsil65Qkqqn9Uhcj0HOBJHYSJHRyIRwOhzFu3DicccYZiMfjKCsrc+3D42tLqt/vNyQzHo/D5/MhHA67rklqaIPBIIYNG4bHHnvMdT36fmg15rxK66+U/snKY3yGgLuyYjweN98dcMABmD59OnbZZZcsLbPU+wJ2jLQoXeRNar20iHJg0sFhWme7pZZVKbCXrp+tdVlZlBaYJUG2Ja+2IS0vtKbowAzbZiwsti3kuN0cGk45H+nFq8/nQygUQjQaxezZs9GrVy8A9cR00KBB+Oabb8y+mUwG//3vf7HbbrshEAgYK6u26IZCIUNceX5abmURBGlF5fgE1JHXyZMn4/zzzze/kajy9/LyckSjUcRiMfh8PkQiEVRXV7u0umVlZdi8ebMh7ZIIS0JMK3JZWZkZG/1+v6cVVhqhrMHIopSRN6mVQnd2SB08lslk8OqrryIUCrkI55FHHrnFKZd4Hi1dyGU1tmidoIUEcGvIgLoJZ6eddsLUqVPNb88//zzuuece2y4sLIoEWhYkyW1Tx/FMJoPrr78exx13nCvgS5Izv9+PHj16GE0qF7nPPPMMbr75ZrzyyitmHolEIgBgCKoMPKOmltZSaaVNJBJIpVKGXHJOjMfjJiia9/z444/j4IMPRiAQMGMZxzFeRzQaRSaTMde8ceNGjBw50pzzgAMOwG233WaO4WVRlZ4syvV4rvLycsyaNQtnnHEGVq9e7VpQyIW/lR9YlCqaJD/Q+llpiWUnr6ysRDgcNr+TjGxNB5FuHC+LsV1Ntn6MGzcOoVDIfNbpczp16oQhQ4YAgFl4RaNRPPjgg1meAmuBsLDY9vCSHujvrrzySrRr1y6v4x1//PHYZ599AMAQTM4R7OdehpB+/fphhx12MN+TqBLpdNpICuR3JK+8XoJ/M7MD/54xYwamT5+OUCiEq6++GsOGDUP79u0bNNDQepvJZPDVV1/hsccewye
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/eroded_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 213,
"id": "9b077539",
"metadata": {},
"outputs": [],
"source": [
"def thick_font(image):\n",
" import numpy as np\n",
" image = cv.bitwise_not(image)\n",
" kernel = np.ones((2,2),np.uint8)\n",
" image = cv.dilate(image, kernel, iterations=1)\n",
" image = cv.bitwise_not(image)\n",
" return (image)"
]
},
{
"cell_type": "code",
"execution_count": 214,
"id": "25bbdacd",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 214,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"dilated_image = thick_font(no_noise)\n",
"cv.imwrite(\"temp/dilated_image.jpg\", dilated_image)"
]
},
{
"cell_type": "code",
"execution_count": 215,
"id": "cff53158",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 673.75x375 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"display(\"temp/dilated_image.jpg\")"
]
},
{
"cell_type": "code",
"execution_count": 216,
"id": "18cd2910",
"metadata": {},
"outputs": [],
"source": [
"def remove_borders(image):\n",
" contours, heiarchy = cv.findContours(image, cv.RETR_EXTERNAL, cv.CHAIN_APPROX_SIMPLE)\n",
" cntsSorted = sorted(contours, key=lambda x:cv.contourArea(x))\n",
" cnt = cntsSorted[-1]\n",
" x, y, w, h = cv.boundingRect(cnt)\n",
" crop = image[y:y+h, x:x+w]\n",
" return (crop)"
]
},
{
"cell_type": "code",
"execution_count": 217,
"id": "40842784",
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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
"text/plain": [
"<Figure size 643.75x287.5 with 1 Axes>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"no_borders = remove_borders(no_noise)\n",
"cv.imwrite(\"temp/no_borders.jpg\", no_borders)\n",
"display('temp/no_borders.jpg')"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.9.15"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
"autoclose": false,
"autocomplete": true,
"bibliofile": "biblio.bib",
"cite_by": "apalike",
"current_citInitial": 1,
"eqLabelWithNumbers": true,
"eqNumInitial": 1,
"hotkeys": {
"equation": "Ctrl-E",
"itemize": "Ctrl-I"
},
"labels_anchors": false,
"latex_user_defs": false,
"report_style_numbering": false,
"user_envs_cfg": false
},
"toc": {
"base_numbering": 1,
"nav_menu": {},
"number_sections": false,
"sideBar": true,
"skip_h1_title": false,
"title_cell": "Table of Contents",
"title_sidebar": "Contents",
"toc_cell": false,
"toc_position": {},
"toc_section_display": true,
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 5
}
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