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+ 
+
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+
+## 
+
+
+##
+
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+
+
+
+
+![](\"https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png\")
\n",
+ "\n",
+ "\n",
+ "![\"Run](\"https://assets.paperspace.io/img/gradient-badge.svg\"){kind=icon}
\n",
+ " ![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
+ " ![\"Open](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
+ "![](\"https://user-images.githubusercontent.com/26833433/202808393-50deb439-ae1b-4246-a685-7560c9b37211.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0eq1SMWl6Sfn"
+ },
+ "source": [
+ "# 2. Validate\n",
+ "Validate a model's accuracy on the [Imagenet](https://image-net.org/) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WQPtK1QYVaD_",
+ "outputId": "20fc0630-141e-4a90-ea06-342cbd7ce496"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar\n",
+ "Resolving image-net.org (image-net.org)... 171.64.68.16\n",
+ "Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected.\n",
+ "HTTP request sent, awaiting response... 200 OK\n",
+ "Length: 6744924160 (6.3G) [application/x-tar]\n",
+ "Saving to: ‘ILSVRC2012_img_val.tar’\n",
+ "\n",
+ "ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s \n",
+ "\n",
+ "2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]\n",
+ "\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download Imagenet val (6.3G, 50000 images)\n",
+ "!bash data/scripts/get_imagenet.sh --val"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "X58w8JLpMnjH",
+ "outputId": "41843132-98e2-4c25-d474-4cd7b246fb8e"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[34m\u001b[1mclassify/val: \u001b[0mdata=../datasets/imagenet, weights=['yolov5s-cls.pt'], batch_size=128, imgsz=224, device=, workers=8, verbose=True, project=runs/val-cls, name=exp, exist_ok=False, half=True, dnn=False\n",
+ "YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
+ "\n",
+ "Fusing layers... \n",
+ "Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs\n",
+ "validating: 100% 391/391 [04:57<00:00, 1.31it/s]\n",
+ " Class Images top1_acc top5_acc\n",
+ " all 50000 0.715 0.902\n",
+ " tench 50 0.94 0.98\n",
+ " goldfish 50 0.88 0.92\n",
+ " great white shark 50 0.78 0.96\n",
+ " tiger shark 50 0.68 0.96\n",
+ " hammerhead shark 50 0.82 0.92\n",
+ " electric ray 50 0.76 0.9\n",
+ " stingray 50 0.7 0.9\n",
+ " cock 50 0.78 0.92\n",
+ " hen 50 0.84 0.96\n",
+ " ostrich 50 0.98 1\n",
+ " brambling 50 0.9 0.96\n",
+ " goldfinch 50 0.92 0.98\n",
+ " house finch 50 0.88 0.96\n",
+ " junco 50 0.94 0.98\n",
+ " indigo bunting 50 0.86 0.88\n",
+ " American robin 50 0.9 0.96\n",
+ " bulbul 50 0.84 0.96\n",
+ " jay 50 0.9 0.96\n",
+ " magpie 50 0.84 0.96\n",
+ " chickadee 50 0.9 1\n",
+ " American dipper 50 0.82 0.92\n",
+ " kite 50 0.76 0.94\n",
+ " bald eagle 50 0.92 1\n",
+ " vulture 50 0.96 1\n",
+ " great grey owl 50 0.94 0.98\n",
+ " fire salamander 50 0.96 0.98\n",
+ " smooth newt 50 0.58 0.94\n",
+ " newt 50 0.74 0.9\n",
+ " spotted salamander 50 0.86 0.94\n",
+ " axolotl 50 0.86 0.96\n",
+ " American bullfrog 50 0.78 0.92\n",
+ " tree frog 50 0.84 0.96\n",
+ " tailed frog 50 0.48 0.8\n",
+ " loggerhead sea turtle 50 0.68 0.94\n",
+ " leatherback sea turtle 50 0.5 0.8\n",
+ " mud turtle 50 0.64 0.84\n",
+ " terrapin 50 0.52 0.98\n",
+ " box turtle 50 0.84 0.98\n",
+ " banded gecko 50 0.7 0.88\n",
+ " green iguana 50 0.76 0.94\n",
+ " Carolina anole 50 0.58 0.96\n",
+ "desert grassland whiptail lizard 50 0.82 0.94\n",
+ " agama 50 0.74 0.92\n",
+ " frilled-necked lizard 50 0.84 0.86\n",
+ " alligator lizard 50 0.58 0.78\n",
+ " Gila monster 50 0.72 0.8\n",
+ " European green lizard 50 0.42 0.9\n",
+ " chameleon 50 0.76 0.84\n",
+ " Komodo dragon 50 0.86 0.96\n",
+ " Nile crocodile 50 0.7 0.84\n",
+ " American alligator 50 0.76 0.96\n",
+ " triceratops 50 0.9 0.94\n",
+ " worm snake 50 0.76 0.88\n",
+ " ring-necked snake 50 0.8 0.92\n",
+ " eastern hog-nosed snake 50 0.58 0.88\n",
+ " smooth green snake 50 0.6 0.94\n",
+ " kingsnake 50 0.82 0.9\n",
+ " garter snake 50 0.88 0.94\n",
+ " water snake 50 0.7 0.94\n",
+ " vine snake 50 0.66 0.76\n",
+ " night snake 50 0.34 0.82\n",
+ " boa constrictor 50 0.8 0.96\n",
+ " African rock python 50 0.48 0.76\n",
+ " Indian cobra 50 0.82 0.94\n",
+ " green mamba 50 0.54 0.86\n",
+ " sea snake 50 0.62 0.9\n",
+ " Saharan horned viper 50 0.56 0.86\n",
+ "eastern diamondback rattlesnake 50 0.6 0.86\n",
+ " sidewinder 50 0.28 0.86\n",
+ " trilobite 50 0.98 0.98\n",
+ " harvestman 50 0.86 0.94\n",
+ " scorpion 50 0.86 0.94\n",
+ " yellow garden spider 50 0.92 0.96\n",
+ " barn spider 50 0.38 0.98\n",
+ " European garden spider 50 0.62 0.98\n",
+ " southern black widow 50 0.88 0.94\n",
+ " tarantula 50 0.94 1\n",
+ " wolf spider 50 0.82 0.92\n",
+ " tick 50 0.74 0.84\n",
+ " centipede 50 0.68 0.82\n",
+ " black grouse 50 0.88 0.98\n",
+ " ptarmigan 50 0.78 0.94\n",
+ " ruffed grouse 50 0.88 1\n",
+ " prairie grouse 50 0.92 1\n",
+ " peacock 50 0.88 0.9\n",
+ " quail 50 0.9 0.94\n",
+ " partridge 50 0.74 0.96\n",
+ " grey parrot 50 0.9 0.96\n",
+ " macaw 50 0.88 0.98\n",
+ "sulphur-crested cockatoo 50 0.86 0.92\n",
+ " lorikeet 50 0.96 1\n",
+ " coucal 50 0.82 0.88\n",
+ " bee eater 50 0.96 0.98\n",
+ " hornbill 50 0.9 0.96\n",
+ " hummingbird 50 0.88 0.96\n",
+ " jacamar 50 0.92 0.94\n",
+ " toucan 50 0.84 0.94\n",
+ " duck 50 0.76 0.94\n",
+ " red-breasted merganser 50 0.86 0.96\n",
+ " goose 50 0.74 0.96\n",
+ " black swan 50 0.94 0.98\n",
+ " tusker 50 0.54 0.92\n",
+ " echidna 50 0.98 1\n",
+ " platypus 50 0.72 0.84\n",
+ " wallaby 50 0.78 0.88\n",
+ " koala 50 0.84 0.92\n",
+ " wombat 50 0.78 0.84\n",
+ " jellyfish 50 0.88 0.96\n",
+ " sea anemone 50 0.72 0.9\n",
+ " brain coral 50 0.88 0.96\n",
+ " flatworm 50 0.8 0.98\n",
+ " nematode 50 0.86 0.9\n",
+ " conch 50 0.74 0.88\n",
+ " snail 50 0.78 0.88\n",
+ " slug 50 0.74 0.82\n",
+ " sea slug 50 0.88 0.98\n",
+ " chiton 50 0.88 0.98\n",
+ " chambered nautilus 50 0.88 0.92\n",
+ " Dungeness crab 50 0.78 0.94\n",
+ " rock crab 50 0.68 0.86\n",
+ " fiddler crab 50 0.64 0.86\n",
+ " red king crab 50 0.76 0.96\n",
+ " American lobster 50 0.78 0.96\n",
+ " spiny lobster 50 0.74 0.88\n",
+ " crayfish 50 0.56 0.86\n",
+ " hermit crab 50 0.78 0.96\n",
+ " isopod 50 0.66 0.78\n",
+ " white stork 50 0.88 0.96\n",
+ " black stork 50 0.84 0.98\n",
+ " spoonbill 50 0.96 1\n",
+ " flamingo 50 0.94 1\n",
+ " little blue heron 50 0.92 0.98\n",
+ " great egret 50 0.9 0.96\n",
+ " bittern 50 0.86 0.94\n",
+ " crane (bird) 50 0.62 0.9\n",
+ " limpkin 50 0.98 1\n",
+ " common gallinule 50 0.92 0.96\n",
+ " American coot 50 0.9 0.98\n",
+ " bustard 50 0.92 0.96\n",
+ " ruddy turnstone 50 0.94 1\n",
+ " dunlin 50 0.86 0.94\n",
+ " common redshank 50 0.9 0.96\n",
+ " dowitcher 50 0.84 0.96\n",
+ " oystercatcher 50 0.86 0.94\n",
+ " pelican 50 0.92 0.96\n",
+ " king penguin 50 0.88 0.96\n",
+ " albatross 50 0.9 1\n",
+ " grey whale 50 0.84 0.92\n",
+ " killer whale 50 0.92 1\n",
+ " dugong 50 0.84 0.96\n",
+ " sea lion 50 0.82 0.92\n",
+ " Chihuahua 50 0.66 0.84\n",
+ " Japanese Chin 50 0.72 0.98\n",
+ " Maltese 50 0.76 0.94\n",
+ " Pekingese 50 0.84 0.94\n",
+ " Shih Tzu 50 0.74 0.96\n",
+ " King Charles Spaniel 50 0.88 0.98\n",
+ " Papillon 50 0.86 0.94\n",
+ " toy terrier 50 0.48 0.94\n",
+ " Rhodesian Ridgeback 50 0.76 0.98\n",
+ " Afghan Hound 50 0.84 1\n",
+ " Basset Hound 50 0.8 0.92\n",
+ " Beagle 50 0.82 0.96\n",
+ " Bloodhound 50 0.48 0.72\n",
+ " Bluetick Coonhound 50 0.86 0.94\n",
+ " Black and Tan Coonhound 50 0.54 0.8\n",
+ "Treeing Walker Coonhound 50 0.66 0.98\n",
+ " English foxhound 50 0.32 0.84\n",
+ " Redbone Coonhound 50 0.62 0.94\n",
+ " borzoi 50 0.92 1\n",
+ " Irish Wolfhound 50 0.48 0.88\n",
+ " Italian Greyhound 50 0.76 0.98\n",
+ " Whippet 50 0.74 0.92\n",
+ " Ibizan Hound 50 0.6 0.86\n",
+ " Norwegian Elkhound 50 0.88 0.98\n",
+ " Otterhound 50 0.62 0.9\n",
+ " Saluki 50 0.72 0.92\n",
+ " Scottish Deerhound 50 0.86 0.98\n",
+ " Weimaraner 50 0.88 0.94\n",
+ "Staffordshire Bull Terrier 50 0.66 0.98\n",
+ "American Staffordshire Terrier 50 0.64 0.92\n",
+ " Bedlington Terrier 50 0.9 0.92\n",
+ " Border Terrier 50 0.86 0.92\n",
+ " Kerry Blue Terrier 50 0.78 0.98\n",
+ " Irish Terrier 50 0.7 0.96\n",
+ " Norfolk Terrier 50 0.68 0.9\n",
+ " Norwich Terrier 50 0.72 1\n",
+ " Yorkshire Terrier 50 0.66 0.9\n",
+ " Wire Fox Terrier 50 0.64 0.98\n",
+ " Lakeland Terrier 50 0.74 0.92\n",
+ " Sealyham Terrier 50 0.76 0.9\n",
+ " Airedale Terrier 50 0.82 0.92\n",
+ " Cairn Terrier 50 0.76 0.9\n",
+ " Australian Terrier 50 0.48 0.84\n",
+ " Dandie Dinmont Terrier 50 0.82 0.92\n",
+ " Boston Terrier 50 0.92 1\n",
+ " Miniature Schnauzer 50 0.68 0.9\n",
+ " Giant Schnauzer 50 0.72 0.98\n",
+ " Standard Schnauzer 50 0.74 1\n",
+ " Scottish Terrier 50 0.76 0.96\n",
+ " Tibetan Terrier 50 0.48 1\n",
+ "Australian Silky Terrier 50 0.66 0.96\n",
+ "Soft-coated Wheaten Terrier 50 0.74 0.96\n",
+ "West Highland White Terrier 50 0.88 0.96\n",
+ " Lhasa Apso 50 0.68 0.96\n",
+ " Flat-Coated Retriever 50 0.72 0.94\n",
+ " Curly-coated Retriever 50 0.82 0.94\n",
+ " Golden Retriever 50 0.86 0.94\n",
+ " Labrador Retriever 50 0.82 0.94\n",
+ "Chesapeake Bay Retriever 50 0.76 0.96\n",
+ "German Shorthaired Pointer 50 0.8 0.96\n",
+ " Vizsla 50 0.68 0.96\n",
+ " English Setter 50 0.7 1\n",
+ " Irish Setter 50 0.8 0.9\n",
+ " Gordon Setter 50 0.84 0.92\n",
+ " Brittany 50 0.84 0.96\n",
+ " Clumber Spaniel 50 0.92 0.96\n",
+ "English Springer Spaniel 50 0.88 1\n",
+ " Welsh Springer Spaniel 50 0.92 1\n",
+ " Cocker Spaniels 50 0.7 0.94\n",
+ " Sussex Spaniel 50 0.72 0.92\n",
+ " Irish Water Spaniel 50 0.88 0.98\n",
+ " Kuvasz 50 0.66 0.9\n",
+ " Schipperke 50 0.9 0.98\n",
+ " Groenendael 50 0.8 0.94\n",
+ " Malinois 50 0.86 0.98\n",
+ " Briard 50 0.52 0.8\n",
+ " Australian Kelpie 50 0.6 0.88\n",
+ " Komondor 50 0.88 0.94\n",
+ " Old English Sheepdog 50 0.94 0.98\n",
+ " Shetland Sheepdog 50 0.74 0.9\n",
+ " collie 50 0.6 0.96\n",
+ " Border Collie 50 0.74 0.96\n",
+ " Bouvier des Flandres 50 0.78 0.94\n",
+ " Rottweiler 50 0.88 0.96\n",
+ " German Shepherd Dog 50 0.8 0.98\n",
+ " Dobermann 50 0.68 0.96\n",
+ " Miniature Pinscher 50 0.76 0.88\n",
+ "Greater Swiss Mountain Dog 50 0.68 0.94\n",
+ " Bernese Mountain Dog 50 0.96 1\n",
+ " Appenzeller Sennenhund 50 0.22 1\n",
+ " Entlebucher Sennenhund 50 0.64 0.98\n",
+ " Boxer 50 0.7 0.92\n",
+ " Bullmastiff 50 0.78 0.98\n",
+ " Tibetan Mastiff 50 0.88 0.96\n",
+ " French Bulldog 50 0.84 0.94\n",
+ " Great Dane 50 0.54 0.9\n",
+ " St. Bernard 50 0.92 1\n",
+ " husky 50 0.46 0.98\n",
+ " Alaskan Malamute 50 0.76 0.96\n",
+ " Siberian Husky 50 0.46 0.98\n",
+ " Dalmatian 50 0.94 0.98\n",
+ " Affenpinscher 50 0.78 0.9\n",
+ " Basenji 50 0.92 0.94\n",
+ " pug 50 0.94 0.98\n",
+ " Leonberger 50 1 1\n",
+ " Newfoundland 50 0.78 0.96\n",
+ " Pyrenean Mountain Dog 50 0.78 0.96\n",
+ " Samoyed 50 0.96 1\n",
+ " Pomeranian 50 0.98 1\n",
+ " Chow Chow 50 0.9 0.96\n",
+ " Keeshond 50 0.88 0.94\n",
+ " Griffon Bruxellois 50 0.84 0.98\n",
+ " Pembroke Welsh Corgi 50 0.82 0.94\n",
+ " Cardigan Welsh Corgi 50 0.66 0.98\n",
+ " Toy Poodle 50 0.52 0.88\n",
+ " Miniature Poodle 50 0.52 0.92\n",
+ " Standard Poodle 50 0.8 1\n",
+ " Mexican hairless dog 50 0.88 0.98\n",
+ " grey wolf 50 0.82 0.92\n",
+ " Alaskan tundra wolf 50 0.78 0.98\n",
+ " red wolf 50 0.48 0.9\n",
+ " coyote 50 0.64 0.86\n",
+ " dingo 50 0.76 0.88\n",
+ " dhole 50 0.9 0.98\n",
+ " African wild dog 50 0.98 1\n",
+ " hyena 50 0.88 0.96\n",
+ " red fox 50 0.54 0.92\n",
+ " kit fox 50 0.72 0.98\n",
+ " Arctic fox 50 0.94 1\n",
+ " grey fox 50 0.7 0.94\n",
+ " tabby cat 50 0.54 0.92\n",
+ " tiger cat 50 0.22 0.94\n",
+ " Persian cat 50 0.9 0.98\n",
+ " Siamese cat 50 0.96 1\n",
+ " Egyptian Mau 50 0.54 0.8\n",
+ " cougar 50 0.9 1\n",
+ " lynx 50 0.72 0.88\n",
+ " leopard 50 0.78 0.98\n",
+ " snow leopard 50 0.9 0.98\n",
+ " jaguar 50 0.7 0.94\n",
+ " lion 50 0.9 0.98\n",
+ " tiger 50 0.92 0.98\n",
+ " cheetah 50 0.94 0.98\n",
+ " brown bear 50 0.94 0.98\n",
+ " American black bear 50 0.8 1\n",
+ " polar bear 50 0.84 0.96\n",
+ " sloth bear 50 0.72 0.92\n",
+ " mongoose 50 0.7 0.92\n",
+ " meerkat 50 0.82 0.92\n",
+ " tiger beetle 50 0.92 0.94\n",
+ " ladybug 50 0.86 0.94\n",
+ " ground beetle 50 0.64 0.94\n",
+ " longhorn beetle 50 0.62 0.88\n",
+ " leaf beetle 50 0.64 0.98\n",
+ " dung beetle 50 0.86 0.98\n",
+ " rhinoceros beetle 50 0.86 0.94\n",
+ " weevil 50 0.9 1\n",
+ " fly 50 0.78 0.94\n",
+ " bee 50 0.68 0.94\n",
+ " ant 50 0.68 0.78\n",
+ " grasshopper 50 0.5 0.92\n",
+ " cricket 50 0.64 0.92\n",
+ " stick insect 50 0.64 0.92\n",
+ " cockroach 50 0.72 0.8\n",
+ " mantis 50 0.64 0.86\n",
+ " cicada 50 0.9 0.96\n",
+ " leafhopper 50 0.88 0.94\n",
+ " lacewing 50 0.78 0.92\n",
+ " dragonfly 50 0.82 0.98\n",
+ " damselfly 50 0.82 1\n",
+ " red admiral 50 0.94 0.96\n",
+ " ringlet 50 0.86 0.98\n",
+ " monarch butterfly 50 0.9 0.92\n",
+ " small white 50 0.9 1\n",
+ " sulphur butterfly 50 0.92 1\n",
+ "gossamer-winged butterfly 50 0.88 1\n",
+ " starfish 50 0.88 0.92\n",
+ " sea urchin 50 0.84 0.94\n",
+ " sea cucumber 50 0.66 0.84\n",
+ " cottontail rabbit 50 0.72 0.94\n",
+ " hare 50 0.84 0.96\n",
+ " Angora rabbit 50 0.94 0.98\n",
+ " hamster 50 0.96 1\n",
+ " porcupine 50 0.88 0.98\n",
+ " fox squirrel 50 0.76 0.94\n",
+ " marmot 50 0.92 0.96\n",
+ " beaver 50 0.78 0.94\n",
+ " guinea pig 50 0.78 0.94\n",
+ " common sorrel 50 0.96 0.98\n",
+ " zebra 50 0.94 0.96\n",
+ " pig 50 0.5 0.76\n",
+ " wild boar 50 0.84 0.96\n",
+ " warthog 50 0.84 0.96\n",
+ " hippopotamus 50 0.88 0.96\n",
+ " ox 50 0.48 0.94\n",
+ " water buffalo 50 0.78 0.94\n",
+ " bison 50 0.88 0.96\n",
+ " ram 50 0.58 0.92\n",
+ " bighorn sheep 50 0.66 1\n",
+ " Alpine ibex 50 0.92 0.98\n",
+ " hartebeest 50 0.94 1\n",
+ " impala 50 0.82 0.96\n",
+ " gazelle 50 0.7 0.96\n",
+ " dromedary 50 0.9 1\n",
+ " llama 50 0.82 0.94\n",
+ " weasel 50 0.44 0.92\n",
+ " mink 50 0.78 0.96\n",
+ " European polecat 50 0.46 0.9\n",
+ " black-footed ferret 50 0.68 0.96\n",
+ " otter 50 0.66 0.88\n",
+ " skunk 50 0.96 0.96\n",
+ " badger 50 0.86 0.92\n",
+ " armadillo 50 0.88 0.9\n",
+ " three-toed sloth 50 0.96 1\n",
+ " orangutan 50 0.78 0.92\n",
+ " gorilla 50 0.82 0.94\n",
+ " chimpanzee 50 0.84 0.94\n",
+ " gibbon 50 0.76 0.86\n",
+ " siamang 50 0.68 0.94\n",
+ " guenon 50 0.8 0.94\n",
+ " patas monkey 50 0.62 0.82\n",
+ " baboon 50 0.9 0.98\n",
+ " macaque 50 0.8 0.86\n",
+ " langur 50 0.6 0.82\n",
+ " black-and-white colobus 50 0.86 0.9\n",
+ " proboscis monkey 50 1 1\n",
+ " marmoset 50 0.74 0.98\n",
+ " white-headed capuchin 50 0.72 0.9\n",
+ " howler monkey 50 0.86 0.94\n",
+ " titi 50 0.5 0.9\n",
+ "Geoffroy's spider monkey 50 0.42 0.8\n",
+ " common squirrel monkey 50 0.76 0.92\n",
+ " ring-tailed lemur 50 0.72 0.94\n",
+ " indri 50 0.9 0.96\n",
+ " Asian elephant 50 0.58 0.92\n",
+ " African bush elephant 50 0.7 0.98\n",
+ " red panda 50 0.94 0.94\n",
+ " giant panda 50 0.94 0.98\n",
+ " snoek 50 0.74 0.9\n",
+ " eel 50 0.6 0.84\n",
+ " coho salmon 50 0.84 0.96\n",
+ " rock beauty 50 0.88 0.98\n",
+ " clownfish 50 0.78 0.98\n",
+ " sturgeon 50 0.68 0.94\n",
+ " garfish 50 0.62 0.8\n",
+ " lionfish 50 0.96 0.96\n",
+ " pufferfish 50 0.88 0.96\n",
+ " abacus 50 0.74 0.88\n",
+ " abaya 50 0.84 0.92\n",
+ " academic gown 50 0.42 0.86\n",
+ " accordion 50 0.8 0.9\n",
+ " acoustic guitar 50 0.5 0.76\n",
+ " aircraft carrier 50 0.8 0.96\n",
+ " airliner 50 0.92 1\n",
+ " airship 50 0.76 0.82\n",
+ " altar 50 0.64 0.98\n",
+ " ambulance 50 0.88 0.98\n",
+ " amphibious vehicle 50 0.64 0.94\n",
+ " analog clock 50 0.52 0.92\n",
+ " apiary 50 0.82 0.96\n",
+ " apron 50 0.7 0.84\n",
+ " waste container 50 0.4 0.8\n",
+ " assault rifle 50 0.42 0.84\n",
+ " backpack 50 0.34 0.64\n",
+ " bakery 50 0.4 0.68\n",
+ " balance beam 50 0.8 0.98\n",
+ " balloon 50 0.86 0.96\n",
+ " ballpoint pen 50 0.52 0.96\n",
+ " Band-Aid 50 0.7 0.9\n",
+ " banjo 50 0.84 1\n",
+ " baluster 50 0.68 0.94\n",
+ " barbell 50 0.56 0.9\n",
+ " barber chair 50 0.7 0.92\n",
+ " barbershop 50 0.54 0.86\n",
+ " barn 50 0.96 0.96\n",
+ " barometer 50 0.84 0.98\n",
+ " barrel 50 0.56 0.88\n",
+ " wheelbarrow 50 0.66 0.88\n",
+ " baseball 50 0.74 0.98\n",
+ " basketball 50 0.88 0.98\n",
+ " bassinet 50 0.66 0.92\n",
+ " bassoon 50 0.74 0.98\n",
+ " swimming cap 50 0.62 0.88\n",
+ " bath towel 50 0.54 0.78\n",
+ " bathtub 50 0.4 0.88\n",
+ " station wagon 50 0.66 0.84\n",
+ " lighthouse 50 0.78 0.94\n",
+ " beaker 50 0.52 0.68\n",
+ " military cap 50 0.84 0.96\n",
+ " beer bottle 50 0.66 0.88\n",
+ " beer glass 50 0.6 0.84\n",
+ " bell-cot 50 0.56 0.96\n",
+ " bib 50 0.58 0.82\n",
+ " tandem bicycle 50 0.86 0.96\n",
+ " bikini 50 0.56 0.88\n",
+ " ring binder 50 0.64 0.84\n",
+ " binoculars 50 0.54 0.78\n",
+ " birdhouse 50 0.86 0.94\n",
+ " boathouse 50 0.74 0.92\n",
+ " bobsleigh 50 0.92 0.96\n",
+ " bolo tie 50 0.8 0.94\n",
+ " poke bonnet 50 0.64 0.86\n",
+ " bookcase 50 0.66 0.92\n",
+ " bookstore 50 0.62 0.88\n",
+ " bottle cap 50 0.58 0.7\n",
+ " bow 50 0.72 0.86\n",
+ " bow tie 50 0.7 0.9\n",
+ " brass 50 0.92 0.96\n",
+ " bra 50 0.5 0.7\n",
+ " breakwater 50 0.62 0.86\n",
+ " breastplate 50 0.4 0.9\n",
+ " broom 50 0.6 0.86\n",
+ " bucket 50 0.66 0.8\n",
+ " buckle 50 0.5 0.68\n",
+ " bulletproof vest 50 0.5 0.78\n",
+ " high-speed train 50 0.94 0.96\n",
+ " butcher shop 50 0.74 0.94\n",
+ " taxicab 50 0.64 0.86\n",
+ " cauldron 50 0.44 0.66\n",
+ " candle 50 0.48 0.74\n",
+ " cannon 50 0.88 0.94\n",
+ " canoe 50 0.94 1\n",
+ " can opener 50 0.66 0.86\n",
+ " cardigan 50 0.68 0.8\n",
+ " car mirror 50 0.94 0.96\n",
+ " carousel 50 0.94 0.98\n",
+ " tool kit 50 0.56 0.78\n",
+ " carton 50 0.42 0.7\n",
+ " car wheel 50 0.38 0.74\n",
+ "automated teller machine 50 0.76 0.94\n",
+ " cassette 50 0.52 0.8\n",
+ " cassette player 50 0.28 0.9\n",
+ " castle 50 0.78 0.88\n",
+ " catamaran 50 0.78 1\n",
+ " CD player 50 0.52 0.82\n",
+ " cello 50 0.82 1\n",
+ " mobile phone 50 0.68 0.86\n",
+ " chain 50 0.38 0.66\n",
+ " chain-link fence 50 0.7 0.84\n",
+ " chain mail 50 0.64 0.9\n",
+ " chainsaw 50 0.84 0.92\n",
+ " chest 50 0.68 0.92\n",
+ " chiffonier 50 0.26 0.64\n",
+ " chime 50 0.62 0.84\n",
+ " china cabinet 50 0.82 0.96\n",
+ " Christmas stocking 50 0.92 0.94\n",
+ " church 50 0.62 0.9\n",
+ " movie theater 50 0.58 0.88\n",
+ " cleaver 50 0.32 0.62\n",
+ " cliff dwelling 50 0.88 1\n",
+ " cloak 50 0.32 0.64\n",
+ " clogs 50 0.58 0.88\n",
+ " cocktail shaker 50 0.62 0.7\n",
+ " coffee mug 50 0.44 0.72\n",
+ " coffeemaker 50 0.64 0.92\n",
+ " coil 50 0.66 0.84\n",
+ " combination lock 50 0.64 0.84\n",
+ " computer keyboard 50 0.7 0.82\n",
+ " confectionery store 50 0.54 0.86\n",
+ " container ship 50 0.82 0.98\n",
+ " convertible 50 0.78 0.98\n",
+ " corkscrew 50 0.82 0.92\n",
+ " cornet 50 0.46 0.88\n",
+ " cowboy boot 50 0.64 0.8\n",
+ " cowboy hat 50 0.64 0.82\n",
+ " cradle 50 0.38 0.8\n",
+ " crane (machine) 50 0.78 0.94\n",
+ " crash helmet 50 0.92 0.96\n",
+ " crate 50 0.52 0.82\n",
+ " infant bed 50 0.74 1\n",
+ " Crock Pot 50 0.78 0.9\n",
+ " croquet ball 50 0.9 0.96\n",
+ " crutch 50 0.46 0.7\n",
+ " cuirass 50 0.54 0.86\n",
+ " dam 50 0.74 0.92\n",
+ " desk 50 0.6 0.86\n",
+ " desktop computer 50 0.54 0.94\n",
+ " rotary dial telephone 50 0.88 0.94\n",
+ " diaper 50 0.68 0.84\n",
+ " digital clock 50 0.54 0.76\n",
+ " digital watch 50 0.58 0.86\n",
+ " dining table 50 0.76 0.9\n",
+ " dishcloth 50 0.94 1\n",
+ " dishwasher 50 0.44 0.78\n",
+ " disc brake 50 0.98 1\n",
+ " dock 50 0.54 0.94\n",
+ " dog sled 50 0.84 1\n",
+ " dome 50 0.72 0.92\n",
+ " doormat 50 0.56 0.82\n",
+ " drilling rig 50 0.84 0.96\n",
+ " drum 50 0.38 0.68\n",
+ " drumstick 50 0.56 0.72\n",
+ " dumbbell 50 0.62 0.9\n",
+ " Dutch oven 50 0.7 0.84\n",
+ " electric fan 50 0.82 0.86\n",
+ " electric guitar 50 0.62 0.84\n",
+ " electric locomotive 50 0.92 0.98\n",
+ " entertainment center 50 0.9 0.98\n",
+ " envelope 50 0.44 0.86\n",
+ " espresso machine 50 0.72 0.94\n",
+ " face powder 50 0.7 0.92\n",
+ " feather boa 50 0.7 0.84\n",
+ " filing cabinet 50 0.88 0.98\n",
+ " fireboat 50 0.94 0.98\n",
+ " fire engine 50 0.84 0.9\n",
+ " fire screen sheet 50 0.62 0.76\n",
+ " flagpole 50 0.74 0.88\n",
+ " flute 50 0.36 0.72\n",
+ " folding chair 50 0.62 0.84\n",
+ " football helmet 50 0.86 0.94\n",
+ " forklift 50 0.8 0.92\n",
+ " fountain 50 0.84 0.94\n",
+ " fountain pen 50 0.76 0.92\n",
+ " four-poster bed 50 0.78 0.94\n",
+ " freight car 50 0.96 1\n",
+ " French horn 50 0.76 0.92\n",
+ " frying pan 50 0.36 0.78\n",
+ " fur coat 50 0.84 0.96\n",
+ " garbage truck 50 0.9 0.98\n",
+ " gas mask 50 0.84 0.92\n",
+ " gas pump 50 0.9 0.98\n",
+ " goblet 50 0.68 0.82\n",
+ " go-kart 50 0.9 1\n",
+ " golf ball 50 0.84 0.9\n",
+ " golf cart 50 0.78 0.86\n",
+ " gondola 50 0.98 0.98\n",
+ " gong 50 0.74 0.92\n",
+ " gown 50 0.62 0.96\n",
+ " grand piano 50 0.7 0.96\n",
+ " greenhouse 50 0.8 0.98\n",
+ " grille 50 0.72 0.9\n",
+ " grocery store 50 0.66 0.94\n",
+ " guillotine 50 0.86 0.92\n",
+ " barrette 50 0.52 0.66\n",
+ " hair spray 50 0.5 0.74\n",
+ " half-track 50 0.78 0.9\n",
+ " hammer 50 0.56 0.76\n",
+ " hamper 50 0.64 0.84\n",
+ " hair dryer 50 0.56 0.74\n",
+ " hand-held computer 50 0.42 0.86\n",
+ " handkerchief 50 0.78 0.94\n",
+ " hard disk drive 50 0.76 0.84\n",
+ " harmonica 50 0.7 0.88\n",
+ " harp 50 0.88 0.96\n",
+ " harvester 50 0.78 1\n",
+ " hatchet 50 0.54 0.74\n",
+ " holster 50 0.66 0.84\n",
+ " home theater 50 0.64 0.94\n",
+ " honeycomb 50 0.56 0.88\n",
+ " hook 50 0.3 0.6\n",
+ " hoop skirt 50 0.64 0.86\n",
+ " horizontal bar 50 0.68 0.98\n",
+ " horse-drawn vehicle 50 0.88 0.94\n",
+ " hourglass 50 0.88 0.96\n",
+ " iPod 50 0.76 0.94\n",
+ " clothes iron 50 0.82 0.88\n",
+ " jack-o'-lantern 50 0.98 0.98\n",
+ " jeans 50 0.68 0.84\n",
+ " jeep 50 0.72 0.9\n",
+ " T-shirt 50 0.72 0.96\n",
+ " jigsaw puzzle 50 0.84 0.94\n",
+ " pulled rickshaw 50 0.86 0.94\n",
+ " joystick 50 0.8 0.9\n",
+ " kimono 50 0.84 0.96\n",
+ " knee pad 50 0.62 0.88\n",
+ " knot 50 0.66 0.8\n",
+ " lab coat 50 0.8 0.96\n",
+ " ladle 50 0.36 0.64\n",
+ " lampshade 50 0.48 0.84\n",
+ " laptop computer 50 0.26 0.88\n",
+ " lawn mower 50 0.78 0.96\n",
+ " lens cap 50 0.46 0.72\n",
+ " paper knife 50 0.26 0.5\n",
+ " library 50 0.54 0.9\n",
+ " lifeboat 50 0.92 0.98\n",
+ " lighter 50 0.56 0.78\n",
+ " limousine 50 0.76 0.92\n",
+ " ocean liner 50 0.88 0.94\n",
+ " lipstick 50 0.74 0.9\n",
+ " slip-on shoe 50 0.74 0.92\n",
+ " lotion 50 0.5 0.86\n",
+ " speaker 50 0.52 0.68\n",
+ " loupe 50 0.32 0.52\n",
+ " sawmill 50 0.72 0.9\n",
+ " magnetic compass 50 0.52 0.82\n",
+ " mail bag 50 0.68 0.92\n",
+ " mailbox 50 0.82 0.92\n",
+ " tights 50 0.22 0.94\n",
+ " tank suit 50 0.24 0.9\n",
+ " manhole cover 50 0.96 0.98\n",
+ " maraca 50 0.74 0.9\n",
+ " marimba 50 0.84 0.94\n",
+ " mask 50 0.44 0.82\n",
+ " match 50 0.66 0.9\n",
+ " maypole 50 0.96 1\n",
+ " maze 50 0.8 0.96\n",
+ " measuring cup 50 0.54 0.76\n",
+ " medicine chest 50 0.6 0.84\n",
+ " megalith 50 0.8 0.92\n",
+ " microphone 50 0.52 0.7\n",
+ " microwave oven 50 0.48 0.72\n",
+ " military uniform 50 0.62 0.84\n",
+ " milk can 50 0.68 0.82\n",
+ " minibus 50 0.7 1\n",
+ " miniskirt 50 0.46 0.76\n",
+ " minivan 50 0.38 0.8\n",
+ " missile 50 0.4 0.84\n",
+ " mitten 50 0.76 0.88\n",
+ " mixing bowl 50 0.8 0.92\n",
+ " mobile home 50 0.54 0.78\n",
+ " Model T 50 0.92 0.96\n",
+ " modem 50 0.58 0.86\n",
+ " monastery 50 0.44 0.9\n",
+ " monitor 50 0.4 0.86\n",
+ " moped 50 0.56 0.94\n",
+ " mortar 50 0.68 0.94\n",
+ " square academic cap 50 0.5 0.84\n",
+ " mosque 50 0.9 1\n",
+ " mosquito net 50 0.9 0.98\n",
+ " scooter 50 0.9 0.98\n",
+ " mountain bike 50 0.78 0.96\n",
+ " tent 50 0.88 0.96\n",
+ " computer mouse 50 0.42 0.82\n",
+ " mousetrap 50 0.76 0.88\n",
+ " moving van 50 0.4 0.72\n",
+ " muzzle 50 0.5 0.72\n",
+ " nail 50 0.68 0.74\n",
+ " neck brace 50 0.56 0.68\n",
+ " necklace 50 0.86 1\n",
+ " nipple 50 0.7 0.88\n",
+ " notebook computer 50 0.34 0.84\n",
+ " obelisk 50 0.8 0.92\n",
+ " oboe 50 0.6 0.84\n",
+ " ocarina 50 0.8 0.86\n",
+ " odometer 50 0.96 1\n",
+ " oil filter 50 0.58 0.82\n",
+ " organ 50 0.82 0.9\n",
+ " oscilloscope 50 0.9 0.96\n",
+ " overskirt 50 0.2 0.7\n",
+ " bullock cart 50 0.7 0.94\n",
+ " oxygen mask 50 0.46 0.84\n",
+ " packet 50 0.5 0.78\n",
+ " paddle 50 0.56 0.94\n",
+ " paddle wheel 50 0.86 0.96\n",
+ " padlock 50 0.74 0.78\n",
+ " paintbrush 50 0.62 0.8\n",
+ " pajamas 50 0.56 0.92\n",
+ " palace 50 0.64 0.96\n",
+ " pan flute 50 0.84 0.86\n",
+ " paper towel 50 0.66 0.84\n",
+ " parachute 50 0.92 0.94\n",
+ " parallel bars 50 0.62 0.96\n",
+ " park bench 50 0.74 0.9\n",
+ " parking meter 50 0.84 0.92\n",
+ " passenger car 50 0.5 0.82\n",
+ " patio 50 0.58 0.84\n",
+ " payphone 50 0.74 0.92\n",
+ " pedestal 50 0.52 0.9\n",
+ " pencil case 50 0.64 0.92\n",
+ " pencil sharpener 50 0.52 0.78\n",
+ " perfume 50 0.7 0.9\n",
+ " Petri dish 50 0.6 0.8\n",
+ " photocopier 50 0.88 0.98\n",
+ " plectrum 50 0.7 0.84\n",
+ " Pickelhaube 50 0.72 0.86\n",
+ " picket fence 50 0.84 0.94\n",
+ " pickup truck 50 0.64 0.92\n",
+ " pier 50 0.52 0.82\n",
+ " piggy bank 50 0.82 0.94\n",
+ " pill bottle 50 0.76 0.86\n",
+ " pillow 50 0.76 0.9\n",
+ " ping-pong ball 50 0.84 0.88\n",
+ " pinwheel 50 0.76 0.88\n",
+ " pirate ship 50 0.76 0.94\n",
+ " pitcher 50 0.46 0.84\n",
+ " hand plane 50 0.84 0.94\n",
+ " planetarium 50 0.88 0.98\n",
+ " plastic bag 50 0.36 0.62\n",
+ " plate rack 50 0.52 0.78\n",
+ " plow 50 0.78 0.88\n",
+ " plunger 50 0.42 0.7\n",
+ " Polaroid camera 50 0.84 0.92\n",
+ " pole 50 0.38 0.74\n",
+ " police van 50 0.76 0.94\n",
+ " poncho 50 0.58 0.86\n",
+ " billiard table 50 0.8 0.88\n",
+ " soda bottle 50 0.56 0.94\n",
+ " pot 50 0.78 0.92\n",
+ " potter's wheel 50 0.9 0.94\n",
+ " power drill 50 0.42 0.72\n",
+ " prayer rug 50 0.7 0.86\n",
+ " printer 50 0.54 0.86\n",
+ " prison 50 0.7 0.9\n",
+ " projectile 50 0.28 0.9\n",
+ " projector 50 0.62 0.84\n",
+ " hockey puck 50 0.92 0.96\n",
+ " punching bag 50 0.6 0.68\n",
+ " purse 50 0.42 0.78\n",
+ " quill 50 0.68 0.84\n",
+ " quilt 50 0.64 0.9\n",
+ " race car 50 0.72 0.92\n",
+ " racket 50 0.72 0.9\n",
+ " radiator 50 0.66 0.76\n",
+ " radio 50 0.64 0.92\n",
+ " radio telescope 50 0.9 0.96\n",
+ " rain barrel 50 0.8 0.98\n",
+ " recreational vehicle 50 0.84 0.94\n",
+ " reel 50 0.72 0.82\n",
+ " reflex camera 50 0.72 0.92\n",
+ " refrigerator 50 0.7 0.9\n",
+ " remote control 50 0.7 0.88\n",
+ " restaurant 50 0.5 0.66\n",
+ " revolver 50 0.82 1\n",
+ " rifle 50 0.38 0.7\n",
+ " rocking chair 50 0.62 0.84\n",
+ " rotisserie 50 0.88 0.92\n",
+ " eraser 50 0.54 0.76\n",
+ " rugby ball 50 0.86 0.94\n",
+ " ruler 50 0.68 0.86\n",
+ " running shoe 50 0.78 0.94\n",
+ " safe 50 0.82 0.92\n",
+ " safety pin 50 0.4 0.62\n",
+ " salt shaker 50 0.66 0.9\n",
+ " sandal 50 0.66 0.86\n",
+ " sarong 50 0.64 0.86\n",
+ " saxophone 50 0.66 0.88\n",
+ " scabbard 50 0.76 0.92\n",
+ " weighing scale 50 0.58 0.78\n",
+ " school bus 50 0.92 1\n",
+ " schooner 50 0.84 1\n",
+ " scoreboard 50 0.9 0.96\n",
+ " CRT screen 50 0.14 0.7\n",
+ " screw 50 0.9 0.98\n",
+ " screwdriver 50 0.3 0.58\n",
+ " seat belt 50 0.88 0.94\n",
+ " sewing machine 50 0.76 0.9\n",
+ " shield 50 0.56 0.82\n",
+ " shoe store 50 0.78 0.96\n",
+ " shoji 50 0.8 0.92\n",
+ " shopping basket 50 0.52 0.88\n",
+ " shopping cart 50 0.76 0.92\n",
+ " shovel 50 0.62 0.84\n",
+ " shower cap 50 0.7 0.84\n",
+ " shower curtain 50 0.64 0.82\n",
+ " ski 50 0.74 0.92\n",
+ " ski mask 50 0.72 0.88\n",
+ " sleeping bag 50 0.68 0.8\n",
+ " slide rule 50 0.72 0.88\n",
+ " sliding door 50 0.44 0.78\n",
+ " slot machine 50 0.94 0.98\n",
+ " snorkel 50 0.86 0.98\n",
+ " snowmobile 50 0.88 1\n",
+ " snowplow 50 0.84 0.98\n",
+ " soap dispenser 50 0.56 0.86\n",
+ " soccer ball 50 0.86 0.96\n",
+ " sock 50 0.62 0.76\n",
+ " solar thermal collector 50 0.72 0.96\n",
+ " sombrero 50 0.6 0.84\n",
+ " soup bowl 50 0.56 0.94\n",
+ " space bar 50 0.34 0.88\n",
+ " space heater 50 0.52 0.74\n",
+ " space shuttle 50 0.82 0.96\n",
+ " spatula 50 0.3 0.6\n",
+ " motorboat 50 0.86 1\n",
+ " spider web 50 0.7 0.9\n",
+ " spindle 50 0.86 0.98\n",
+ " sports car 50 0.6 0.94\n",
+ " spotlight 50 0.26 0.6\n",
+ " stage 50 0.68 0.86\n",
+ " steam locomotive 50 0.94 1\n",
+ " through arch bridge 50 0.84 0.96\n",
+ " steel drum 50 0.82 0.9\n",
+ " stethoscope 50 0.6 0.82\n",
+ " scarf 50 0.5 0.92\n",
+ " stone wall 50 0.76 0.9\n",
+ " stopwatch 50 0.58 0.9\n",
+ " stove 50 0.46 0.74\n",
+ " strainer 50 0.64 0.84\n",
+ " tram 50 0.88 0.96\n",
+ " stretcher 50 0.6 0.8\n",
+ " couch 50 0.8 0.96\n",
+ " stupa 50 0.88 0.88\n",
+ " submarine 50 0.72 0.92\n",
+ " suit 50 0.4 0.78\n",
+ " sundial 50 0.58 0.74\n",
+ " sunglass 50 0.14 0.58\n",
+ " sunglasses 50 0.28 0.58\n",
+ " sunscreen 50 0.32 0.7\n",
+ " suspension bridge 50 0.6 0.94\n",
+ " mop 50 0.74 0.92\n",
+ " sweatshirt 50 0.28 0.66\n",
+ " swimsuit 50 0.52 0.82\n",
+ " swing 50 0.76 0.84\n",
+ " switch 50 0.56 0.76\n",
+ " syringe 50 0.62 0.82\n",
+ " table lamp 50 0.6 0.88\n",
+ " tank 50 0.8 0.96\n",
+ " tape player 50 0.46 0.76\n",
+ " teapot 50 0.84 1\n",
+ " teddy bear 50 0.82 0.94\n",
+ " television 50 0.6 0.9\n",
+ " tennis ball 50 0.7 0.94\n",
+ " thatched roof 50 0.88 0.9\n",
+ " front curtain 50 0.8 0.92\n",
+ " thimble 50 0.6 0.8\n",
+ " threshing machine 50 0.56 0.88\n",
+ " throne 50 0.72 0.82\n",
+ " tile roof 50 0.72 0.94\n",
+ " toaster 50 0.66 0.84\n",
+ " tobacco shop 50 0.42 0.7\n",
+ " toilet seat 50 0.62 0.88\n",
+ " torch 50 0.64 0.84\n",
+ " totem pole 50 0.92 0.98\n",
+ " tow truck 50 0.62 0.88\n",
+ " toy store 50 0.6 0.94\n",
+ " tractor 50 0.76 0.98\n",
+ " semi-trailer truck 50 0.78 0.92\n",
+ " tray 50 0.46 0.64\n",
+ " trench coat 50 0.54 0.72\n",
+ " tricycle 50 0.72 0.94\n",
+ " trimaran 50 0.7 0.98\n",
+ " tripod 50 0.58 0.86\n",
+ " triumphal arch 50 0.92 0.98\n",
+ " trolleybus 50 0.9 1\n",
+ " trombone 50 0.54 0.88\n",
+ " tub 50 0.24 0.82\n",
+ " turnstile 50 0.84 0.94\n",
+ " typewriter keyboard 50 0.68 0.98\n",
+ " umbrella 50 0.52 0.7\n",
+ " unicycle 50 0.74 0.96\n",
+ " upright piano 50 0.76 0.9\n",
+ " vacuum cleaner 50 0.62 0.9\n",
+ " vase 50 0.5 0.78\n",
+ " vault 50 0.76 0.92\n",
+ " velvet 50 0.2 0.42\n",
+ " vending machine 50 0.9 1\n",
+ " vestment 50 0.54 0.82\n",
+ " viaduct 50 0.78 0.86\n",
+ " violin 50 0.68 0.78\n",
+ " volleyball 50 0.86 1\n",
+ " waffle iron 50 0.72 0.88\n",
+ " wall clock 50 0.54 0.88\n",
+ " wallet 50 0.52 0.9\n",
+ " wardrobe 50 0.68 0.88\n",
+ " military aircraft 50 0.9 0.98\n",
+ " sink 50 0.72 0.96\n",
+ " washing machine 50 0.78 0.94\n",
+ " water bottle 50 0.54 0.74\n",
+ " water jug 50 0.22 0.74\n",
+ " water tower 50 0.9 0.96\n",
+ " whiskey jug 50 0.64 0.74\n",
+ " whistle 50 0.72 0.84\n",
+ " wig 50 0.84 0.9\n",
+ " window screen 50 0.68 0.8\n",
+ " window shade 50 0.52 0.76\n",
+ " Windsor tie 50 0.22 0.66\n",
+ " wine bottle 50 0.42 0.82\n",
+ " wing 50 0.54 0.96\n",
+ " wok 50 0.46 0.82\n",
+ " wooden spoon 50 0.58 0.8\n",
+ " wool 50 0.32 0.82\n",
+ " split-rail fence 50 0.74 0.9\n",
+ " shipwreck 50 0.84 0.96\n",
+ " yawl 50 0.78 0.96\n",
+ " yurt 50 0.84 1\n",
+ " website 50 0.98 1\n",
+ " comic book 50 0.62 0.9\n",
+ " crossword 50 0.84 0.88\n",
+ " traffic sign 50 0.78 0.9\n",
+ " traffic light 50 0.8 0.94\n",
+ " dust jacket 50 0.72 0.94\n",
+ " menu 50 0.82 0.96\n",
+ " plate 50 0.44 0.88\n",
+ " guacamole 50 0.8 0.92\n",
+ " consomme 50 0.54 0.88\n",
+ " hot pot 50 0.86 0.98\n",
+ " trifle 50 0.92 0.98\n",
+ " ice cream 50 0.68 0.94\n",
+ " ice pop 50 0.62 0.84\n",
+ " baguette 50 0.62 0.88\n",
+ " bagel 50 0.64 0.92\n",
+ " pretzel 50 0.72 0.88\n",
+ " cheeseburger 50 0.9 1\n",
+ " hot dog 50 0.74 0.94\n",
+ " mashed potato 50 0.74 0.9\n",
+ " cabbage 50 0.84 0.96\n",
+ " broccoli 50 0.9 0.96\n",
+ " cauliflower 50 0.82 1\n",
+ " zucchini 50 0.74 0.9\n",
+ " spaghetti squash 50 0.8 0.96\n",
+ " acorn squash 50 0.82 0.96\n",
+ " butternut squash 50 0.7 0.94\n",
+ " cucumber 50 0.6 0.96\n",
+ " artichoke 50 0.84 0.94\n",
+ " bell pepper 50 0.84 0.98\n",
+ " cardoon 50 0.88 0.94\n",
+ " mushroom 50 0.38 0.92\n",
+ " Granny Smith 50 0.9 0.96\n",
+ " strawberry 50 0.6 0.88\n",
+ " orange 50 0.7 0.92\n",
+ " lemon 50 0.78 0.98\n",
+ " fig 50 0.82 0.96\n",
+ " pineapple 50 0.86 0.96\n",
+ " banana 50 0.84 0.96\n",
+ " jackfruit 50 0.9 0.98\n",
+ " custard apple 50 0.86 0.96\n",
+ " pomegranate 50 0.82 0.98\n",
+ " hay 50 0.8 0.92\n",
+ " carbonara 50 0.88 0.94\n",
+ " chocolate syrup 50 0.46 0.84\n",
+ " dough 50 0.4 0.6\n",
+ " meatloaf 50 0.58 0.84\n",
+ " pizza 50 0.84 0.96\n",
+ " pot pie 50 0.68 0.9\n",
+ " burrito 50 0.8 0.98\n",
+ " red wine 50 0.54 0.82\n",
+ " espresso 50 0.64 0.88\n",
+ " cup 50 0.38 0.7\n",
+ " eggnog 50 0.38 0.7\n",
+ " alp 50 0.54 0.88\n",
+ " bubble 50 0.8 0.96\n",
+ " cliff 50 0.64 1\n",
+ " coral reef 50 0.72 0.96\n",
+ " geyser 50 0.94 1\n",
+ " lakeshore 50 0.54 0.88\n",
+ " promontory 50 0.58 0.94\n",
+ " shoal 50 0.6 0.96\n",
+ " seashore 50 0.44 0.78\n",
+ " valley 50 0.72 0.94\n",
+ " volcano 50 0.78 0.96\n",
+ " baseball player 50 0.72 0.94\n",
+ " bridegroom 50 0.72 0.88\n",
+ " scuba diver 50 0.8 1\n",
+ " rapeseed 50 0.94 0.98\n",
+ " daisy 50 0.96 0.98\n",
+ " yellow lady's slipper 50 1 1\n",
+ " corn 50 0.4 0.88\n",
+ " acorn 50 0.92 0.98\n",
+ " rose hip 50 0.92 0.98\n",
+ " horse chestnut seed 50 0.94 0.98\n",
+ " coral fungus 50 0.96 0.96\n",
+ " agaric 50 0.82 0.94\n",
+ " gyromitra 50 0.98 1\n",
+ " stinkhorn mushroom 50 0.8 0.94\n",
+ " earth star 50 0.98 1\n",
+ " hen-of-the-woods 50 0.8 0.96\n",
+ " bolete 50 0.74 0.94\n",
+ " ear 50 0.48 0.94\n",
+ " toilet paper 50 0.36 0.68\n",
+ "Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224)\n",
+ "Results saved to \u001b[1mruns/val-cls/exp\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Validate YOLOv5s on Imagenet val\n",
+ "!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZY2VXXXu74w5"
+ },
+ "source": [
+ "# 3. Train\n",
+ "\n",
+ "![](\"https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png\"/)
![](\"https://user-images.githubusercontent.com/26833433/202802162-92e60571-ab58-4409-948d-b31fddcd3c6f.png\"/)
![\"Comet](\"https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Lay2WsTjNJzP"
+ },
+ "source": [
+ "## ClearML Logging and Automation 🌟 NEW\n",
+ "\n",
+ "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
+ "\n",
+ "- `pip install clearml`\n",
+ "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
+ "\n",
+ "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
+ "\n",
+ "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
+ "\n",
+ "\n",
+ "![\"ClearML](\"https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-WPvRbS5Swl6"
+ },
+ "source": [
+ "## Local Logging\n",
+ "\n",
+ "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
+ "\n",
+ "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
+ "\n",
+ "![\"Local](\"https://user-images.githubusercontent.com/26833433/183222430-e1abd1b7-782c-4cde-b04d-ad52926bf818.jpg\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zelyeqbyt3GD"
+ },
+ "source": [
+ "# Environments\n",
+ "\n",
+ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
+ "\n",
+ "- **Notebooks** with free GPU: \n",
+ "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
+ "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
+ "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) ![\"Docker](\"https://img.shields.io/docker/pulls/ultralytics/yolov5?logo=docker\")
\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6Qu7Iesl0p54"
+ },
+ "source": [
+ "# Status\n",
+ "\n",
+ "\n",
+ "\n",
+ "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IEijrePND_2I"
+ },
+ "source": [
+ "# Appendix\n",
+ "\n",
+ "Additional content below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GMusP4OAxFu6"
+ },
+ "outputs": [],
+ "source": [
+ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
+ "\n",
+ "model = torch.hub.load(\n",
+ " \"ultralytics/yolov5\", \"yolov5s\", force_reload=True, trust_repo=True\n",
+ ") # or yolov5n - yolov5x6 or custom\n",
+ "im = \"https://ultralytics.com/images/zidane.jpg\" # file, Path, PIL.Image, OpenCV, nparray, list\n",
+ "results = model(im) # inference\n",
+ "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "YOLOv5 Classification Tutorial",
+ "provenance": []
+ },
+ "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": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Transfer Learning/Accident_Classifier/classify/val.py b/Transfer Learning/Accident_Classifier/classify/val.py
new file mode 100644
index 00000000..8ce48f06
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/classify/val.py
@@ -0,0 +1,178 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""
+Validate a trained YOLOv5 classification model on a classification dataset.
+
+Usage:
+ $ bash data/scripts/get_imagenet.sh --val # download ImageNet val split (6.3G, 50000 images)
+ $ python classify/val.py --weights yolov5m-cls.pt --data ../datasets/imagenet --img 224 # validate ImageNet
+
+Usage - formats:
+ $ python classify/val.py --weights yolov5s-cls.pt # PyTorch
+ yolov5s-cls.torchscript # TorchScript
+ yolov5s-cls.onnx # ONNX Runtime or OpenCV DNN with --dnn
+ yolov5s-cls_openvino_model # OpenVINO
+ yolov5s-cls.engine # TensorRT
+ yolov5s-cls.mlmodel # CoreML (macOS-only)
+ yolov5s-cls_saved_model # TensorFlow SavedModel
+ yolov5s-cls.pb # TensorFlow GraphDef
+ yolov5s-cls.tflite # TensorFlow Lite
+ yolov5s-cls_edgetpu.tflite # TensorFlow Edge TPU
+ yolov5s-cls_paddle_model # PaddlePaddle
+"""
+
+import argparse
+import os
+import sys
+from pathlib import Path
+
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
+
+from models.common import DetectMultiBackend
+from utils.dataloaders import create_classification_dataloader
+from utils.general import (
+ LOGGER,
+ TQDM_BAR_FORMAT,
+ Profile,
+ check_img_size,
+ check_requirements,
+ colorstr,
+ increment_path,
+ print_args,
+)
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+@smart_inference_mode()
+def run(
+ data=ROOT / "../datasets/mnist", # dataset dir
+ weights=ROOT / "yolov5s-cls.pt", # model.pt path(s)
+ batch_size=128, # batch size
+ imgsz=224, # inference size (pixels)
+ device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
+ workers=8, # max dataloader workers (per RANK in DDP mode)
+ verbose=False, # verbose output
+ project=ROOT / "runs/val-cls", # save to project/name
+ name="exp", # save to project/name
+ exist_ok=False, # existing project/name ok, do not increment
+ half=False, # use FP16 half-precision inference
+ dnn=False, # use OpenCV DNN for ONNX inference
+ model=None,
+ dataloader=None,
+ criterion=None,
+ pbar=None,
+):
+ """Validates a YOLOv5 classification model on a dataset, computing metrics like top1 and top5 accuracy."""
+ # Initialize/load model and set device
+ training = model is not None
+ if training: # called by train.py
+ device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
+ half &= device.type != "cpu" # half precision only supported on CUDA
+ model.half() if half else model.float()
+ else: # called directly
+ device = select_device(device, batch_size=batch_size)
+
+ # Directories
+ save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
+ save_dir.mkdir(parents=True, exist_ok=True) # make dir
+
+ # Load model
+ model = DetectMultiBackend(weights, device=device, dnn=dnn, fp16=half)
+ stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+ imgsz = check_img_size(imgsz, s=stride) # check image size
+ half = model.fp16 # FP16 supported on limited backends with CUDA
+ if engine:
+ batch_size = model.batch_size
+ else:
+ device = model.device
+ if not (pt or jit):
+ batch_size = 1 # export.py models default to batch-size 1
+ LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
+
+ # Dataloader
+ data = Path(data)
+ test_dir = data / "test" if (data / "test").exists() else data / "val" # data/test or data/val
+ dataloader = create_classification_dataloader(
+ path=test_dir, imgsz=imgsz, batch_size=batch_size, augment=False, rank=-1, workers=workers
+ )
+
+ model.eval()
+ pred, targets, loss, dt = [], [], 0, (Profile(device=device), Profile(device=device), Profile(device=device))
+ n = len(dataloader) # number of batches
+ action = "validating" if dataloader.dataset.root.stem == "val" else "testing"
+ desc = f"{pbar.desc[:-36]}{action:>36}" if pbar else f"{action}"
+ bar = tqdm(dataloader, desc, n, not training, bar_format=TQDM_BAR_FORMAT, position=0)
+ with torch.cuda.amp.autocast(enabled=device.type != "cpu"):
+ for images, labels in bar:
+ with dt[0]:
+ images, labels = images.to(device, non_blocking=True), labels.to(device)
+
+ with dt[1]:
+ y = model(images)
+
+ with dt[2]:
+ pred.append(y.argsort(1, descending=True)[:, :5])
+ targets.append(labels)
+ if criterion:
+ loss += criterion(y, labels)
+
+ loss /= n
+ pred, targets = torch.cat(pred), torch.cat(targets)
+ correct = (targets[:, None] == pred).float()
+ acc = torch.stack((correct[:, 0], correct.max(1).values), dim=1) # (top1, top5) accuracy
+ top1, top5 = acc.mean(0).tolist()
+
+ if pbar:
+ pbar.desc = f"{pbar.desc[:-36]}{loss:>12.3g}{top1:>12.3g}{top5:>12.3g}"
+ if verbose: # all classes
+ LOGGER.info(f"{'Class':>24}{'Images':>12}{'top1_acc':>12}{'top5_acc':>12}")
+ LOGGER.info(f"{'all':>24}{targets.shape[0]:>12}{top1:>12.3g}{top5:>12.3g}")
+ for i, c in model.names.items():
+ acc_i = acc[targets == i]
+ top1i, top5i = acc_i.mean(0).tolist()
+ LOGGER.info(f"{c:>24}{acc_i.shape[0]:>12}{top1i:>12.3g}{top5i:>12.3g}")
+
+ # Print results
+ t = tuple(x.t / len(dataloader.dataset.samples) * 1e3 for x in dt) # speeds per image
+ shape = (1, 3, imgsz, imgsz)
+ LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms post-process per image at shape {shape}" % t)
+ LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
+
+ return top1, top5, loss
+
+
+def parse_opt():
+ """Parses and returns command line arguments for YOLOv5 model evaluation and inference settings."""
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--data", type=str, default=ROOT / "../datasets/mnist", help="dataset path")
+ parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-cls.pt", help="model.pt path(s)")
+ parser.add_argument("--batch-size", type=int, default=128, help="batch size")
+ parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=224, help="inference size (pixels)")
+ parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+ parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+ parser.add_argument("--verbose", nargs="?", const=True, default=True, help="verbose output")
+ parser.add_argument("--project", default=ROOT / "runs/val-cls", help="save to project/name")
+ parser.add_argument("--name", default="exp", help="save to project/name")
+ parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+ parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+ parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+ opt = parser.parse_args()
+ print_args(vars(opt))
+ return opt
+
+
+def main(opt):
+ """Executes the YOLOv5 model prediction workflow, handling argument parsing and requirement checks."""
+ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+ run(**vars(opt))
+
+
+if __name__ == "__main__":
+ opt = parse_opt()
+ main(opt)
diff --git a/Transfer Learning/Accident_Classifier/data/Argoverse.yaml b/Transfer Learning/Accident_Classifier/data/Argoverse.yaml
new file mode 100644
index 00000000..366552ea
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/Argoverse.yaml
@@ -0,0 +1,72 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Argoverse-HD dataset (ring-front-center camera) http://www.cs.cmu.edu/~mengtial/proj/streaming/ by Argo AI
+# Example usage: python train.py --data Argoverse.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── Argoverse ← downloads here (31.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Argoverse # dataset root dir
+train: Argoverse-1.1/images/train/ # train images (relative to 'path') 39384 images
+val: Argoverse-1.1/images/val/ # val images (relative to 'path') 15062 images
+test: Argoverse-1.1/images/test/ # test images (optional) https://eval.ai/web/challenges/challenge-page/800/overview
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: bus
+ 5: truck
+ 6: traffic_light
+ 7: stop_sign
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import json
+
+ from tqdm import tqdm
+ from utils.general import download, Path
+
+
+ def argoverse2yolo(set):
+ labels = {}
+ a = json.load(open(set, "rb"))
+ for annot in tqdm(a['annotations'], desc=f"Converting {set} to YOLOv5 format..."):
+ img_id = annot['image_id']
+ img_name = a['images'][img_id]['name']
+ img_label_name = f'{img_name[:-3]}txt'
+
+ cls = annot['category_id'] # instance class id
+ x_center, y_center, width, height = annot['bbox']
+ x_center = (x_center + width / 2) / 1920.0 # offset and scale
+ y_center = (y_center + height / 2) / 1200.0 # offset and scale
+ width /= 1920.0 # scale
+ height /= 1200.0 # scale
+
+ img_dir = set.parents[2] / 'Argoverse-1.1' / 'labels' / a['seq_dirs'][a['images'][annot['image_id']]['sid']]
+ if not img_dir.exists():
+ img_dir.mkdir(parents=True, exist_ok=True)
+
+ k = str(img_dir / img_label_name)
+ if k not in labels:
+ labels[k] = []
+ labels[k].append(f"{cls} {x_center} {y_center} {width} {height}\n")
+
+ for k in labels:
+ with open(k, "w") as f:
+ f.writelines(labels[k])
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://argoverse-hd.s3.us-east-2.amazonaws.com/Argoverse-HD-Full.zip']
+ download(urls, dir=dir, delete=False)
+
+ # Convert
+ annotations_dir = 'Argoverse-HD/annotations/'
+ (dir / 'Argoverse-1.1' / 'tracking').rename(dir / 'Argoverse-1.1' / 'images') # rename 'tracking' to 'images'
+ for d in "train.json", "val.json":
+ argoverse2yolo(dir / annotations_dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/Transfer Learning/Accident_Classifier/data/GlobalWheat2020.yaml b/Transfer Learning/Accident_Classifier/data/GlobalWheat2020.yaml
new file mode 100644
index 00000000..acb88290
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/GlobalWheat2020.yaml
@@ -0,0 +1,52 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Global Wheat 2020 dataset http://www.global-wheat.com/ by University of Saskatchewan
+# Example usage: python train.py --data GlobalWheat2020.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── GlobalWheat2020 ← downloads here (7.0 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/GlobalWheat2020 # dataset root dir
+train: # train images (relative to 'path') 3422 images
+ - images/arvalis_1
+ - images/arvalis_2
+ - images/arvalis_3
+ - images/ethz_1
+ - images/rres_1
+ - images/inrae_1
+ - images/usask_1
+val: # val images (relative to 'path') 748 images (WARNING: train set contains ethz_1)
+ - images/ethz_1
+test: # test images (optional) 1276 images
+ - images/utokyo_1
+ - images/utokyo_2
+ - images/nau_1
+ - images/uq_1
+
+# Classes
+names:
+ 0: wheat_head
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from utils.general import download, Path
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://zenodo.org/record/4298502/files/global-wheat-codalab-official.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/GlobalWheat2020_labels.zip']
+ download(urls, dir=dir)
+
+ # Make Directories
+ for p in 'annotations', 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+
+ # Move
+ for p in 'arvalis_1', 'arvalis_2', 'arvalis_3', 'ethz_1', 'rres_1', 'inrae_1', 'usask_1', \
+ 'utokyo_1', 'utokyo_2', 'nau_1', 'uq_1':
+ (dir / p).rename(dir / 'images' / p) # move to /images
+ f = (dir / p).with_suffix('.json') # json file
+ if f.exists():
+ f.rename((dir / 'annotations' / p).with_suffix('.json')) # move to /annotations
diff --git a/Transfer Learning/Accident_Classifier/data/ImageNet.yaml b/Transfer Learning/Accident_Classifier/data/ImageNet.yaml
new file mode 100644
index 00000000..979a0e4d
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/ImageNet.yaml
@@ -0,0 +1,1020 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+# └── imagenet ← downloads here (144 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: tench
+ 1: goldfish
+ 2: great white shark
+ 3: tiger shark
+ 4: hammerhead shark
+ 5: electric ray
+ 6: stingray
+ 7: cock
+ 8: hen
+ 9: ostrich
+ 10: brambling
+ 11: goldfinch
+ 12: house finch
+ 13: junco
+ 14: indigo bunting
+ 15: American robin
+ 16: bulbul
+ 17: jay
+ 18: magpie
+ 19: chickadee
+ 20: American dipper
+ 21: kite
+ 22: bald eagle
+ 23: vulture
+ 24: great grey owl
+ 25: fire salamander
+ 26: smooth newt
+ 27: newt
+ 28: spotted salamander
+ 29: axolotl
+ 30: American bullfrog
+ 31: tree frog
+ 32: tailed frog
+ 33: loggerhead sea turtle
+ 34: leatherback sea turtle
+ 35: mud turtle
+ 36: terrapin
+ 37: box turtle
+ 38: banded gecko
+ 39: green iguana
+ 40: Carolina anole
+ 41: desert grassland whiptail lizard
+ 42: agama
+ 43: frilled-necked lizard
+ 44: alligator lizard
+ 45: Gila monster
+ 46: European green lizard
+ 47: chameleon
+ 48: Komodo dragon
+ 49: Nile crocodile
+ 50: American alligator
+ 51: triceratops
+ 52: worm snake
+ 53: ring-necked snake
+ 54: eastern hog-nosed snake
+ 55: smooth green snake
+ 56: kingsnake
+ 57: garter snake
+ 58: water snake
+ 59: vine snake
+ 60: night snake
+ 61: boa constrictor
+ 62: African rock python
+ 63: Indian cobra
+ 64: green mamba
+ 65: sea snake
+ 66: Saharan horned viper
+ 67: eastern diamondback rattlesnake
+ 68: sidewinder
+ 69: trilobite
+ 70: harvestman
+ 71: scorpion
+ 72: yellow garden spider
+ 73: barn spider
+ 74: European garden spider
+ 75: southern black widow
+ 76: tarantula
+ 77: wolf spider
+ 78: tick
+ 79: centipede
+ 80: black grouse
+ 81: ptarmigan
+ 82: ruffed grouse
+ 83: prairie grouse
+ 84: peacock
+ 85: quail
+ 86: partridge
+ 87: grey parrot
+ 88: macaw
+ 89: sulphur-crested cockatoo
+ 90: lorikeet
+ 91: coucal
+ 92: bee eater
+ 93: hornbill
+ 94: hummingbird
+ 95: jacamar
+ 96: toucan
+ 97: duck
+ 98: red-breasted merganser
+ 99: goose
+ 100: black swan
+ 101: tusker
+ 102: echidna
+ 103: platypus
+ 104: wallaby
+ 105: koala
+ 106: wombat
+ 107: jellyfish
+ 108: sea anemone
+ 109: brain coral
+ 110: flatworm
+ 111: nematode
+ 112: conch
+ 113: snail
+ 114: slug
+ 115: sea slug
+ 116: chiton
+ 117: chambered nautilus
+ 118: Dungeness crab
+ 119: rock crab
+ 120: fiddler crab
+ 121: red king crab
+ 122: American lobster
+ 123: spiny lobster
+ 124: crayfish
+ 125: hermit crab
+ 126: isopod
+ 127: white stork
+ 128: black stork
+ 129: spoonbill
+ 130: flamingo
+ 131: little blue heron
+ 132: great egret
+ 133: bittern
+ 134: crane (bird)
+ 135: limpkin
+ 136: common gallinule
+ 137: American coot
+ 138: bustard
+ 139: ruddy turnstone
+ 140: dunlin
+ 141: common redshank
+ 142: dowitcher
+ 143: oystercatcher
+ 144: pelican
+ 145: king penguin
+ 146: albatross
+ 147: grey whale
+ 148: killer whale
+ 149: dugong
+ 150: sea lion
+ 151: Chihuahua
+ 152: Japanese Chin
+ 153: Maltese
+ 154: Pekingese
+ 155: Shih Tzu
+ 156: King Charles Spaniel
+ 157: Papillon
+ 158: toy terrier
+ 159: Rhodesian Ridgeback
+ 160: Afghan Hound
+ 161: Basset Hound
+ 162: Beagle
+ 163: Bloodhound
+ 164: Bluetick Coonhound
+ 165: Black and Tan Coonhound
+ 166: Treeing Walker Coonhound
+ 167: English foxhound
+ 168: Redbone Coonhound
+ 169: borzoi
+ 170: Irish Wolfhound
+ 171: Italian Greyhound
+ 172: Whippet
+ 173: Ibizan Hound
+ 174: Norwegian Elkhound
+ 175: Otterhound
+ 176: Saluki
+ 177: Scottish Deerhound
+ 178: Weimaraner
+ 179: Staffordshire Bull Terrier
+ 180: American Staffordshire Terrier
+ 181: Bedlington Terrier
+ 182: Border Terrier
+ 183: Kerry Blue Terrier
+ 184: Irish Terrier
+ 185: Norfolk Terrier
+ 186: Norwich Terrier
+ 187: Yorkshire Terrier
+ 188: Wire Fox Terrier
+ 189: Lakeland Terrier
+ 190: Sealyham Terrier
+ 191: Airedale Terrier
+ 192: Cairn Terrier
+ 193: Australian Terrier
+ 194: Dandie Dinmont Terrier
+ 195: Boston Terrier
+ 196: Miniature Schnauzer
+ 197: Giant Schnauzer
+ 198: Standard Schnauzer
+ 199: Scottish Terrier
+ 200: Tibetan Terrier
+ 201: Australian Silky Terrier
+ 202: Soft-coated Wheaten Terrier
+ 203: West Highland White Terrier
+ 204: Lhasa Apso
+ 205: Flat-Coated Retriever
+ 206: Curly-coated Retriever
+ 207: Golden Retriever
+ 208: Labrador Retriever
+ 209: Chesapeake Bay Retriever
+ 210: German Shorthaired Pointer
+ 211: Vizsla
+ 212: English Setter
+ 213: Irish Setter
+ 214: Gordon Setter
+ 215: Brittany
+ 216: Clumber Spaniel
+ 217: English Springer Spaniel
+ 218: Welsh Springer Spaniel
+ 219: Cocker Spaniels
+ 220: Sussex Spaniel
+ 221: Irish Water Spaniel
+ 222: Kuvasz
+ 223: Schipperke
+ 224: Groenendael
+ 225: Malinois
+ 226: Briard
+ 227: Australian Kelpie
+ 228: Komondor
+ 229: Old English Sheepdog
+ 230: Shetland Sheepdog
+ 231: collie
+ 232: Border Collie
+ 233: Bouvier des Flandres
+ 234: Rottweiler
+ 235: German Shepherd Dog
+ 236: Dobermann
+ 237: Miniature Pinscher
+ 238: Greater Swiss Mountain Dog
+ 239: Bernese Mountain Dog
+ 240: Appenzeller Sennenhund
+ 241: Entlebucher Sennenhund
+ 242: Boxer
+ 243: Bullmastiff
+ 244: Tibetan Mastiff
+ 245: French Bulldog
+ 246: Great Dane
+ 247: St. Bernard
+ 248: husky
+ 249: Alaskan Malamute
+ 250: Siberian Husky
+ 251: Dalmatian
+ 252: Affenpinscher
+ 253: Basenji
+ 254: pug
+ 255: Leonberger
+ 256: Newfoundland
+ 257: Pyrenean Mountain Dog
+ 258: Samoyed
+ 259: Pomeranian
+ 260: Chow Chow
+ 261: Keeshond
+ 262: Griffon Bruxellois
+ 263: Pembroke Welsh Corgi
+ 264: Cardigan Welsh Corgi
+ 265: Toy Poodle
+ 266: Miniature Poodle
+ 267: Standard Poodle
+ 268: Mexican hairless dog
+ 269: grey wolf
+ 270: Alaskan tundra wolf
+ 271: red wolf
+ 272: coyote
+ 273: dingo
+ 274: dhole
+ 275: African wild dog
+ 276: hyena
+ 277: red fox
+ 278: kit fox
+ 279: Arctic fox
+ 280: grey fox
+ 281: tabby cat
+ 282: tiger cat
+ 283: Persian cat
+ 284: Siamese cat
+ 285: Egyptian Mau
+ 286: cougar
+ 287: lynx
+ 288: leopard
+ 289: snow leopard
+ 290: jaguar
+ 291: lion
+ 292: tiger
+ 293: cheetah
+ 294: brown bear
+ 295: American black bear
+ 296: polar bear
+ 297: sloth bear
+ 298: mongoose
+ 299: meerkat
+ 300: tiger beetle
+ 301: ladybug
+ 302: ground beetle
+ 303: longhorn beetle
+ 304: leaf beetle
+ 305: dung beetle
+ 306: rhinoceros beetle
+ 307: weevil
+ 308: fly
+ 309: bee
+ 310: ant
+ 311: grasshopper
+ 312: cricket
+ 313: stick insect
+ 314: cockroach
+ 315: mantis
+ 316: cicada
+ 317: leafhopper
+ 318: lacewing
+ 319: dragonfly
+ 320: damselfly
+ 321: red admiral
+ 322: ringlet
+ 323: monarch butterfly
+ 324: small white
+ 325: sulphur butterfly
+ 326: gossamer-winged butterfly
+ 327: starfish
+ 328: sea urchin
+ 329: sea cucumber
+ 330: cottontail rabbit
+ 331: hare
+ 332: Angora rabbit
+ 333: hamster
+ 334: porcupine
+ 335: fox squirrel
+ 336: marmot
+ 337: beaver
+ 338: guinea pig
+ 339: common sorrel
+ 340: zebra
+ 341: pig
+ 342: wild boar
+ 343: warthog
+ 344: hippopotamus
+ 345: ox
+ 346: water buffalo
+ 347: bison
+ 348: ram
+ 349: bighorn sheep
+ 350: Alpine ibex
+ 351: hartebeest
+ 352: impala
+ 353: gazelle
+ 354: dromedary
+ 355: llama
+ 356: weasel
+ 357: mink
+ 358: European polecat
+ 359: black-footed ferret
+ 360: otter
+ 361: skunk
+ 362: badger
+ 363: armadillo
+ 364: three-toed sloth
+ 365: orangutan
+ 366: gorilla
+ 367: chimpanzee
+ 368: gibbon
+ 369: siamang
+ 370: guenon
+ 371: patas monkey
+ 372: baboon
+ 373: macaque
+ 374: langur
+ 375: black-and-white colobus
+ 376: proboscis monkey
+ 377: marmoset
+ 378: white-headed capuchin
+ 379: howler monkey
+ 380: titi
+ 381: Geoffroy's spider monkey
+ 382: common squirrel monkey
+ 383: ring-tailed lemur
+ 384: indri
+ 385: Asian elephant
+ 386: African bush elephant
+ 387: red panda
+ 388: giant panda
+ 389: snoek
+ 390: eel
+ 391: coho salmon
+ 392: rock beauty
+ 393: clownfish
+ 394: sturgeon
+ 395: garfish
+ 396: lionfish
+ 397: pufferfish
+ 398: abacus
+ 399: abaya
+ 400: academic gown
+ 401: accordion
+ 402: acoustic guitar
+ 403: aircraft carrier
+ 404: airliner
+ 405: airship
+ 406: altar
+ 407: ambulance
+ 408: amphibious vehicle
+ 409: analog clock
+ 410: apiary
+ 411: apron
+ 412: waste container
+ 413: assault rifle
+ 414: backpack
+ 415: bakery
+ 416: balance beam
+ 417: balloon
+ 418: ballpoint pen
+ 419: Band-Aid
+ 420: banjo
+ 421: baluster
+ 422: barbell
+ 423: barber chair
+ 424: barbershop
+ 425: barn
+ 426: barometer
+ 427: barrel
+ 428: wheelbarrow
+ 429: baseball
+ 430: basketball
+ 431: bassinet
+ 432: bassoon
+ 433: swimming cap
+ 434: bath towel
+ 435: bathtub
+ 436: station wagon
+ 437: lighthouse
+ 438: beaker
+ 439: military cap
+ 440: beer bottle
+ 441: beer glass
+ 442: bell-cot
+ 443: bib
+ 444: tandem bicycle
+ 445: bikini
+ 446: ring binder
+ 447: binoculars
+ 448: birdhouse
+ 449: boathouse
+ 450: bobsleigh
+ 451: bolo tie
+ 452: poke bonnet
+ 453: bookcase
+ 454: bookstore
+ 455: bottle cap
+ 456: bow
+ 457: bow tie
+ 458: brass
+ 459: bra
+ 460: breakwater
+ 461: breastplate
+ 462: broom
+ 463: bucket
+ 464: buckle
+ 465: bulletproof vest
+ 466: high-speed train
+ 467: butcher shop
+ 468: taxicab
+ 469: cauldron
+ 470: candle
+ 471: cannon
+ 472: canoe
+ 473: can opener
+ 474: cardigan
+ 475: car mirror
+ 476: carousel
+ 477: tool kit
+ 478: carton
+ 479: car wheel
+ 480: automated teller machine
+ 481: cassette
+ 482: cassette player
+ 483: castle
+ 484: catamaran
+ 485: CD player
+ 486: cello
+ 487: mobile phone
+ 488: chain
+ 489: chain-link fence
+ 490: chain mail
+ 491: chainsaw
+ 492: chest
+ 493: chiffonier
+ 494: chime
+ 495: china cabinet
+ 496: Christmas stocking
+ 497: church
+ 498: movie theater
+ 499: cleaver
+ 500: cliff dwelling
+ 501: cloak
+ 502: clogs
+ 503: cocktail shaker
+ 504: coffee mug
+ 505: coffeemaker
+ 506: coil
+ 507: combination lock
+ 508: computer keyboard
+ 509: confectionery store
+ 510: container ship
+ 511: convertible
+ 512: corkscrew
+ 513: cornet
+ 514: cowboy boot
+ 515: cowboy hat
+ 516: cradle
+ 517: crane (machine)
+ 518: crash helmet
+ 519: crate
+ 520: infant bed
+ 521: Crock Pot
+ 522: croquet ball
+ 523: crutch
+ 524: cuirass
+ 525: dam
+ 526: desk
+ 527: desktop computer
+ 528: rotary dial telephone
+ 529: diaper
+ 530: digital clock
+ 531: digital watch
+ 532: dining table
+ 533: dishcloth
+ 534: dishwasher
+ 535: disc brake
+ 536: dock
+ 537: dog sled
+ 538: dome
+ 539: doormat
+ 540: drilling rig
+ 541: drum
+ 542: drumstick
+ 543: dumbbell
+ 544: Dutch oven
+ 545: electric fan
+ 546: electric guitar
+ 547: electric locomotive
+ 548: entertainment center
+ 549: envelope
+ 550: espresso machine
+ 551: face powder
+ 552: feather boa
+ 553: filing cabinet
+ 554: fireboat
+ 555: fire engine
+ 556: fire screen sheet
+ 557: flagpole
+ 558: flute
+ 559: folding chair
+ 560: football helmet
+ 561: forklift
+ 562: fountain
+ 563: fountain pen
+ 564: four-poster bed
+ 565: freight car
+ 566: French horn
+ 567: frying pan
+ 568: fur coat
+ 569: garbage truck
+ 570: gas mask
+ 571: gas pump
+ 572: goblet
+ 573: go-kart
+ 574: golf ball
+ 575: golf cart
+ 576: gondola
+ 577: gong
+ 578: gown
+ 579: grand piano
+ 580: greenhouse
+ 581: grille
+ 582: grocery store
+ 583: guillotine
+ 584: barrette
+ 585: hair spray
+ 586: half-track
+ 587: hammer
+ 588: hamper
+ 589: hair dryer
+ 590: hand-held computer
+ 591: handkerchief
+ 592: hard disk drive
+ 593: harmonica
+ 594: harp
+ 595: harvester
+ 596: hatchet
+ 597: holster
+ 598: home theater
+ 599: honeycomb
+ 600: hook
+ 601: hoop skirt
+ 602: horizontal bar
+ 603: horse-drawn vehicle
+ 604: hourglass
+ 605: iPod
+ 606: clothes iron
+ 607: jack-o'-lantern
+ 608: jeans
+ 609: jeep
+ 610: T-shirt
+ 611: jigsaw puzzle
+ 612: pulled rickshaw
+ 613: joystick
+ 614: kimono
+ 615: knee pad
+ 616: knot
+ 617: lab coat
+ 618: ladle
+ 619: lampshade
+ 620: laptop computer
+ 621: lawn mower
+ 622: lens cap
+ 623: paper knife
+ 624: library
+ 625: lifeboat
+ 626: lighter
+ 627: limousine
+ 628: ocean liner
+ 629: lipstick
+ 630: slip-on shoe
+ 631: lotion
+ 632: speaker
+ 633: loupe
+ 634: sawmill
+ 635: magnetic compass
+ 636: mail bag
+ 637: mailbox
+ 638: tights
+ 639: tank suit
+ 640: manhole cover
+ 641: maraca
+ 642: marimba
+ 643: mask
+ 644: match
+ 645: maypole
+ 646: maze
+ 647: measuring cup
+ 648: medicine chest
+ 649: megalith
+ 650: microphone
+ 651: microwave oven
+ 652: military uniform
+ 653: milk can
+ 654: minibus
+ 655: miniskirt
+ 656: minivan
+ 657: missile
+ 658: mitten
+ 659: mixing bowl
+ 660: mobile home
+ 661: Model T
+ 662: modem
+ 663: monastery
+ 664: monitor
+ 665: moped
+ 666: mortar
+ 667: square academic cap
+ 668: mosque
+ 669: mosquito net
+ 670: scooter
+ 671: mountain bike
+ 672: tent
+ 673: computer mouse
+ 674: mousetrap
+ 675: moving van
+ 676: muzzle
+ 677: nail
+ 678: neck brace
+ 679: necklace
+ 680: nipple
+ 681: notebook computer
+ 682: obelisk
+ 683: oboe
+ 684: ocarina
+ 685: odometer
+ 686: oil filter
+ 687: organ
+ 688: oscilloscope
+ 689: overskirt
+ 690: bullock cart
+ 691: oxygen mask
+ 692: packet
+ 693: paddle
+ 694: paddle wheel
+ 695: padlock
+ 696: paintbrush
+ 697: pajamas
+ 698: palace
+ 699: pan flute
+ 700: paper towel
+ 701: parachute
+ 702: parallel bars
+ 703: park bench
+ 704: parking meter
+ 705: passenger car
+ 706: patio
+ 707: payphone
+ 708: pedestal
+ 709: pencil case
+ 710: pencil sharpener
+ 711: perfume
+ 712: Petri dish
+ 713: photocopier
+ 714: plectrum
+ 715: Pickelhaube
+ 716: picket fence
+ 717: pickup truck
+ 718: pier
+ 719: piggy bank
+ 720: pill bottle
+ 721: pillow
+ 722: ping-pong ball
+ 723: pinwheel
+ 724: pirate ship
+ 725: pitcher
+ 726: hand plane
+ 727: planetarium
+ 728: plastic bag
+ 729: plate rack
+ 730: plow
+ 731: plunger
+ 732: Polaroid camera
+ 733: pole
+ 734: police van
+ 735: poncho
+ 736: billiard table
+ 737: soda bottle
+ 738: pot
+ 739: potter's wheel
+ 740: power drill
+ 741: prayer rug
+ 742: printer
+ 743: prison
+ 744: projectile
+ 745: projector
+ 746: hockey puck
+ 747: punching bag
+ 748: purse
+ 749: quill
+ 750: quilt
+ 751: race car
+ 752: racket
+ 753: radiator
+ 754: radio
+ 755: radio telescope
+ 756: rain barrel
+ 757: recreational vehicle
+ 758: reel
+ 759: reflex camera
+ 760: refrigerator
+ 761: remote control
+ 762: restaurant
+ 763: revolver
+ 764: rifle
+ 765: rocking chair
+ 766: rotisserie
+ 767: eraser
+ 768: rugby ball
+ 769: ruler
+ 770: running shoe
+ 771: safe
+ 772: safety pin
+ 773: salt shaker
+ 774: sandal
+ 775: sarong
+ 776: saxophone
+ 777: scabbard
+ 778: weighing scale
+ 779: school bus
+ 780: schooner
+ 781: scoreboard
+ 782: CRT screen
+ 783: screw
+ 784: screwdriver
+ 785: seat belt
+ 786: sewing machine
+ 787: shield
+ 788: shoe store
+ 789: shoji
+ 790: shopping basket
+ 791: shopping cart
+ 792: shovel
+ 793: shower cap
+ 794: shower curtain
+ 795: ski
+ 796: ski mask
+ 797: sleeping bag
+ 798: slide rule
+ 799: sliding door
+ 800: slot machine
+ 801: snorkel
+ 802: snowmobile
+ 803: snowplow
+ 804: soap dispenser
+ 805: soccer ball
+ 806: sock
+ 807: solar thermal collector
+ 808: sombrero
+ 809: soup bowl
+ 810: space bar
+ 811: space heater
+ 812: space shuttle
+ 813: spatula
+ 814: motorboat
+ 815: spider web
+ 816: spindle
+ 817: sports car
+ 818: spotlight
+ 819: stage
+ 820: steam locomotive
+ 821: through arch bridge
+ 822: steel drum
+ 823: stethoscope
+ 824: scarf
+ 825: stone wall
+ 826: stopwatch
+ 827: stove
+ 828: strainer
+ 829: tram
+ 830: stretcher
+ 831: couch
+ 832: stupa
+ 833: submarine
+ 834: suit
+ 835: sundial
+ 836: sunglass
+ 837: sunglasses
+ 838: sunscreen
+ 839: suspension bridge
+ 840: mop
+ 841: sweatshirt
+ 842: swimsuit
+ 843: swing
+ 844: switch
+ 845: syringe
+ 846: table lamp
+ 847: tank
+ 848: tape player
+ 849: teapot
+ 850: teddy bear
+ 851: television
+ 852: tennis ball
+ 853: thatched roof
+ 854: front curtain
+ 855: thimble
+ 856: threshing machine
+ 857: throne
+ 858: tile roof
+ 859: toaster
+ 860: tobacco shop
+ 861: toilet seat
+ 862: torch
+ 863: totem pole
+ 864: tow truck
+ 865: toy store
+ 866: tractor
+ 867: semi-trailer truck
+ 868: tray
+ 869: trench coat
+ 870: tricycle
+ 871: trimaran
+ 872: tripod
+ 873: triumphal arch
+ 874: trolleybus
+ 875: trombone
+ 876: tub
+ 877: turnstile
+ 878: typewriter keyboard
+ 879: umbrella
+ 880: unicycle
+ 881: upright piano
+ 882: vacuum cleaner
+ 883: vase
+ 884: vault
+ 885: velvet
+ 886: vending machine
+ 887: vestment
+ 888: viaduct
+ 889: violin
+ 890: volleyball
+ 891: waffle iron
+ 892: wall clock
+ 893: wallet
+ 894: wardrobe
+ 895: military aircraft
+ 896: sink
+ 897: washing machine
+ 898: water bottle
+ 899: water jug
+ 900: water tower
+ 901: whiskey jug
+ 902: whistle
+ 903: wig
+ 904: window screen
+ 905: window shade
+ 906: Windsor tie
+ 907: wine bottle
+ 908: wing
+ 909: wok
+ 910: wooden spoon
+ 911: wool
+ 912: split-rail fence
+ 913: shipwreck
+ 914: yawl
+ 915: yurt
+ 916: website
+ 917: comic book
+ 918: crossword
+ 919: traffic sign
+ 920: traffic light
+ 921: dust jacket
+ 922: menu
+ 923: plate
+ 924: guacamole
+ 925: consomme
+ 926: hot pot
+ 927: trifle
+ 928: ice cream
+ 929: ice pop
+ 930: baguette
+ 931: bagel
+ 932: pretzel
+ 933: cheeseburger
+ 934: hot dog
+ 935: mashed potato
+ 936: cabbage
+ 937: broccoli
+ 938: cauliflower
+ 939: zucchini
+ 940: spaghetti squash
+ 941: acorn squash
+ 942: butternut squash
+ 943: cucumber
+ 944: artichoke
+ 945: bell pepper
+ 946: cardoon
+ 947: mushroom
+ 948: Granny Smith
+ 949: strawberry
+ 950: orange
+ 951: lemon
+ 952: fig
+ 953: pineapple
+ 954: banana
+ 955: jackfruit
+ 956: custard apple
+ 957: pomegranate
+ 958: hay
+ 959: carbonara
+ 960: chocolate syrup
+ 961: dough
+ 962: meatloaf
+ 963: pizza
+ 964: pot pie
+ 965: burrito
+ 966: red wine
+ 967: espresso
+ 968: cup
+ 969: eggnog
+ 970: alp
+ 971: bubble
+ 972: cliff
+ 973: coral reef
+ 974: geyser
+ 975: lakeshore
+ 976: promontory
+ 977: shoal
+ 978: seashore
+ 979: valley
+ 980: volcano
+ 981: baseball player
+ 982: bridegroom
+ 983: scuba diver
+ 984: rapeseed
+ 985: daisy
+ 986: yellow lady's slipper
+ 987: corn
+ 988: acorn
+ 989: rose hip
+ 990: horse chestnut seed
+ 991: coral fungus
+ 992: agaric
+ 993: gyromitra
+ 994: stinkhorn mushroom
+ 995: earth star
+ 996: hen-of-the-woods
+ 997: bolete
+ 998: ear
+ 999: toilet paper
+
+# Download script/URL (optional)
+download: data/scripts/get_imagenet.sh
diff --git a/Transfer Learning/Accident_Classifier/data/ImageNet10.yaml b/Transfer Learning/Accident_Classifier/data/ImageNet10.yaml
new file mode 100644
index 00000000..2189def7
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/ImageNet10.yaml
@@ -0,0 +1,30 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+# └── imagenet10 ← downloads here
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet10 # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: tench
+ 1: goldfish
+ 2: great white shark
+ 3: tiger shark
+ 4: hammerhead shark
+ 5: electric ray
+ 6: stingray
+ 7: cock
+ 8: hen
+ 9: ostrich
+
+# Download script/URL (optional)
+download: data/scripts/get_imagenet10.sh
diff --git a/Transfer Learning/Accident_Classifier/data/ImageNet100.yaml b/Transfer Learning/Accident_Classifier/data/ImageNet100.yaml
new file mode 100644
index 00000000..560cdecd
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/ImageNet100.yaml
@@ -0,0 +1,119 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+# └── imagenet100 ← downloads here
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet100 # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: tench
+ 1: goldfish
+ 2: great white shark
+ 3: tiger shark
+ 4: hammerhead shark
+ 5: electric ray
+ 6: stingray
+ 7: cock
+ 8: hen
+ 9: ostrich
+ 10: brambling
+ 11: goldfinch
+ 12: house finch
+ 13: junco
+ 14: indigo bunting
+ 15: American robin
+ 16: bulbul
+ 17: jay
+ 18: magpie
+ 19: chickadee
+ 20: American dipper
+ 21: kite
+ 22: bald eagle
+ 23: vulture
+ 24: great grey owl
+ 25: fire salamander
+ 26: smooth newt
+ 27: newt
+ 28: spotted salamander
+ 29: axolotl
+ 30: American bullfrog
+ 31: tree frog
+ 32: tailed frog
+ 33: loggerhead sea turtle
+ 34: leatherback sea turtle
+ 35: mud turtle
+ 36: terrapin
+ 37: box turtle
+ 38: banded gecko
+ 39: green iguana
+ 40: Carolina anole
+ 41: desert grassland whiptail lizard
+ 42: agama
+ 43: frilled-necked lizard
+ 44: alligator lizard
+ 45: Gila monster
+ 46: European green lizard
+ 47: chameleon
+ 48: Komodo dragon
+ 49: Nile crocodile
+ 50: American alligator
+ 51: triceratops
+ 52: worm snake
+ 53: ring-necked snake
+ 54: eastern hog-nosed snake
+ 55: smooth green snake
+ 56: kingsnake
+ 57: garter snake
+ 58: water snake
+ 59: vine snake
+ 60: night snake
+ 61: boa constrictor
+ 62: African rock python
+ 63: Indian cobra
+ 64: green mamba
+ 65: sea snake
+ 66: Saharan horned viper
+ 67: eastern diamondback rattlesnake
+ 68: sidewinder
+ 69: trilobite
+ 70: harvestman
+ 71: scorpion
+ 72: yellow garden spider
+ 73: barn spider
+ 74: European garden spider
+ 75: southern black widow
+ 76: tarantula
+ 77: wolf spider
+ 78: tick
+ 79: centipede
+ 80: black grouse
+ 81: ptarmigan
+ 82: ruffed grouse
+ 83: prairie grouse
+ 84: peacock
+ 85: quail
+ 86: partridge
+ 87: grey parrot
+ 88: macaw
+ 89: sulphur-crested cockatoo
+ 90: lorikeet
+ 91: coucal
+ 92: bee eater
+ 93: hornbill
+ 94: hummingbird
+ 95: jacamar
+ 96: toucan
+ 97: duck
+ 98: red-breasted merganser
+ 99: goose
+# Download script/URL (optional)
+download: data/scripts/get_imagenet100.sh
diff --git a/Transfer Learning/Accident_Classifier/data/ImageNet1000.yaml b/Transfer Learning/Accident_Classifier/data/ImageNet1000.yaml
new file mode 100644
index 00000000..aa17e9e0
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/ImageNet1000.yaml
@@ -0,0 +1,1020 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# ImageNet-1k dataset https://www.image-net.org/index.php by Stanford University
+# Simplified class names from https://github.com/anishathalye/imagenet-simple-labels
+# Example usage: python classify/train.py --data imagenet
+# parent
+# ├── yolov5
+# └── datasets
+# └── imagenet100 ← downloads here
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/imagenet1000 # dataset root dir
+train: train # train images (relative to 'path') 1281167 images
+val: val # val images (relative to 'path') 50000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: tench
+ 1: goldfish
+ 2: great white shark
+ 3: tiger shark
+ 4: hammerhead shark
+ 5: electric ray
+ 6: stingray
+ 7: cock
+ 8: hen
+ 9: ostrich
+ 10: brambling
+ 11: goldfinch
+ 12: house finch
+ 13: junco
+ 14: indigo bunting
+ 15: American robin
+ 16: bulbul
+ 17: jay
+ 18: magpie
+ 19: chickadee
+ 20: American dipper
+ 21: kite
+ 22: bald eagle
+ 23: vulture
+ 24: great grey owl
+ 25: fire salamander
+ 26: smooth newt
+ 27: newt
+ 28: spotted salamander
+ 29: axolotl
+ 30: American bullfrog
+ 31: tree frog
+ 32: tailed frog
+ 33: loggerhead sea turtle
+ 34: leatherback sea turtle
+ 35: mud turtle
+ 36: terrapin
+ 37: box turtle
+ 38: banded gecko
+ 39: green iguana
+ 40: Carolina anole
+ 41: desert grassland whiptail lizard
+ 42: agama
+ 43: frilled-necked lizard
+ 44: alligator lizard
+ 45: Gila monster
+ 46: European green lizard
+ 47: chameleon
+ 48: Komodo dragon
+ 49: Nile crocodile
+ 50: American alligator
+ 51: triceratops
+ 52: worm snake
+ 53: ring-necked snake
+ 54: eastern hog-nosed snake
+ 55: smooth green snake
+ 56: kingsnake
+ 57: garter snake
+ 58: water snake
+ 59: vine snake
+ 60: night snake
+ 61: boa constrictor
+ 62: African rock python
+ 63: Indian cobra
+ 64: green mamba
+ 65: sea snake
+ 66: Saharan horned viper
+ 67: eastern diamondback rattlesnake
+ 68: sidewinder
+ 69: trilobite
+ 70: harvestman
+ 71: scorpion
+ 72: yellow garden spider
+ 73: barn spider
+ 74: European garden spider
+ 75: southern black widow
+ 76: tarantula
+ 77: wolf spider
+ 78: tick
+ 79: centipede
+ 80: black grouse
+ 81: ptarmigan
+ 82: ruffed grouse
+ 83: prairie grouse
+ 84: peacock
+ 85: quail
+ 86: partridge
+ 87: grey parrot
+ 88: macaw
+ 89: sulphur-crested cockatoo
+ 90: lorikeet
+ 91: coucal
+ 92: bee eater
+ 93: hornbill
+ 94: hummingbird
+ 95: jacamar
+ 96: toucan
+ 97: duck
+ 98: red-breasted merganser
+ 99: goose
+ 100: black swan
+ 101: tusker
+ 102: echidna
+ 103: platypus
+ 104: wallaby
+ 105: koala
+ 106: wombat
+ 107: jellyfish
+ 108: sea anemone
+ 109: brain coral
+ 110: flatworm
+ 111: nematode
+ 112: conch
+ 113: snail
+ 114: slug
+ 115: sea slug
+ 116: chiton
+ 117: chambered nautilus
+ 118: Dungeness crab
+ 119: rock crab
+ 120: fiddler crab
+ 121: red king crab
+ 122: American lobster
+ 123: spiny lobster
+ 124: crayfish
+ 125: hermit crab
+ 126: isopod
+ 127: white stork
+ 128: black stork
+ 129: spoonbill
+ 130: flamingo
+ 131: little blue heron
+ 132: great egret
+ 133: bittern
+ 134: crane (bird)
+ 135: limpkin
+ 136: common gallinule
+ 137: American coot
+ 138: bustard
+ 139: ruddy turnstone
+ 140: dunlin
+ 141: common redshank
+ 142: dowitcher
+ 143: oystercatcher
+ 144: pelican
+ 145: king penguin
+ 146: albatross
+ 147: grey whale
+ 148: killer whale
+ 149: dugong
+ 150: sea lion
+ 151: Chihuahua
+ 152: Japanese Chin
+ 153: Maltese
+ 154: Pekingese
+ 155: Shih Tzu
+ 156: King Charles Spaniel
+ 157: Papillon
+ 158: toy terrier
+ 159: Rhodesian Ridgeback
+ 160: Afghan Hound
+ 161: Basset Hound
+ 162: Beagle
+ 163: Bloodhound
+ 164: Bluetick Coonhound
+ 165: Black and Tan Coonhound
+ 166: Treeing Walker Coonhound
+ 167: English foxhound
+ 168: Redbone Coonhound
+ 169: borzoi
+ 170: Irish Wolfhound
+ 171: Italian Greyhound
+ 172: Whippet
+ 173: Ibizan Hound
+ 174: Norwegian Elkhound
+ 175: Otterhound
+ 176: Saluki
+ 177: Scottish Deerhound
+ 178: Weimaraner
+ 179: Staffordshire Bull Terrier
+ 180: American Staffordshire Terrier
+ 181: Bedlington Terrier
+ 182: Border Terrier
+ 183: Kerry Blue Terrier
+ 184: Irish Terrier
+ 185: Norfolk Terrier
+ 186: Norwich Terrier
+ 187: Yorkshire Terrier
+ 188: Wire Fox Terrier
+ 189: Lakeland Terrier
+ 190: Sealyham Terrier
+ 191: Airedale Terrier
+ 192: Cairn Terrier
+ 193: Australian Terrier
+ 194: Dandie Dinmont Terrier
+ 195: Boston Terrier
+ 196: Miniature Schnauzer
+ 197: Giant Schnauzer
+ 198: Standard Schnauzer
+ 199: Scottish Terrier
+ 200: Tibetan Terrier
+ 201: Australian Silky Terrier
+ 202: Soft-coated Wheaten Terrier
+ 203: West Highland White Terrier
+ 204: Lhasa Apso
+ 205: Flat-Coated Retriever
+ 206: Curly-coated Retriever
+ 207: Golden Retriever
+ 208: Labrador Retriever
+ 209: Chesapeake Bay Retriever
+ 210: German Shorthaired Pointer
+ 211: Vizsla
+ 212: English Setter
+ 213: Irish Setter
+ 214: Gordon Setter
+ 215: Brittany
+ 216: Clumber Spaniel
+ 217: English Springer Spaniel
+ 218: Welsh Springer Spaniel
+ 219: Cocker Spaniels
+ 220: Sussex Spaniel
+ 221: Irish Water Spaniel
+ 222: Kuvasz
+ 223: Schipperke
+ 224: Groenendael
+ 225: Malinois
+ 226: Briard
+ 227: Australian Kelpie
+ 228: Komondor
+ 229: Old English Sheepdog
+ 230: Shetland Sheepdog
+ 231: collie
+ 232: Border Collie
+ 233: Bouvier des Flandres
+ 234: Rottweiler
+ 235: German Shepherd Dog
+ 236: Dobermann
+ 237: Miniature Pinscher
+ 238: Greater Swiss Mountain Dog
+ 239: Bernese Mountain Dog
+ 240: Appenzeller Sennenhund
+ 241: Entlebucher Sennenhund
+ 242: Boxer
+ 243: Bullmastiff
+ 244: Tibetan Mastiff
+ 245: French Bulldog
+ 246: Great Dane
+ 247: St. Bernard
+ 248: husky
+ 249: Alaskan Malamute
+ 250: Siberian Husky
+ 251: Dalmatian
+ 252: Affenpinscher
+ 253: Basenji
+ 254: pug
+ 255: Leonberger
+ 256: Newfoundland
+ 257: Pyrenean Mountain Dog
+ 258: Samoyed
+ 259: Pomeranian
+ 260: Chow Chow
+ 261: Keeshond
+ 262: Griffon Bruxellois
+ 263: Pembroke Welsh Corgi
+ 264: Cardigan Welsh Corgi
+ 265: Toy Poodle
+ 266: Miniature Poodle
+ 267: Standard Poodle
+ 268: Mexican hairless dog
+ 269: grey wolf
+ 270: Alaskan tundra wolf
+ 271: red wolf
+ 272: coyote
+ 273: dingo
+ 274: dhole
+ 275: African wild dog
+ 276: hyena
+ 277: red fox
+ 278: kit fox
+ 279: Arctic fox
+ 280: grey fox
+ 281: tabby cat
+ 282: tiger cat
+ 283: Persian cat
+ 284: Siamese cat
+ 285: Egyptian Mau
+ 286: cougar
+ 287: lynx
+ 288: leopard
+ 289: snow leopard
+ 290: jaguar
+ 291: lion
+ 292: tiger
+ 293: cheetah
+ 294: brown bear
+ 295: American black bear
+ 296: polar bear
+ 297: sloth bear
+ 298: mongoose
+ 299: meerkat
+ 300: tiger beetle
+ 301: ladybug
+ 302: ground beetle
+ 303: longhorn beetle
+ 304: leaf beetle
+ 305: dung beetle
+ 306: rhinoceros beetle
+ 307: weevil
+ 308: fly
+ 309: bee
+ 310: ant
+ 311: grasshopper
+ 312: cricket
+ 313: stick insect
+ 314: cockroach
+ 315: mantis
+ 316: cicada
+ 317: leafhopper
+ 318: lacewing
+ 319: dragonfly
+ 320: damselfly
+ 321: red admiral
+ 322: ringlet
+ 323: monarch butterfly
+ 324: small white
+ 325: sulphur butterfly
+ 326: gossamer-winged butterfly
+ 327: starfish
+ 328: sea urchin
+ 329: sea cucumber
+ 330: cottontail rabbit
+ 331: hare
+ 332: Angora rabbit
+ 333: hamster
+ 334: porcupine
+ 335: fox squirrel
+ 336: marmot
+ 337: beaver
+ 338: guinea pig
+ 339: common sorrel
+ 340: zebra
+ 341: pig
+ 342: wild boar
+ 343: warthog
+ 344: hippopotamus
+ 345: ox
+ 346: water buffalo
+ 347: bison
+ 348: ram
+ 349: bighorn sheep
+ 350: Alpine ibex
+ 351: hartebeest
+ 352: impala
+ 353: gazelle
+ 354: dromedary
+ 355: llama
+ 356: weasel
+ 357: mink
+ 358: European polecat
+ 359: black-footed ferret
+ 360: otter
+ 361: skunk
+ 362: badger
+ 363: armadillo
+ 364: three-toed sloth
+ 365: orangutan
+ 366: gorilla
+ 367: chimpanzee
+ 368: gibbon
+ 369: siamang
+ 370: guenon
+ 371: patas monkey
+ 372: baboon
+ 373: macaque
+ 374: langur
+ 375: black-and-white colobus
+ 376: proboscis monkey
+ 377: marmoset
+ 378: white-headed capuchin
+ 379: howler monkey
+ 380: titi
+ 381: Geoffroy's spider monkey
+ 382: common squirrel monkey
+ 383: ring-tailed lemur
+ 384: indri
+ 385: Asian elephant
+ 386: African bush elephant
+ 387: red panda
+ 388: giant panda
+ 389: snoek
+ 390: eel
+ 391: coho salmon
+ 392: rock beauty
+ 393: clownfish
+ 394: sturgeon
+ 395: garfish
+ 396: lionfish
+ 397: pufferfish
+ 398: abacus
+ 399: abaya
+ 400: academic gown
+ 401: accordion
+ 402: acoustic guitar
+ 403: aircraft carrier
+ 404: airliner
+ 405: airship
+ 406: altar
+ 407: ambulance
+ 408: amphibious vehicle
+ 409: analog clock
+ 410: apiary
+ 411: apron
+ 412: waste container
+ 413: assault rifle
+ 414: backpack
+ 415: bakery
+ 416: balance beam
+ 417: balloon
+ 418: ballpoint pen
+ 419: Band-Aid
+ 420: banjo
+ 421: baluster
+ 422: barbell
+ 423: barber chair
+ 424: barbershop
+ 425: barn
+ 426: barometer
+ 427: barrel
+ 428: wheelbarrow
+ 429: baseball
+ 430: basketball
+ 431: bassinet
+ 432: bassoon
+ 433: swimming cap
+ 434: bath towel
+ 435: bathtub
+ 436: station wagon
+ 437: lighthouse
+ 438: beaker
+ 439: military cap
+ 440: beer bottle
+ 441: beer glass
+ 442: bell-cot
+ 443: bib
+ 444: tandem bicycle
+ 445: bikini
+ 446: ring binder
+ 447: binoculars
+ 448: birdhouse
+ 449: boathouse
+ 450: bobsleigh
+ 451: bolo tie
+ 452: poke bonnet
+ 453: bookcase
+ 454: bookstore
+ 455: bottle cap
+ 456: bow
+ 457: bow tie
+ 458: brass
+ 459: bra
+ 460: breakwater
+ 461: breastplate
+ 462: broom
+ 463: bucket
+ 464: buckle
+ 465: bulletproof vest
+ 466: high-speed train
+ 467: butcher shop
+ 468: taxicab
+ 469: cauldron
+ 470: candle
+ 471: cannon
+ 472: canoe
+ 473: can opener
+ 474: cardigan
+ 475: car mirror
+ 476: carousel
+ 477: tool kit
+ 478: carton
+ 479: car wheel
+ 480: automated teller machine
+ 481: cassette
+ 482: cassette player
+ 483: castle
+ 484: catamaran
+ 485: CD player
+ 486: cello
+ 487: mobile phone
+ 488: chain
+ 489: chain-link fence
+ 490: chain mail
+ 491: chainsaw
+ 492: chest
+ 493: chiffonier
+ 494: chime
+ 495: china cabinet
+ 496: Christmas stocking
+ 497: church
+ 498: movie theater
+ 499: cleaver
+ 500: cliff dwelling
+ 501: cloak
+ 502: clogs
+ 503: cocktail shaker
+ 504: coffee mug
+ 505: coffeemaker
+ 506: coil
+ 507: combination lock
+ 508: computer keyboard
+ 509: confectionery store
+ 510: container ship
+ 511: convertible
+ 512: corkscrew
+ 513: cornet
+ 514: cowboy boot
+ 515: cowboy hat
+ 516: cradle
+ 517: crane (machine)
+ 518: crash helmet
+ 519: crate
+ 520: infant bed
+ 521: Crock Pot
+ 522: croquet ball
+ 523: crutch
+ 524: cuirass
+ 525: dam
+ 526: desk
+ 527: desktop computer
+ 528: rotary dial telephone
+ 529: diaper
+ 530: digital clock
+ 531: digital watch
+ 532: dining table
+ 533: dishcloth
+ 534: dishwasher
+ 535: disc brake
+ 536: dock
+ 537: dog sled
+ 538: dome
+ 539: doormat
+ 540: drilling rig
+ 541: drum
+ 542: drumstick
+ 543: dumbbell
+ 544: Dutch oven
+ 545: electric fan
+ 546: electric guitar
+ 547: electric locomotive
+ 548: entertainment center
+ 549: envelope
+ 550: espresso machine
+ 551: face powder
+ 552: feather boa
+ 553: filing cabinet
+ 554: fireboat
+ 555: fire engine
+ 556: fire screen sheet
+ 557: flagpole
+ 558: flute
+ 559: folding chair
+ 560: football helmet
+ 561: forklift
+ 562: fountain
+ 563: fountain pen
+ 564: four-poster bed
+ 565: freight car
+ 566: French horn
+ 567: frying pan
+ 568: fur coat
+ 569: garbage truck
+ 570: gas mask
+ 571: gas pump
+ 572: goblet
+ 573: go-kart
+ 574: golf ball
+ 575: golf cart
+ 576: gondola
+ 577: gong
+ 578: gown
+ 579: grand piano
+ 580: greenhouse
+ 581: grille
+ 582: grocery store
+ 583: guillotine
+ 584: barrette
+ 585: hair spray
+ 586: half-track
+ 587: hammer
+ 588: hamper
+ 589: hair dryer
+ 590: hand-held computer
+ 591: handkerchief
+ 592: hard disk drive
+ 593: harmonica
+ 594: harp
+ 595: harvester
+ 596: hatchet
+ 597: holster
+ 598: home theater
+ 599: honeycomb
+ 600: hook
+ 601: hoop skirt
+ 602: horizontal bar
+ 603: horse-drawn vehicle
+ 604: hourglass
+ 605: iPod
+ 606: clothes iron
+ 607: jack-o'-lantern
+ 608: jeans
+ 609: jeep
+ 610: T-shirt
+ 611: jigsaw puzzle
+ 612: pulled rickshaw
+ 613: joystick
+ 614: kimono
+ 615: knee pad
+ 616: knot
+ 617: lab coat
+ 618: ladle
+ 619: lampshade
+ 620: laptop computer
+ 621: lawn mower
+ 622: lens cap
+ 623: paper knife
+ 624: library
+ 625: lifeboat
+ 626: lighter
+ 627: limousine
+ 628: ocean liner
+ 629: lipstick
+ 630: slip-on shoe
+ 631: lotion
+ 632: speaker
+ 633: loupe
+ 634: sawmill
+ 635: magnetic compass
+ 636: mail bag
+ 637: mailbox
+ 638: tights
+ 639: tank suit
+ 640: manhole cover
+ 641: maraca
+ 642: marimba
+ 643: mask
+ 644: match
+ 645: maypole
+ 646: maze
+ 647: measuring cup
+ 648: medicine chest
+ 649: megalith
+ 650: microphone
+ 651: microwave oven
+ 652: military uniform
+ 653: milk can
+ 654: minibus
+ 655: miniskirt
+ 656: minivan
+ 657: missile
+ 658: mitten
+ 659: mixing bowl
+ 660: mobile home
+ 661: Model T
+ 662: modem
+ 663: monastery
+ 664: monitor
+ 665: moped
+ 666: mortar
+ 667: square academic cap
+ 668: mosque
+ 669: mosquito net
+ 670: scooter
+ 671: mountain bike
+ 672: tent
+ 673: computer mouse
+ 674: mousetrap
+ 675: moving van
+ 676: muzzle
+ 677: nail
+ 678: neck brace
+ 679: necklace
+ 680: nipple
+ 681: notebook computer
+ 682: obelisk
+ 683: oboe
+ 684: ocarina
+ 685: odometer
+ 686: oil filter
+ 687: organ
+ 688: oscilloscope
+ 689: overskirt
+ 690: bullock cart
+ 691: oxygen mask
+ 692: packet
+ 693: paddle
+ 694: paddle wheel
+ 695: padlock
+ 696: paintbrush
+ 697: pajamas
+ 698: palace
+ 699: pan flute
+ 700: paper towel
+ 701: parachute
+ 702: parallel bars
+ 703: park bench
+ 704: parking meter
+ 705: passenger car
+ 706: patio
+ 707: payphone
+ 708: pedestal
+ 709: pencil case
+ 710: pencil sharpener
+ 711: perfume
+ 712: Petri dish
+ 713: photocopier
+ 714: plectrum
+ 715: Pickelhaube
+ 716: picket fence
+ 717: pickup truck
+ 718: pier
+ 719: piggy bank
+ 720: pill bottle
+ 721: pillow
+ 722: ping-pong ball
+ 723: pinwheel
+ 724: pirate ship
+ 725: pitcher
+ 726: hand plane
+ 727: planetarium
+ 728: plastic bag
+ 729: plate rack
+ 730: plow
+ 731: plunger
+ 732: Polaroid camera
+ 733: pole
+ 734: police van
+ 735: poncho
+ 736: billiard table
+ 737: soda bottle
+ 738: pot
+ 739: potter's wheel
+ 740: power drill
+ 741: prayer rug
+ 742: printer
+ 743: prison
+ 744: projectile
+ 745: projector
+ 746: hockey puck
+ 747: punching bag
+ 748: purse
+ 749: quill
+ 750: quilt
+ 751: race car
+ 752: racket
+ 753: radiator
+ 754: radio
+ 755: radio telescope
+ 756: rain barrel
+ 757: recreational vehicle
+ 758: reel
+ 759: reflex camera
+ 760: refrigerator
+ 761: remote control
+ 762: restaurant
+ 763: revolver
+ 764: rifle
+ 765: rocking chair
+ 766: rotisserie
+ 767: eraser
+ 768: rugby ball
+ 769: ruler
+ 770: running shoe
+ 771: safe
+ 772: safety pin
+ 773: salt shaker
+ 774: sandal
+ 775: sarong
+ 776: saxophone
+ 777: scabbard
+ 778: weighing scale
+ 779: school bus
+ 780: schooner
+ 781: scoreboard
+ 782: CRT screen
+ 783: screw
+ 784: screwdriver
+ 785: seat belt
+ 786: sewing machine
+ 787: shield
+ 788: shoe store
+ 789: shoji
+ 790: shopping basket
+ 791: shopping cart
+ 792: shovel
+ 793: shower cap
+ 794: shower curtain
+ 795: ski
+ 796: ski mask
+ 797: sleeping bag
+ 798: slide rule
+ 799: sliding door
+ 800: slot machine
+ 801: snorkel
+ 802: snowmobile
+ 803: snowplow
+ 804: soap dispenser
+ 805: soccer ball
+ 806: sock
+ 807: solar thermal collector
+ 808: sombrero
+ 809: soup bowl
+ 810: space bar
+ 811: space heater
+ 812: space shuttle
+ 813: spatula
+ 814: motorboat
+ 815: spider web
+ 816: spindle
+ 817: sports car
+ 818: spotlight
+ 819: stage
+ 820: steam locomotive
+ 821: through arch bridge
+ 822: steel drum
+ 823: stethoscope
+ 824: scarf
+ 825: stone wall
+ 826: stopwatch
+ 827: stove
+ 828: strainer
+ 829: tram
+ 830: stretcher
+ 831: couch
+ 832: stupa
+ 833: submarine
+ 834: suit
+ 835: sundial
+ 836: sunglass
+ 837: sunglasses
+ 838: sunscreen
+ 839: suspension bridge
+ 840: mop
+ 841: sweatshirt
+ 842: swimsuit
+ 843: swing
+ 844: switch
+ 845: syringe
+ 846: table lamp
+ 847: tank
+ 848: tape player
+ 849: teapot
+ 850: teddy bear
+ 851: television
+ 852: tennis ball
+ 853: thatched roof
+ 854: front curtain
+ 855: thimble
+ 856: threshing machine
+ 857: throne
+ 858: tile roof
+ 859: toaster
+ 860: tobacco shop
+ 861: toilet seat
+ 862: torch
+ 863: totem pole
+ 864: tow truck
+ 865: toy store
+ 866: tractor
+ 867: semi-trailer truck
+ 868: tray
+ 869: trench coat
+ 870: tricycle
+ 871: trimaran
+ 872: tripod
+ 873: triumphal arch
+ 874: trolleybus
+ 875: trombone
+ 876: tub
+ 877: turnstile
+ 878: typewriter keyboard
+ 879: umbrella
+ 880: unicycle
+ 881: upright piano
+ 882: vacuum cleaner
+ 883: vase
+ 884: vault
+ 885: velvet
+ 886: vending machine
+ 887: vestment
+ 888: viaduct
+ 889: violin
+ 890: volleyball
+ 891: waffle iron
+ 892: wall clock
+ 893: wallet
+ 894: wardrobe
+ 895: military aircraft
+ 896: sink
+ 897: washing machine
+ 898: water bottle
+ 899: water jug
+ 900: water tower
+ 901: whiskey jug
+ 902: whistle
+ 903: wig
+ 904: window screen
+ 905: window shade
+ 906: Windsor tie
+ 907: wine bottle
+ 908: wing
+ 909: wok
+ 910: wooden spoon
+ 911: wool
+ 912: split-rail fence
+ 913: shipwreck
+ 914: yawl
+ 915: yurt
+ 916: website
+ 917: comic book
+ 918: crossword
+ 919: traffic sign
+ 920: traffic light
+ 921: dust jacket
+ 922: menu
+ 923: plate
+ 924: guacamole
+ 925: consomme
+ 926: hot pot
+ 927: trifle
+ 928: ice cream
+ 929: ice pop
+ 930: baguette
+ 931: bagel
+ 932: pretzel
+ 933: cheeseburger
+ 934: hot dog
+ 935: mashed potato
+ 936: cabbage
+ 937: broccoli
+ 938: cauliflower
+ 939: zucchini
+ 940: spaghetti squash
+ 941: acorn squash
+ 942: butternut squash
+ 943: cucumber
+ 944: artichoke
+ 945: bell pepper
+ 946: cardoon
+ 947: mushroom
+ 948: Granny Smith
+ 949: strawberry
+ 950: orange
+ 951: lemon
+ 952: fig
+ 953: pineapple
+ 954: banana
+ 955: jackfruit
+ 956: custard apple
+ 957: pomegranate
+ 958: hay
+ 959: carbonara
+ 960: chocolate syrup
+ 961: dough
+ 962: meatloaf
+ 963: pizza
+ 964: pot pie
+ 965: burrito
+ 966: red wine
+ 967: espresso
+ 968: cup
+ 969: eggnog
+ 970: alp
+ 971: bubble
+ 972: cliff
+ 973: coral reef
+ 974: geyser
+ 975: lakeshore
+ 976: promontory
+ 977: shoal
+ 978: seashore
+ 979: valley
+ 980: volcano
+ 981: baseball player
+ 982: bridegroom
+ 983: scuba diver
+ 984: rapeseed
+ 985: daisy
+ 986: yellow lady's slipper
+ 987: corn
+ 988: acorn
+ 989: rose hip
+ 990: horse chestnut seed
+ 991: coral fungus
+ 992: agaric
+ 993: gyromitra
+ 994: stinkhorn mushroom
+ 995: earth star
+ 996: hen-of-the-woods
+ 997: bolete
+ 998: ear
+ 999: toilet paper
+
+# Download script/URL (optional)
+download: data/scripts/get_imagenet1000.sh
diff --git a/Transfer Learning/Accident_Classifier/data/Objects365.yaml b/Transfer Learning/Accident_Classifier/data/Objects365.yaml
new file mode 100644
index 00000000..f1f0a1ae
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/Objects365.yaml
@@ -0,0 +1,436 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Objects365 dataset https://www.objects365.org/ by Megvii
+# Example usage: python train.py --data Objects365.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── Objects365 ← downloads here (712 GB = 367G data + 345G zips)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/Objects365 # dataset root dir
+train: images/train # train images (relative to 'path') 1742289 images
+val: images/val # val images (relative to 'path') 80000 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: Person
+ 1: Sneakers
+ 2: Chair
+ 3: Other Shoes
+ 4: Hat
+ 5: Car
+ 6: Lamp
+ 7: Glasses
+ 8: Bottle
+ 9: Desk
+ 10: Cup
+ 11: Street Lights
+ 12: Cabinet/shelf
+ 13: Handbag/Satchel
+ 14: Bracelet
+ 15: Plate
+ 16: Picture/Frame
+ 17: Helmet
+ 18: Book
+ 19: Gloves
+ 20: Storage box
+ 21: Boat
+ 22: Leather Shoes
+ 23: Flower
+ 24: Bench
+ 25: Potted Plant
+ 26: Bowl/Basin
+ 27: Flag
+ 28: Pillow
+ 29: Boots
+ 30: Vase
+ 31: Microphone
+ 32: Necklace
+ 33: Ring
+ 34: SUV
+ 35: Wine Glass
+ 36: Belt
+ 37: Monitor/TV
+ 38: Backpack
+ 39: Umbrella
+ 40: Traffic Light
+ 41: Speaker
+ 42: Watch
+ 43: Tie
+ 44: Trash bin Can
+ 45: Slippers
+ 46: Bicycle
+ 47: Stool
+ 48: Barrel/bucket
+ 49: Van
+ 50: Couch
+ 51: Sandals
+ 52: Basket
+ 53: Drum
+ 54: Pen/Pencil
+ 55: Bus
+ 56: Wild Bird
+ 57: High Heels
+ 58: Motorcycle
+ 59: Guitar
+ 60: Carpet
+ 61: Cell Phone
+ 62: Bread
+ 63: Camera
+ 64: Canned
+ 65: Truck
+ 66: Traffic cone
+ 67: Cymbal
+ 68: Lifesaver
+ 69: Towel
+ 70: Stuffed Toy
+ 71: Candle
+ 72: Sailboat
+ 73: Laptop
+ 74: Awning
+ 75: Bed
+ 76: Faucet
+ 77: Tent
+ 78: Horse
+ 79: Mirror
+ 80: Power outlet
+ 81: Sink
+ 82: Apple
+ 83: Air Conditioner
+ 84: Knife
+ 85: Hockey Stick
+ 86: Paddle
+ 87: Pickup Truck
+ 88: Fork
+ 89: Traffic Sign
+ 90: Balloon
+ 91: Tripod
+ 92: Dog
+ 93: Spoon
+ 94: Clock
+ 95: Pot
+ 96: Cow
+ 97: Cake
+ 98: Dinning Table
+ 99: Sheep
+ 100: Hanger
+ 101: Blackboard/Whiteboard
+ 102: Napkin
+ 103: Other Fish
+ 104: Orange/Tangerine
+ 105: Toiletry
+ 106: Keyboard
+ 107: Tomato
+ 108: Lantern
+ 109: Machinery Vehicle
+ 110: Fan
+ 111: Green Vegetables
+ 112: Banana
+ 113: Baseball Glove
+ 114: Airplane
+ 115: Mouse
+ 116: Train
+ 117: Pumpkin
+ 118: Soccer
+ 119: Skiboard
+ 120: Luggage
+ 121: Nightstand
+ 122: Tea pot
+ 123: Telephone
+ 124: Trolley
+ 125: Head Phone
+ 126: Sports Car
+ 127: Stop Sign
+ 128: Dessert
+ 129: Scooter
+ 130: Stroller
+ 131: Crane
+ 132: Remote
+ 133: Refrigerator
+ 134: Oven
+ 135: Lemon
+ 136: Duck
+ 137: Baseball Bat
+ 138: Surveillance Camera
+ 139: Cat
+ 140: Jug
+ 141: Broccoli
+ 142: Piano
+ 143: Pizza
+ 144: Elephant
+ 145: Skateboard
+ 146: Surfboard
+ 147: Gun
+ 148: Skating and Skiing shoes
+ 149: Gas stove
+ 150: Donut
+ 151: Bow Tie
+ 152: Carrot
+ 153: Toilet
+ 154: Kite
+ 155: Strawberry
+ 156: Other Balls
+ 157: Shovel
+ 158: Pepper
+ 159: Computer Box
+ 160: Toilet Paper
+ 161: Cleaning Products
+ 162: Chopsticks
+ 163: Microwave
+ 164: Pigeon
+ 165: Baseball
+ 166: Cutting/chopping Board
+ 167: Coffee Table
+ 168: Side Table
+ 169: Scissors
+ 170: Marker
+ 171: Pie
+ 172: Ladder
+ 173: Snowboard
+ 174: Cookies
+ 175: Radiator
+ 176: Fire Hydrant
+ 177: Basketball
+ 178: Zebra
+ 179: Grape
+ 180: Giraffe
+ 181: Potato
+ 182: Sausage
+ 183: Tricycle
+ 184: Violin
+ 185: Egg
+ 186: Fire Extinguisher
+ 187: Candy
+ 188: Fire Truck
+ 189: Billiards
+ 190: Converter
+ 191: Bathtub
+ 192: Wheelchair
+ 193: Golf Club
+ 194: Briefcase
+ 195: Cucumber
+ 196: Cigar/Cigarette
+ 197: Paint Brush
+ 198: Pear
+ 199: Heavy Truck
+ 200: Hamburger
+ 201: Extractor
+ 202: Extension Cord
+ 203: Tong
+ 204: Tennis Racket
+ 205: Folder
+ 206: American Football
+ 207: earphone
+ 208: Mask
+ 209: Kettle
+ 210: Tennis
+ 211: Ship
+ 212: Swing
+ 213: Coffee Machine
+ 214: Slide
+ 215: Carriage
+ 216: Onion
+ 217: Green beans
+ 218: Projector
+ 219: Frisbee
+ 220: Washing Machine/Drying Machine
+ 221: Chicken
+ 222: Printer
+ 223: Watermelon
+ 224: Saxophone
+ 225: Tissue
+ 226: Toothbrush
+ 227: Ice cream
+ 228: Hot-air balloon
+ 229: Cello
+ 230: French Fries
+ 231: Scale
+ 232: Trophy
+ 233: Cabbage
+ 234: Hot dog
+ 235: Blender
+ 236: Peach
+ 237: Rice
+ 238: Wallet/Purse
+ 239: Volleyball
+ 240: Deer
+ 241: Goose
+ 242: Tape
+ 243: Tablet
+ 244: Cosmetics
+ 245: Trumpet
+ 246: Pineapple
+ 247: Golf Ball
+ 248: Ambulance
+ 249: Parking meter
+ 250: Mango
+ 251: Key
+ 252: Hurdle
+ 253: Fishing Rod
+ 254: Medal
+ 255: Flute
+ 256: Brush
+ 257: Penguin
+ 258: Megaphone
+ 259: Corn
+ 260: Lettuce
+ 261: Garlic
+ 262: Swan
+ 263: Helicopter
+ 264: Green Onion
+ 265: Sandwich
+ 266: Nuts
+ 267: Speed Limit Sign
+ 268: Induction Cooker
+ 269: Broom
+ 270: Trombone
+ 271: Plum
+ 272: Rickshaw
+ 273: Goldfish
+ 274: Kiwi fruit
+ 275: Router/modem
+ 276: Poker Card
+ 277: Toaster
+ 278: Shrimp
+ 279: Sushi
+ 280: Cheese
+ 281: Notepaper
+ 282: Cherry
+ 283: Pliers
+ 284: CD
+ 285: Pasta
+ 286: Hammer
+ 287: Cue
+ 288: Avocado
+ 289: Hamimelon
+ 290: Flask
+ 291: Mushroom
+ 292: Screwdriver
+ 293: Soap
+ 294: Recorder
+ 295: Bear
+ 296: Eggplant
+ 297: Board Eraser
+ 298: Coconut
+ 299: Tape Measure/Ruler
+ 300: Pig
+ 301: Showerhead
+ 302: Globe
+ 303: Chips
+ 304: Steak
+ 305: Crosswalk Sign
+ 306: Stapler
+ 307: Camel
+ 308: Formula 1
+ 309: Pomegranate
+ 310: Dishwasher
+ 311: Crab
+ 312: Hoverboard
+ 313: Meat ball
+ 314: Rice Cooker
+ 315: Tuba
+ 316: Calculator
+ 317: Papaya
+ 318: Antelope
+ 319: Parrot
+ 320: Seal
+ 321: Butterfly
+ 322: Dumbbell
+ 323: Donkey
+ 324: Lion
+ 325: Urinal
+ 326: Dolphin
+ 327: Electric Drill
+ 328: Hair Dryer
+ 329: Egg tart
+ 330: Jellyfish
+ 331: Treadmill
+ 332: Lighter
+ 333: Grapefruit
+ 334: Game board
+ 335: Mop
+ 336: Radish
+ 337: Baozi
+ 338: Target
+ 339: French
+ 340: Spring Rolls
+ 341: Monkey
+ 342: Rabbit
+ 343: Pencil Case
+ 344: Yak
+ 345: Red Cabbage
+ 346: Binoculars
+ 347: Asparagus
+ 348: Barbell
+ 349: Scallop
+ 350: Noddles
+ 351: Comb
+ 352: Dumpling
+ 353: Oyster
+ 354: Table Tennis paddle
+ 355: Cosmetics Brush/Eyeliner Pencil
+ 356: Chainsaw
+ 357: Eraser
+ 358: Lobster
+ 359: Durian
+ 360: Okra
+ 361: Lipstick
+ 362: Cosmetics Mirror
+ 363: Curling
+ 364: Table Tennis
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from tqdm import tqdm
+
+ from utils.general import Path, check_requirements, download, np, xyxy2xywhn
+
+ check_requirements('pycocotools>=2.0')
+ from pycocotools.coco import COCO
+
+ # Make Directories
+ dir = Path(yaml['path']) # dataset root dir
+ for p in 'images', 'labels':
+ (dir / p).mkdir(parents=True, exist_ok=True)
+ for q in 'train', 'val':
+ (dir / p / q).mkdir(parents=True, exist_ok=True)
+
+ # Train, Val Splits
+ for split, patches in [('train', 50 + 1), ('val', 43 + 1)]:
+ print(f"Processing {split} in {patches} patches ...")
+ images, labels = dir / 'images' / split, dir / 'labels' / split
+
+ # Download
+ url = f"https://dorc.ks3-cn-beijing.ksyun.com/data-set/2020Objects365%E6%95%B0%E6%8D%AE%E9%9B%86/{split}/"
+ if split == 'train':
+ download([f'{url}zhiyuan_objv2_{split}.tar.gz'], dir=dir, delete=False) # annotations json
+ download([f'{url}patch{i}.tar.gz' for i in range(patches)], dir=images, curl=True, delete=False, threads=8)
+ elif split == 'val':
+ download([f'{url}zhiyuan_objv2_{split}.json'], dir=dir, delete=False) # annotations json
+ download([f'{url}images/v1/patch{i}.tar.gz' for i in range(15 + 1)], dir=images, curl=True, delete=False, threads=8)
+ download([f'{url}images/v2/patch{i}.tar.gz' for i in range(16, patches)], dir=images, curl=True, delete=False, threads=8)
+
+ # Move
+ for f in tqdm(images.rglob('*.jpg'), desc=f'Moving {split} images'):
+ f.rename(images / f.name) # move to /images/{split}
+
+ # Labels
+ coco = COCO(dir / f'zhiyuan_objv2_{split}.json')
+ names = [x["name"] for x in coco.loadCats(coco.getCatIds())]
+ for cid, cat in enumerate(names):
+ catIds = coco.getCatIds(catNms=[cat])
+ imgIds = coco.getImgIds(catIds=catIds)
+ for im in tqdm(coco.loadImgs(imgIds), desc=f'Class {cid + 1}/{len(names)} {cat}'):
+ width, height = im["width"], im["height"]
+ path = Path(im["file_name"]) # image filename
+ try:
+ with open(labels / path.with_suffix('.txt').name, 'a') as file:
+ annIds = coco.getAnnIds(imgIds=im["id"], catIds=catIds, iscrowd=False)
+ for a in coco.loadAnns(annIds):
+ x, y, w, h = a['bbox'] # bounding box in xywh (xy top-left corner)
+ xyxy = np.array([x, y, x + w, y + h])[None] # pixels(1,4)
+ x, y, w, h = xyxy2xywhn(xyxy, w=width, h=height, clip=True)[0] # normalized and clipped
+ file.write(f"{cid} {x:.5f} {y:.5f} {w:.5f} {h:.5f}\n")
+ except Exception as e:
+ print(e)
diff --git a/Transfer Learning/Accident_Classifier/data/SKU-110K.yaml b/Transfer Learning/Accident_Classifier/data/SKU-110K.yaml
new file mode 100644
index 00000000..b012bec3
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/SKU-110K.yaml
@@ -0,0 +1,51 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# SKU-110K retail items dataset https://github.com/eg4000/SKU110K_CVPR19 by Trax Retail
+# Example usage: python train.py --data SKU-110K.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── SKU-110K ← downloads here (13.6 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/SKU-110K # dataset root dir
+train: train.txt # train images (relative to 'path') 8219 images
+val: val.txt # val images (relative to 'path') 588 images
+test: test.txt # test images (optional) 2936 images
+
+# Classes
+names:
+ 0: object
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import shutil
+ from tqdm import tqdm
+ from utils.general import np, pd, Path, download, xyxy2xywh
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ parent = Path(dir.parent) # download dir
+ urls = ['http://trax-geometry.s3.amazonaws.com/cvpr_challenge/SKU110K_fixed.tar.gz']
+ download(urls, dir=parent, delete=False)
+
+ # Rename directories
+ if dir.exists():
+ shutil.rmtree(dir)
+ (parent / 'SKU110K_fixed').rename(dir) # rename dir
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # create labels dir
+
+ # Convert labels
+ names = 'image', 'x1', 'y1', 'x2', 'y2', 'class', 'image_width', 'image_height' # column names
+ for d in 'annotations_train.csv', 'annotations_val.csv', 'annotations_test.csv':
+ x = pd.read_csv(dir / 'annotations' / d, names=names).values # annotations
+ images, unique_images = x[:, 0], np.unique(x[:, 0])
+ with open((dir / d).with_suffix('.txt').__str__().replace('annotations_', ''), 'w') as f:
+ f.writelines(f'./images/{s}\n' for s in unique_images)
+ for im in tqdm(unique_images, desc=f'Converting {dir / d}'):
+ cls = 0 # single-class dataset
+ with open((dir / 'labels' / im).with_suffix('.txt'), 'a') as f:
+ for r in x[images == im]:
+ w, h = r[6], r[7] # image width, height
+ xywh = xyxy2xywh(np.array([[r[1] / w, r[2] / h, r[3] / w, r[4] / h]]))[0] # instance
+ f.write(f"{cls} {xywh[0]:.5f} {xywh[1]:.5f} {xywh[2]:.5f} {xywh[3]:.5f}\n") # write label
diff --git a/Transfer Learning/Accident_Classifier/data/VOC.yaml b/Transfer Learning/Accident_Classifier/data/VOC.yaml
new file mode 100644
index 00000000..227d91d7
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/VOC.yaml
@@ -0,0 +1,98 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# PASCAL VOC dataset http://host.robots.ox.ac.uk/pascal/VOC by University of Oxford
+# Example usage: python train.py --data VOC.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── VOC ← downloads here (2.8 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VOC
+train: # train images (relative to 'path') 16551 images
+ - images/train2012
+ - images/train2007
+ - images/val2012
+ - images/val2007
+val: # val images (relative to 'path') 4952 images
+ - images/test2007
+test: # test images (optional)
+ - images/test2007
+
+# Classes
+names:
+ 0: aeroplane
+ 1: bicycle
+ 2: bird
+ 3: boat
+ 4: bottle
+ 5: bus
+ 6: car
+ 7: cat
+ 8: chair
+ 9: cow
+ 10: diningtable
+ 11: dog
+ 12: horse
+ 13: motorbike
+ 14: person
+ 15: pottedplant
+ 16: sheep
+ 17: sofa
+ 18: train
+ 19: tvmonitor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ import xml.etree.ElementTree as ET
+
+ from tqdm import tqdm
+ from utils.general import download, Path
+
+
+ def convert_label(path, lb_path, year, image_id):
+ def convert_box(size, box):
+ dw, dh = 1. / size[0], 1. / size[1]
+ x, y, w, h = (box[0] + box[1]) / 2.0 - 1, (box[2] + box[3]) / 2.0 - 1, box[1] - box[0], box[3] - box[2]
+ return x * dw, y * dh, w * dw, h * dh
+
+ in_file = open(path / f'VOC{year}/Annotations/{image_id}.xml')
+ out_file = open(lb_path, 'w')
+ tree = ET.parse(in_file)
+ root = tree.getroot()
+ size = root.find('size')
+ w = int(size.find('width').text)
+ h = int(size.find('height').text)
+
+ names = list(yaml['names'].values()) # names list
+ for obj in root.iter('object'):
+ cls = obj.find('name').text
+ if cls in names and int(obj.find('difficult').text) != 1:
+ xmlbox = obj.find('bndbox')
+ bb = convert_box((w, h), [float(xmlbox.find(x).text) for x in ('xmin', 'xmax', 'ymin', 'ymax')])
+ cls_id = names.index(cls) # class id
+ out_file.write(" ".join([str(a) for a in (cls_id, *bb)]) + '\n')
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+ urls = [f'{url}VOCtrainval_06-Nov-2007.zip', # 446MB, 5012 images
+ f'{url}VOCtest_06-Nov-2007.zip', # 438MB, 4953 images
+ f'{url}VOCtrainval_11-May-2012.zip'] # 1.95GB, 17126 images
+ download(urls, dir=dir / 'images', delete=False, curl=True, threads=3)
+
+ # Convert
+ path = dir / 'images/VOCdevkit'
+ for year, image_set in ('2012', 'train'), ('2012', 'val'), ('2007', 'train'), ('2007', 'val'), ('2007', 'test'):
+ imgs_path = dir / 'images' / f'{image_set}{year}'
+ lbs_path = dir / 'labels' / f'{image_set}{year}'
+ imgs_path.mkdir(exist_ok=True, parents=True)
+ lbs_path.mkdir(exist_ok=True, parents=True)
+
+ with open(path / f'VOC{year}/ImageSets/Main/{image_set}.txt') as f:
+ image_ids = f.read().strip().split()
+ for id in tqdm(image_ids, desc=f'{image_set}{year}'):
+ f = path / f'VOC{year}/JPEGImages/{id}.jpg' # old img path
+ lb_path = (lbs_path / f.name).with_suffix('.txt') # new label path
+ f.rename(imgs_path / f.name) # move image
+ convert_label(path, lb_path, year, id) # convert labels to YOLO format
diff --git a/Transfer Learning/Accident_Classifier/data/VisDrone.yaml b/Transfer Learning/Accident_Classifier/data/VisDrone.yaml
new file mode 100644
index 00000000..20ff1d39
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/VisDrone.yaml
@@ -0,0 +1,68 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# VisDrone2019-DET dataset https://github.com/VisDrone/VisDrone-Dataset by Tianjin University
+# Example usage: python train.py --data VisDrone.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── VisDrone ← downloads here (2.3 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/VisDrone # dataset root dir
+train: VisDrone2019-DET-train/images # train images (relative to 'path') 6471 images
+val: VisDrone2019-DET-val/images # val images (relative to 'path') 548 images
+test: VisDrone2019-DET-test-dev/images # test images (optional) 1610 images
+
+# Classes
+names:
+ 0: pedestrian
+ 1: people
+ 2: bicycle
+ 3: car
+ 4: van
+ 5: truck
+ 6: tricycle
+ 7: awning-tricycle
+ 8: bus
+ 9: motor
+
+# Download script/URL (optional) ---------------------------------------------------------------------------------------
+download: |
+ from utils.general import download, os, Path
+
+ def visdrone2yolo(dir):
+ from PIL import Image
+ from tqdm import tqdm
+
+ def convert_box(size, box):
+ # Convert VisDrone box to YOLO xywh box
+ dw = 1. / size[0]
+ dh = 1. / size[1]
+ return (box[0] + box[2] / 2) * dw, (box[1] + box[3] / 2) * dh, box[2] * dw, box[3] * dh
+
+ (dir / 'labels').mkdir(parents=True, exist_ok=True) # make labels directory
+ pbar = tqdm((dir / 'annotations').glob('*.txt'), desc=f'Converting {dir}')
+ for f in pbar:
+ img_size = Image.open((dir / 'images' / f.name).with_suffix('.jpg')).size
+ lines = []
+ with open(f, 'r') as file: # read annotation.txt
+ for row in [x.split(',') for x in file.read().strip().splitlines()]:
+ if row[4] == '0': # VisDrone 'ignored regions' class 0
+ continue
+ cls = int(row[5]) - 1
+ box = convert_box(img_size, tuple(map(int, row[:4])))
+ lines.append(f"{cls} {' '.join(f'{x:.6f}' for x in box)}\n")
+ with open(str(f).replace(os.sep + 'annotations' + os.sep, os.sep + 'labels' + os.sep), 'w') as fl:
+ fl.writelines(lines) # write label.txt
+
+
+ # Download
+ dir = Path(yaml['path']) # dataset root dir
+ urls = ['https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-train.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-val.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-dev.zip',
+ 'https://github.com/ultralytics/assets/releases/download/v0.0.0/VisDrone2019-DET-test-challenge.zip']
+ download(urls, dir=dir, curl=True, threads=4)
+
+ # Convert
+ for d in 'VisDrone2019-DET-train', 'VisDrone2019-DET-val', 'VisDrone2019-DET-test-dev':
+ visdrone2yolo(dir / d) # convert VisDrone annotations to YOLO labels
diff --git a/Transfer Learning/Accident_Classifier/data/coco.yaml b/Transfer Learning/Accident_Classifier/data/coco.yaml
new file mode 100644
index 00000000..816efa5c
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/coco.yaml
@@ -0,0 +1,114 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# COCO 2017 dataset http://cocodataset.org by Microsoft
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco ← downloads here (20.1 GB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco # dataset root dir
+train: train2017.txt # train images (relative to 'path') 118287 images
+val: val2017.txt # val images (relative to 'path') 5000 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: |
+ from utils.general import download, Path
+
+
+ # Download labels
+ segments = False # segment or box labels
+ dir = Path(yaml['path']) # dataset root dir
+ url = 'https://github.com/ultralytics/assets/releases/download/v0.0.0/'
+ urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
+ download(urls, dir=dir.parent)
+
+ # Download data
+ urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
+ 'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
+ 'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
+ download(urls, dir=dir / 'images', threads=3)
diff --git a/Transfer Learning/Accident_Classifier/data/coco128-seg.yaml b/Transfer Learning/Accident_Classifier/data/coco128-seg.yaml
new file mode 100644
index 00000000..aea711c9
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/coco128-seg.yaml
@@ -0,0 +1,99 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# COCO128-seg dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco128-seg ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128-seg # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128-seg.zip
diff --git a/Transfer Learning/Accident_Classifier/data/coco128.yaml b/Transfer Learning/Accident_Classifier/data/coco128.yaml
new file mode 100644
index 00000000..2ed35c06
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/coco128.yaml
@@ -0,0 +1,99 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# COCO128 dataset https://www.kaggle.com/ultralytics/coco128 (first 128 images from COCO train2017) by Ultralytics
+# Example usage: python train.py --data coco128.yaml
+# parent
+# ├── yolov5
+# └── datasets
+# └── coco128 ← downloads here (7 MB)
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco128 # dataset root dir
+train: images/train2017 # train images (relative to 'path') 128 images
+val: images/train2017 # val images (relative to 'path') 128 images
+test: # test images (optional)
+
+# Classes
+names:
+ 0: person
+ 1: bicycle
+ 2: car
+ 3: motorcycle
+ 4: airplane
+ 5: bus
+ 6: train
+ 7: truck
+ 8: boat
+ 9: traffic light
+ 10: fire hydrant
+ 11: stop sign
+ 12: parking meter
+ 13: bench
+ 14: bird
+ 15: cat
+ 16: dog
+ 17: horse
+ 18: sheep
+ 19: cow
+ 20: elephant
+ 21: bear
+ 22: zebra
+ 23: giraffe
+ 24: backpack
+ 25: umbrella
+ 26: handbag
+ 27: tie
+ 28: suitcase
+ 29: frisbee
+ 30: skis
+ 31: snowboard
+ 32: sports ball
+ 33: kite
+ 34: baseball bat
+ 35: baseball glove
+ 36: skateboard
+ 37: surfboard
+ 38: tennis racket
+ 39: bottle
+ 40: wine glass
+ 41: cup
+ 42: fork
+ 43: knife
+ 44: spoon
+ 45: bowl
+ 46: banana
+ 47: apple
+ 48: sandwich
+ 49: orange
+ 50: broccoli
+ 51: carrot
+ 52: hot dog
+ 53: pizza
+ 54: donut
+ 55: cake
+ 56: chair
+ 57: couch
+ 58: potted plant
+ 59: bed
+ 60: dining table
+ 61: toilet
+ 62: tv
+ 63: laptop
+ 64: mouse
+ 65: remote
+ 66: keyboard
+ 67: cell phone
+ 68: microwave
+ 69: oven
+ 70: toaster
+ 71: sink
+ 72: refrigerator
+ 73: book
+ 74: clock
+ 75: vase
+ 76: scissors
+ 77: teddy bear
+ 78: hair drier
+ 79: toothbrush
+
+# Download script/URL (optional)
+download: https://github.com/ultralytics/assets/releases/download/v0.0.0/coco128.zip
diff --git a/Transfer Learning/Accident_Classifier/data/hyps/hyp.Objects365.yaml b/Transfer Learning/Accident_Classifier/data/hyps/hyp.Objects365.yaml
new file mode 100644
index 00000000..7a6c507c
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/hyps/hyp.Objects365.yaml
@@ -0,0 +1,34 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Hyperparameters for Objects365 training
+# python train.py --weights yolov5m.pt --data Objects365.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.00258
+lrf: 0.17
+momentum: 0.779
+weight_decay: 0.00058
+warmup_epochs: 1.33
+warmup_momentum: 0.86
+warmup_bias_lr: 0.0711
+box: 0.0539
+cls: 0.299
+cls_pw: 0.825
+obj: 0.632
+obj_pw: 1.0
+iou_t: 0.2
+anchor_t: 3.44
+anchors: 3.2
+fl_gamma: 0.0
+hsv_h: 0.0188
+hsv_s: 0.704
+hsv_v: 0.36
+degrees: 0.0
+translate: 0.0902
+scale: 0.491
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 1.0
+mixup: 0.0
+copy_paste: 0.0
diff --git a/Transfer Learning/Accident_Classifier/data/hyps/hyp.VOC.yaml b/Transfer Learning/Accident_Classifier/data/hyps/hyp.VOC.yaml
new file mode 100644
index 00000000..c04c63e2
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/hyps/hyp.VOC.yaml
@@ -0,0 +1,40 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Hyperparameters for VOC training
+# python train.py --batch 128 --weights yolov5m6.pt --data VOC.yaml --epochs 50 --img 512 --hyp hyp.scratch-med.yaml --evolve
+# See Hyperparameter Evolution tutorial for details https://github.com/ultralytics/yolov5#tutorials
+
+# YOLOv5 Hyperparameter Evolution Results
+# Best generation: 467
+# Last generation: 996
+# metrics/precision, metrics/recall, metrics/mAP_0.5, metrics/mAP_0.5:0.95, val/box_loss, val/obj_loss, val/cls_loss
+# 0.87729, 0.85125, 0.91286, 0.72664, 0.0076739, 0.0042529, 0.0013865
+
+lr0: 0.00334
+lrf: 0.15135
+momentum: 0.74832
+weight_decay: 0.00025
+warmup_epochs: 3.3835
+warmup_momentum: 0.59462
+warmup_bias_lr: 0.18657
+box: 0.02
+cls: 0.21638
+cls_pw: 0.5
+obj: 0.51728
+obj_pw: 0.67198
+iou_t: 0.2
+anchor_t: 3.3744
+fl_gamma: 0.0
+hsv_h: 0.01041
+hsv_s: 0.54703
+hsv_v: 0.27739
+degrees: 0.0
+translate: 0.04591
+scale: 0.75544
+shear: 0.0
+perspective: 0.0
+flipud: 0.0
+fliplr: 0.5
+mosaic: 0.85834
+mixup: 0.04266
+copy_paste: 0.0
+anchors: 3.412
diff --git a/Transfer Learning/Accident_Classifier/data/hyps/hyp.no-augmentation.yaml b/Transfer Learning/Accident_Classifier/data/hyps/hyp.no-augmentation.yaml
new file mode 100644
index 00000000..adc360bb
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/hyps/hyp.no-augmentation.yaml
@@ -0,0 +1,35 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Hyperparameters when using Albumentations frameworks
+# python train.py --hyp hyp.no-augmentation.yaml
+# See https://github.com/ultralytics/yolov5/pull/3882 for YOLOv5 + Albumentations Usage examples
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+# this parameters are all zero since we want to use albumentation framework
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0 # image HSV-Hue augmentation (fraction)
+hsv_s: 0 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0 # image translation (+/- fraction)
+scale: 0 # image scale (+/- gain)
+shear: 0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.0 # image flip left-right (probability)
+mosaic: 0.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-high.yaml b/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-high.yaml
new file mode 100644
index 00000000..3e913e36
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-high.yaml
@@ -0,0 +1,34 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Hyperparameters for high-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.9 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.1 # image mixup (probability)
+copy_paste: 0.1 # segment copy-paste (probability)
diff --git a/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-low.yaml b/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-low.yaml
new file mode 100644
index 00000000..ff0d1e7f
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-low.yaml
@@ -0,0 +1,34 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Hyperparameters for low-augmentation COCO training from scratch
+# python train.py --batch 64 --cfg yolov5n6.yaml --weights '' --data coco.yaml --img 640 --epochs 300 --linear
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.01 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.5 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 1.0 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.5 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.0 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-med.yaml b/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-med.yaml
new file mode 100644
index 00000000..c2fba1fc
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/hyps/hyp.scratch-med.yaml
@@ -0,0 +1,34 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Hyperparameters for medium-augmentation COCO training from scratch
+# python train.py --batch 32 --cfg yolov5m6.yaml --weights '' --data coco.yaml --img 1280 --epochs 300
+# See tutorials for hyperparameter evolution https://github.com/ultralytics/yolov5#tutorials
+
+lr0: 0.01 # initial learning rate (SGD=1E-2, Adam=1E-3)
+lrf: 0.1 # final OneCycleLR learning rate (lr0 * lrf)
+momentum: 0.937 # SGD momentum/Adam beta1
+weight_decay: 0.0005 # optimizer weight decay 5e-4
+warmup_epochs: 3.0 # warmup epochs (fractions ok)
+warmup_momentum: 0.8 # warmup initial momentum
+warmup_bias_lr: 0.1 # warmup initial bias lr
+box: 0.05 # box loss gain
+cls: 0.3 # cls loss gain
+cls_pw: 1.0 # cls BCELoss positive_weight
+obj: 0.7 # obj loss gain (scale with pixels)
+obj_pw: 1.0 # obj BCELoss positive_weight
+iou_t: 0.20 # IoU training threshold
+anchor_t: 4.0 # anchor-multiple threshold
+# anchors: 3 # anchors per output layer (0 to ignore)
+fl_gamma: 0.0 # focal loss gamma (efficientDet default gamma=1.5)
+hsv_h: 0.015 # image HSV-Hue augmentation (fraction)
+hsv_s: 0.7 # image HSV-Saturation augmentation (fraction)
+hsv_v: 0.4 # image HSV-Value augmentation (fraction)
+degrees: 0.0 # image rotation (+/- deg)
+translate: 0.1 # image translation (+/- fraction)
+scale: 0.9 # image scale (+/- gain)
+shear: 0.0 # image shear (+/- deg)
+perspective: 0.0 # image perspective (+/- fraction), range 0-0.001
+flipud: 0.0 # image flip up-down (probability)
+fliplr: 0.5 # image flip left-right (probability)
+mosaic: 1.0 # image mosaic (probability)
+mixup: 0.1 # image mixup (probability)
+copy_paste: 0.0 # segment copy-paste (probability)
diff --git a/Transfer Learning/Accident_Classifier/data/images/bus.jpg b/Transfer Learning/Accident_Classifier/data/images/bus.jpg
new file mode 100644
index 00000000..b43e3111
Binary files /dev/null and b/Transfer Learning/Accident_Classifier/data/images/bus.jpg differ
diff --git a/Transfer Learning/Accident_Classifier/data/images/zidane.jpg b/Transfer Learning/Accident_Classifier/data/images/zidane.jpg
new file mode 100644
index 00000000..92d72ea1
Binary files /dev/null and b/Transfer Learning/Accident_Classifier/data/images/zidane.jpg differ
diff --git a/Transfer Learning/Accident_Classifier/data/scripts/download_weights.sh b/Transfer Learning/Accident_Classifier/data/scripts/download_weights.sh
new file mode 100644
index 00000000..e408959b
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/data/scripts/download_weights.sh
@@ -0,0 +1,22 @@
+#!/bin/bash
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Download latest models from https://github.com/ultralytics/yolov5/releases
+# Example usage: bash data/scripts/download_weights.sh
+# parent
+# └── yolov5
+# ├── yolov5s.pt ← downloads here
+# ├── yolov5m.pt
+# └── ...
+
+python - <\n",
+ "\n",
+ "\n",
+ "\n",
+ " \n",
+ " \n",
+ "![](\"https://user-images.githubusercontent.com/26833433/199030123-08c72f8d-6871-4116-8ed3-c373642cf28e.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0eq1SMWl6Sfn"
+ },
+ "source": [
+ "# 2. Validate\n",
+ "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "WQPtK1QYVaD_",
+ "outputId": "9d751d8c-bee8-4339-cf30-9854ca530449"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Downloading https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017labels-segments.zip ...\n",
+ "Downloading http://images.cocodataset.org/zips/val2017.zip ...\n",
+ "######################################################################## 100.0%\n",
+ "######################################################################## 100.0%\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Download COCO val\n",
+ "!bash data/scripts/get_coco.sh --val --segments # download (780M - 5000 images)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "X58w8JLpMnjH",
+ "outputId": "a140d67a-02da-479e-9ddb-7d54bf9e407a"
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "\u001b[34m\u001b[1msegment/val: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s-seg.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=False, project=runs/val-seg, name=exp, exist_ok=False, half=True, dnn=False\n",
+ "YOLOv5 🚀 v7.0-2-gc9d47ae Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)\n",
+ "\n",
+ "Fusing layers... \n",
+ "YOLOv5s-seg summary: 224 layers, 7611485 parameters, 0 gradients, 26.4 GFLOPs\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:03<00:00, 1361.31it/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
+ " Class Images Instances Box(P R mAP50 mAP50-95) Mask(P R mAP50 mAP50-95): 100% 157/157 [01:54<00:00, 1.37it/s]\n",
+ " all 5000 36335 0.673 0.517 0.566 0.373 0.672 0.49 0.532 0.319\n",
+ "Speed: 0.6ms pre-process, 4.4ms inference, 2.9ms NMS per image at shape (32, 3, 640, 640)\n",
+ "Results saved to \u001b[1mruns/val-seg/exp\u001b[0m\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Validate YOLOv5s-seg on COCO val\n",
+ "!python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 --half"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZY2VXXXu74w5"
+ },
+ "source": [
+ "# 3. Train\n",
+ "\n",
+ "
![](\"https://robflow-public-assets.s3.amazonaws.com/how-to-train-yolov5-segmentation-annotation.gif\"/)
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Lay2WsTjNJzP"
+ },
+ "source": [
+ "## ClearML Logging and Automation 🌟 NEW\n",
+ "\n",
+ "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
+ "\n",
+ "- `pip install clearml`\n",
+ "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
+ "\n",
+ "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
+ "\n",
+ "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
+ "\n",
+ "\n",
+ ""
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-WPvRbS5Swl6"
+ },
+ "source": [
+ "## Local Logging\n",
+ "\n",
+ "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
+ "\n",
+ "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices. \n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zelyeqbyt3GD"
+ },
+ "source": [
+ "# Environments\n",
+ "\n",
+ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
+ "\n",
+ "- **Notebooks** with free GPU: \n",
+ "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
+ "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
+ "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6Qu7Iesl0p54"
+ },
+ "source": [
+ "# Status\n",
+ "\n",
+ "\n",
+ "\n",
+ "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IEijrePND_2I"
+ },
+ "source": [
+ "# Appendix\n",
+ "\n",
+ "Additional content below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "id": "GMusP4OAxFu6"
+ },
+ "outputs": [],
+ "source": [
+ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
+ "\n",
+ "model = torch.hub.load(\n",
+ " \"ultralytics/yolov5\", \"yolov5s-seg\", force_reload=True, trust_repo=True\n",
+ ") # or yolov5n - yolov5x6 or custom\n",
+ "im = \"https://ultralytics.com/images/zidane.jpg\" # file, Path, PIL.Image, OpenCV, nparray, list\n",
+ "results = model(im) # inference\n",
+ "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
+ ]
+ }
+ ],
+ "metadata": {
+ "accelerator": "GPU",
+ "colab": {
+ "name": "YOLOv5 Segmentation Tutorial",
+ "provenance": [],
+ "toc_visible": true
+ },
+ "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": "3.7.12"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/Transfer Learning/Accident_Classifier/segment/val.py b/Transfer Learning/Accident_Classifier/segment/val.py
new file mode 100644
index 00000000..60a7fe7c
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/segment/val.py
@@ -0,0 +1,522 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""
+Validate a trained YOLOv5 segment model on a segment dataset.
+
+Usage:
+ $ bash data/scripts/get_coco.sh --val --segments # download COCO-segments val split (1G, 5000 images)
+ $ python segment/val.py --weights yolov5s-seg.pt --data coco.yaml --img 640 # validate COCO-segments
+
+Usage - formats:
+ $ python segment/val.py --weights yolov5s-seg.pt # PyTorch
+ yolov5s-seg.torchscript # TorchScript
+ yolov5s-seg.onnx # ONNX Runtime or OpenCV DNN with --dnn
+ yolov5s-seg_openvino_label # OpenVINO
+ yolov5s-seg.engine # TensorRT
+ yolov5s-seg.mlmodel # CoreML (macOS-only)
+ yolov5s-seg_saved_model # TensorFlow SavedModel
+ yolov5s-seg.pb # TensorFlow GraphDef
+ yolov5s-seg.tflite # TensorFlow Lite
+ yolov5s-seg_edgetpu.tflite # TensorFlow Edge TPU
+ yolov5s-seg_paddle_model # PaddlePaddle
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+from multiprocessing.pool import ThreadPool
+from pathlib import Path
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[1] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
+
+import torch.nn.functional as F
+
+from models.common import DetectMultiBackend
+from models.yolo import SegmentationModel
+from utils.callbacks import Callbacks
+from utils.general import (
+ LOGGER,
+ NUM_THREADS,
+ TQDM_BAR_FORMAT,
+ Profile,
+ check_dataset,
+ check_img_size,
+ check_requirements,
+ check_yaml,
+ coco80_to_coco91_class,
+ colorstr,
+ increment_path,
+ non_max_suppression,
+ print_args,
+ scale_boxes,
+ xywh2xyxy,
+ xyxy2xywh,
+)
+from utils.metrics import ConfusionMatrix, box_iou
+from utils.plots import output_to_target, plot_val_study
+from utils.segment.dataloaders import create_dataloader
+from utils.segment.general import mask_iou, process_mask, process_mask_native, scale_image
+from utils.segment.metrics import Metrics, ap_per_class_box_and_mask
+from utils.segment.plots import plot_images_and_masks
+from utils.torch_utils import de_parallel, select_device, smart_inference_mode
+
+
+def save_one_txt(predn, save_conf, shape, file):
+ """Saves detection results in txt format; includes class, xywh (normalized), optionally confidence if `save_conf` is
+ True.
+ """
+ gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
+ for *xyxy, conf, cls in predn.tolist():
+ xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
+ line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
+ with open(file, "a") as f:
+ f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+
+def save_one_json(predn, jdict, path, class_map, pred_masks):
+ """
+ Saves a JSON file with detection results including bounding boxes, category IDs, scores, and segmentation masks.
+
+ Example JSON result: {"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}.
+ """
+ from pycocotools.mask import encode
+
+ def single_encode(x):
+ """Encodes binary mask arrays into RLE (Run-Length Encoding) format for JSON serialization."""
+ rle = encode(np.asarray(x[:, :, None], order="F", dtype="uint8"))[0]
+ rle["counts"] = rle["counts"].decode("utf-8")
+ return rle
+
+ image_id = int(path.stem) if path.stem.isnumeric() else path.stem
+ box = xyxy2xywh(predn[:, :4]) # xywh
+ box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
+ pred_masks = np.transpose(pred_masks, (2, 0, 1))
+ with ThreadPool(NUM_THREADS) as pool:
+ rles = pool.map(single_encode, pred_masks)
+ for i, (p, b) in enumerate(zip(predn.tolist(), box.tolist())):
+ jdict.append(
+ {
+ "image_id": image_id,
+ "category_id": class_map[int(p[5])],
+ "bbox": [round(x, 3) for x in b],
+ "score": round(p[4], 5),
+ "segmentation": rles[i],
+ }
+ )
+
+
+def process_batch(detections, labels, iouv, pred_masks=None, gt_masks=None, overlap=False, masks=False):
+ """
+ Return correct prediction matrix
+ Arguments:
+ detections (array[N, 6]), x1, y1, x2, y2, conf, class
+ labels (array[M, 5]), class, x1, y1, x2, y2
+ Returns:
+ correct (array[N, 10]), for 10 IoU levels.
+ """
+ if masks:
+ if overlap:
+ nl = len(labels)
+ index = torch.arange(nl, device=gt_masks.device).view(nl, 1, 1) + 1
+ gt_masks = gt_masks.repeat(nl, 1, 1) # shape(1,640,640) -> (n,640,640)
+ gt_masks = torch.where(gt_masks == index, 1.0, 0.0)
+ if gt_masks.shape[1:] != pred_masks.shape[1:]:
+ gt_masks = F.interpolate(gt_masks[None], pred_masks.shape[1:], mode="bilinear", align_corners=False)[0]
+ gt_masks = gt_masks.gt_(0.5)
+ iou = mask_iou(gt_masks.view(gt_masks.shape[0], -1), pred_masks.view(pred_masks.shape[0], -1))
+ else: # boxes
+ iou = box_iou(labels[:, 1:], detections[:, :4])
+
+ correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
+ correct_class = labels[:, 0:1] == detections[:, 5]
+ for i in range(len(iouv)):
+ x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
+ if x[0].shape[0]:
+ matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
+ if x[0].shape[0] > 1:
+ matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+ # matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+ correct[matches[:, 1].astype(int), i] = True
+ return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
+
+
+@smart_inference_mode()
+def run(
+ data,
+ weights=None, # model.pt path(s)
+ batch_size=32, # batch size
+ imgsz=640, # inference size (pixels)
+ conf_thres=0.001, # confidence threshold
+ iou_thres=0.6, # NMS IoU threshold
+ max_det=300, # maximum detections per image
+ task="val", # train, val, test, speed or study
+ device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
+ workers=8, # max dataloader workers (per RANK in DDP mode)
+ single_cls=False, # treat as single-class dataset
+ augment=False, # augmented inference
+ verbose=False, # verbose output
+ save_txt=False, # save results to *.txt
+ save_hybrid=False, # save label+prediction hybrid results to *.txt
+ save_conf=False, # save confidences in --save-txt labels
+ save_json=False, # save a COCO-JSON results file
+ project=ROOT / "runs/val-seg", # save to project/name
+ name="exp", # save to project/name
+ exist_ok=False, # existing project/name ok, do not increment
+ half=True, # use FP16 half-precision inference
+ dnn=False, # use OpenCV DNN for ONNX inference
+ model=None,
+ dataloader=None,
+ save_dir=Path(""),
+ plots=True,
+ overlap=False,
+ mask_downsample_ratio=1,
+ compute_loss=None,
+ callbacks=Callbacks(),
+):
+ """Validates a YOLOv5 segmentation model on specified dataset, producing metrics, plots, and optional JSON
+ output.
+ """
+ if save_json:
+ check_requirements("pycocotools>=2.0.6")
+ process = process_mask_native # more accurate
+ else:
+ process = process_mask # faster
+
+ # Initialize/load model and set device
+ training = model is not None
+ if training: # called by train.py
+ device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
+ half &= device.type != "cpu" # half precision only supported on CUDA
+ model.half() if half else model.float()
+ nm = de_parallel(model).model[-1].nm # number of masks
+ else: # called directly
+ device = select_device(device, batch_size=batch_size)
+
+ # Directories
+ save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
+ (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
+
+ # Load model
+ model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+ stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+ imgsz = check_img_size(imgsz, s=stride) # check image size
+ half = model.fp16 # FP16 supported on limited backends with CUDA
+ nm = de_parallel(model).model.model[-1].nm if isinstance(model, SegmentationModel) else 32 # number of masks
+ if engine:
+ batch_size = model.batch_size
+ else:
+ device = model.device
+ if not (pt or jit):
+ batch_size = 1 # export.py models default to batch-size 1
+ LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
+
+ # Data
+ data = check_dataset(data) # check
+
+ # Configure
+ model.eval()
+ cuda = device.type != "cpu"
+ is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset
+ nc = 1 if single_cls else int(data["nc"]) # number of classes
+ iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
+ niou = iouv.numel()
+
+ # Dataloader
+ if not training:
+ if pt and not single_cls: # check --weights are trained on --data
+ ncm = model.model.nc
+ assert ncm == nc, (
+ f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} "
+ f"classes). Pass correct combination of --weights and --data that are trained together."
+ )
+ model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
+ pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks
+ task = task if task in ("train", "val", "test") else "val" # path to train/val/test images
+ dataloader = create_dataloader(
+ data[task],
+ imgsz,
+ batch_size,
+ stride,
+ single_cls,
+ pad=pad,
+ rect=rect,
+ workers=workers,
+ prefix=colorstr(f"{task}: "),
+ overlap_mask=overlap,
+ mask_downsample_ratio=mask_downsample_ratio,
+ )[0]
+
+ seen = 0
+ confusion_matrix = ConfusionMatrix(nc=nc)
+ names = model.names if hasattr(model, "names") else model.module.names # get class names
+ if isinstance(names, (list, tuple)): # old format
+ names = dict(enumerate(names))
+ class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
+ s = ("%22s" + "%11s" * 10) % (
+ "Class",
+ "Images",
+ "Instances",
+ "Box(P",
+ "R",
+ "mAP50",
+ "mAP50-95)",
+ "Mask(P",
+ "R",
+ "mAP50",
+ "mAP50-95)",
+ )
+ dt = Profile(device=device), Profile(device=device), Profile(device=device)
+ metrics = Metrics()
+ loss = torch.zeros(4, device=device)
+ jdict, stats = [], []
+ # callbacks.run('on_val_start')
+ pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
+ for batch_i, (im, targets, paths, shapes, masks) in enumerate(pbar):
+ # callbacks.run('on_val_batch_start')
+ with dt[0]:
+ if cuda:
+ im = im.to(device, non_blocking=True)
+ targets = targets.to(device)
+ masks = masks.to(device)
+ masks = masks.float()
+ im = im.half() if half else im.float() # uint8 to fp16/32
+ im /= 255 # 0 - 255 to 0.0 - 1.0
+ nb, _, height, width = im.shape # batch size, channels, height, width
+
+ # Inference
+ with dt[1]:
+ preds, protos, train_out = model(im) if compute_loss else (*model(im, augment=augment)[:2], None)
+
+ # Loss
+ if compute_loss:
+ loss += compute_loss((train_out, protos), targets, masks)[1] # box, obj, cls
+
+ # NMS
+ targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
+ lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
+ with dt[2]:
+ preds = non_max_suppression(
+ preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det, nm=nm
+ )
+
+ # Metrics
+ plot_masks = [] # masks for plotting
+ for si, (pred, proto) in enumerate(zip(preds, protos)):
+ labels = targets[targets[:, 0] == si, 1:]
+ nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
+ path, shape = Path(paths[si]), shapes[si][0]
+ correct_masks = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
+ correct_bboxes = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
+ seen += 1
+
+ if npr == 0:
+ if nl:
+ stats.append((correct_masks, correct_bboxes, *torch.zeros((2, 0), device=device), labels[:, 0]))
+ if plots:
+ confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
+ continue
+
+ # Masks
+ midx = [si] if overlap else targets[:, 0] == si
+ gt_masks = masks[midx]
+ pred_masks = process(proto, pred[:, 6:], pred[:, :4], shape=im[si].shape[1:])
+
+ # Predictions
+ if single_cls:
+ pred[:, 5] = 0
+ predn = pred.clone()
+ scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
+
+ # Evaluate
+ if nl:
+ tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
+ scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
+ labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
+ correct_bboxes = process_batch(predn, labelsn, iouv)
+ correct_masks = process_batch(predn, labelsn, iouv, pred_masks, gt_masks, overlap=overlap, masks=True)
+ if plots:
+ confusion_matrix.process_batch(predn, labelsn)
+ stats.append((correct_masks, correct_bboxes, pred[:, 4], pred[:, 5], labels[:, 0])) # (conf, pcls, tcls)
+
+ pred_masks = torch.as_tensor(pred_masks, dtype=torch.uint8)
+ if plots and batch_i < 3:
+ plot_masks.append(pred_masks[:15]) # filter top 15 to plot
+
+ # Save/log
+ if save_txt:
+ save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
+ if save_json:
+ pred_masks = scale_image(
+ im[si].shape[1:], pred_masks.permute(1, 2, 0).contiguous().cpu().numpy(), shape, shapes[si][1]
+ )
+ save_one_json(predn, jdict, path, class_map, pred_masks) # append to COCO-JSON dictionary
+ # callbacks.run('on_val_image_end', pred, predn, path, names, im[si])
+
+ # Plot images
+ if plots and batch_i < 3:
+ if len(plot_masks):
+ plot_masks = torch.cat(plot_masks, dim=0)
+ plot_images_and_masks(im, targets, masks, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names)
+ plot_images_and_masks(
+ im,
+ output_to_target(preds, max_det=15),
+ plot_masks,
+ paths,
+ save_dir / f"val_batch{batch_i}_pred.jpg",
+ names,
+ ) # pred
+
+ # callbacks.run('on_val_batch_end')
+
+ # Compute metrics
+ stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
+ if len(stats) and stats[0].any():
+ results = ap_per_class_box_and_mask(*stats, plot=plots, save_dir=save_dir, names=names)
+ metrics.update(results)
+ nt = np.bincount(stats[4].astype(int), minlength=nc) # number of targets per class
+
+ # Print results
+ pf = "%22s" + "%11i" * 2 + "%11.3g" * 8 # print format
+ LOGGER.info(pf % ("all", seen, nt.sum(), *metrics.mean_results()))
+ if nt.sum() == 0:
+ LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels")
+
+ # Print results per class
+ if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
+ for i, c in enumerate(metrics.ap_class_index):
+ LOGGER.info(pf % (names[c], seen, nt[c], *metrics.class_result(i)))
+
+ # Print speeds
+ t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
+ if not training:
+ shape = (batch_size, 3, imgsz, imgsz)
+ LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t)
+
+ # Plots
+ if plots:
+ confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
+ # callbacks.run('on_val_end')
+
+ mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask = metrics.mean_results()
+
+ # Save JSON
+ if save_json and len(jdict):
+ w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights
+ anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations
+ pred_json = str(save_dir / f"{w}_predictions.json") # predictions
+ LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...")
+ with open(pred_json, "w") as f:
+ json.dump(jdict, f)
+
+ try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
+ from pycocotools.coco import COCO
+ from pycocotools.cocoeval import COCOeval
+
+ anno = COCO(anno_json) # init annotations api
+ pred = anno.loadRes(pred_json) # init predictions api
+ results = []
+ for eval in COCOeval(anno, pred, "bbox"), COCOeval(anno, pred, "segm"):
+ if is_coco:
+ eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # img ID to evaluate
+ eval.evaluate()
+ eval.accumulate()
+ eval.summarize()
+ results.extend(eval.stats[:2]) # update results (mAP@0.5:0.95, mAP@0.5)
+ map_bbox, map50_bbox, map_mask, map50_mask = results
+ except Exception as e:
+ LOGGER.info(f"pycocotools unable to run: {e}")
+
+ # Return results
+ model.float() # for training
+ if not training:
+ s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
+ LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+ final_metric = mp_bbox, mr_bbox, map50_bbox, map_bbox, mp_mask, mr_mask, map50_mask, map_mask
+ return (*final_metric, *(loss.cpu() / len(dataloader)).tolist()), metrics.get_maps(nc), t
+
+
+def parse_opt():
+ """Parses command line arguments for configuring YOLOv5 options like dataset path, weights, batch size, and
+ inference settings.
+ """
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--data", type=str, default=ROOT / "data/coco128-seg.yaml", help="dataset.yaml path")
+ parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s-seg.pt", help="model path(s)")
+ parser.add_argument("--batch-size", type=int, default=32, help="batch size")
+ parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
+ parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold")
+ parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
+ parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
+ parser.add_argument("--task", default="val", help="train, val, test, speed or study")
+ parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+ parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+ parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset")
+ parser.add_argument("--augment", action="store_true", help="augmented inference")
+ parser.add_argument("--verbose", action="store_true", help="report mAP by class")
+ parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
+ parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt")
+ parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
+ parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file")
+ parser.add_argument("--project", default=ROOT / "runs/val-seg", help="save results to project/name")
+ parser.add_argument("--name", default="exp", help="save to project/name")
+ parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+ parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+ parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+ opt = parser.parse_args()
+ opt.data = check_yaml(opt.data) # check YAML
+ # opt.save_json |= opt.data.endswith('coco.yaml')
+ opt.save_txt |= opt.save_hybrid
+ print_args(vars(opt))
+ return opt
+
+
+def main(opt):
+ """Executes YOLOv5 tasks including training, validation, testing, speed, and study with configurable options."""
+ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+
+ if opt.task in ("train", "val", "test"): # run normally
+ if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
+ LOGGER.warning(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
+ if opt.save_hybrid:
+ LOGGER.warning("WARNING ⚠️ --save-hybrid returns high mAP from hybrid labels, not from predictions alone")
+ run(**vars(opt))
+
+ else:
+ weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
+ opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results
+ if opt.task == "speed": # speed benchmarks
+ # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
+ opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
+ for opt.weights in weights:
+ run(**vars(opt), plots=False)
+
+ elif opt.task == "study": # speed vs mAP benchmarks
+ # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
+ for opt.weights in weights:
+ f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to
+ x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
+ for opt.imgsz in x: # img-size
+ LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
+ r, _, t = run(**vars(opt), plots=False)
+ y.append(r + t) # results and times
+ np.savetxt(f, y, fmt="%10.4g") # save
+ subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
+ plot_val_study(x=x) # plot
+ else:
+ raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
+
+
+if __name__ == "__main__":
+ opt = parse_opt()
+ main(opt)
diff --git a/Transfer Learning/Accident_Classifier/train.py b/Transfer Learning/Accident_Classifier/train.py
new file mode 100644
index 00000000..b4395d7e
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/train.py
@@ -0,0 +1,986 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""
+Train a YOLOv5 model on a custom dataset. Models and datasets download automatically from the latest YOLOv5 release.
+
+Usage - Single-GPU training:
+ $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)
+ $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
+
+Usage - Multi-GPU DDP training:
+ $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights yolov5s.pt --img 640 --device 0,1,2,3
+
+Models: https://github.com/ultralytics/yolov5/tree/master/models
+Datasets: https://github.com/ultralytics/yolov5/tree/master/data
+Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
+"""
+
+import argparse
+import math
+import os
+import random
+import subprocess
+import sys
+import time
+from copy import deepcopy
+from datetime import datetime, timedelta
+from pathlib import Path
+
+try:
+ import comet_ml # must be imported before torch (if installed)
+except ImportError:
+ comet_ml = None
+
+import numpy as np
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import yaml
+from torch.optim import lr_scheduler
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
+
+import val as validate # for end-of-epoch mAP
+from models.experimental import attempt_load
+from models.yolo import Model
+from utils.autoanchor import check_anchors
+from utils.autobatch import check_train_batch_size
+from utils.callbacks import Callbacks
+from utils.dataloaders import create_dataloader
+from utils.downloads import attempt_download, is_url
+from utils.general import (
+ LOGGER,
+ TQDM_BAR_FORMAT,
+ check_amp,
+ check_dataset,
+ check_file,
+ check_git_info,
+ check_git_status,
+ check_img_size,
+ check_requirements,
+ check_suffix,
+ check_yaml,
+ colorstr,
+ get_latest_run,
+ increment_path,
+ init_seeds,
+ intersect_dicts,
+ labels_to_class_weights,
+ labels_to_image_weights,
+ methods,
+ one_cycle,
+ print_args,
+ print_mutation,
+ strip_optimizer,
+ yaml_save,
+)
+from utils.loggers import LOGGERS, Loggers
+from utils.loggers.comet.comet_utils import check_comet_resume
+from utils.loss import ComputeLoss
+from utils.metrics import fitness
+from utils.plots import plot_evolve
+from utils.torch_utils import (
+ EarlyStopping,
+ ModelEMA,
+ de_parallel,
+ select_device,
+ smart_DDP,
+ smart_optimizer,
+ smart_resume,
+ torch_distributed_zero_first,
+)
+
+LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv("RANK", -1))
+WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
+GIT_INFO = check_git_info()
+
+
+def train(hyp, opt, device, callbacks):
+ """
+ Train a YOLOv5 model on a custom dataset using specified hyperparameters, options, and device, managing datasets,
+ model architecture, loss computation, and optimizer steps.
+
+ Args:
+ hyp (str | dict): Path to the hyperparameters YAML file or a dictionary of hyperparameters.
+ opt (argparse.Namespace): Parsed command-line arguments containing training options.
+ device (torch.device): Device on which training occurs, e.g., 'cuda' or 'cpu'.
+ callbacks (Callbacks): Callback functions for various training events.
+
+ Returns:
+ None
+
+ Models and datasets download automatically from the latest YOLOv5 release.
+
+ Example:
+ Single-GPU training:
+ ```bash
+ $ python train.py --data coco128.yaml --weights yolov5s.pt --img 640 # from pretrained (recommended)
+ $ python train.py --data coco128.yaml --weights '' --cfg yolov5s.yaml --img 640 # from scratch
+ ```
+
+ Multi-GPU DDP training:
+ ```bash
+ $ python -m torch.distributed.run --nproc_per_node 4 --master_port 1 train.py --data coco128.yaml --weights
+ yolov5s.pt --img 640 --device 0,1,2,3
+ ```
+
+ For more usage details, refer to:
+ - Models: https://github.com/ultralytics/yolov5/tree/master/models
+ - Datasets: https://github.com/ultralytics/yolov5/tree/master/data
+ - Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
+ """
+ save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = (
+ Path(opt.save_dir),
+ opt.epochs,
+ opt.batch_size,
+ opt.weights,
+ opt.single_cls,
+ opt.evolve,
+ opt.data,
+ opt.cfg,
+ opt.resume,
+ opt.noval,
+ opt.nosave,
+ opt.workers,
+ opt.freeze,
+ )
+ callbacks.run("on_pretrain_routine_start")
+
+ # Directories
+ w = save_dir / "weights" # weights dir
+ (w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
+ last, best = w / "last.pt", w / "best.pt"
+
+ # Hyperparameters
+ if isinstance(hyp, str):
+ with open(hyp, errors="ignore") as f:
+ hyp = yaml.safe_load(f) # load hyps dict
+ LOGGER.info(colorstr("hyperparameters: ") + ", ".join(f"{k}={v}" for k, v in hyp.items()))
+ opt.hyp = hyp.copy() # for saving hyps to checkpoints
+
+ # Save run settings
+ if not evolve:
+ yaml_save(save_dir / "hyp.yaml", hyp)
+ yaml_save(save_dir / "opt.yaml", vars(opt))
+
+ # Loggers
+ data_dict = None
+ if RANK in {-1, 0}:
+ include_loggers = list(LOGGERS)
+ if getattr(opt, "ndjson_console", False):
+ include_loggers.append("ndjson_console")
+ if getattr(opt, "ndjson_file", False):
+ include_loggers.append("ndjson_file")
+
+ loggers = Loggers(
+ save_dir=save_dir,
+ weights=weights,
+ opt=opt,
+ hyp=hyp,
+ logger=LOGGER,
+ include=tuple(include_loggers),
+ )
+
+ # Register actions
+ for k in methods(loggers):
+ callbacks.register_action(k, callback=getattr(loggers, k))
+
+ # Process custom dataset artifact link
+ data_dict = loggers.remote_dataset
+ if resume: # If resuming runs from remote artifact
+ weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
+
+ # Config
+ plots = not evolve and not opt.noplots # create plots
+ cuda = device.type != "cpu"
+ init_seeds(opt.seed + 1 + RANK, deterministic=True)
+ with torch_distributed_zero_first(LOCAL_RANK):
+ data_dict = data_dict or check_dataset(data) # check if None
+ train_path, val_path = data_dict["train"], data_dict["val"]
+ nc = 1 if single_cls else int(data_dict["nc"]) # number of classes
+ names = {0: "item"} if single_cls and len(data_dict["names"]) != 1 else data_dict["names"] # class names
+ is_coco = isinstance(val_path, str) and val_path.endswith("coco/val2017.txt") # COCO dataset
+
+ # Model
+ check_suffix(weights, ".pt") # check weights
+ pretrained = weights.endswith(".pt")
+ if pretrained:
+ with torch_distributed_zero_first(LOCAL_RANK):
+ weights = attempt_download(weights) # download if not found locally
+ ckpt = torch.load(weights, map_location="cpu") # load checkpoint to CPU to avoid CUDA memory leak
+ model = Model(cfg or ckpt["model"].yaml, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create
+ exclude = ["anchor"] if (cfg or hyp.get("anchors")) and not resume else [] # exclude keys
+ csd = ckpt["model"].float().state_dict() # checkpoint state_dict as FP32
+ csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
+ model.load_state_dict(csd, strict=False) # load
+ LOGGER.info(f"Transferred {len(csd)}/{len(model.state_dict())} items from {weights}") # report
+ else:
+ model = Model(cfg, ch=3, nc=nc, anchors=hyp.get("anchors")).to(device) # create
+ amp = check_amp(model) # check AMP
+
+ # Freeze
+ freeze = [f"model.{x}." for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
+ for k, v in model.named_parameters():
+ v.requires_grad = True # train all layers
+ # v.register_hook(lambda x: torch.nan_to_num(x)) # NaN to 0 (commented for erratic training results)
+ if any(x in k for x in freeze):
+ LOGGER.info(f"freezing {k}")
+ v.requires_grad = False
+
+ # Image size
+ gs = max(int(model.stride.max()), 32) # grid size (max stride)
+ imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
+
+ # Batch size
+ if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
+ batch_size = check_train_batch_size(model, imgsz, amp)
+ loggers.on_params_update({"batch_size": batch_size})
+
+ # Optimizer
+ nbs = 64 # nominal batch size
+ accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
+ hyp["weight_decay"] *= batch_size * accumulate / nbs # scale weight_decay
+ optimizer = smart_optimizer(model, opt.optimizer, hyp["lr0"], hyp["momentum"], hyp["weight_decay"])
+
+ # Scheduler
+ if opt.cos_lr:
+ lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf']
+ else:
+
+ def lf(x):
+ """Linear learning rate scheduler function with decay calculated by epoch proportion."""
+ return (1 - x / epochs) * (1.0 - hyp["lrf"]) + hyp["lrf"] # linear
+
+ scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
+
+ # EMA
+ ema = ModelEMA(model) if RANK in {-1, 0} else None
+
+ # Resume
+ best_fitness, start_epoch = 0.0, 0
+ if pretrained:
+ if resume:
+ best_fitness, start_epoch, epochs = smart_resume(ckpt, optimizer, ema, weights, epochs, resume)
+ del ckpt, csd
+
+ # DP mode
+ if cuda and RANK == -1 and torch.cuda.device_count() > 1:
+ LOGGER.warning(
+ "WARNING ⚠️ DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n"
+ "See Multi-GPU Tutorial at https://docs.ultralytics.com/yolov5/tutorials/multi_gpu_training to get started."
+ )
+ model = torch.nn.DataParallel(model)
+
+ # SyncBatchNorm
+ if opt.sync_bn and cuda and RANK != -1:
+ model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
+ LOGGER.info("Using SyncBatchNorm()")
+
+ # Trainloader
+ train_loader, dataset = create_dataloader(
+ train_path,
+ imgsz,
+ batch_size // WORLD_SIZE,
+ gs,
+ single_cls,
+ hyp=hyp,
+ augment=True,
+ cache=None if opt.cache == "val" else opt.cache,
+ rect=opt.rect,
+ rank=LOCAL_RANK,
+ workers=workers,
+ image_weights=opt.image_weights,
+ quad=opt.quad,
+ prefix=colorstr("train: "),
+ shuffle=True,
+ seed=opt.seed,
+ )
+ labels = np.concatenate(dataset.labels, 0)
+ mlc = int(labels[:, 0].max()) # max label class
+ assert mlc < nc, f"Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}"
+
+ # Process 0
+ if RANK in {-1, 0}:
+ val_loader = create_dataloader(
+ val_path,
+ imgsz,
+ batch_size // WORLD_SIZE * 2,
+ gs,
+ single_cls,
+ hyp=hyp,
+ cache=None if noval else opt.cache,
+ rect=True,
+ rank=-1,
+ workers=workers * 2,
+ pad=0.5,
+ prefix=colorstr("val: "),
+ )[0]
+
+ if not resume:
+ if not opt.noautoanchor:
+ check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz) # run AutoAnchor
+ model.half().float() # pre-reduce anchor precision
+
+ callbacks.run("on_pretrain_routine_end", labels, names)
+
+ # DDP mode
+ if cuda and RANK != -1:
+ model = smart_DDP(model)
+
+ # Model attributes
+ nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
+ hyp["box"] *= 3 / nl # scale to layers
+ hyp["cls"] *= nc / 80 * 3 / nl # scale to classes and layers
+ hyp["obj"] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
+ hyp["label_smoothing"] = opt.label_smoothing
+ model.nc = nc # attach number of classes to model
+ model.hyp = hyp # attach hyperparameters to model
+ model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
+ model.names = names
+
+ # Start training
+ t0 = time.time()
+ nb = len(train_loader) # number of batches
+ nw = max(round(hyp["warmup_epochs"] * nb), 100) # number of warmup iterations, max(3 epochs, 100 iterations)
+ # nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
+ last_opt_step = -1
+ maps = np.zeros(nc) # mAP per class
+ results = (0, 0, 0, 0, 0, 0, 0) # P, R, mAP@.5, mAP@.5-.95, val_loss(box, obj, cls)
+ scheduler.last_epoch = start_epoch - 1 # do not move
+ scaler = torch.cuda.amp.GradScaler(enabled=amp)
+ stopper, stop = EarlyStopping(patience=opt.patience), False
+ compute_loss = ComputeLoss(model) # init loss class
+ callbacks.run("on_train_start")
+ LOGGER.info(
+ f'Image sizes {imgsz} train, {imgsz} val\n'
+ f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
+ f"Logging results to {colorstr('bold', save_dir)}\n"
+ f'Starting training for {epochs} epochs...'
+ )
+ for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
+ callbacks.run("on_train_epoch_start")
+ model.train()
+
+ # Update image weights (optional, single-GPU only)
+ if opt.image_weights:
+ cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
+ iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
+ dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
+
+ # Update mosaic border (optional)
+ # b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
+ # dataset.mosaic_border = [b - imgsz, -b] # height, width borders
+
+ mloss = torch.zeros(3, device=device) # mean losses
+ if RANK != -1:
+ train_loader.sampler.set_epoch(epoch)
+ pbar = enumerate(train_loader)
+ LOGGER.info(("\n" + "%11s" * 7) % ("Epoch", "GPU_mem", "box_loss", "obj_loss", "cls_loss", "Instances", "Size"))
+ if RANK in {-1, 0}:
+ pbar = tqdm(pbar, total=nb, bar_format=TQDM_BAR_FORMAT) # progress bar
+ optimizer.zero_grad()
+ for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
+ callbacks.run("on_train_batch_start")
+ ni = i + nb * epoch # number integrated batches (since train start)
+ imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
+
+ # Warmup
+ if ni <= nw:
+ xi = [0, nw] # x interp
+ # compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
+ accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
+ for j, x in enumerate(optimizer.param_groups):
+ # bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
+ x["lr"] = np.interp(ni, xi, [hyp["warmup_bias_lr"] if j == 0 else 0.0, x["initial_lr"] * lf(epoch)])
+ if "momentum" in x:
+ x["momentum"] = np.interp(ni, xi, [hyp["warmup_momentum"], hyp["momentum"]])
+
+ # Multi-scale
+ if opt.multi_scale:
+ sz = random.randrange(int(imgsz * 0.5), int(imgsz * 1.5) + gs) // gs * gs # size
+ sf = sz / max(imgs.shape[2:]) # scale factor
+ if sf != 1:
+ ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
+ imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False)
+
+ # Forward
+ with torch.cuda.amp.autocast(amp):
+ pred = model(imgs) # forward
+ loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
+ if RANK != -1:
+ loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
+ if opt.quad:
+ loss *= 4.0
+
+ # Backward
+ scaler.scale(loss).backward()
+
+ # Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
+ if ni - last_opt_step >= accumulate:
+ scaler.unscale_(optimizer) # unscale gradients
+ torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=10.0) # clip gradients
+ scaler.step(optimizer) # optimizer.step
+ scaler.update()
+ optimizer.zero_grad()
+ if ema:
+ ema.update(model)
+ last_opt_step = ni
+
+ # Log
+ if RANK in {-1, 0}:
+ mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
+ mem = f"{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G" # (GB)
+ pbar.set_description(
+ ("%11s" * 2 + "%11.4g" * 5)
+ % (f"{epoch}/{epochs - 1}", mem, *mloss, targets.shape[0], imgs.shape[-1])
+ )
+ callbacks.run("on_train_batch_end", model, ni, imgs, targets, paths, list(mloss))
+ if callbacks.stop_training:
+ return
+ # end batch ------------------------------------------------------------------------------------------------
+
+ # Scheduler
+ lr = [x["lr"] for x in optimizer.param_groups] # for loggers
+ scheduler.step()
+
+ if RANK in {-1, 0}:
+ # mAP
+ callbacks.run("on_train_epoch_end", epoch=epoch)
+ ema.update_attr(model, include=["yaml", "nc", "hyp", "names", "stride", "class_weights"])
+ final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
+ if not noval or final_epoch: # Calculate mAP
+ results, maps, _ = validate.run(
+ data_dict,
+ batch_size=batch_size // WORLD_SIZE * 2,
+ imgsz=imgsz,
+ half=amp,
+ model=ema.ema,
+ single_cls=single_cls,
+ dataloader=val_loader,
+ save_dir=save_dir,
+ plots=False,
+ callbacks=callbacks,
+ compute_loss=compute_loss,
+ )
+
+ # Update best mAP
+ fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, mAP@.5, mAP@.5-.95]
+ stop = stopper(epoch=epoch, fitness=fi) # early stop check
+ if fi > best_fitness:
+ best_fitness = fi
+ log_vals = list(mloss) + list(results) + lr
+ callbacks.run("on_fit_epoch_end", log_vals, epoch, best_fitness, fi)
+
+ # Save model
+ if (not nosave) or (final_epoch and not evolve): # if save
+ ckpt = {
+ "epoch": epoch,
+ "best_fitness": best_fitness,
+ "model": deepcopy(de_parallel(model)).half(),
+ "ema": deepcopy(ema.ema).half(),
+ "updates": ema.updates,
+ "optimizer": optimizer.state_dict(),
+ "opt": vars(opt),
+ "git": GIT_INFO, # {remote, branch, commit} if a git repo
+ "date": datetime.now().isoformat(),
+ }
+
+ # Save last, best and delete
+ torch.save(ckpt, last)
+ if best_fitness == fi:
+ torch.save(ckpt, best)
+ if opt.save_period > 0 and epoch % opt.save_period == 0:
+ torch.save(ckpt, w / f"epoch{epoch}.pt")
+ del ckpt
+ callbacks.run("on_model_save", last, epoch, final_epoch, best_fitness, fi)
+
+ # EarlyStopping
+ if RANK != -1: # if DDP training
+ broadcast_list = [stop if RANK == 0 else None]
+ dist.broadcast_object_list(broadcast_list, 0) # broadcast 'stop' to all ranks
+ if RANK != 0:
+ stop = broadcast_list[0]
+ if stop:
+ break # must break all DDP ranks
+
+ # end epoch ----------------------------------------------------------------------------------------------------
+ # end training -----------------------------------------------------------------------------------------------------
+ if RANK in {-1, 0}:
+ LOGGER.info(f"\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.")
+ for f in last, best:
+ if f.exists():
+ strip_optimizer(f) # strip optimizers
+ if f is best:
+ LOGGER.info(f"\nValidating {f}...")
+ results, _, _ = validate.run(
+ data_dict,
+ batch_size=batch_size // WORLD_SIZE * 2,
+ imgsz=imgsz,
+ model=attempt_load(f, device).half(),
+ iou_thres=0.65 if is_coco else 0.60, # best pycocotools at iou 0.65
+ single_cls=single_cls,
+ dataloader=val_loader,
+ save_dir=save_dir,
+ save_json=is_coco,
+ verbose=True,
+ plots=plots,
+ callbacks=callbacks,
+ compute_loss=compute_loss,
+ ) # val best model with plots
+ if is_coco:
+ callbacks.run("on_fit_epoch_end", list(mloss) + list(results) + lr, epoch, best_fitness, fi)
+
+ callbacks.run("on_train_end", last, best, epoch, results)
+
+ torch.cuda.empty_cache()
+ return results
+
+
+def parse_opt(known=False):
+ """
+ Parse command-line arguments for YOLOv5 training, validation, and testing.
+
+ Args:
+ known (bool, optional): If True, parses known arguments, ignoring the unknown. Defaults to False.
+
+ Returns:
+ (argparse.Namespace): Parsed command-line arguments containing options for YOLOv5 execution.
+
+ Example:
+ ```python
+ from ultralytics.yolo import parse_opt
+ opt = parse_opt()
+ print(opt)
+ ```
+
+ Links:
+ - Models: https://github.com/ultralytics/yolov5/tree/master/models
+ - Datasets: https://github.com/ultralytics/yolov5/tree/master/data
+ - Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
+ """
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path")
+ parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
+ parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
+ parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path")
+ parser.add_argument("--epochs", type=int, default=100, help="total training epochs")
+ parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch")
+ parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)")
+ parser.add_argument("--rect", action="store_true", help="rectangular training")
+ parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training")
+ parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
+ parser.add_argument("--noval", action="store_true", help="only validate final epoch")
+ parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor")
+ parser.add_argument("--noplots", action="store_true", help="save no plot files")
+ parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations")
+ parser.add_argument(
+ "--evolve_population", type=str, default=ROOT / "data/hyps", help="location for loading population"
+ )
+ parser.add_argument("--resume_evolve", type=str, default=None, help="resume evolve from last generation")
+ parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
+ parser.add_argument("--cache", type=str, nargs="?", const="ram", help="image --cache ram/disk")
+ parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training")
+ parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+ parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%")
+ parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class")
+ parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer")
+ parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode")
+ parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+ parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name")
+ parser.add_argument("--name", default="exp", help="save to project/name")
+ parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+ parser.add_argument("--quad", action="store_true", help="quad dataloader")
+ parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler")
+ parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon")
+ parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)")
+ parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2")
+ parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)")
+ parser.add_argument("--seed", type=int, default=0, help="Global training seed")
+ parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
+
+ # Logger arguments
+ parser.add_argument("--entity", default=None, help="Entity")
+ parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='Upload data, "val" option')
+ parser.add_argument("--bbox_interval", type=int, default=-1, help="Set bounding-box image logging interval")
+ parser.add_argument("--artifact_alias", type=str, default="latest", help="Version of dataset artifact to use")
+
+ # NDJSON logging
+ parser.add_argument("--ndjson-console", action="store_true", help="Log ndjson to console")
+ parser.add_argument("--ndjson-file", action="store_true", help="Log ndjson to file")
+
+ return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+def main(opt, callbacks=Callbacks()):
+ """
+ Runs the main entry point for training or hyperparameter evolution with specified options and optional callbacks.
+
+ Args:
+ opt (argparse.Namespace): The command-line arguments parsed for YOLOv5 training and evolution.
+ callbacks (ultralytics.utils.callbacks.Callbacks, optional): Callback functions for various training stages.
+ Defaults to Callbacks().
+
+ Returns:
+ None
+
+ Note:
+ For detailed usage, refer to:
+ https://github.com/ultralytics/yolov5/tree/master/models
+ """
+ if RANK in {-1, 0}:
+ print_args(vars(opt))
+ check_git_status()
+ check_requirements(ROOT / "requirements.txt")
+
+ # Resume (from specified or most recent last.pt)
+ if opt.resume and not check_comet_resume(opt) and not opt.evolve:
+ last = Path(check_file(opt.resume) if isinstance(opt.resume, str) else get_latest_run())
+ opt_yaml = last.parent.parent / "opt.yaml" # train options yaml
+ opt_data = opt.data # original dataset
+ if opt_yaml.is_file():
+ with open(opt_yaml, errors="ignore") as f:
+ d = yaml.safe_load(f)
+ else:
+ d = torch.load(last, map_location="cpu")["opt"]
+ opt = argparse.Namespace(**d) # replace
+ opt.cfg, opt.weights, opt.resume = "", str(last), True # reinstate
+ if is_url(opt_data):
+ opt.data = check_file(opt_data) # avoid HUB resume auth timeout
+ else:
+ opt.data, opt.cfg, opt.hyp, opt.weights, opt.project = (
+ check_file(opt.data),
+ check_yaml(opt.cfg),
+ check_yaml(opt.hyp),
+ str(opt.weights),
+ str(opt.project),
+ ) # checks
+ assert len(opt.cfg) or len(opt.weights), "either --cfg or --weights must be specified"
+ if opt.evolve:
+ if opt.project == str(ROOT / "runs/train"): # if default project name, rename to runs/evolve
+ opt.project = str(ROOT / "runs/evolve")
+ opt.exist_ok, opt.resume = opt.resume, False # pass resume to exist_ok and disable resume
+ if opt.name == "cfg":
+ opt.name = Path(opt.cfg).stem # use model.yaml as name
+ opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok))
+
+ # DDP mode
+ device = select_device(opt.device, batch_size=opt.batch_size)
+ if LOCAL_RANK != -1:
+ msg = "is not compatible with YOLOv5 Multi-GPU DDP training"
+ assert not opt.image_weights, f"--image-weights {msg}"
+ assert not opt.evolve, f"--evolve {msg}"
+ assert opt.batch_size != -1, f"AutoBatch with --batch-size -1 {msg}, please pass a valid --batch-size"
+ assert opt.batch_size % WORLD_SIZE == 0, f"--batch-size {opt.batch_size} must be multiple of WORLD_SIZE"
+ assert torch.cuda.device_count() > LOCAL_RANK, "insufficient CUDA devices for DDP command"
+ torch.cuda.set_device(LOCAL_RANK)
+ device = torch.device("cuda", LOCAL_RANK)
+ dist.init_process_group(
+ backend="nccl" if dist.is_nccl_available() else "gloo", timeout=timedelta(seconds=10800)
+ )
+
+ # Train
+ if not opt.evolve:
+ train(opt.hyp, opt, device, callbacks)
+
+ # Evolve hyperparameters (optional)
+ else:
+ # Hyperparameter evolution metadata (including this hyperparameter True-False, lower_limit, upper_limit)
+ meta = {
+ "lr0": (False, 1e-5, 1e-1), # initial learning rate (SGD=1E-2, Adam=1E-3)
+ "lrf": (False, 0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
+ "momentum": (False, 0.6, 0.98), # SGD momentum/Adam beta1
+ "weight_decay": (False, 0.0, 0.001), # optimizer weight decay
+ "warmup_epochs": (False, 0.0, 5.0), # warmup epochs (fractions ok)
+ "warmup_momentum": (False, 0.0, 0.95), # warmup initial momentum
+ "warmup_bias_lr": (False, 0.0, 0.2), # warmup initial bias lr
+ "box": (False, 0.02, 0.2), # box loss gain
+ "cls": (False, 0.2, 4.0), # cls loss gain
+ "cls_pw": (False, 0.5, 2.0), # cls BCELoss positive_weight
+ "obj": (False, 0.2, 4.0), # obj loss gain (scale with pixels)
+ "obj_pw": (False, 0.5, 2.0), # obj BCELoss positive_weight
+ "iou_t": (False, 0.1, 0.7), # IoU training threshold
+ "anchor_t": (False, 2.0, 8.0), # anchor-multiple threshold
+ "anchors": (False, 2.0, 10.0), # anchors per output grid (0 to ignore)
+ "fl_gamma": (False, 0.0, 2.0), # focal loss gamma (efficientDet default gamma=1.5)
+ "hsv_h": (True, 0.0, 0.1), # image HSV-Hue augmentation (fraction)
+ "hsv_s": (True, 0.0, 0.9), # image HSV-Saturation augmentation (fraction)
+ "hsv_v": (True, 0.0, 0.9), # image HSV-Value augmentation (fraction)
+ "degrees": (True, 0.0, 45.0), # image rotation (+/- deg)
+ "translate": (True, 0.0, 0.9), # image translation (+/- fraction)
+ "scale": (True, 0.0, 0.9), # image scale (+/- gain)
+ "shear": (True, 0.0, 10.0), # image shear (+/- deg)
+ "perspective": (True, 0.0, 0.001), # image perspective (+/- fraction), range 0-0.001
+ "flipud": (True, 0.0, 1.0), # image flip up-down (probability)
+ "fliplr": (True, 0.0, 1.0), # image flip left-right (probability)
+ "mosaic": (True, 0.0, 1.0), # image mixup (probability)
+ "mixup": (True, 0.0, 1.0), # image mixup (probability)
+ "copy_paste": (True, 0.0, 1.0),
+ } # segment copy-paste (probability)
+
+ # GA configs
+ pop_size = 50
+ mutation_rate_min = 0.01
+ mutation_rate_max = 0.5
+ crossover_rate_min = 0.5
+ crossover_rate_max = 1
+ min_elite_size = 2
+ max_elite_size = 5
+ tournament_size_min = 2
+ tournament_size_max = 10
+
+ with open(opt.hyp, errors="ignore") as f:
+ hyp = yaml.safe_load(f) # load hyps dict
+ if "anchors" not in hyp: # anchors commented in hyp.yaml
+ hyp["anchors"] = 3
+ if opt.noautoanchor:
+ del hyp["anchors"], meta["anchors"]
+ opt.noval, opt.nosave, save_dir = True, True, Path(opt.save_dir) # only val/save final epoch
+ # ei = [isinstance(x, (int, float)) for x in hyp.values()] # evolvable indices
+ evolve_yaml, evolve_csv = save_dir / "hyp_evolve.yaml", save_dir / "evolve.csv"
+ if opt.bucket:
+ # download evolve.csv if exists
+ subprocess.run(
+ [
+ "gsutil",
+ "cp",
+ f"gs://{opt.bucket}/evolve.csv",
+ str(evolve_csv),
+ ]
+ )
+
+ # Delete the items in meta dictionary whose first value is False
+ del_ = [item for item, value_ in meta.items() if value_[0] is False]
+ hyp_GA = hyp.copy() # Make a copy of hyp dictionary
+ for item in del_:
+ del meta[item] # Remove the item from meta dictionary
+ del hyp_GA[item] # Remove the item from hyp_GA dictionary
+
+ # Set lower_limit and upper_limit arrays to hold the search space boundaries
+ lower_limit = np.array([meta[k][1] for k in hyp_GA.keys()])
+ upper_limit = np.array([meta[k][2] for k in hyp_GA.keys()])
+
+ # Create gene_ranges list to hold the range of values for each gene in the population
+ gene_ranges = [(lower_limit[i], upper_limit[i]) for i in range(len(upper_limit))]
+
+ # Initialize the population with initial_values or random values
+ initial_values = []
+
+ # If resuming evolution from a previous checkpoint
+ if opt.resume_evolve is not None:
+ assert os.path.isfile(ROOT / opt.resume_evolve), "evolve population path is wrong!"
+ with open(ROOT / opt.resume_evolve, errors="ignore") as f:
+ evolve_population = yaml.safe_load(f)
+ for value in evolve_population.values():
+ value = np.array([value[k] for k in hyp_GA.keys()])
+ initial_values.append(list(value))
+
+ # If not resuming from a previous checkpoint, generate initial values from .yaml files in opt.evolve_population
+ else:
+ yaml_files = [f for f in os.listdir(opt.evolve_population) if f.endswith(".yaml")]
+ for file_name in yaml_files:
+ with open(os.path.join(opt.evolve_population, file_name)) as yaml_file:
+ value = yaml.safe_load(yaml_file)
+ value = np.array([value[k] for k in hyp_GA.keys()])
+ initial_values.append(list(value))
+
+ # Generate random values within the search space for the rest of the population
+ if initial_values is None:
+ population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size)]
+ elif pop_size > 1:
+ population = [generate_individual(gene_ranges, len(hyp_GA)) for _ in range(pop_size - len(initial_values))]
+ for initial_value in initial_values:
+ population = [initial_value] + population
+
+ # Run the genetic algorithm for a fixed number of generations
+ list_keys = list(hyp_GA.keys())
+ for generation in range(opt.evolve):
+ if generation >= 1:
+ save_dict = {}
+ for i in range(len(population)):
+ little_dict = {list_keys[j]: float(population[i][j]) for j in range(len(population[i]))}
+ save_dict[f"gen{str(generation)}number{str(i)}"] = little_dict
+
+ with open(save_dir / "evolve_population.yaml", "w") as outfile:
+ yaml.dump(save_dict, outfile, default_flow_style=False)
+
+ # Adaptive elite size
+ elite_size = min_elite_size + int((max_elite_size - min_elite_size) * (generation / opt.evolve))
+ # Evaluate the fitness of each individual in the population
+ fitness_scores = []
+ for individual in population:
+ for key, value in zip(hyp_GA.keys(), individual):
+ hyp_GA[key] = value
+ hyp.update(hyp_GA)
+ results = train(hyp.copy(), opt, device, callbacks)
+ callbacks = Callbacks()
+ # Write mutation results
+ keys = (
+ "metrics/precision",
+ "metrics/recall",
+ "metrics/mAP_0.5",
+ "metrics/mAP_0.5:0.95",
+ "val/box_loss",
+ "val/obj_loss",
+ "val/cls_loss",
+ )
+ print_mutation(keys, results, hyp.copy(), save_dir, opt.bucket)
+ fitness_scores.append(results[2])
+
+ # Select the fittest individuals for reproduction using adaptive tournament selection
+ selected_indices = []
+ for _ in range(pop_size - elite_size):
+ # Adaptive tournament size
+ tournament_size = max(
+ max(2, tournament_size_min),
+ int(min(tournament_size_max, pop_size) - (generation / (opt.evolve / 10))),
+ )
+ # Perform tournament selection to choose the best individual
+ tournament_indices = random.sample(range(pop_size), tournament_size)
+ tournament_fitness = [fitness_scores[j] for j in tournament_indices]
+ winner_index = tournament_indices[tournament_fitness.index(max(tournament_fitness))]
+ selected_indices.append(winner_index)
+
+ # Add the elite individuals to the selected indices
+ elite_indices = [i for i in range(pop_size) if fitness_scores[i] in sorted(fitness_scores)[-elite_size:]]
+ selected_indices.extend(elite_indices)
+ # Create the next generation through crossover and mutation
+ next_generation = []
+ for _ in range(pop_size):
+ parent1_index = selected_indices[random.randint(0, pop_size - 1)]
+ parent2_index = selected_indices[random.randint(0, pop_size - 1)]
+ # Adaptive crossover rate
+ crossover_rate = max(
+ crossover_rate_min, min(crossover_rate_max, crossover_rate_max - (generation / opt.evolve))
+ )
+ if random.uniform(0, 1) < crossover_rate:
+ crossover_point = random.randint(1, len(hyp_GA) - 1)
+ child = population[parent1_index][:crossover_point] + population[parent2_index][crossover_point:]
+ else:
+ child = population[parent1_index]
+ # Adaptive mutation rate
+ mutation_rate = max(
+ mutation_rate_min, min(mutation_rate_max, mutation_rate_max - (generation / opt.evolve))
+ )
+ for j in range(len(hyp_GA)):
+ if random.uniform(0, 1) < mutation_rate:
+ child[j] += random.uniform(-0.1, 0.1)
+ child[j] = min(max(child[j], gene_ranges[j][0]), gene_ranges[j][1])
+ next_generation.append(child)
+ # Replace the old population with the new generation
+ population = next_generation
+ # Print the best solution found
+ best_index = fitness_scores.index(max(fitness_scores))
+ best_individual = population[best_index]
+ print("Best solution found:", best_individual)
+ # Plot results
+ plot_evolve(evolve_csv)
+ LOGGER.info(
+ f'Hyperparameter evolution finished {opt.evolve} generations\n'
+ f"Results saved to {colorstr('bold', save_dir)}\n"
+ f'Usage example: $ python train.py --hyp {evolve_yaml}'
+ )
+
+
+def generate_individual(input_ranges, individual_length):
+ """
+ Generate an individual with random hyperparameters within specified ranges.
+
+ Args:
+ input_ranges (list[tuple[float, float]]): List of tuples where each tuple contains the lower and upper bounds
+ for the corresponding gene (hyperparameter).
+ individual_length (int): The number of genes (hyperparameters) in the individual.
+
+ Returns:
+ list[float]: A list representing a generated individual with random gene values within the specified ranges.
+
+ Example:
+ ```python
+ input_ranges = [(0.01, 0.1), (0.1, 1.0), (0.9, 2.0)]
+ individual_length = 3
+ individual = generate_individual(input_ranges, individual_length)
+ print(individual) # Output: [0.035, 0.678, 1.456] (example output)
+ ```
+
+ Note:
+ The individual returned will have a length equal to `individual_length`, with each gene value being a floating-point
+ number within its specified range in `input_ranges`.
+ """
+ individual = []
+ for i in range(individual_length):
+ lower_bound, upper_bound = input_ranges[i]
+ individual.append(random.uniform(lower_bound, upper_bound))
+ return individual
+
+
+def run(**kwargs):
+ """
+ Execute YOLOv5 training with specified options, allowing optional overrides through keyword arguments.
+
+ Args:
+ weights (str, optional): Path to initial weights. Defaults to ROOT / 'yolov5s.pt'.
+ cfg (str, optional): Path to model YAML configuration. Defaults to an empty string.
+ data (str, optional): Path to dataset YAML configuration. Defaults to ROOT / 'data/coco128.yaml'.
+ hyp (str, optional): Path to hyperparameters YAML configuration. Defaults to ROOT / 'data/hyps/hyp.scratch-low.yaml'.
+ epochs (int, optional): Total number of training epochs. Defaults to 100.
+ batch_size (int, optional): Total batch size for all GPUs. Use -1 for automatic batch size determination. Defaults to 16.
+ imgsz (int, optional): Image size (pixels) for training and validation. Defaults to 640.
+ rect (bool, optional): Use rectangular training. Defaults to False.
+ resume (bool | str, optional): Resume most recent training with an optional path. Defaults to False.
+ nosave (bool, optional): Only save the final checkpoint. Defaults to False.
+ noval (bool, optional): Only validate at the final epoch. Defaults to False.
+ noautoanchor (bool, optional): Disable AutoAnchor. Defaults to False.
+ noplots (bool, optional): Do not save plot files. Defaults to False.
+ evolve (int, optional): Evolve hyperparameters for a specified number of generations. Use 300 if provided without a
+ value.
+ evolve_population (str, optional): Directory for loading population during evolution. Defaults to ROOT / 'data/ hyps'.
+ resume_evolve (str, optional): Resume hyperparameter evolution from the last generation. Defaults to None.
+ bucket (str, optional): gsutil bucket for saving checkpoints. Defaults to an empty string.
+ cache (str, optional): Cache image data in 'ram' or 'disk'. Defaults to None.
+ image_weights (bool, optional): Use weighted image selection for training. Defaults to False.
+ device (str, optional): CUDA device identifier, e.g., '0', '0,1,2,3', or 'cpu'. Defaults to an empty string.
+ multi_scale (bool, optional): Use multi-scale training, varying image size by ±50%. Defaults to False.
+ single_cls (bool, optional): Train with multi-class data as single-class. Defaults to False.
+ optimizer (str, optional): Optimizer type, choices are ['SGD', 'Adam', 'AdamW']. Defaults to 'SGD'.
+ sync_bn (bool, optional): Use synchronized BatchNorm, only available in DDP mode. Defaults to False.
+ workers (int, optional): Maximum dataloader workers per rank in DDP mode. Defaults to 8.
+ project (str, optional): Directory for saving training runs. Defaults to ROOT / 'runs/train'.
+ name (str, optional): Name for saving the training run. Defaults to 'exp'.
+ exist_ok (bool, optional): Allow existing project/name without incrementing. Defaults to False.
+ quad (bool, optional): Use quad dataloader. Defaults to False.
+ cos_lr (bool, optional): Use cosine learning rate scheduler. Defaults to False.
+ label_smoothing (float, optional): Label smoothing epsilon value. Defaults to 0.0.
+ patience (int, optional): Patience for early stopping, measured in epochs without improvement. Defaults to 100.
+ freeze (list, optional): Layers to freeze, e.g., backbone=10, first 3 layers = [0, 1, 2]. Defaults to [0].
+ save_period (int, optional): Frequency in epochs to save checkpoints. Disabled if < 1. Defaults to -1.
+ seed (int, optional): Global training random seed. Defaults to 0.
+ local_rank (int, optional): Automatic DDP Multi-GPU argument. Do not modify. Defaults to -1.
+
+ Returns:
+ None: The function initiates YOLOv5 training or hyperparameter evolution based on the provided options.
+
+ Examples:
+ ```python
+ import train
+ train.run(data='coco128.yaml', imgsz=320, weights='yolov5m.pt')
+ ```
+
+ Notes:
+ - Models: https://github.com/ultralytics/yolov5/tree/master/models
+ - Datasets: https://github.com/ultralytics/yolov5/tree/master/data
+ - Tutorial: https://docs.ultralytics.com/yolov5/tutorials/train_custom_data
+ """
+ opt = parse_opt(True)
+ for k, v in kwargs.items():
+ setattr(opt, k, v)
+ main(opt)
+ return opt
+
+
+if __name__ == "__main__":
+ opt = parse_opt()
+ main(opt)
diff --git a/Transfer Learning/Accident_Classifier/tutorial.ipynb b/Transfer Learning/Accident_Classifier/tutorial.ipynb
new file mode 100644
index 00000000..ebc6c0b2
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/tutorial.ipynb
@@ -0,0 +1,604 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "name": "YOLOv5 Tutorial",
+ "provenance": []
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "accelerator": "GPU"
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "t6MPjfT5NrKQ"
+ },
+ "source": [
+ "\n",
+ "\n",
+ "[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [العربية](https://docs.ultralytics.com/ar/)\n",
+ "\n",
+ " \n",
+ " \n",
+ " \n",
+ "\n",
+ "This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.![](\"https://user-images.githubusercontent.com/26833433/127574988-6a558aa1-d268-44b9-bf6b-62d4c605cc72.jpg\")
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "0eq1SMWl6Sfn"
+ },
+ "source": [
+ "# 2. Validate\n",
+ "Validate a model's accuracy on the [COCO](https://cocodataset.org/#home) dataset's `val` or `test` splits. Models are downloaded automatically from the [latest YOLOv5 release](https://github.com/ultralytics/yolov5/releases). To show results by class use the `--verbose` flag."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "WQPtK1QYVaD_",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "cf7d52f0-281c-4c96-a488-79f5908f8426"
+ },
+ "source": [
+ "# Download COCO val\n",
+ "torch.hub.download_url_to_file('https://github.com/ultralytics/assets/releases/download/v0.0.0/coco2017val.zip', 'tmp.zip') # download (780M - 5000 images)\n",
+ "!unzip -q tmp.zip -d ../datasets && rm tmp.zip # unzip"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stderr",
+ "text": [
+ "100%|██████████| 780M/780M [00:12<00:00, 66.6MB/s]\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "X58w8JLpMnjH",
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "outputId": "3e234e05-ee8b-4ad1-b1a4-f6a55d5e4f3d"
+ },
+ "source": [
+ "# Validate YOLOv5s on COCO val\n",
+ "!python val.py --weights yolov5s.pt --data coco.yaml --img 640 --half"
+ ],
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "\u001b[34m\u001b[1mval: \u001b[0mdata=/content/yolov5/data/coco.yaml, weights=['yolov5s.pt'], batch_size=32, imgsz=640, conf_thres=0.001, iou_thres=0.6, max_det=300, task=val, device=, workers=8, single_cls=False, augment=False, verbose=False, save_txt=False, save_hybrid=False, save_conf=False, save_json=True, project=runs/val, name=exp, exist_ok=False, half=True, dnn=False\n",
+ "YOLOv5 🚀 v7.0-136-g71244ae Python-3.9.16 torch-2.0.0+cu118 CUDA:0 (Tesla T4, 15102MiB)\n",
+ "\n",
+ "Fusing layers... \n",
+ "YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mScanning /content/datasets/coco/val2017... 4952 images, 48 backgrounds, 0 corrupt: 100% 5000/5000 [00:02<00:00, 2024.59it/s]\n",
+ "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: /content/datasets/coco/val2017.cache\n",
+ " Class Images Instances P R mAP50 mAP50-95: 100% 157/157 [01:25<00:00, 1.84it/s]\n",
+ " all 5000 36335 0.671 0.519 0.566 0.371\n",
+ "Speed: 0.1ms pre-process, 3.1ms inference, 2.3ms NMS per image at shape (32, 3, 640, 640)\n",
+ "\n",
+ "Evaluating pycocotools mAP... saving runs/val/exp/yolov5s_predictions.json...\n",
+ "loading annotations into memory...\n",
+ "Done (t=0.43s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Loading and preparing results...\n",
+ "DONE (t=5.32s)\n",
+ "creating index...\n",
+ "index created!\n",
+ "Running per image evaluation...\n",
+ "Evaluate annotation type *bbox*\n",
+ "DONE (t=78.89s).\n",
+ "Accumulating evaluation results...\n",
+ "DONE (t=14.51s).\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.374\n",
+ " Average Precision (AP) @[ IoU=0.50 | area= all | maxDets=100 ] = 0.572\n",
+ " Average Precision (AP) @[ IoU=0.75 | area= all | maxDets=100 ] = 0.402\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.211\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.423\n",
+ " Average Precision (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.489\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 1 ] = 0.311\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets= 10 ] = 0.516\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= all | maxDets=100 ] = 0.566\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.378\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.625\n",
+ " Average Recall (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.722\n",
+ "Results saved to \u001b[1mruns/val/exp\u001b[0m\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "ZY2VXXXu74w5"
+ },
+ "source": [
+ "# 3. Train\n",
+ "\n",
+ "
"
+ ],
+ "metadata": {
+ "id": "nWOsI5wJR1o3"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## ClearML Logging and Automation 🌟 NEW\n",
+ "\n",
+ "[ClearML](https://cutt.ly/yolov5-notebook-clearml) is completely integrated into YOLOv5 to track your experimentation, manage dataset versions and even remotely execute training runs. To enable ClearML (check cells above):\n",
+ "\n",
+ "- `pip install clearml`\n",
+ "- run `clearml-init` to connect to a ClearML server (**deploy your own [open-source server](https://github.com/allegroai/clearml-server)**, or use our [free hosted server](https://cutt.ly/yolov5-notebook-clearml))\n",
+ "\n",
+ "You'll get all the great expected features from an experiment manager: live updates, model upload, experiment comparison etc. but ClearML also tracks uncommitted changes and installed packages for example. Thanks to that ClearML Tasks (which is what we call experiments) are also reproducible on different machines! With only 1 extra line, we can schedule a YOLOv5 training task on a queue to be executed by any number of ClearML Agents (workers).\n",
+ "\n",
+ "You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!\n",
+ "\n",
+ "\n",
+ ""
+ ],
+ "metadata": {
+ "id": "Lay2WsTjNJzP"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "-WPvRbS5Swl6"
+ },
+ "source": [
+ "## Local Logging\n",
+ "\n",
+ "Training results are automatically logged with [Tensorboard](https://www.tensorflow.org/tensorboard) and [CSV](https://github.com/ultralytics/yolov5/pull/4148) loggers to `runs/train`, with a new experiment directory created for each new training as `runs/train/exp2`, `runs/train/exp3`, etc.\n",
+ "\n",
+ "This directory contains train and val statistics, mosaics, labels, predictions and augmentated mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.\n",
+ "\n",
+ "\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "Zelyeqbyt3GD"
+ },
+ "source": [
+ "# Environments\n",
+ "\n",
+ "YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled):\n",
+ "\n",
+ "- **Notebooks** with free GPU: \n",
+ "- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/)\n",
+ "- **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/aws_quickstart_tutorial/)\n",
+ "- **Docker Image**. See [Docker Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/docker_image_quickstart_tutorial/) \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "6Qu7Iesl0p54"
+ },
+ "source": [
+ "# Status\n",
+ "\n",
+ "\n",
+ "\n",
+ "If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training ([train.py](https://github.com/ultralytics/yolov5/blob/master/train.py)), testing ([val.py](https://github.com/ultralytics/yolov5/blob/master/val.py)), inference ([detect.py](https://github.com/ultralytics/yolov5/blob/master/detect.py)) and export ([export.py](https://github.com/ultralytics/yolov5/blob/master/export.py)) on macOS, Windows, and Ubuntu every 24 hours and on every commit.\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "IEijrePND_2I"
+ },
+ "source": [
+ "# Appendix\n",
+ "\n",
+ "Additional content below."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "metadata": {
+ "id": "GMusP4OAxFu6"
+ },
+ "source": [
+ "# YOLOv5 PyTorch HUB Inference (DetectionModels only)\n",
+ "import torch\n",
+ "\n",
+ "model = torch.hub.load('ultralytics/yolov5', 'yolov5s', force_reload=True, trust_repo=True) # or yolov5n - yolov5x6 or custom\n",
+ "im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list\n",
+ "results = model(im) # inference\n",
+ "results.print() # or .show(), .save(), .crop(), .pandas(), etc."
+ ],
+ "execution_count": null,
+ "outputs": []
+ }
+ ]
+}
diff --git a/Transfer Learning/Accident_Classifier/utils/__init__.py b/Transfer Learning/Accident_Classifier/utils/__init__.py
new file mode 100644
index 00000000..185afd69
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/__init__.py
@@ -0,0 +1,97 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""utils/initialization."""
+
+import contextlib
+import platform
+import threading
+
+
+def emojis(str=""):
+ """Returns an emoji-safe version of a string, stripped of emojis on Windows platforms."""
+ return str.encode().decode("ascii", "ignore") if platform.system() == "Windows" else str
+
+
+class TryExcept(contextlib.ContextDecorator):
+ """A context manager and decorator for error handling that prints an optional message with emojis on exception."""
+
+ def __init__(self, msg=""):
+ """Initializes TryExcept with an optional message, used as a decorator or context manager for error handling."""
+ self.msg = msg
+
+ def __enter__(self):
+ """Enter the runtime context related to this object for error handling with an optional message."""
+ pass
+
+ def __exit__(self, exc_type, value, traceback):
+ """Context manager exit method that prints an error message with emojis if an exception occurred, always returns
+ True.
+ """
+ if value:
+ print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}"))
+ return True
+
+
+def threaded(func):
+ """Decorator @threaded to run a function in a separate thread, returning the thread instance."""
+
+ def wrapper(*args, **kwargs):
+ """Runs the decorated function in a separate daemon thread and returns the thread instance."""
+ thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True)
+ thread.start()
+ return thread
+
+ return wrapper
+
+
+def join_threads(verbose=False):
+ """
+ Joins all daemon threads, optionally printing their names if verbose is True.
+
+ Example: atexit.register(lambda: join_threads())
+ """
+ main_thread = threading.current_thread()
+ for t in threading.enumerate():
+ if t is not main_thread:
+ if verbose:
+ print(f"Joining thread {t.name}")
+ t.join()
+
+
+def notebook_init(verbose=True):
+ """Initializes notebook environment by checking requirements, cleaning up, and displaying system info."""
+ print("Checking setup...")
+
+ import os
+ import shutil
+
+ from ultralytics.utils.checks import check_requirements
+
+ from utils.general import check_font, is_colab
+ from utils.torch_utils import select_device # imports
+
+ check_font()
+
+ import psutil
+
+ if check_requirements("wandb", install=False):
+ os.system("pip uninstall -y wandb") # eliminate unexpected account creation prompt with infinite hang
+ if is_colab():
+ shutil.rmtree("/content/sample_data", ignore_errors=True) # remove colab /sample_data directory
+
+ # System info
+ display = None
+ if verbose:
+ gb = 1 << 30 # bytes to GiB (1024 ** 3)
+ ram = psutil.virtual_memory().total
+ total, used, free = shutil.disk_usage("/")
+ with contextlib.suppress(Exception): # clear display if ipython is installed
+ from IPython import display
+
+ display.clear_output()
+ s = f"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)"
+ else:
+ s = ""
+
+ select_device(newline=False)
+ print(emojis(f"Setup complete ✅ {s}"))
+ return display
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diff --git a/Transfer Learning/Accident_Classifier/utils/activations.py b/Transfer Learning/Accident_Classifier/utils/activations.py
new file mode 100644
index 00000000..44bec8cd
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/activations.py
@@ -0,0 +1,134 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Activation functions."""
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+
+class SiLU(nn.Module):
+ """Applies the Sigmoid-weighted Linear Unit (SiLU) activation function, also known as Swish."""
+
+ @staticmethod
+ def forward(x):
+ """
+ Applies the Sigmoid-weighted Linear Unit (SiLU) activation function.
+
+ https://arxiv.org/pdf/1606.08415.pdf.
+ """
+ return x * torch.sigmoid(x)
+
+
+class Hardswish(nn.Module):
+ """Applies the Hardswish activation function, which is efficient for mobile and embedded devices."""
+
+ @staticmethod
+ def forward(x):
+ """
+ Applies the Hardswish activation function, compatible with TorchScript, CoreML, and ONNX.
+
+ Equivalent to x * F.hardsigmoid(x)
+ """
+ return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX
+
+
+class Mish(nn.Module):
+ """Mish activation https://github.com/digantamisra98/Mish."""
+
+ @staticmethod
+ def forward(x):
+ """Applies the Mish activation function, a smooth alternative to ReLU."""
+ return x * F.softplus(x).tanh()
+
+
+class MemoryEfficientMish(nn.Module):
+ """Efficiently applies the Mish activation function using custom autograd for reduced memory usage."""
+
+ class F(torch.autograd.Function):
+ """Implements a custom autograd function for memory-efficient Mish activation."""
+
+ @staticmethod
+ def forward(ctx, x):
+ """Applies the Mish activation function, a smooth ReLU alternative, to the input tensor `x`."""
+ ctx.save_for_backward(x)
+ return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x)))
+
+ @staticmethod
+ def backward(ctx, grad_output):
+ """Computes the gradient of the Mish activation function with respect to input `x`."""
+ x = ctx.saved_tensors[0]
+ sx = torch.sigmoid(x)
+ fx = F.softplus(x).tanh()
+ return grad_output * (fx + x * sx * (1 - fx * fx))
+
+ def forward(self, x):
+ """Applies the Mish activation function to the input tensor `x`."""
+ return self.F.apply(x)
+
+
+class FReLU(nn.Module):
+ """FReLU activation https://arxiv.org/abs/2007.11824."""
+
+ def __init__(self, c1, k=3): # ch_in, kernel
+ """Initializes FReLU activation with channel `c1` and kernel size `k`."""
+ super().__init__()
+ self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False)
+ self.bn = nn.BatchNorm2d(c1)
+
+ def forward(self, x):
+ """
+ Applies FReLU activation with max operation between input and BN-convolved input.
+
+ https://arxiv.org/abs/2007.11824
+ """
+ return torch.max(x, self.bn(self.conv(x)))
+
+
+class AconC(nn.Module):
+ """
+ ACON activation (activate or not) function.
+
+ AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
+ See "Activate or Not: Learning Customized Activation" https://arxiv.org/pdf/2009.04759.pdf.
+ """
+
+ def __init__(self, c1):
+ """Initializes AconC with learnable parameters p1, p2, and beta for channel-wise activation control."""
+ super().__init__()
+ self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
+ self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
+ self.beta = nn.Parameter(torch.ones(1, c1, 1, 1))
+
+ def forward(self, x):
+ """Applies AconC activation function with learnable parameters for channel-wise control on input tensor x."""
+ dpx = (self.p1 - self.p2) * x
+ return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x
+
+
+class MetaAconC(nn.Module):
+ """
+ ACON activation (activate or not) function.
+
+ AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter
+ See "Activate or Not: Learning Customized Activation" https://arxiv.org/pdf/2009.04759.pdf.
+ """
+
+ def __init__(self, c1, k=1, s=1, r=16):
+ """Initializes MetaAconC with params: channel_in (c1), kernel size (k=1), stride (s=1), reduction (r=16)."""
+ super().__init__()
+ c2 = max(r, c1 // r)
+ self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1))
+ self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1))
+ self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True)
+ self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True)
+ # self.bn1 = nn.BatchNorm2d(c2)
+ # self.bn2 = nn.BatchNorm2d(c1)
+
+ def forward(self, x):
+ """Applies a forward pass transforming input `x` using learnable parameters and sigmoid activation."""
+ y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True)
+ # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891
+ # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable
+ beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed
+ dpx = (self.p1 - self.p2) * x
+ return dpx * torch.sigmoid(beta * dpx) + self.p2 * x
diff --git a/Transfer Learning/Accident_Classifier/utils/augmentations.py b/Transfer Learning/Accident_Classifier/utils/augmentations.py
new file mode 100644
index 00000000..af4c4057
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/augmentations.py
@@ -0,0 +1,448 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Image augmentation functions."""
+
+import math
+import random
+
+import cv2
+import numpy as np
+import torch
+import torchvision.transforms as T
+import torchvision.transforms.functional as TF
+
+from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
+from utils.metrics import bbox_ioa
+
+IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean
+IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation
+
+
+class Albumentations:
+ """Provides optional data augmentation for YOLOv5 using Albumentations library if installed."""
+
+ def __init__(self, size=640):
+ """Initializes Albumentations class for optional data augmentation in YOLOv5 with specified input size."""
+ self.transform = None
+ prefix = colorstr("albumentations: ")
+ try:
+ import albumentations as A
+
+ check_version(A.__version__, "1.0.3", hard=True) # version requirement
+
+ T = [
+ A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0),
+ A.Blur(p=0.01),
+ A.MedianBlur(p=0.01),
+ A.ToGray(p=0.01),
+ A.CLAHE(p=0.01),
+ A.RandomBrightnessContrast(p=0.0),
+ A.RandomGamma(p=0.0),
+ A.ImageCompression(quality_lower=75, p=0.0),
+ ] # transforms
+ self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"]))
+
+ LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
+ except ImportError: # package not installed, skip
+ pass
+ except Exception as e:
+ LOGGER.info(f"{prefix}{e}")
+
+ def __call__(self, im, labels, p=1.0):
+ """Applies transformations to an image and labels with probability `p`, returning updated image and labels."""
+ if self.transform and random.random() < p:
+ new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed
+ im, labels = new["image"], np.array([[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])])
+ return im, labels
+
+
+def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False):
+ """
+ Applies ImageNet normalization to RGB images in BCHW format, modifying them in-place if specified.
+
+ Example: y = (x - mean) / std
+ """
+ return TF.normalize(x, mean, std, inplace=inplace)
+
+
+def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD):
+ """Reverses ImageNet normalization for BCHW format RGB images by applying `x = x * std + mean`."""
+ for i in range(3):
+ x[:, i] = x[:, i] * std[i] + mean[i]
+ return x
+
+
+def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5):
+ """Applies HSV color-space augmentation to an image with random gains for hue, saturation, and value."""
+ if hgain or sgain or vgain:
+ r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains
+ hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV))
+ dtype = im.dtype # uint8
+
+ x = np.arange(0, 256, dtype=r.dtype)
+ lut_hue = ((x * r[0]) % 180).astype(dtype)
+ lut_sat = np.clip(x * r[1], 0, 255).astype(dtype)
+ lut_val = np.clip(x * r[2], 0, 255).astype(dtype)
+
+ im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val)))
+ cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed
+
+
+def hist_equalize(im, clahe=True, bgr=False):
+ """Equalizes image histogram, with optional CLAHE, for BGR or RGB image with shape (n,m,3) and range 0-255."""
+ yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV)
+ if clahe:
+ c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8))
+ yuv[:, :, 0] = c.apply(yuv[:, :, 0])
+ else:
+ yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram
+ return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB
+
+
+def replicate(im, labels):
+ """
+ Replicates half of the smallest object labels in an image for data augmentation.
+
+ Returns augmented image and labels.
+ """
+ h, w = im.shape[:2]
+ boxes = labels[:, 1:].astype(int)
+ x1, y1, x2, y2 = boxes.T
+ s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels)
+ for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices
+ x1b, y1b, x2b, y2b = boxes[i]
+ bh, bw = y2b - y1b, x2b - x1b
+ yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y
+ x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh]
+ im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax]
+ labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0)
+
+ return im, labels
+
+
+def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32):
+ """Resizes and pads image to new_shape with stride-multiple constraints, returns resized image, ratio, padding."""
+ shape = im.shape[:2] # current shape [height, width]
+ if isinstance(new_shape, int):
+ new_shape = (new_shape, new_shape)
+
+ # Scale ratio (new / old)
+ r = min(new_shape[0] / shape[0], new_shape[1] / shape[1])
+ if not scaleup: # only scale down, do not scale up (for better val mAP)
+ r = min(r, 1.0)
+
+ # Compute padding
+ ratio = r, r # width, height ratios
+ new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r))
+ dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding
+ if auto: # minimum rectangle
+ dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding
+ elif scaleFill: # stretch
+ dw, dh = 0.0, 0.0
+ new_unpad = (new_shape[1], new_shape[0])
+ ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios
+
+ dw /= 2 # divide padding into 2 sides
+ dh /= 2
+
+ if shape[::-1] != new_unpad: # resize
+ im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR)
+ top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1))
+ left, right = int(round(dw - 0.1)), int(round(dw + 0.1))
+ im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border
+ return im, ratio, (dw, dh)
+
+
+def random_perspective(
+ im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0)
+):
+ # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10))
+ # targets = [cls, xyxy]
+ """Applies random perspective transformation to an image, modifying the image and corresponding labels."""
+ height = im.shape[0] + border[0] * 2 # shape(h,w,c)
+ width = im.shape[1] + border[1] * 2
+
+ # Center
+ C = np.eye(3)
+ C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
+ C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
+
+ # Perspective
+ P = np.eye(3)
+ P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
+ P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
+
+ # Rotation and Scale
+ R = np.eye(3)
+ a = random.uniform(-degrees, degrees)
+ # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
+ s = random.uniform(1 - scale, 1 + scale)
+ # s = 2 ** random.uniform(-scale, scale)
+ R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+ # Shear
+ S = np.eye(3)
+ S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
+ S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
+
+ # Translation
+ T = np.eye(3)
+ T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
+ T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
+
+ # Combined rotation matrix
+ M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
+ if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
+ if perspective:
+ im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
+ else: # affine
+ im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
+
+ # Visualize
+ # import matplotlib.pyplot as plt
+ # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+ # ax[0].imshow(im[:, :, ::-1]) # base
+ # ax[1].imshow(im2[:, :, ::-1]) # warped
+
+ # Transform label coordinates
+ n = len(targets)
+ if n:
+ use_segments = any(x.any() for x in segments) and len(segments) == n
+ new = np.zeros((n, 4))
+ if use_segments: # warp segments
+ segments = resample_segments(segments) # upsample
+ for i, segment in enumerate(segments):
+ xy = np.ones((len(segment), 3))
+ xy[:, :2] = segment
+ xy = xy @ M.T # transform
+ xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
+
+ # clip
+ new[i] = segment2box(xy, width, height)
+
+ else: # warp boxes
+ xy = np.ones((n * 4, 3))
+ xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1
+ xy = xy @ M.T # transform
+ xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine
+
+ # create new boxes
+ x = xy[:, [0, 2, 4, 6]]
+ y = xy[:, [1, 3, 5, 7]]
+ new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T
+
+ # clip
+ new[:, [0, 2]] = new[:, [0, 2]].clip(0, width)
+ new[:, [1, 3]] = new[:, [1, 3]].clip(0, height)
+
+ # filter candidates
+ i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10)
+ targets = targets[i]
+ targets[:, 1:5] = new[i]
+
+ return im, targets
+
+
+def copy_paste(im, labels, segments, p=0.5):
+ """
+ Applies Copy-Paste augmentation by flipping and merging segments and labels on an image.
+
+ Details at https://arxiv.org/abs/2012.07177.
+ """
+ n = len(segments)
+ if p and n:
+ h, w, c = im.shape # height, width, channels
+ im_new = np.zeros(im.shape, np.uint8)
+ for j in random.sample(range(n), k=round(p * n)):
+ l, s = labels[j], segments[j]
+ box = w - l[3], l[2], w - l[1], l[4]
+ ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area
+ if (ioa < 0.30).all(): # allow 30% obscuration of existing labels
+ labels = np.concatenate((labels, [[l[0], *box]]), 0)
+ segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1))
+ cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED)
+
+ result = cv2.flip(im, 1) # augment segments (flip left-right)
+ i = cv2.flip(im_new, 1).astype(bool)
+ im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug
+
+ return im, labels, segments
+
+
+def cutout(im, labels, p=0.5):
+ """
+ Applies cutout augmentation to an image with optional label adjustment, using random masks of varying sizes.
+
+ Details at https://arxiv.org/abs/1708.04552.
+ """
+ if random.random() < p:
+ h, w = im.shape[:2]
+ scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction
+ for s in scales:
+ mask_h = random.randint(1, int(h * s)) # create random masks
+ mask_w = random.randint(1, int(w * s))
+
+ # box
+ xmin = max(0, random.randint(0, w) - mask_w // 2)
+ ymin = max(0, random.randint(0, h) - mask_h // 2)
+ xmax = min(w, xmin + mask_w)
+ ymax = min(h, ymin + mask_h)
+
+ # apply random color mask
+ im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)]
+
+ # return unobscured labels
+ if len(labels) and s > 0.03:
+ box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32)
+ ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area
+ labels = labels[ioa < 0.60] # remove >60% obscured labels
+
+ return labels
+
+
+def mixup(im, labels, im2, labels2):
+ """
+ Applies MixUp augmentation by blending images and labels.
+
+ See https://arxiv.org/pdf/1710.09412.pdf for details.
+ """
+ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
+ im = (im * r + im2 * (1 - r)).astype(np.uint8)
+ labels = np.concatenate((labels, labels2), 0)
+ return im, labels
+
+
+def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16):
+ """
+ Filters bounding box candidates by minimum width-height threshold `wh_thr` (pixels), aspect ratio threshold
+ `ar_thr`, and area ratio threshold `area_thr`.
+
+ box1(4,n) is before augmentation, box2(4,n) is after augmentation.
+ """
+ w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
+ w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
+ ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
+ return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates
+
+
+def classify_albumentations(
+ augment=True,
+ size=224,
+ scale=(0.08, 1.0),
+ ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33
+ hflip=0.5,
+ vflip=0.0,
+ jitter=0.4,
+ mean=IMAGENET_MEAN,
+ std=IMAGENET_STD,
+ auto_aug=False,
+):
+ # YOLOv5 classification Albumentations (optional, only used if package is installed)
+ """Sets up and returns Albumentations transforms for YOLOv5 classification tasks depending on augmentation
+ settings.
+ """
+ prefix = colorstr("albumentations: ")
+ try:
+ import albumentations as A
+ from albumentations.pytorch import ToTensorV2
+
+ check_version(A.__version__, "1.0.3", hard=True) # version requirement
+ if augment: # Resize and crop
+ T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)]
+ if auto_aug:
+ # TODO: implement AugMix, AutoAug & RandAug in albumentation
+ LOGGER.info(f"{prefix}auto augmentations are currently not supported")
+ else:
+ if hflip > 0:
+ T += [A.HorizontalFlip(p=hflip)]
+ if vflip > 0:
+ T += [A.VerticalFlip(p=vflip)]
+ if jitter > 0:
+ color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, saturation, 0 hue
+ T += [A.ColorJitter(*color_jitter, 0)]
+ else: # Use fixed crop for eval set (reproducibility)
+ T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)]
+ T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor
+ LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p))
+ return A.Compose(T)
+
+ except ImportError: # package not installed, skip
+ LOGGER.warning(f"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)")
+ except Exception as e:
+ LOGGER.info(f"{prefix}{e}")
+
+
+def classify_transforms(size=224):
+ """Applies a series of transformations including center crop, ToTensor, and normalization for classification."""
+ assert isinstance(size, int), f"ERROR: classify_transforms size {size} must be integer, not (list, tuple)"
+ # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
+ return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)])
+
+
+class LetterBox:
+ """Resizes and pads images to specified dimensions while maintaining aspect ratio for YOLOv5 preprocessing."""
+
+ def __init__(self, size=(640, 640), auto=False, stride=32):
+ """Initializes a LetterBox object for YOLOv5 image preprocessing with optional auto sizing and stride
+ adjustment.
+ """
+ super().__init__()
+ self.h, self.w = (size, size) if isinstance(size, int) else size
+ self.auto = auto # pass max size integer, automatically solve for short side using stride
+ self.stride = stride # used with auto
+
+ def __call__(self, im):
+ """
+ Resizes and pads input image `im` (HWC format) to specified dimensions, maintaining aspect ratio.
+
+ im = np.array HWC
+ """
+ imh, imw = im.shape[:2]
+ r = min(self.h / imh, self.w / imw) # ratio of new/old
+ h, w = round(imh * r), round(imw * r) # resized image
+ hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w
+ top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1)
+ im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype)
+ im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR)
+ return im_out
+
+
+class CenterCrop:
+ """Applies center crop to an image, resizing it to the specified size while maintaining aspect ratio."""
+
+ def __init__(self, size=640):
+ """Initializes CenterCrop for image preprocessing, accepting single int or tuple for size, defaults to 640."""
+ super().__init__()
+ self.h, self.w = (size, size) if isinstance(size, int) else size
+
+ def __call__(self, im):
+ """
+ Applies center crop to the input image and resizes it to a specified size, maintaining aspect ratio.
+
+ im = np.array HWC
+ """
+ imh, imw = im.shape[:2]
+ m = min(imh, imw) # min dimension
+ top, left = (imh - m) // 2, (imw - m) // 2
+ return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR)
+
+
+class ToTensor:
+ """Converts BGR np.array image from HWC to RGB CHW format, normalizes to [0, 1], and supports FP16 if half=True."""
+
+ def __init__(self, half=False):
+ """Initializes ToTensor for YOLOv5 image preprocessing, with optional half precision (half=True for FP16)."""
+ super().__init__()
+ self.half = half
+
+ def __call__(self, im):
+ """
+ Converts BGR np.array image from HWC to RGB CHW format, and normalizes to [0, 1], with support for FP16 if
+ `half=True`.
+
+ im = np.array HWC in BGR order
+ """
+ im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous
+ im = torch.from_numpy(im) # to torch
+ im = im.half() if self.half else im.float() # uint8 to fp16/32
+ im /= 255.0 # 0-255 to 0.0-1.0
+ return im
diff --git a/Transfer Learning/Accident_Classifier/utils/autoanchor.py b/Transfer Learning/Accident_Classifier/utils/autoanchor.py
new file mode 100644
index 00000000..00eee2eb
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/autoanchor.py
@@ -0,0 +1,175 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""AutoAnchor utils."""
+
+import random
+
+import numpy as np
+import torch
+import yaml
+from tqdm import tqdm
+
+from utils import TryExcept
+from utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr
+
+PREFIX = colorstr("AutoAnchor: ")
+
+
+def check_anchor_order(m):
+ """Checks and corrects anchor order against stride in YOLOv5 Detect() module if necessary."""
+ a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer
+ da = a[-1] - a[0] # delta a
+ ds = m.stride[-1] - m.stride[0] # delta s
+ if da and (da.sign() != ds.sign()): # same order
+ LOGGER.info(f"{PREFIX}Reversing anchor order")
+ m.anchors[:] = m.anchors.flip(0)
+
+
+@TryExcept(f"{PREFIX}ERROR")
+def check_anchors(dataset, model, thr=4.0, imgsz=640):
+ """Evaluates anchor fit to dataset and adjusts if necessary, supporting customizable threshold and image size."""
+ m = model.module.model[-1] if hasattr(model, "module") else model.model[-1] # Detect()
+ shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+ scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale
+ wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh
+
+ def metric(k): # compute metric
+ """Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation."""
+ r = wh[:, None] / k[None]
+ x = torch.min(r, 1 / r).min(2)[0] # ratio metric
+ best = x.max(1)[0] # best_x
+ aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold
+ bpr = (best > 1 / thr).float().mean() # best possible recall
+ return bpr, aat
+
+ stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides
+ anchors = m.anchors.clone() * stride # current anchors
+ bpr, aat = metric(anchors.cpu().view(-1, 2))
+ s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). "
+ if bpr > 0.98: # threshold to recompute
+ LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅")
+ else:
+ LOGGER.info(f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...")
+ na = m.anchors.numel() // 2 # number of anchors
+ anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False)
+ new_bpr = metric(anchors)[0]
+ if new_bpr > bpr: # replace anchors
+ anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors)
+ m.anchors[:] = anchors.clone().view_as(m.anchors)
+ check_anchor_order(m) # must be in pixel-space (not grid-space)
+ m.anchors /= stride
+ s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)"
+ else:
+ s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)"
+ LOGGER.info(s)
+
+
+def kmean_anchors(dataset="./data/coco128.yaml", n=9, img_size=640, thr=4.0, gen=1000, verbose=True):
+ """
+ Creates kmeans-evolved anchors from training dataset.
+
+ Arguments:
+ dataset: path to data.yaml, or a loaded dataset
+ n: number of anchors
+ img_size: image size used for training
+ thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0
+ gen: generations to evolve anchors using genetic algorithm
+ verbose: print all results
+
+ Return:
+ k: kmeans evolved anchors
+
+ Usage:
+ from utils.autoanchor import *; _ = kmean_anchors()
+ """
+ from scipy.cluster.vq import kmeans
+
+ npr = np.random
+ thr = 1 / thr
+
+ def metric(k, wh): # compute metrics
+ """Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation."""
+ r = wh[:, None] / k[None]
+ x = torch.min(r, 1 / r).min(2)[0] # ratio metric
+ # x = wh_iou(wh, torch.tensor(k)) # iou metric
+ return x, x.max(1)[0] # x, best_x
+
+ def anchor_fitness(k): # mutation fitness
+ """Evaluates fitness of YOLOv5 anchors by computing recall and ratio metrics for an anchor evolution process."""
+ _, best = metric(torch.tensor(k, dtype=torch.float32), wh)
+ return (best * (best > thr).float()).mean() # fitness
+
+ def print_results(k, verbose=True):
+ """Sorts and logs kmeans-evolved anchor metrics and best possible recall values for YOLOv5 anchor evaluation."""
+ k = k[np.argsort(k.prod(1))] # sort small to large
+ x, best = metric(k, wh0)
+ bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr
+ s = (
+ f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n"
+ f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, "
+ f"past_thr={x[x > thr].mean():.3f}-mean: "
+ )
+ for x in k:
+ s += "%i,%i, " % (round(x[0]), round(x[1]))
+ if verbose:
+ LOGGER.info(s[:-2])
+ return k
+
+ if isinstance(dataset, str): # *.yaml file
+ with open(dataset, errors="ignore") as f:
+ data_dict = yaml.safe_load(f) # model dict
+ from utils.dataloaders import LoadImagesAndLabels
+
+ dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True)
+
+ # Get label wh
+ shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True)
+ wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh
+
+ # Filter
+ i = (wh0 < 3.0).any(1).sum()
+ if i:
+ LOGGER.info(f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size")
+ wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels
+ # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1
+
+ # Kmeans init
+ try:
+ LOGGER.info(f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...")
+ assert n <= len(wh) # apply overdetermined constraint
+ s = wh.std(0) # sigmas for whitening
+ k = kmeans(wh / s, n, iter=30)[0] * s # points
+ assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar
+ except Exception:
+ LOGGER.warning(f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init")
+ k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init
+ wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0))
+ k = print_results(k, verbose=False)
+
+ # Plot
+ # k, d = [None] * 20, [None] * 20
+ # for i in tqdm(range(1, 21)):
+ # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance
+ # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True)
+ # ax = ax.ravel()
+ # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.')
+ # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh
+ # ax[0].hist(wh[wh[:, 0]<100, 0],400)
+ # ax[1].hist(wh[wh[:, 1]<100, 1],400)
+ # fig.savefig('wh.png', dpi=200)
+
+ # Evolve
+ f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma
+ pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar
+ for _ in pbar:
+ v = np.ones(sh)
+ while (v == 1).all(): # mutate until a change occurs (prevent duplicates)
+ v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0)
+ kg = (k.copy() * v).clip(min=2.0)
+ fg = anchor_fitness(kg)
+ if fg > f:
+ f, k = fg, kg.copy()
+ pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}"
+ if verbose:
+ print_results(k, verbose)
+
+ return print_results(k).astype(np.float32)
diff --git a/Transfer Learning/Accident_Classifier/utils/autobatch.py b/Transfer Learning/Accident_Classifier/utils/autobatch.py
new file mode 100644
index 00000000..08a0de84
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/autobatch.py
@@ -0,0 +1,70 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Auto-batch utils."""
+
+from copy import deepcopy
+
+import numpy as np
+import torch
+
+from utils.general import LOGGER, colorstr
+from utils.torch_utils import profile
+
+
+def check_train_batch_size(model, imgsz=640, amp=True):
+ """Checks and computes optimal training batch size for YOLOv5 model, given image size and AMP setting."""
+ with torch.cuda.amp.autocast(amp):
+ return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size
+
+
+def autobatch(model, imgsz=640, fraction=0.8, batch_size=16):
+ """Estimates optimal YOLOv5 batch size using `fraction` of CUDA memory."""
+ # Usage:
+ # import torch
+ # from utils.autobatch import autobatch
+ # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False)
+ # print(autobatch(model))
+
+ # Check device
+ prefix = colorstr("AutoBatch: ")
+ LOGGER.info(f"{prefix}Computing optimal batch size for --imgsz {imgsz}")
+ device = next(model.parameters()).device # get model device
+ if device.type == "cpu":
+ LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}")
+ return batch_size
+ if torch.backends.cudnn.benchmark:
+ LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}")
+ return batch_size
+
+ # Inspect CUDA memory
+ gb = 1 << 30 # bytes to GiB (1024 ** 3)
+ d = str(device).upper() # 'CUDA:0'
+ properties = torch.cuda.get_device_properties(device) # device properties
+ t = properties.total_memory / gb # GiB total
+ r = torch.cuda.memory_reserved(device) / gb # GiB reserved
+ a = torch.cuda.memory_allocated(device) / gb # GiB allocated
+ f = t - (r + a) # GiB free
+ LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free")
+
+ # Profile batch sizes
+ batch_sizes = [1, 2, 4, 8, 16]
+ try:
+ img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes]
+ results = profile(img, model, n=3, device=device)
+ except Exception as e:
+ LOGGER.warning(f"{prefix}{e}")
+
+ # Fit a solution
+ y = [x[2] for x in results if x] # memory [2]
+ p = np.polyfit(batch_sizes[: len(y)], y, deg=1) # first degree polynomial fit
+ b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size)
+ if None in results: # some sizes failed
+ i = results.index(None) # first fail index
+ if b >= batch_sizes[i]: # y intercept above failure point
+ b = batch_sizes[max(i - 1, 0)] # select prior safe point
+ if b < 1 or b > 1024: # b outside of safe range
+ b = batch_size
+ LOGGER.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.")
+
+ fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted
+ LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅")
+ return b
diff --git a/Transfer Learning/Accident_Classifier/utils/aws/__init__.py b/Transfer Learning/Accident_Classifier/utils/aws/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/Transfer Learning/Accident_Classifier/utils/aws/mime.sh b/Transfer Learning/Accident_Classifier/utils/aws/mime.sh
new file mode 100644
index 00000000..c319a83c
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/aws/mime.sh
@@ -0,0 +1,26 @@
+# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/
+# This script will run on every instance restart, not only on first start
+# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA ---
+
+Content-Type: multipart/mixed; boundary="//"
+MIME-Version: 1.0
+
+--//
+Content-Type: text/cloud-config; charset="us-ascii"
+MIME-Version: 1.0
+Content-Transfer-Encoding: 7bit
+Content-Disposition: attachment; filename="cloud-config.txt"
+
+#cloud-config
+cloud_final_modules:
+- [scripts-user, always]
+
+--//
+Content-Type: text/x-shellscript; charset="us-ascii"
+MIME-Version: 1.0
+Content-Transfer-Encoding: 7bit
+Content-Disposition: attachment; filename="userdata.txt"
+
+#!/bin/bash
+# --- paste contents of userdata.sh here ---
+--//
diff --git a/Transfer Learning/Accident_Classifier/utils/aws/resume.py b/Transfer Learning/Accident_Classifier/utils/aws/resume.py
new file mode 100644
index 00000000..ea432a16
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/aws/resume.py
@@ -0,0 +1,41 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+# Resume all interrupted trainings in yolov5/ dir including DDP trainings
+# Usage: $ python utils/aws/resume.py
+
+import os
+import sys
+from pathlib import Path
+
+import torch
+import yaml
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[2] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+
+port = 0 # --master_port
+path = Path("").resolve()
+for last in path.rglob("*/**/last.pt"):
+ ckpt = torch.load(last)
+ if ckpt["optimizer"] is None:
+ continue
+
+ # Load opt.yaml
+ with open(last.parent.parent / "opt.yaml", errors="ignore") as f:
+ opt = yaml.safe_load(f)
+
+ # Get device count
+ d = opt["device"].split(",") # devices
+ nd = len(d) # number of devices
+ ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel
+
+ if ddp: # multi-GPU
+ port += 1
+ cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}"
+ else: # single-GPU
+ cmd = f"python train.py --resume {last}"
+
+ cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread
+ print(cmd)
+ os.system(cmd)
diff --git a/Transfer Learning/Accident_Classifier/utils/aws/userdata.sh b/Transfer Learning/Accident_Classifier/utils/aws/userdata.sh
new file mode 100644
index 00000000..5fc1332a
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/aws/userdata.sh
@@ -0,0 +1,27 @@
+#!/bin/bash
+# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html
+# This script will run only once on first instance start (for a re-start script see mime.sh)
+# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir
+# Use >300 GB SSD
+
+cd home/ubuntu
+if [ ! -d yolov5 ]; then
+ echo "Running first-time script." # install dependencies, download COCO, pull Docker
+ git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5
+ cd yolov5
+ bash data/scripts/get_coco.sh && echo "COCO done." &
+ sudo docker pull ultralytics/yolov5:latest && echo "Docker done." &
+ python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." &
+ wait && echo "All tasks done." # finish background tasks
+else
+ echo "Running re-start script." # resume interrupted runs
+ i=0
+ list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour'
+ while IFS= read -r id; do
+ ((i++))
+ echo "restarting container $i: $id"
+ sudo docker start $id
+ # sudo docker exec -it $id python train.py --resume # single-GPU
+ sudo docker exec -d $id python utils/aws/resume.py # multi-scenario
+ done <<<"$list"
+fi
diff --git a/Transfer Learning/Accident_Classifier/utils/callbacks.py b/Transfer Learning/Accident_Classifier/utils/callbacks.py
new file mode 100644
index 00000000..21c587bd
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/callbacks.py
@@ -0,0 +1,72 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Callback utils."""
+
+import threading
+
+
+class Callbacks:
+ """Handles all registered callbacks for YOLOv5 Hooks."""
+
+ def __init__(self):
+ """Initializes a Callbacks object to manage registered YOLOv5 training event hooks."""
+ self._callbacks = {
+ "on_pretrain_routine_start": [],
+ "on_pretrain_routine_end": [],
+ "on_train_start": [],
+ "on_train_epoch_start": [],
+ "on_train_batch_start": [],
+ "optimizer_step": [],
+ "on_before_zero_grad": [],
+ "on_train_batch_end": [],
+ "on_train_epoch_end": [],
+ "on_val_start": [],
+ "on_val_batch_start": [],
+ "on_val_image_end": [],
+ "on_val_batch_end": [],
+ "on_val_end": [],
+ "on_fit_epoch_end": [], # fit = train + val
+ "on_model_save": [],
+ "on_train_end": [],
+ "on_params_update": [],
+ "teardown": [],
+ }
+ self.stop_training = False # set True to interrupt training
+
+ def register_action(self, hook, name="", callback=None):
+ """
+ Register a new action to a callback hook.
+
+ Args:
+ hook: The callback hook name to register the action to
+ name: The name of the action for later reference
+ callback: The callback to fire
+ """
+ assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
+ assert callable(callback), f"callback '{callback}' is not callable"
+ self._callbacks[hook].append({"name": name, "callback": callback})
+
+ def get_registered_actions(self, hook=None):
+ """
+ Returns all the registered actions by callback hook.
+
+ Args:
+ hook: The name of the hook to check, defaults to all
+ """
+ return self._callbacks[hook] if hook else self._callbacks
+
+ def run(self, hook, *args, thread=False, **kwargs):
+ """
+ Loop through the registered actions and fire all callbacks on main thread.
+
+ Args:
+ hook: The name of the hook to check, defaults to all
+ args: Arguments to receive from YOLOv5
+ thread: (boolean) Run callbacks in daemon thread
+ kwargs: Keyword Arguments to receive from YOLOv5
+ """
+ assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}"
+ for logger in self._callbacks[hook]:
+ if thread:
+ threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start()
+ else:
+ logger["callback"](*args, **kwargs)
diff --git a/Transfer Learning/Accident_Classifier/utils/dataloaders.py b/Transfer Learning/Accident_Classifier/utils/dataloaders.py
new file mode 100644
index 00000000..61358eb9
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/dataloaders.py
@@ -0,0 +1,1380 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Dataloaders and dataset utils."""
+
+import contextlib
+import glob
+import hashlib
+import json
+import math
+import os
+import random
+import shutil
+import time
+from itertools import repeat
+from multiprocessing.pool import Pool, ThreadPool
+from pathlib import Path
+from threading import Thread
+from urllib.parse import urlparse
+
+import numpy as np
+import psutil
+import torch
+import torch.nn.functional as F
+import torchvision
+import yaml
+from PIL import ExifTags, Image, ImageOps
+from torch.utils.data import DataLoader, Dataset, dataloader, distributed
+from tqdm import tqdm
+
+from utils.augmentations import (
+ Albumentations,
+ augment_hsv,
+ classify_albumentations,
+ classify_transforms,
+ copy_paste,
+ letterbox,
+ mixup,
+ random_perspective,
+)
+from utils.general import (
+ DATASETS_DIR,
+ LOGGER,
+ NUM_THREADS,
+ TQDM_BAR_FORMAT,
+ check_dataset,
+ check_requirements,
+ check_yaml,
+ clean_str,
+ cv2,
+ is_colab,
+ is_kaggle,
+ segments2boxes,
+ unzip_file,
+ xyn2xy,
+ xywh2xyxy,
+ xywhn2xyxy,
+ xyxy2xywhn,
+)
+from utils.torch_utils import torch_distributed_zero_first
+
+# Parameters
+HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data"
+IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # include image suffixes
+VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv" # include video suffixes
+LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv("RANK", -1))
+WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
+PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders
+
+# Get orientation exif tag
+for orientation in ExifTags.TAGS.keys():
+ if ExifTags.TAGS[orientation] == "Orientation":
+ break
+
+
+def get_hash(paths):
+ """Generates a single SHA256 hash for a list of file or directory paths by combining their sizes and paths."""
+ size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes
+ h = hashlib.sha256(str(size).encode()) # hash sizes
+ h.update("".join(paths).encode()) # hash paths
+ return h.hexdigest() # return hash
+
+
+def exif_size(img):
+ """Returns corrected PIL image size (width, height) considering EXIF orientation."""
+ s = img.size # (width, height)
+ with contextlib.suppress(Exception):
+ rotation = dict(img._getexif().items())[orientation]
+ if rotation in [6, 8]: # rotation 270 or 90
+ s = (s[1], s[0])
+ return s
+
+
+def exif_transpose(image):
+ """
+ Transpose a PIL image accordingly if it has an EXIF Orientation tag.
+ Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose().
+
+ :param image: The image to transpose.
+ :return: An image.
+ """
+ exif = image.getexif()
+ orientation = exif.get(0x0112, 1) # default 1
+ if orientation > 1:
+ method = {
+ 2: Image.FLIP_LEFT_RIGHT,
+ 3: Image.ROTATE_180,
+ 4: Image.FLIP_TOP_BOTTOM,
+ 5: Image.TRANSPOSE,
+ 6: Image.ROTATE_270,
+ 7: Image.TRANSVERSE,
+ 8: Image.ROTATE_90,
+ }.get(orientation)
+ if method is not None:
+ image = image.transpose(method)
+ del exif[0x0112]
+ image.info["exif"] = exif.tobytes()
+ return image
+
+
+def seed_worker(worker_id):
+ """
+ Sets the seed for a dataloader worker to ensure reproducibility, based on PyTorch's randomness notes.
+
+ See https://pytorch.org/docs/stable/notes/randomness.html#dataloader.
+ """
+ worker_seed = torch.initial_seed() % 2**32
+ np.random.seed(worker_seed)
+ random.seed(worker_seed)
+
+
+# Inherit from DistributedSampler and override iterator
+# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py
+class SmartDistributedSampler(distributed.DistributedSampler):
+ """A distributed sampler ensuring deterministic shuffling and balanced data distribution across GPUs."""
+
+ def __iter__(self):
+ """Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed."""
+ g = torch.Generator()
+ g.manual_seed(self.seed + self.epoch)
+
+ # determine the eventual size (n) of self.indices (DDP indices)
+ n = int((len(self.dataset) - self.rank - 1) / self.num_replicas) + 1 # num_replicas == WORLD_SIZE
+ idx = torch.randperm(n, generator=g)
+ if not self.shuffle:
+ idx = idx.sort()[0]
+
+ idx = idx.tolist()
+ if self.drop_last:
+ idx = idx[: self.num_samples]
+ else:
+ padding_size = self.num_samples - len(idx)
+ if padding_size <= len(idx):
+ idx += idx[:padding_size]
+ else:
+ idx += (idx * math.ceil(padding_size / len(idx)))[:padding_size]
+
+ return iter(idx)
+
+
+def create_dataloader(
+ path,
+ imgsz,
+ batch_size,
+ stride,
+ single_cls=False,
+ hyp=None,
+ augment=False,
+ cache=False,
+ pad=0.0,
+ rect=False,
+ rank=-1,
+ workers=8,
+ image_weights=False,
+ quad=False,
+ prefix="",
+ shuffle=False,
+ seed=0,
+):
+ """Creates and returns a configured DataLoader instance for loading and processing image datasets."""
+ if rect and shuffle:
+ LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
+ shuffle = False
+ with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
+ dataset = LoadImagesAndLabels(
+ path,
+ imgsz,
+ batch_size,
+ augment=augment, # augmentation
+ hyp=hyp, # hyperparameters
+ rect=rect, # rectangular batches
+ cache_images=cache,
+ single_cls=single_cls,
+ stride=int(stride),
+ pad=pad,
+ image_weights=image_weights,
+ prefix=prefix,
+ rank=rank,
+ )
+
+ batch_size = min(batch_size, len(dataset))
+ nd = torch.cuda.device_count() # number of CUDA devices
+ nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers
+ sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle)
+ loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates
+ generator = torch.Generator()
+ generator.manual_seed(6148914691236517205 + seed + RANK)
+ return loader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=shuffle and sampler is None,
+ num_workers=nw,
+ sampler=sampler,
+ pin_memory=PIN_MEMORY,
+ collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn,
+ worker_init_fn=seed_worker,
+ generator=generator,
+ ), dataset
+
+
+class InfiniteDataLoader(dataloader.DataLoader):
+ """
+ Dataloader that reuses workers.
+
+ Uses same syntax as vanilla DataLoader
+ """
+
+ def __init__(self, *args, **kwargs):
+ """Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax, augmenting with a
+ repeating sampler.
+ """
+ super().__init__(*args, **kwargs)
+ object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler))
+ self.iterator = super().__iter__()
+
+ def __len__(self):
+ """Returns the length of the batch sampler's sampler in the InfiniteDataLoader."""
+ return len(self.batch_sampler.sampler)
+
+ def __iter__(self):
+ """Yields batches of data indefinitely in a loop by resetting the sampler when exhausted."""
+ for _ in range(len(self)):
+ yield next(self.iterator)
+
+
+class _RepeatSampler:
+ """
+ Sampler that repeats forever.
+
+ Args:
+ sampler (Sampler)
+ """
+
+ def __init__(self, sampler):
+ """Initializes a perpetual sampler wrapping a provided `Sampler` instance for endless data iteration."""
+ self.sampler = sampler
+
+ def __iter__(self):
+ """Returns an infinite iterator over the dataset by repeatedly yielding from the given sampler."""
+ while True:
+ yield from iter(self.sampler)
+
+
+class LoadScreenshots:
+ """Loads and processes screenshots for YOLOv5 detection from specified screen regions using mss."""
+
+ def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None):
+ """
+ Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and
+ transforms.
+
+ Source = [screen_number left top width height] (pixels)
+ """
+ check_requirements("mss")
+ import mss
+
+ source, *params = source.split()
+ self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0
+ if len(params) == 1:
+ self.screen = int(params[0])
+ elif len(params) == 4:
+ left, top, width, height = (int(x) for x in params)
+ elif len(params) == 5:
+ self.screen, left, top, width, height = (int(x) for x in params)
+ self.img_size = img_size
+ self.stride = stride
+ self.transforms = transforms
+ self.auto = auto
+ self.mode = "stream"
+ self.frame = 0
+ self.sct = mss.mss()
+
+ # Parse monitor shape
+ monitor = self.sct.monitors[self.screen]
+ self.top = monitor["top"] if top is None else (monitor["top"] + top)
+ self.left = monitor["left"] if left is None else (monitor["left"] + left)
+ self.width = width or monitor["width"]
+ self.height = height or monitor["height"]
+ self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height}
+
+ def __iter__(self):
+ """Iterates over itself, enabling use in loops and iterable contexts."""
+ return self
+
+ def __next__(self):
+ """Captures and returns the next screen frame as a BGR numpy array, cropping to only the first three channels
+ from BGRA.
+ """
+ im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR
+ s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: "
+
+ if self.transforms:
+ im = self.transforms(im0) # transforms
+ else:
+ im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize
+ im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ im = np.ascontiguousarray(im) # contiguous
+ self.frame += 1
+ return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s
+
+
+class LoadImages:
+ """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`."""
+
+ def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+ """Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths."""
+ if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line
+ path = Path(path).read_text().rsplit()
+ files = []
+ for p in sorted(path) if isinstance(path, (list, tuple)) else [path]:
+ p = str(Path(p).resolve())
+ if "*" in p:
+ files.extend(sorted(glob.glob(p, recursive=True))) # glob
+ elif os.path.isdir(p):
+ files.extend(sorted(glob.glob(os.path.join(p, "*.*")))) # dir
+ elif os.path.isfile(p):
+ files.append(p) # files
+ else:
+ raise FileNotFoundError(f"{p} does not exist")
+
+ images = [x for x in files if x.split(".")[-1].lower() in IMG_FORMATS]
+ videos = [x for x in files if x.split(".")[-1].lower() in VID_FORMATS]
+ ni, nv = len(images), len(videos)
+
+ self.img_size = img_size
+ self.stride = stride
+ self.files = images + videos
+ self.nf = ni + nv # number of files
+ self.video_flag = [False] * ni + [True] * nv
+ self.mode = "image"
+ self.auto = auto
+ self.transforms = transforms # optional
+ self.vid_stride = vid_stride # video frame-rate stride
+ if any(videos):
+ self._new_video(videos[0]) # new video
+ else:
+ self.cap = None
+ assert self.nf > 0, (
+ f"No images or videos found in {p}. "
+ f"Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}"
+ )
+
+ def __iter__(self):
+ """Initializes iterator by resetting count and returns the iterator object itself."""
+ self.count = 0
+ return self
+
+ def __next__(self):
+ """Advances to the next file in the dataset, raising StopIteration if at the end."""
+ if self.count == self.nf:
+ raise StopIteration
+ path = self.files[self.count]
+
+ if self.video_flag[self.count]:
+ # Read video
+ self.mode = "video"
+ for _ in range(self.vid_stride):
+ self.cap.grab()
+ ret_val, im0 = self.cap.retrieve()
+ while not ret_val:
+ self.count += 1
+ self.cap.release()
+ if self.count == self.nf: # last video
+ raise StopIteration
+ path = self.files[self.count]
+ self._new_video(path)
+ ret_val, im0 = self.cap.read()
+
+ self.frame += 1
+ # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False
+ s = f"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: "
+
+ else:
+ # Read image
+ self.count += 1
+ im0 = cv2.imread(path) # BGR
+ assert im0 is not None, f"Image Not Found {path}"
+ s = f"image {self.count}/{self.nf} {path}: "
+
+ if self.transforms:
+ im = self.transforms(im0) # transforms
+ else:
+ im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize
+ im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ im = np.ascontiguousarray(im) # contiguous
+
+ return path, im, im0, self.cap, s
+
+ def _new_video(self, path):
+ """Initializes a new video capture object with path, frame count adjusted by stride, and orientation
+ metadata.
+ """
+ self.frame = 0
+ self.cap = cv2.VideoCapture(path)
+ self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride)
+ self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees
+ # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493
+
+ def _cv2_rotate(self, im):
+ """Rotates a cv2 image based on its orientation; supports 0, 90, and 180 degrees rotations."""
+ if self.orientation == 0:
+ return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE)
+ elif self.orientation == 180:
+ return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE)
+ elif self.orientation == 90:
+ return cv2.rotate(im, cv2.ROTATE_180)
+ return im
+
+ def __len__(self):
+ """Returns the number of files in the dataset."""
+ return self.nf # number of files
+
+
+class LoadStreams:
+ """Loads and processes video streams for YOLOv5, supporting various sources including YouTube and IP cameras."""
+
+ def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1):
+ """Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including
+ YouTube.
+ """
+ torch.backends.cudnn.benchmark = True # faster for fixed-size inference
+ self.mode = "stream"
+ self.img_size = img_size
+ self.stride = stride
+ self.vid_stride = vid_stride # video frame-rate stride
+ sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources]
+ n = len(sources)
+ self.sources = [clean_str(x) for x in sources] # clean source names for later
+ self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n
+ for i, s in enumerate(sources): # index, source
+ # Start thread to read frames from video stream
+ st = f"{i + 1}/{n}: {s}... "
+ if urlparse(s).hostname in ("www.youtube.com", "youtube.com", "youtu.be"): # if source is YouTube video
+ # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4'
+ check_requirements(("pafy", "youtube_dl==2020.12.2"))
+ import pafy
+
+ s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL
+ s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam
+ if s == 0:
+ assert not is_colab(), "--source 0 webcam unsupported on Colab. Rerun command in a local environment."
+ assert not is_kaggle(), "--source 0 webcam unsupported on Kaggle. Rerun command in a local environment."
+ cap = cv2.VideoCapture(s)
+ assert cap.isOpened(), f"{st}Failed to open {s}"
+ w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH))
+ h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
+ fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan
+ self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float("inf") # infinite stream fallback
+ self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback
+
+ _, self.imgs[i] = cap.read() # guarantee first frame
+ self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True)
+ LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)")
+ self.threads[i].start()
+ LOGGER.info("") # newline
+
+ # check for common shapes
+ s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs])
+ self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal
+ self.auto = auto and self.rect
+ self.transforms = transforms # optional
+ if not self.rect:
+ LOGGER.warning("WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.")
+
+ def update(self, i, cap, stream):
+ """Reads frames from stream `i`, updating imgs array; handles stream reopening on signal loss."""
+ n, f = 0, self.frames[i] # frame number, frame array
+ while cap.isOpened() and n < f:
+ n += 1
+ cap.grab() # .read() = .grab() followed by .retrieve()
+ if n % self.vid_stride == 0:
+ success, im = cap.retrieve()
+ if success:
+ self.imgs[i] = im
+ else:
+ LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.")
+ self.imgs[i] = np.zeros_like(self.imgs[i])
+ cap.open(stream) # re-open stream if signal was lost
+ time.sleep(0.0) # wait time
+
+ def __iter__(self):
+ """Resets and returns the iterator for iterating over video frames or images in a dataset."""
+ self.count = -1
+ return self
+
+ def __next__(self):
+ """Iterates over video frames or images, halting on thread stop or 'q' key press, raising `StopIteration` when
+ done.
+ """
+ self.count += 1
+ if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord("q"): # q to quit
+ cv2.destroyAllWindows()
+ raise StopIteration
+
+ im0 = self.imgs.copy()
+ if self.transforms:
+ im = np.stack([self.transforms(x) for x in im0]) # transforms
+ else:
+ im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize
+ im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW
+ im = np.ascontiguousarray(im) # contiguous
+
+ return self.sources, im, im0, None, ""
+
+ def __len__(self):
+ """Returns the number of sources in the dataset, supporting up to 32 streams at 30 FPS over 30 years."""
+ return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years
+
+
+def img2label_paths(img_paths):
+ """Generates label file paths from corresponding image file paths by replacing `/images/` with `/labels/` and
+ extension with `.txt`.
+ """
+ sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings
+ return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths]
+
+
+class LoadImagesAndLabels(Dataset):
+ """Loads images and their corresponding labels for training and validation in YOLOv5."""
+
+ cache_version = 0.6 # dataset labels *.cache version
+ rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4]
+
+ def __init__(
+ self,
+ path,
+ img_size=640,
+ batch_size=16,
+ augment=False,
+ hyp=None,
+ rect=False,
+ image_weights=False,
+ cache_images=False,
+ single_cls=False,
+ stride=32,
+ pad=0.0,
+ min_items=0,
+ prefix="",
+ rank=-1,
+ seed=0,
+ ):
+ """Initializes the YOLOv5 dataset loader, handling images and their labels, caching, and preprocessing."""
+ self.img_size = img_size
+ self.augment = augment
+ self.hyp = hyp
+ self.image_weights = image_weights
+ self.rect = False if image_weights else rect
+ self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training)
+ self.mosaic_border = [-img_size // 2, -img_size // 2]
+ self.stride = stride
+ self.path = path
+ self.albumentations = Albumentations(size=img_size) if augment else None
+
+ try:
+ f = [] # image files
+ for p in path if isinstance(path, list) else [path]:
+ p = Path(p) # os-agnostic
+ if p.is_dir(): # dir
+ f += glob.glob(str(p / "**" / "*.*"), recursive=True)
+ # f = list(p.rglob('*.*')) # pathlib
+ elif p.is_file(): # file
+ with open(p) as t:
+ t = t.read().strip().splitlines()
+ parent = str(p.parent) + os.sep
+ f += [x.replace("./", parent, 1) if x.startswith("./") else x for x in t] # to global path
+ # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib)
+ else:
+ raise FileNotFoundError(f"{prefix}{p} does not exist")
+ self.im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS)
+ # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib
+ assert self.im_files, f"{prefix}No images found"
+ except Exception as e:
+ raise Exception(f"{prefix}Error loading data from {path}: {e}\n{HELP_URL}") from e
+
+ # Check cache
+ self.label_files = img2label_paths(self.im_files) # labels
+ cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix(".cache")
+ try:
+ cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict
+ assert cache["version"] == self.cache_version # matches current version
+ assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash
+ except Exception:
+ cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops
+
+ # Display cache
+ nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total
+ if exists and LOCAL_RANK in {-1, 0}:
+ d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt"
+ tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results
+ if cache["msgs"]:
+ LOGGER.info("\n".join(cache["msgs"])) # display warnings
+ assert nf > 0 or not augment, f"{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}"
+
+ # Read cache
+ [cache.pop(k) for k in ("hash", "version", "msgs")] # remove items
+ labels, shapes, self.segments = zip(*cache.values())
+ nl = len(np.concatenate(labels, 0)) # number of labels
+ assert nl > 0 or not augment, f"{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}"
+ self.labels = list(labels)
+ self.shapes = np.array(shapes)
+ self.im_files = list(cache.keys()) # update
+ self.label_files = img2label_paths(cache.keys()) # update
+
+ # Filter images
+ if min_items:
+ include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int)
+ LOGGER.info(f"{prefix}{n - len(include)}/{n} images filtered from dataset")
+ self.im_files = [self.im_files[i] for i in include]
+ self.label_files = [self.label_files[i] for i in include]
+ self.labels = [self.labels[i] for i in include]
+ self.segments = [self.segments[i] for i in include]
+ self.shapes = self.shapes[include] # wh
+
+ # Create indices
+ n = len(self.shapes) # number of images
+ bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index
+ nb = bi[-1] + 1 # number of batches
+ self.batch = bi # batch index of image
+ self.n = n
+ self.indices = np.arange(n)
+ if rank > -1: # DDP indices (see: SmartDistributedSampler)
+ # force each rank (i.e. GPU process) to sample the same subset of data on every epoch
+ self.indices = self.indices[np.random.RandomState(seed=seed).permutation(n) % WORLD_SIZE == RANK]
+
+ # Update labels
+ include_class = [] # filter labels to include only these classes (optional)
+ self.segments = list(self.segments)
+ include_class_array = np.array(include_class).reshape(1, -1)
+ for i, (label, segment) in enumerate(zip(self.labels, self.segments)):
+ if include_class:
+ j = (label[:, 0:1] == include_class_array).any(1)
+ self.labels[i] = label[j]
+ if segment:
+ self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem]
+ if single_cls: # single-class training, merge all classes into 0
+ self.labels[i][:, 0] = 0
+
+ # Rectangular Training
+ if self.rect:
+ # Sort by aspect ratio
+ s = self.shapes # wh
+ ar = s[:, 1] / s[:, 0] # aspect ratio
+ irect = ar.argsort()
+ self.im_files = [self.im_files[i] for i in irect]
+ self.label_files = [self.label_files[i] for i in irect]
+ self.labels = [self.labels[i] for i in irect]
+ self.segments = [self.segments[i] for i in irect]
+ self.shapes = s[irect] # wh
+ ar = ar[irect]
+
+ # Set training image shapes
+ shapes = [[1, 1]] * nb
+ for i in range(nb):
+ ari = ar[bi == i]
+ mini, maxi = ari.min(), ari.max()
+ if maxi < 1:
+ shapes[i] = [maxi, 1]
+ elif mini > 1:
+ shapes[i] = [1, 1 / mini]
+
+ self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride
+
+ # Cache images into RAM/disk for faster training
+ if cache_images == "ram" and not self.check_cache_ram(prefix=prefix):
+ cache_images = False
+ self.ims = [None] * n
+ self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files]
+ if cache_images:
+ b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
+ self.im_hw0, self.im_hw = [None] * n, [None] * n
+ fcn = self.cache_images_to_disk if cache_images == "disk" else self.load_image
+ results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices)
+ pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0)
+ for i, x in pbar:
+ if cache_images == "disk":
+ b += self.npy_files[i].stat().st_size
+ else: # 'ram'
+ self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i)
+ b += self.ims[i].nbytes * WORLD_SIZE
+ pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})"
+ pbar.close()
+
+ def check_cache_ram(self, safety_margin=0.1, prefix=""):
+ """Checks if available RAM is sufficient for caching images, adjusting for a safety margin."""
+ b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes
+ n = min(self.n, 30) # extrapolate from 30 random images
+ for _ in range(n):
+ im = cv2.imread(random.choice(self.im_files)) # sample image
+ ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio
+ b += im.nbytes * ratio**2
+ mem_required = b * self.n / n # GB required to cache dataset into RAM
+ mem = psutil.virtual_memory()
+ cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question
+ if not cache:
+ LOGGER.info(
+ f'{prefix}{mem_required / gb:.1f}GB RAM required, '
+ f'{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, '
+ f"{'caching images ✅' if cache else 'not caching images ⚠️'}"
+ )
+ return cache
+
+ def cache_labels(self, path=Path("./labels.cache"), prefix=""):
+ """Caches dataset labels, verifies images, reads shapes, and tracks dataset integrity."""
+ x = {} # dict
+ nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages
+ desc = f"{prefix}Scanning {path.parent / path.stem}..."
+ with Pool(NUM_THREADS) as pool:
+ pbar = tqdm(
+ pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))),
+ desc=desc,
+ total=len(self.im_files),
+ bar_format=TQDM_BAR_FORMAT,
+ )
+ for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar:
+ nm += nm_f
+ nf += nf_f
+ ne += ne_f
+ nc += nc_f
+ if im_file:
+ x[im_file] = [lb, shape, segments]
+ if msg:
+ msgs.append(msg)
+ pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt"
+
+ pbar.close()
+ if msgs:
+ LOGGER.info("\n".join(msgs))
+ if nf == 0:
+ LOGGER.warning(f"{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}")
+ x["hash"] = get_hash(self.label_files + self.im_files)
+ x["results"] = nf, nm, ne, nc, len(self.im_files)
+ x["msgs"] = msgs # warnings
+ x["version"] = self.cache_version # cache version
+ try:
+ np.save(path, x) # save cache for next time
+ path.with_suffix(".cache.npy").rename(path) # remove .npy suffix
+ LOGGER.info(f"{prefix}New cache created: {path}")
+ except Exception as e:
+ LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}") # not writeable
+ return x
+
+ def __len__(self):
+ """Returns the number of images in the dataset."""
+ return len(self.im_files)
+
+ # def __iter__(self):
+ # self.count = -1
+ # print('ran dataset iter')
+ # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF)
+ # return self
+
+ def __getitem__(self, index):
+ """Fetches the dataset item at the given index, considering linear, shuffled, or weighted sampling."""
+ index = self.indices[index] # linear, shuffled, or image_weights
+
+ hyp = self.hyp
+ mosaic = self.mosaic and random.random() < hyp["mosaic"]
+ if mosaic:
+ # Load mosaic
+ img, labels = self.load_mosaic(index)
+ shapes = None
+
+ # MixUp augmentation
+ if random.random() < hyp["mixup"]:
+ img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices)))
+
+ else:
+ # Load image
+ img, (h0, w0), (h, w) = self.load_image(index)
+
+ # Letterbox
+ shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
+ img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
+ shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
+
+ labels = self.labels[index].copy()
+ if labels.size: # normalized xywh to pixel xyxy format
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
+
+ if self.augment:
+ img, labels = random_perspective(
+ img,
+ labels,
+ degrees=hyp["degrees"],
+ translate=hyp["translate"],
+ scale=hyp["scale"],
+ shear=hyp["shear"],
+ perspective=hyp["perspective"],
+ )
+
+ nl = len(labels) # number of labels
+ if nl:
+ labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
+
+ if self.augment:
+ # Albumentations
+ img, labels = self.albumentations(img, labels)
+ nl = len(labels) # update after albumentations
+
+ # HSV color-space
+ augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"])
+
+ # Flip up-down
+ if random.random() < hyp["flipud"]:
+ img = np.flipud(img)
+ if nl:
+ labels[:, 2] = 1 - labels[:, 2]
+
+ # Flip left-right
+ if random.random() < hyp["fliplr"]:
+ img = np.fliplr(img)
+ if nl:
+ labels[:, 1] = 1 - labels[:, 1]
+
+ # Cutouts
+ # labels = cutout(img, labels, p=0.5)
+ # nl = len(labels) # update after cutout
+
+ labels_out = torch.zeros((nl, 6))
+ if nl:
+ labels_out[:, 1:] = torch.from_numpy(labels)
+
+ # Convert
+ img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ img = np.ascontiguousarray(img)
+
+ return torch.from_numpy(img), labels_out, self.im_files[index], shapes
+
+ def load_image(self, i):
+ """
+ Loads an image by index, returning the image, its original dimensions, and resized dimensions.
+
+ Returns (im, original hw, resized hw)
+ """
+ im, f, fn = (
+ self.ims[i],
+ self.im_files[i],
+ self.npy_files[i],
+ )
+ if im is None: # not cached in RAM
+ if fn.exists(): # load npy
+ im = np.load(fn)
+ else: # read image
+ im = cv2.imread(f) # BGR
+ assert im is not None, f"Image Not Found {f}"
+ h0, w0 = im.shape[:2] # orig hw
+ r = self.img_size / max(h0, w0) # ratio
+ if r != 1: # if sizes are not equal
+ interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
+ im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)
+ return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized
+ return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized
+
+ def cache_images_to_disk(self, i):
+ """Saves an image to disk as an *.npy file for quicker loading, identified by index `i`."""
+ f = self.npy_files[i]
+ if not f.exists():
+ np.save(f.as_posix(), cv2.imread(self.im_files[i]))
+
+ def load_mosaic(self, index):
+ """Loads a 4-image mosaic for YOLOv5, combining 1 selected and 3 random images, with labels and segments."""
+ labels4, segments4 = [], []
+ s = self.img_size
+ yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y
+ indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices
+ random.shuffle(indices)
+ for i, index in enumerate(indices):
+ # Load image
+ img, _, (h, w) = self.load_image(index)
+
+ # place img in img4
+ if i == 0: # top left
+ img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
+ x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
+ elif i == 1: # top right
+ x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+ x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+ elif i == 2: # bottom left
+ x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+ elif i == 3: # bottom right
+ x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+ img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
+ padw = x1a - x1b
+ padh = y1a - y1b
+
+ # Labels
+ labels, segments = self.labels[index].copy(), self.segments[index].copy()
+ if labels.size:
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
+ segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
+ labels4.append(labels)
+ segments4.extend(segments)
+
+ # Concat/clip labels
+ labels4 = np.concatenate(labels4, 0)
+ for x in (labels4[:, 1:], *segments4):
+ np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
+ # img4, labels4 = replicate(img4, labels4) # replicate
+
+ # Augment
+ img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"])
+ img4, labels4 = random_perspective(
+ img4,
+ labels4,
+ segments4,
+ degrees=self.hyp["degrees"],
+ translate=self.hyp["translate"],
+ scale=self.hyp["scale"],
+ shear=self.hyp["shear"],
+ perspective=self.hyp["perspective"],
+ border=self.mosaic_border,
+ ) # border to remove
+
+ return img4, labels4
+
+ def load_mosaic9(self, index):
+ """Loads 1 image + 8 random images into a 9-image mosaic for augmented YOLOv5 training, returning labels and
+ segments.
+ """
+ labels9, segments9 = [], []
+ s = self.img_size
+ indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices
+ random.shuffle(indices)
+ hp, wp = -1, -1 # height, width previous
+ for i, index in enumerate(indices):
+ # Load image
+ img, _, (h, w) = self.load_image(index)
+
+ # place img in img9
+ if i == 0: # center
+ img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
+ h0, w0 = h, w
+ c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates
+ elif i == 1: # top
+ c = s, s - h, s + w, s
+ elif i == 2: # top right
+ c = s + wp, s - h, s + wp + w, s
+ elif i == 3: # right
+ c = s + w0, s, s + w0 + w, s + h
+ elif i == 4: # bottom right
+ c = s + w0, s + hp, s + w0 + w, s + hp + h
+ elif i == 5: # bottom
+ c = s + w0 - w, s + h0, s + w0, s + h0 + h
+ elif i == 6: # bottom left
+ c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h
+ elif i == 7: # left
+ c = s - w, s + h0 - h, s, s + h0
+ elif i == 8: # top left
+ c = s - w, s + h0 - hp - h, s, s + h0 - hp
+
+ padx, pady = c[:2]
+ x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords
+
+ # Labels
+ labels, segments = self.labels[index].copy(), self.segments[index].copy()
+ if labels.size:
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format
+ segments = [xyn2xy(x, w, h, padx, pady) for x in segments]
+ labels9.append(labels)
+ segments9.extend(segments)
+
+ # Image
+ img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :] # img9[ymin:ymax, xmin:xmax]
+ hp, wp = h, w # height, width previous
+
+ # Offset
+ yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y
+ img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s]
+
+ # Concat/clip labels
+ labels9 = np.concatenate(labels9, 0)
+ labels9[:, [1, 3]] -= xc
+ labels9[:, [2, 4]] -= yc
+ c = np.array([xc, yc]) # centers
+ segments9 = [x - c for x in segments9]
+
+ for x in (labels9[:, 1:], *segments9):
+ np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
+ # img9, labels9 = replicate(img9, labels9) # replicate
+
+ # Augment
+ img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp["copy_paste"])
+ img9, labels9 = random_perspective(
+ img9,
+ labels9,
+ segments9,
+ degrees=self.hyp["degrees"],
+ translate=self.hyp["translate"],
+ scale=self.hyp["scale"],
+ shear=self.hyp["shear"],
+ perspective=self.hyp["perspective"],
+ border=self.mosaic_border,
+ ) # border to remove
+
+ return img9, labels9
+
+ @staticmethod
+ def collate_fn(batch):
+ """Batches images, labels, paths, and shapes, assigning unique indices to targets in merged label tensor."""
+ im, label, path, shapes = zip(*batch) # transposed
+ for i, lb in enumerate(label):
+ lb[:, 0] = i # add target image index for build_targets()
+ return torch.stack(im, 0), torch.cat(label, 0), path, shapes
+
+ @staticmethod
+ def collate_fn4(batch):
+ """Bundles a batch's data by quartering the number of shapes and paths, preparing it for model input."""
+ im, label, path, shapes = zip(*batch) # transposed
+ n = len(shapes) // 4
+ im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n]
+
+ ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]])
+ wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]])
+ s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale
+ for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW
+ i *= 4
+ if random.random() < 0.5:
+ im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode="bilinear", align_corners=False)[
+ 0
+ ].type(im[i].type())
+ lb = label[i]
+ else:
+ im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2)
+ lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s
+ im4.append(im1)
+ label4.append(lb)
+
+ for i, lb in enumerate(label4):
+ lb[:, 0] = i # add target image index for build_targets()
+
+ return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4
+
+
+# Ancillary functions --------------------------------------------------------------------------------------------------
+def flatten_recursive(path=DATASETS_DIR / "coco128"):
+ """Flattens a directory by copying all files from subdirectories to a new top-level directory, preserving
+ filenames.
+ """
+ new_path = Path(f"{str(path)}_flat")
+ if os.path.exists(new_path):
+ shutil.rmtree(new_path) # delete output folder
+ os.makedirs(new_path) # make new output folder
+ for file in tqdm(glob.glob(f"{str(Path(path))}/**/*.*", recursive=True)):
+ shutil.copyfile(file, new_path / Path(file).name)
+
+
+def extract_boxes(path=DATASETS_DIR / "coco128"):
+ """
+ Converts a detection dataset to a classification dataset, creating a directory for each class and extracting
+ bounding boxes.
+
+ Example: from utils.dataloaders import *; extract_boxes()
+ """
+ path = Path(path) # images dir
+ shutil.rmtree(path / "classification") if (path / "classification").is_dir() else None # remove existing
+ files = list(path.rglob("*.*"))
+ n = len(files) # number of files
+ for im_file in tqdm(files, total=n):
+ if im_file.suffix[1:] in IMG_FORMATS:
+ # image
+ im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB
+ h, w = im.shape[:2]
+
+ # labels
+ lb_file = Path(img2label_paths([str(im_file)])[0])
+ if Path(lb_file).exists():
+ with open(lb_file) as f:
+ lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels
+
+ for j, x in enumerate(lb):
+ c = int(x[0]) # class
+ f = (path / "classification") / f"{c}" / f"{path.stem}_{im_file.stem}_{j}.jpg" # new filename
+ if not f.parent.is_dir():
+ f.parent.mkdir(parents=True)
+
+ b = x[1:] * [w, h, w, h] # box
+ # b[2:] = b[2:].max() # rectangle to square
+ b[2:] = b[2:] * 1.2 + 3 # pad
+ b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int)
+
+ b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image
+ b[[1, 3]] = np.clip(b[[1, 3]], 0, h)
+ assert cv2.imwrite(str(f), im[b[1] : b[3], b[0] : b[2]]), f"box failure in {f}"
+
+
+def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False):
+ """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files
+ Usage: from utils.dataloaders import *; autosplit().
+
+ Arguments:
+ path: Path to images directory
+ weights: Train, val, test weights (list, tuple)
+ annotated_only: Only use images with an annotated txt file
+ """
+ path = Path(path) # images dir
+ files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS) # image files only
+ n = len(files) # number of files
+ random.seed(0) # for reproducibility
+ indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split
+
+ txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"] # 3 txt files
+ for x in txt:
+ if (path.parent / x).exists():
+ (path.parent / x).unlink() # remove existing
+
+ print(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only)
+ for i, img in tqdm(zip(indices, files), total=n):
+ if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label
+ with open(path.parent / txt[i], "a") as f:
+ f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n") # add image to txt file
+
+
+def verify_image_label(args):
+ """Verifies a single image-label pair, ensuring image format, size, and legal label values."""
+ im_file, lb_file, prefix = args
+ nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, "", [] # number (missing, found, empty, corrupt), message, segments
+ try:
+ # verify images
+ im = Image.open(im_file)
+ im.verify() # PIL verify
+ shape = exif_size(im) # image size
+ assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels"
+ assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}"
+ if im.format.lower() in ("jpg", "jpeg"):
+ with open(im_file, "rb") as f:
+ f.seek(-2, 2)
+ if f.read() != b"\xff\xd9": # corrupt JPEG
+ ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100)
+ msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved"
+
+ # verify labels
+ if os.path.isfile(lb_file):
+ nf = 1 # label found
+ with open(lb_file) as f:
+ lb = [x.split() for x in f.read().strip().splitlines() if len(x)]
+ if any(len(x) > 6 for x in lb): # is segment
+ classes = np.array([x[0] for x in lb], dtype=np.float32)
+ segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...)
+ lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh)
+ lb = np.array(lb, dtype=np.float32)
+ nl = len(lb)
+ if nl:
+ assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected"
+ assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}"
+ assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}"
+ _, i = np.unique(lb, axis=0, return_index=True)
+ if len(i) < nl: # duplicate row check
+ lb = lb[i] # remove duplicates
+ if segments:
+ segments = [segments[x] for x in i]
+ msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed"
+ else:
+ ne = 1 # label empty
+ lb = np.zeros((0, 5), dtype=np.float32)
+ else:
+ nm = 1 # label missing
+ lb = np.zeros((0, 5), dtype=np.float32)
+ return im_file, lb, shape, segments, nm, nf, ne, nc, msg
+ except Exception as e:
+ nc = 1
+ msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}"
+ return [None, None, None, None, nm, nf, ne, nc, msg]
+
+
+class HUBDatasetStats:
+ """
+ Class for generating HUB dataset JSON and `-hub` dataset directory.
+
+ Arguments:
+ path: Path to data.yaml or data.zip (with data.yaml inside data.zip)
+ autodownload: Attempt to download dataset if not found locally
+
+ Usage
+ from utils.dataloaders import HUBDatasetStats
+ stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1
+ stats = HUBDatasetStats('path/to/coco128.zip') # usage 2
+ stats.get_json(save=False)
+ stats.process_images()
+ """
+
+ def __init__(self, path="coco128.yaml", autodownload=False):
+ """Initializes HUBDatasetStats with optional auto-download for datasets, given a path to dataset YAML or ZIP
+ file.
+ """
+ zipped, data_dir, yaml_path = self._unzip(Path(path))
+ try:
+ with open(check_yaml(yaml_path), errors="ignore") as f:
+ data = yaml.safe_load(f) # data dict
+ if zipped:
+ data["path"] = data_dir
+ except Exception as e:
+ raise Exception("error/HUB/dataset_stats/yaml_load") from e
+
+ check_dataset(data, autodownload) # download dataset if missing
+ self.hub_dir = Path(data["path"] + "-hub")
+ self.im_dir = self.hub_dir / "images"
+ self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images
+ self.stats = {"nc": data["nc"], "names": list(data["names"].values())} # statistics dictionary
+ self.data = data
+
+ @staticmethod
+ def _find_yaml(dir):
+ """Finds and returns the path to a single '.yaml' file in the specified directory, preferring files that match
+ the directory name.
+ """
+ files = list(dir.glob("*.yaml")) or list(dir.rglob("*.yaml")) # try root level first and then recursive
+ assert files, f"No *.yaml file found in {dir}"
+ if len(files) > 1:
+ files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name
+ assert files, f"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed"
+ assert len(files) == 1, f"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}"
+ return files[0]
+
+ def _unzip(self, path):
+ """Unzips a .zip file at 'path', returning success status, unzipped directory, and path to YAML file within."""
+ if not str(path).endswith(".zip"): # path is data.yaml
+ return False, None, path
+ assert Path(path).is_file(), f"Error unzipping {path}, file not found"
+ unzip_file(path, path=path.parent)
+ dir = path.with_suffix("") # dataset directory == zip name
+ assert dir.is_dir(), f"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/"
+ return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path
+
+ def _hub_ops(self, f, max_dim=1920):
+ """Resizes and saves an image at reduced quality for web/app viewing, supporting both PIL and OpenCV."""
+ f_new = self.im_dir / Path(f).name # dataset-hub image filename
+ try: # use PIL
+ im = Image.open(f)
+ r = max_dim / max(im.height, im.width) # ratio
+ if r < 1.0: # image too large
+ im = im.resize((int(im.width * r), int(im.height * r)))
+ im.save(f_new, "JPEG", quality=50, optimize=True) # save
+ except Exception as e: # use OpenCV
+ LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}")
+ im = cv2.imread(f)
+ im_height, im_width = im.shape[:2]
+ r = max_dim / max(im_height, im_width) # ratio
+ if r < 1.0: # image too large
+ im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA)
+ cv2.imwrite(str(f_new), im)
+
+ def get_json(self, save=False, verbose=False):
+ """Generates dataset JSON for Ultralytics HUB, optionally saves or prints it; save=bool, verbose=bool."""
+
+ def _round(labels):
+ """Rounds class labels to integers and coordinates to 4 decimal places for improved label accuracy."""
+ return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels]
+
+ for split in "train", "val", "test":
+ if self.data.get(split) is None:
+ self.stats[split] = None # i.e. no test set
+ continue
+ dataset = LoadImagesAndLabels(self.data[split]) # load dataset
+ x = np.array(
+ [
+ np.bincount(label[:, 0].astype(int), minlength=self.data["nc"])
+ for label in tqdm(dataset.labels, total=dataset.n, desc="Statistics")
+ ]
+ ) # shape(128x80)
+ self.stats[split] = {
+ "instance_stats": {"total": int(x.sum()), "per_class": x.sum(0).tolist()},
+ "image_stats": {
+ "total": dataset.n,
+ "unlabelled": int(np.all(x == 0, 1).sum()),
+ "per_class": (x > 0).sum(0).tolist(),
+ },
+ "labels": [{str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)],
+ }
+
+ # Save, print and return
+ if save:
+ stats_path = self.hub_dir / "stats.json"
+ print(f"Saving {stats_path.resolve()}...")
+ with open(stats_path, "w") as f:
+ json.dump(self.stats, f) # save stats.json
+ if verbose:
+ print(json.dumps(self.stats, indent=2, sort_keys=False))
+ return self.stats
+
+ def process_images(self):
+ """Compresses images for Ultralytics HUB across 'train', 'val', 'test' splits and saves to specified
+ directory.
+ """
+ for split in "train", "val", "test":
+ if self.data.get(split) is None:
+ continue
+ dataset = LoadImagesAndLabels(self.data[split]) # load dataset
+ desc = f"{split} images"
+ for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc):
+ pass
+ print(f"Done. All images saved to {self.im_dir}")
+ return self.im_dir
+
+
+# Classification dataloaders -------------------------------------------------------------------------------------------
+class ClassificationDataset(torchvision.datasets.ImageFolder):
+ """
+ YOLOv5 Classification Dataset.
+
+ Arguments:
+ root: Dataset path
+ transform: torchvision transforms, used by default
+ album_transform: Albumentations transforms, used if installed
+ """
+
+ def __init__(self, root, augment, imgsz, cache=False):
+ """Initializes YOLOv5 Classification Dataset with optional caching, augmentations, and transforms for image
+ classification.
+ """
+ super().__init__(root=root)
+ self.torch_transforms = classify_transforms(imgsz)
+ self.album_transforms = classify_albumentations(augment, imgsz) if augment else None
+ self.cache_ram = cache is True or cache == "ram"
+ self.cache_disk = cache == "disk"
+ self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im
+
+ def __getitem__(self, i):
+ """Fetches and transforms an image sample by index, supporting RAM/disk caching and Augmentations."""
+ f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image
+ if self.cache_ram and im is None:
+ im = self.samples[i][3] = cv2.imread(f)
+ elif self.cache_disk:
+ if not fn.exists(): # load npy
+ np.save(fn.as_posix(), cv2.imread(f))
+ im = np.load(fn)
+ else: # read image
+ im = cv2.imread(f) # BGR
+ if self.album_transforms:
+ sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
+ else:
+ sample = self.torch_transforms(im)
+ return sample, j
+
+
+def create_classification_dataloader(
+ path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True
+):
+ # Returns Dataloader object to be used with YOLOv5 Classifier
+ """Creates a DataLoader for image classification, supporting caching, augmentation, and distributed training."""
+ with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
+ dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache)
+ batch_size = min(batch_size, len(dataset))
+ nd = torch.cuda.device_count()
+ nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers])
+ sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
+ generator = torch.Generator()
+ generator.manual_seed(6148914691236517205 + RANK)
+ return InfiniteDataLoader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=shuffle and sampler is None,
+ num_workers=nw,
+ sampler=sampler,
+ pin_memory=PIN_MEMORY,
+ worker_init_fn=seed_worker,
+ generator=generator,
+ ) # or DataLoader(persistent_workers=True)
diff --git a/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile b/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile
new file mode 100644
index 00000000..f4727162
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile
@@ -0,0 +1,73 @@
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference
+
+# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch
+FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime
+
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+
+# Install linux packages
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt update
+RUN TZ=Etc/UTC apt install -y tzdata
+RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg
+# RUN alias python=python3
+
+# Security updates
+# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796
+RUN apt upgrade --no-install-recommends -y openssl
+
+# Create working directory
+RUN rm -rf /usr/src/app && mkdir -p /usr/src/app
+WORKDIR /usr/src/app
+
+# Copy contents
+COPY . /usr/src/app
+
+# Install pip packages
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \
+ coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0'
+ # tensorflow tensorflowjs \
+
+# Set environment variables
+ENV OMP_NUM_THREADS=1
+
+# Cleanup
+ENV DEBIAN_FRONTEND teletype
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t
+
+# Pull and Run
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t
+
+# Pull and Run with local directory access
+# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t
+
+# Kill all
+# sudo docker kill $(sudo docker ps -q)
+
+# Kill all image-based
+# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest)
+
+# DockerHub tag update
+# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew
+
+# Clean up
+# sudo docker system prune -a --volumes
+
+# Update Ubuntu drivers
+# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/
+
+# DDP test
+# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3
+
+# GCP VM from Image
+# docker.io/ultralytics/yolov5:latest
diff --git a/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile-arm64 b/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile-arm64
new file mode 100644
index 00000000..0de85bf8
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile-arm64
@@ -0,0 +1,40 @@
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM arm64v8/ubuntu:22.10
+
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+
+# Install linux packages
+ENV DEBIAN_FRONTEND noninteractive
+RUN apt update
+RUN TZ=Etc/UTC apt install -y tzdata
+RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev
+# RUN alias python=python3
+
+# Install pip packages
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
+ coremltools onnx onnxruntime
+ # tensorflow-aarch64 tensorflowjs \
+
+# Create working directory
+RUN mkdir -p /usr/src/app
+WORKDIR /usr/src/app
+
+# Copy contents
+COPY . /usr/src/app
+ENV DEBIAN_FRONTEND teletype
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t
+
+# Pull and Run
+# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
diff --git a/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile-cpu b/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile-cpu
new file mode 100644
index 00000000..573ad327
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/docker/Dockerfile-cpu
@@ -0,0 +1,42 @@
+# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license
+# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5
+# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments
+
+# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu
+FROM ubuntu:23.10
+
+# Downloads to user config dir
+ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/
+
+# Install linux packages
+# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package
+RUN apt update \
+ && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0
+# RUN alias python=python3
+
+# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error
+RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED
+
+# Install pip packages
+COPY requirements.txt .
+RUN python3 -m pip install --upgrade pip wheel
+RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \
+ coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \
+ # tensorflow tensorflowjs \
+ --extra-index-url https://download.pytorch.org/whl/cpu
+
+# Create working directory
+RUN mkdir -p /usr/src/app
+WORKDIR /usr/src/app
+
+# Copy contents
+COPY . /usr/src/app
+
+
+# Usage Examples -------------------------------------------------------------------------------------------------------
+
+# Build and Push
+# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t
+
+# Pull and Run
+# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t
diff --git a/Transfer Learning/Accident_Classifier/utils/downloads.py b/Transfer Learning/Accident_Classifier/utils/downloads.py
new file mode 100644
index 00000000..c7e2273c
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/downloads.py
@@ -0,0 +1,136 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Download utils."""
+
+import logging
+import subprocess
+import urllib
+from pathlib import Path
+
+import requests
+import torch
+
+
+def is_url(url, check=True):
+ """Determines if a string is a URL and optionally checks its existence online, returning a boolean."""
+ try:
+ url = str(url)
+ result = urllib.parse.urlparse(url)
+ assert all([result.scheme, result.netloc]) # check if is url
+ return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online
+ except (AssertionError, urllib.request.HTTPError):
+ return False
+
+
+def gsutil_getsize(url=""):
+ """
+ Returns the size in bytes of a file at a Google Cloud Storage URL using `gsutil du`.
+
+ Returns 0 if the command fails or output is empty.
+ """
+ output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8")
+ return int(output.split()[0]) if output else 0
+
+
+def url_getsize(url="https://ultralytics.com/images/bus.jpg"):
+ """Returns the size in bytes of a downloadable file at a given URL; defaults to -1 if not found."""
+ response = requests.head(url, allow_redirects=True)
+ return int(response.headers.get("content-length", -1))
+
+
+def curl_download(url, filename, *, silent: bool = False) -> bool:
+ """Download a file from a url to a filename using curl."""
+ silent_option = "sS" if silent else "" # silent
+ proc = subprocess.run(
+ [
+ "curl",
+ "-#",
+ f"-{silent_option}L",
+ url,
+ "--output",
+ filename,
+ "--retry",
+ "9",
+ "-C",
+ "-",
+ ]
+ )
+ return proc.returncode == 0
+
+
+def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""):
+ """
+ Downloads a file from a URL (or alternate URL) to a specified path if file is above a minimum size.
+
+ Removes incomplete downloads.
+ """
+ from utils.general import LOGGER
+
+ file = Path(file)
+ assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}"
+ try: # url1
+ LOGGER.info(f"Downloading {url} to {file}...")
+ torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO)
+ assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check
+ except Exception as e: # url2
+ if file.exists():
+ file.unlink() # remove partial downloads
+ LOGGER.info(f"ERROR: {e}\nRe-attempting {url2 or url} to {file}...")
+ # curl download, retry and resume on fail
+ curl_download(url2 or url, file)
+ finally:
+ if not file.exists() or file.stat().st_size < min_bytes: # check
+ if file.exists():
+ file.unlink() # remove partial downloads
+ LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}")
+ LOGGER.info("")
+
+
+def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"):
+ """Downloads a file from GitHub release assets or via direct URL if not found locally, supporting backup
+ versions.
+ """
+ from utils.general import LOGGER
+
+ def github_assets(repository, version="latest"):
+ """Fetches GitHub repository release tag and asset names using the GitHub API."""
+ if version != "latest":
+ version = f"tags/{version}" # i.e. tags/v7.0
+ response = requests.get(f"https://api.github.com/repos/{repository}/releases/{version}").json() # github api
+ return response["tag_name"], [x["name"] for x in response["assets"]] # tag, assets
+
+ file = Path(str(file).strip().replace("'", ""))
+ if not file.exists():
+ # URL specified
+ name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc.
+ if str(file).startswith(("http:/", "https:/")): # download
+ url = str(file).replace(":/", "://") # Pathlib turns :// -> :/
+ file = name.split("?")[0] # parse authentication https://url.com/file.txt?auth...
+ if Path(file).is_file():
+ LOGGER.info(f"Found {url} locally at {file}") # file already exists
+ else:
+ safe_download(file=file, url=url, min_bytes=1e5)
+ return file
+
+ # GitHub assets
+ assets = [f"yolov5{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "6", "-cls", "-seg")] # default
+ try:
+ tag, assets = github_assets(repo, release)
+ except Exception:
+ try:
+ tag, assets = github_assets(repo) # latest release
+ except Exception:
+ try:
+ tag = subprocess.check_output("git tag", shell=True, stderr=subprocess.STDOUT).decode().split()[-1]
+ except Exception:
+ tag = release
+
+ if name in assets:
+ file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required)
+ safe_download(
+ file,
+ url=f"https://github.com/{repo}/releases/download/{tag}/{name}",
+ min_bytes=1e5,
+ error_msg=f"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}",
+ )
+
+ return str(file)
diff --git a/Transfer Learning/Accident_Classifier/utils/flask_rest_api/README.md b/Transfer Learning/Accident_Classifier/utils/flask_rest_api/README.md
new file mode 100644
index 00000000..d3ffaa20
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/flask_rest_api/README.md
@@ -0,0 +1,70 @@
+# Flask REST API
+
+[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/).
+
+## Requirements
+
+[Flask](https://palletsprojects.com/projects/flask/) is required. Install with:
+
+```shell
+$ pip install Flask
+```
+
+## Run
+
+After Flask installation run:
+
+```shell
+$ python3 restapi.py --port 5000
+```
+
+Then use [curl](https://curl.se/) to perform a request:
+
+```shell
+$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s'
+```
+
+The model inference results are returned as a JSON response:
+
+```json
+[
+ {
+ "class": 0,
+ "confidence": 0.8900438547,
+ "height": 0.9318675399,
+ "name": "person",
+ "width": 0.3264600933,
+ "xcenter": 0.7438579798,
+ "ycenter": 0.5207948685
+ },
+ {
+ "class": 0,
+ "confidence": 0.8440024257,
+ "height": 0.7155083418,
+ "name": "person",
+ "width": 0.6546785235,
+ "xcenter": 0.427829951,
+ "ycenter": 0.6334488392
+ },
+ {
+ "class": 27,
+ "confidence": 0.3771208823,
+ "height": 0.3902671337,
+ "name": "tie",
+ "width": 0.0696444362,
+ "xcenter": 0.3675483763,
+ "ycenter": 0.7991207838
+ },
+ {
+ "class": 27,
+ "confidence": 0.3527112305,
+ "height": 0.1540903747,
+ "name": "tie",
+ "width": 0.0336618312,
+ "xcenter": 0.7814827561,
+ "ycenter": 0.5065554976
+ }
+]
+```
+
+An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py`
diff --git a/Transfer Learning/Accident_Classifier/utils/flask_rest_api/example_request.py b/Transfer Learning/Accident_Classifier/utils/flask_rest_api/example_request.py
new file mode 100644
index 00000000..10424900
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/flask_rest_api/example_request.py
@@ -0,0 +1,17 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Perform test request."""
+
+import pprint
+
+import requests
+
+DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s"
+IMAGE = "zidane.jpg"
+
+# Read image
+with open(IMAGE, "rb") as f:
+ image_data = f.read()
+
+response = requests.post(DETECTION_URL, files={"image": image_data}).json()
+
+pprint.pprint(response)
diff --git a/Transfer Learning/Accident_Classifier/utils/flask_rest_api/restapi.py b/Transfer Learning/Accident_Classifier/utils/flask_rest_api/restapi.py
new file mode 100644
index 00000000..7e03d3a6
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/flask_rest_api/restapi.py
@@ -0,0 +1,49 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Run a Flask REST API exposing one or more YOLOv5s models."""
+
+import argparse
+import io
+
+import torch
+from flask import Flask, request
+from PIL import Image
+
+app = Flask(__name__)
+models = {}
+
+DETECTION_URL = "/v1/object-detection/
+
+## About ClearML
+
+[ClearML](https://clear.ml) is an [open-source](https://github.com/allegroai/clearml) toolbox designed to save you time ⏱️.
+
+🔨 Track every YOLOv5 training run in the experiment manager
+
+🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool
+
+🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent
+
+🔬 Get the very best mAP using ClearML Hyperparameter Optimization
+
+🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving
+
+And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
+
+
+
+## 🦾 Setting Things Up
+
+To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one:
+
+Either sign up for free to the [ClearML Hosted Service](https://clear.ml) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go!
+
+1. Install the `clearml` python package:
+
+ ```bash
+ pip install clearml
+ ```
+
+2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions:
+
+ ```bash
+ clearml-init
+ ```
+
+That's it! You're done 😎
+
+## 🚀 Training YOLOv5 With ClearML
+
+To enable ClearML experiment tracking, simply install the ClearML pip package.
+
+```bash
+pip install clearml>=1.2.0
+```
+
+This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager.
+
+If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name!
+
+```bash
+python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
+```
+
+or with custom project and task name:
+
+```bash
+python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache
+```
+
+This will capture:
+
+- Source code + uncommitted changes
+- Installed packages
+- (Hyper)parameters
+- Model files (use `--save-period n` to save a checkpoint every n epochs)
+- Console output
+- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...)
+- General info such as machine details, runtime, creation date etc.
+- All produced plots such as label correlogram and confusion matrix
+- Images with bounding boxes per epoch
+- Mosaic per epoch
+- Validation images per epoch
+- ...
+
+That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them!
+
+There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
+
+## 🔗 Dataset Version Management
+
+Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment!
+
+
+
+### Prepare Your Dataset
+
+The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure:
+
+```
+..
+|_ yolov5
+|_ datasets
+ |_ coco128
+ |_ images
+ |_ labels
+ |_ LICENSE
+ |_ README.txt
+```
+
+But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure.
+
+Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls.
+
+Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`.
+
+```
+..
+|_ yolov5
+|_ datasets
+ |_ coco128
+ |_ images
+ |_ labels
+ |_ coco128.yaml # <---- HERE!
+ |_ LICENSE
+ |_ README.txt
+```
+
+### Upload Your Dataset
+
+To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command:
+
+```bash
+cd coco128
+clearml-data sync --project YOLOv5 --name coco128 --folder .
+```
+
+The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other:
+
+```bash
+# Optionally add --parent 
+
+# YOLOv5 with Comet
+
+This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2)
+
+# About Comet
+
+Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models.
+
+Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes!
+
+# Getting Started
+
+## Install Comet
+
+```shell
+pip install comet_ml
+```
+
+## Configure Comet Credentials
+
+There are two ways to configure Comet with YOLOv5.
+
+You can either set your credentials through environment variables
+
+**Environment Variables**
+
+```shell
+export COMET_API_KEY=
+
+# Try out an Example!
+
+Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)
+
+Or better yet, try it out yourself in this Colab Notebook
+
+[](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb)
+
+# Log automatically
+
+By default, Comet will log the following items
+
+## Metrics
+
+- Box Loss, Object Loss, Classification Loss for the training and validation data
+- mAP_0.5, mAP_0.5:0.95 metrics for the validation data.
+- Precision and Recall for the validation data
+
+## Parameters
+
+- Model Hyperparameters
+- All parameters passed through the command line options
+
+## Visualizations
+
+- Confusion Matrix of the model predictions on the validation data
+- Plots for the PR and F1 curves across all classes
+- Correlogram of the Class Labels
+
+# Configure Comet Logging
+
+Comet can be configured to log additional data either through command line flags passed to the training script or through environment variables.
+
+```shell
+export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online
+export COMET_MODEL_NAME=
+
+You can preview the data directly in the Comet UI. 
+
+Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file 
+
+### Using a saved Artifact
+
+If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL.
+
+```
+# contents of artifact.yaml file
+path: "comet://
+
+## Resuming a Training Run
+
+If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path.
+
+The Run Path has the following format `comet://
diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/comet/__init__.py b/Transfer Learning/Accident_Classifier/utils/loggers/comet/__init__.py
new file mode 100644
index 00000000..846dcb42
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/loggers/comet/__init__.py
@@ -0,0 +1,551 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+import glob
+import json
+import logging
+import os
+import sys
+from pathlib import Path
+
+logger = logging.getLogger(__name__)
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[3] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+
+try:
+ import comet_ml
+
+ # Project Configuration
+ config = comet_ml.config.get_config()
+ COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
+except ImportError:
+ comet_ml = None
+ COMET_PROJECT_NAME = None
+
+import PIL
+import torch
+import torchvision.transforms as T
+import yaml
+
+from utils.dataloaders import img2label_paths
+from utils.general import check_dataset, scale_boxes, xywh2xyxy
+from utils.metrics import box_iou
+
+COMET_PREFIX = "comet://"
+
+COMET_MODE = os.getenv("COMET_MODE", "online")
+
+# Model Saving Settings
+COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
+
+# Dataset Artifact Settings
+COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true"
+
+# Evaluation Settings
+COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true"
+COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true"
+COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100))
+
+# Confusion Matrix Settings
+CONF_THRES = float(os.getenv("CONF_THRES", 0.001))
+IOU_THRES = float(os.getenv("IOU_THRES", 0.6))
+
+# Batch Logging Settings
+COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true"
+COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1)
+COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1)
+COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true"
+
+RANK = int(os.getenv("RANK", -1))
+
+to_pil = T.ToPILImage()
+
+
+class CometLogger:
+ """Log metrics, parameters, source code, models and much more with Comet."""
+
+ def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None:
+ """Initializes CometLogger with given options, hyperparameters, run ID, job type, and additional experiment
+ arguments.
+ """
+ self.job_type = job_type
+ self.opt = opt
+ self.hyp = hyp
+
+ # Comet Flags
+ self.comet_mode = COMET_MODE
+
+ self.save_model = opt.save_period > -1
+ self.model_name = COMET_MODEL_NAME
+
+ # Batch Logging Settings
+ self.log_batch_metrics = COMET_LOG_BATCH_METRICS
+ self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL
+
+ # Dataset Artifact Settings
+ self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET
+ self.resume = self.opt.resume
+
+ # Default parameters to pass to Experiment objects
+ self.default_experiment_kwargs = {
+ "log_code": False,
+ "log_env_gpu": True,
+ "log_env_cpu": True,
+ "project_name": COMET_PROJECT_NAME,
+ }
+ self.default_experiment_kwargs.update(experiment_kwargs)
+ self.experiment = self._get_experiment(self.comet_mode, run_id)
+ self.experiment.set_name(self.opt.name)
+
+ self.data_dict = self.check_dataset(self.opt.data)
+ self.class_names = self.data_dict["names"]
+ self.num_classes = self.data_dict["nc"]
+
+ self.logged_images_count = 0
+ self.max_images = COMET_MAX_IMAGE_UPLOADS
+
+ if run_id is None:
+ self.experiment.log_other("Created from", "YOLOv5")
+ if not isinstance(self.experiment, comet_ml.OfflineExperiment):
+ workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:]
+ self.experiment.log_other(
+ "Run Path",
+ f"{workspace}/{project_name}/{experiment_id}",
+ )
+ self.log_parameters(vars(opt))
+ self.log_parameters(self.opt.hyp)
+ self.log_asset_data(
+ self.opt.hyp,
+ name="hyperparameters.json",
+ metadata={"type": "hyp-config-file"},
+ )
+ self.log_asset(
+ f"{self.opt.save_dir}/opt.yaml",
+ metadata={"type": "opt-config-file"},
+ )
+
+ self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX
+
+ if hasattr(self.opt, "conf_thres"):
+ self.conf_thres = self.opt.conf_thres
+ else:
+ self.conf_thres = CONF_THRES
+ if hasattr(self.opt, "iou_thres"):
+ self.iou_thres = self.opt.iou_thres
+ else:
+ self.iou_thres = IOU_THRES
+
+ self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres})
+
+ self.comet_log_predictions = COMET_LOG_PREDICTIONS
+ if self.opt.bbox_interval == -1:
+ self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10
+ else:
+ self.comet_log_prediction_interval = self.opt.bbox_interval
+
+ if self.comet_log_predictions:
+ self.metadata_dict = {}
+ self.logged_image_names = []
+
+ self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS
+
+ self.experiment.log_others(
+ {
+ "comet_mode": COMET_MODE,
+ "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS,
+ "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS,
+ "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS,
+ "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX,
+ "comet_model_name": COMET_MODEL_NAME,
+ }
+ )
+
+ # Check if running the Experiment with the Comet Optimizer
+ if hasattr(self.opt, "comet_optimizer_id"):
+ self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id)
+ self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective)
+ self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric)
+ self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp))
+
+ def _get_experiment(self, mode, experiment_id=None):
+ """Returns a new or existing Comet.ml experiment based on mode and optional experiment_id."""
+ if mode == "offline":
+ return (
+ comet_ml.ExistingOfflineExperiment(
+ previous_experiment=experiment_id,
+ **self.default_experiment_kwargs,
+ )
+ if experiment_id is not None
+ else comet_ml.OfflineExperiment(
+ **self.default_experiment_kwargs,
+ )
+ )
+ try:
+ if experiment_id is not None:
+ return comet_ml.ExistingExperiment(
+ previous_experiment=experiment_id,
+ **self.default_experiment_kwargs,
+ )
+
+ return comet_ml.Experiment(**self.default_experiment_kwargs)
+
+ except ValueError:
+ logger.warning(
+ "COMET WARNING: "
+ "Comet credentials have not been set. "
+ "Comet will default to offline logging. "
+ "Please set your credentials to enable online logging."
+ )
+ return self._get_experiment("offline", experiment_id)
+
+ return
+
+ def log_metrics(self, log_dict, **kwargs):
+ """Logs metrics to the current experiment, accepting a dictionary of metric names and values."""
+ self.experiment.log_metrics(log_dict, **kwargs)
+
+ def log_parameters(self, log_dict, **kwargs):
+ """Logs parameters to the current experiment, accepting a dictionary of parameter names and values."""
+ self.experiment.log_parameters(log_dict, **kwargs)
+
+ def log_asset(self, asset_path, **kwargs):
+ """Logs a file or directory as an asset to the current experiment."""
+ self.experiment.log_asset(asset_path, **kwargs)
+
+ def log_asset_data(self, asset, **kwargs):
+ """Logs in-memory data as an asset to the current experiment, with optional kwargs."""
+ self.experiment.log_asset_data(asset, **kwargs)
+
+ def log_image(self, img, **kwargs):
+ """Logs an image to the current experiment with optional kwargs."""
+ self.experiment.log_image(img, **kwargs)
+
+ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
+ """Logs model checkpoint to experiment with path, options, epoch, fitness, and best model flag."""
+ if not self.save_model:
+ return
+
+ model_metadata = {
+ "fitness_score": fitness_score[-1],
+ "epochs_trained": epoch + 1,
+ "save_period": opt.save_period,
+ "total_epochs": opt.epochs,
+ }
+
+ model_files = glob.glob(f"{path}/*.pt")
+ for model_path in model_files:
+ name = Path(model_path).name
+
+ self.experiment.log_model(
+ self.model_name,
+ file_or_folder=model_path,
+ file_name=name,
+ metadata=model_metadata,
+ overwrite=True,
+ )
+
+ def check_dataset(self, data_file):
+ """Validates the dataset configuration by loading the YAML file specified in `data_file`."""
+ with open(data_file) as f:
+ data_config = yaml.safe_load(f)
+
+ path = data_config.get("path")
+ if path and path.startswith(COMET_PREFIX):
+ path = data_config["path"].replace(COMET_PREFIX, "")
+ return self.download_dataset_artifact(path)
+ self.log_asset(self.opt.data, metadata={"type": "data-config-file"})
+
+ return check_dataset(data_file)
+
+ def log_predictions(self, image, labelsn, path, shape, predn):
+ """Logs predictions with IOU filtering, given image, labels, path, shape, and predictions."""
+ if self.logged_images_count >= self.max_images:
+ return
+ detections = predn[predn[:, 4] > self.conf_thres]
+ iou = box_iou(labelsn[:, 1:], detections[:, :4])
+ mask, _ = torch.where(iou > self.iou_thres)
+ if len(mask) == 0:
+ return
+
+ filtered_detections = detections[mask]
+ filtered_labels = labelsn[mask]
+
+ image_id = path.split("/")[-1].split(".")[0]
+ image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}"
+ if image_name not in self.logged_image_names:
+ native_scale_image = PIL.Image.open(path)
+ self.log_image(native_scale_image, name=image_name)
+ self.logged_image_names.append(image_name)
+
+ metadata = [
+ {
+ "label": f"{self.class_names[int(cls)]}-gt",
+ "score": 100,
+ "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
+ }
+ for cls, *xyxy in filtered_labels.tolist()
+ ]
+ metadata.extend(
+ {
+ "label": f"{self.class_names[int(cls)]}",
+ "score": conf * 100,
+ "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]},
+ }
+ for *xyxy, conf, cls in filtered_detections.tolist()
+ )
+ self.metadata_dict[image_name] = metadata
+ self.logged_images_count += 1
+
+ return
+
+ def preprocess_prediction(self, image, labels, shape, pred):
+ """Processes prediction data, resizing labels and adding dataset metadata."""
+ nl, _ = labels.shape[0], pred.shape[0]
+
+ # Predictions
+ if self.opt.single_cls:
+ pred[:, 5] = 0
+
+ predn = pred.clone()
+ scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1])
+
+ labelsn = None
+ if nl:
+ tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
+ scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels
+ labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
+ scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred
+
+ return predn, labelsn
+
+ def add_assets_to_artifact(self, artifact, path, asset_path, split):
+ """Adds image and label assets to a wandb artifact given dataset split and paths."""
+ img_paths = sorted(glob.glob(f"{asset_path}/*"))
+ label_paths = img2label_paths(img_paths)
+
+ for image_file, label_file in zip(img_paths, label_paths):
+ image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file])
+
+ try:
+ artifact.add(
+ image_file,
+ logical_path=image_logical_path,
+ metadata={"split": split},
+ )
+ artifact.add(
+ label_file,
+ logical_path=label_logical_path,
+ metadata={"split": split},
+ )
+ except ValueError as e:
+ logger.error("COMET ERROR: Error adding file to Artifact. Skipping file.")
+ logger.error(f"COMET ERROR: {e}")
+ continue
+
+ return artifact
+
+ def upload_dataset_artifact(self):
+ """Uploads a YOLOv5 dataset as an artifact to the Comet.ml platform."""
+ dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset")
+ path = str((ROOT / Path(self.data_dict["path"])).resolve())
+
+ metadata = self.data_dict.copy()
+ for key in ["train", "val", "test"]:
+ split_path = metadata.get(key)
+ if split_path is not None:
+ metadata[key] = split_path.replace(path, "")
+
+ artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata)
+ for key in metadata.keys():
+ if key in ["train", "val", "test"]:
+ if isinstance(self.upload_dataset, str) and (key != self.upload_dataset):
+ continue
+
+ asset_path = self.data_dict.get(key)
+ if asset_path is not None:
+ artifact = self.add_assets_to_artifact(artifact, path, asset_path, key)
+
+ self.experiment.log_artifact(artifact)
+
+ return
+
+ def download_dataset_artifact(self, artifact_path):
+ """Downloads a dataset artifact to a specified directory using the experiment's logged artifact."""
+ logged_artifact = self.experiment.get_artifact(artifact_path)
+ artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name)
+ logged_artifact.download(artifact_save_dir)
+
+ metadata = logged_artifact.metadata
+ data_dict = metadata.copy()
+ data_dict["path"] = artifact_save_dir
+
+ metadata_names = metadata.get("names")
+ if isinstance(metadata_names, dict):
+ data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()}
+ elif isinstance(metadata_names, list):
+ data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)}
+ else:
+ raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary"
+
+ return self.update_data_paths(data_dict)
+
+ def update_data_paths(self, data_dict):
+ """Updates data paths in the dataset dictionary, defaulting 'path' to an empty string if not present."""
+ path = data_dict.get("path", "")
+
+ for split in ["train", "val", "test"]:
+ if data_dict.get(split):
+ split_path = data_dict.get(split)
+ data_dict[split] = (
+ f"{path}/{split_path}" if isinstance(split, str) else [f"{path}/{x}" for x in split_path]
+ )
+
+ return data_dict
+
+ def on_pretrain_routine_end(self, paths):
+ """Called at the end of pretraining routine to handle paths if training is not being resumed."""
+ if self.opt.resume:
+ return
+
+ for path in paths:
+ self.log_asset(str(path))
+
+ if self.upload_dataset and not self.resume:
+ self.upload_dataset_artifact()
+
+ return
+
+ def on_train_start(self):
+ """Logs hyperparameters at the start of training."""
+ self.log_parameters(self.hyp)
+
+ def on_train_epoch_start(self):
+ """Called at the start of each training epoch."""
+ return
+
+ def on_train_epoch_end(self, epoch):
+ """Updates the current epoch in the experiment tracking at the end of each epoch."""
+ self.experiment.curr_epoch = epoch
+
+ return
+
+ def on_train_batch_start(self):
+ """Called at the start of each training batch."""
+ return
+
+ def on_train_batch_end(self, log_dict, step):
+ """Callback function that updates and logs metrics at the end of each training batch if conditions are met."""
+ self.experiment.curr_step = step
+ if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0):
+ self.log_metrics(log_dict, step=step)
+
+ return
+
+ def on_train_end(self, files, save_dir, last, best, epoch, results):
+ """Logs metadata and optionally saves model files at the end of training."""
+ if self.comet_log_predictions:
+ curr_epoch = self.experiment.curr_epoch
+ self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch)
+
+ for f in files:
+ self.log_asset(f, metadata={"epoch": epoch})
+ self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch})
+
+ if not self.opt.evolve:
+ model_path = str(best if best.exists() else last)
+ name = Path(model_path).name
+ if self.save_model:
+ self.experiment.log_model(
+ self.model_name,
+ file_or_folder=model_path,
+ file_name=name,
+ overwrite=True,
+ )
+
+ # Check if running Experiment with Comet Optimizer
+ if hasattr(self.opt, "comet_optimizer_id"):
+ metric = results.get(self.opt.comet_optimizer_metric)
+ self.experiment.log_other("optimizer_metric_value", metric)
+
+ self.finish_run()
+
+ def on_val_start(self):
+ """Called at the start of validation, currently a placeholder with no functionality."""
+ return
+
+ def on_val_batch_start(self):
+ """Placeholder called at the start of a validation batch with no current functionality."""
+ return
+
+ def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs):
+ """Callback executed at the end of a validation batch, conditionally logs predictions to Comet ML."""
+ if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)):
+ return
+
+ for si, pred in enumerate(outputs):
+ if len(pred) == 0:
+ continue
+
+ image = images[si]
+ labels = targets[targets[:, 0] == si, 1:]
+ shape = shapes[si]
+ path = paths[si]
+ predn, labelsn = self.preprocess_prediction(image, labels, shape, pred)
+ if labelsn is not None:
+ self.log_predictions(image, labelsn, path, shape, predn)
+
+ return
+
+ def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix):
+ """Logs per-class metrics to Comet.ml after validation if enabled and more than one class exists."""
+ if self.comet_log_per_class_metrics and self.num_classes > 1:
+ for i, c in enumerate(ap_class):
+ class_name = self.class_names[c]
+ self.experiment.log_metrics(
+ {
+ "mAP@.5": ap50[i],
+ "mAP@.5:.95": ap[i],
+ "precision": p[i],
+ "recall": r[i],
+ "f1": f1[i],
+ "true_positives": tp[i],
+ "false_positives": fp[i],
+ "support": nt[c],
+ },
+ prefix=class_name,
+ )
+
+ if self.comet_log_confusion_matrix:
+ epoch = self.experiment.curr_epoch
+ class_names = list(self.class_names.values())
+ class_names.append("background")
+ num_classes = len(class_names)
+
+ self.experiment.log_confusion_matrix(
+ matrix=confusion_matrix.matrix,
+ max_categories=num_classes,
+ labels=class_names,
+ epoch=epoch,
+ column_label="Actual Category",
+ row_label="Predicted Category",
+ file_name=f"confusion-matrix-epoch-{epoch}.json",
+ )
+
+ def on_fit_epoch_end(self, result, epoch):
+ """Logs metrics at the end of each training epoch."""
+ self.log_metrics(result, epoch=epoch)
+
+ def on_model_save(self, last, epoch, final_epoch, best_fitness, fi):
+ """Callback to save model checkpoints periodically if conditions are met."""
+ if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1:
+ self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi)
+
+ def on_params_update(self, params):
+ """Logs updated parameters during training."""
+ self.log_parameters(params)
+
+ def finish_run(self):
+ """Ends the current experiment and logs its completion."""
+ self.experiment.end()
diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/comet/__pycache__/__init__.cpython-310.pyc b/Transfer Learning/Accident_Classifier/utils/loggers/comet/__pycache__/__init__.cpython-310.pyc
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diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/comet/__pycache__/comet_utils.cpython-310.pyc b/Transfer Learning/Accident_Classifier/utils/loggers/comet/__pycache__/comet_utils.cpython-310.pyc
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diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/comet/comet_utils.py b/Transfer Learning/Accident_Classifier/utils/loggers/comet/comet_utils.py
new file mode 100644
index 00000000..cf936ab4
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/loggers/comet/comet_utils.py
@@ -0,0 +1,151 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+import logging
+import os
+from urllib.parse import urlparse
+
+try:
+ import comet_ml
+except ImportError:
+ comet_ml = None
+
+import yaml
+
+logger = logging.getLogger(__name__)
+
+COMET_PREFIX = "comet://"
+COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5")
+COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt")
+
+
+def download_model_checkpoint(opt, experiment):
+ """Downloads YOLOv5 model checkpoint from Comet ML experiment, updating `opt.weights` with download path."""
+ model_dir = f"{opt.project}/{experiment.name}"
+ os.makedirs(model_dir, exist_ok=True)
+
+ model_name = COMET_MODEL_NAME
+ model_asset_list = experiment.get_model_asset_list(model_name)
+
+ if len(model_asset_list) == 0:
+ logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}")
+ return
+
+ model_asset_list = sorted(
+ model_asset_list,
+ key=lambda x: x["step"],
+ reverse=True,
+ )
+ logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list}
+
+ resource_url = urlparse(opt.weights)
+ checkpoint_filename = resource_url.query
+
+ if checkpoint_filename:
+ asset_id = logged_checkpoint_map.get(checkpoint_filename)
+ else:
+ asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME)
+ checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME
+
+ if asset_id is None:
+ logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment")
+ return
+
+ try:
+ logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}")
+ asset_filename = checkpoint_filename
+
+ model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
+ model_download_path = f"{model_dir}/{asset_filename}"
+ with open(model_download_path, "wb") as f:
+ f.write(model_binary)
+
+ opt.weights = model_download_path
+
+ except Exception as e:
+ logger.warning("COMET WARNING: Unable to download checkpoint from Comet")
+ logger.exception(e)
+
+
+def set_opt_parameters(opt, experiment):
+ """
+ Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run.
+
+ Args:
+ opt (argparse.Namespace): Namespace of command line options
+ experiment (comet_ml.APIExperiment): Comet API Experiment object
+ """
+ asset_list = experiment.get_asset_list()
+ resume_string = opt.resume
+
+ for asset in asset_list:
+ if asset["fileName"] == "opt.yaml":
+ asset_id = asset["assetId"]
+ asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False)
+ opt_dict = yaml.safe_load(asset_binary)
+ for key, value in opt_dict.items():
+ setattr(opt, key, value)
+ opt.resume = resume_string
+
+ # Save hyperparameters to YAML file
+ # Necessary to pass checks in training script
+ save_dir = f"{opt.project}/{experiment.name}"
+ os.makedirs(save_dir, exist_ok=True)
+
+ hyp_yaml_path = f"{save_dir}/hyp.yaml"
+ with open(hyp_yaml_path, "w") as f:
+ yaml.dump(opt.hyp, f)
+ opt.hyp = hyp_yaml_path
+
+
+def check_comet_weights(opt):
+ """
+ Downloads model weights from Comet and updates the weights path to point to saved weights location.
+
+ Args:
+ opt (argparse.Namespace): Command Line arguments passed
+ to YOLOv5 training script
+
+ Returns:
+ None/bool: Return True if weights are successfully downloaded
+ else return None
+ """
+ if comet_ml is None:
+ return
+
+ if isinstance(opt.weights, str) and opt.weights.startswith(COMET_PREFIX):
+ api = comet_ml.API()
+ resource = urlparse(opt.weights)
+ experiment_path = f"{resource.netloc}{resource.path}"
+ experiment = api.get(experiment_path)
+ download_model_checkpoint(opt, experiment)
+ return True
+
+ return None
+
+
+def check_comet_resume(opt):
+ """
+ Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters.
+
+ Args:
+ opt (argparse.Namespace): Command Line arguments passed
+ to YOLOv5 training script
+
+ Returns:
+ None/bool: Return True if the run is restored successfully
+ else return None
+ """
+ if comet_ml is None:
+ return
+
+ if isinstance(opt.resume, str) and opt.resume.startswith(COMET_PREFIX):
+ api = comet_ml.API()
+ resource = urlparse(opt.resume)
+ experiment_path = f"{resource.netloc}{resource.path}"
+ experiment = api.get(experiment_path)
+ set_opt_parameters(opt, experiment)
+ download_model_checkpoint(opt, experiment)
+
+ return True
+
+ return None
diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/comet/hpo.py b/Transfer Learning/Accident_Classifier/utils/loggers/comet/hpo.py
new file mode 100644
index 00000000..c225ebbd
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/loggers/comet/hpo.py
@@ -0,0 +1,126 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+import argparse
+import json
+import logging
+import os
+import sys
+from pathlib import Path
+
+import comet_ml
+
+logger = logging.getLogger(__name__)
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[3] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+
+from train import train
+from utils.callbacks import Callbacks
+from utils.general import increment_path
+from utils.torch_utils import select_device
+
+# Project Configuration
+config = comet_ml.config.get_config()
+COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5")
+
+
+def get_args(known=False):
+ """Parses command-line arguments for YOLOv5 training, supporting configuration of weights, data paths,
+ hyperparameters, and more.
+ """
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path")
+ parser.add_argument("--cfg", type=str, default="", help="model.yaml path")
+ parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
+ parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path")
+ parser.add_argument("--epochs", type=int, default=300, help="total training epochs")
+ parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch")
+ parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)")
+ parser.add_argument("--rect", action="store_true", help="rectangular training")
+ parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training")
+ parser.add_argument("--nosave", action="store_true", help="only save final checkpoint")
+ parser.add_argument("--noval", action="store_true", help="only validate final epoch")
+ parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor")
+ parser.add_argument("--noplots", action="store_true", help="save no plot files")
+ parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations")
+ parser.add_argument("--bucket", type=str, default="", help="gsutil bucket")
+ parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"')
+ parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training")
+ parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+ parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%")
+ parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class")
+ parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer")
+ parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode")
+ parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+ parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name")
+ parser.add_argument("--name", default="exp", help="save to project/name")
+ parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+ parser.add_argument("--quad", action="store_true", help="quad dataloader")
+ parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler")
+ parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon")
+ parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)")
+ parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2")
+ parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)")
+ parser.add_argument("--seed", type=int, default=0, help="Global training seed")
+ parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify")
+
+ # Weights & Biases arguments
+ parser.add_argument("--entity", default=None, help="W&B: Entity")
+ parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='W&B: Upload data, "val" option')
+ parser.add_argument("--bbox_interval", type=int, default=-1, help="W&B: Set bounding-box image logging interval")
+ parser.add_argument("--artifact_alias", type=str, default="latest", help="W&B: Version of dataset artifact to use")
+
+ # Comet Arguments
+ parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.")
+ parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.")
+ parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.")
+ parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.")
+ parser.add_argument(
+ "--comet_optimizer_workers",
+ type=int,
+ default=1,
+ help="Comet: Number of Parallel Workers to use with the Comet Optimizer.",
+ )
+
+ return parser.parse_known_args()[0] if known else parser.parse_args()
+
+
+def run(parameters, opt):
+ """Executes YOLOv5 training with given hyperparameters and options, setting up device and training directories."""
+ hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]}
+
+ opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve))
+ opt.batch_size = parameters.get("batch_size")
+ opt.epochs = parameters.get("epochs")
+
+ device = select_device(opt.device, batch_size=opt.batch_size)
+ train(hyp_dict, opt, device, callbacks=Callbacks())
+
+
+if __name__ == "__main__":
+ opt = get_args(known=True)
+
+ opt.weights = str(opt.weights)
+ opt.cfg = str(opt.cfg)
+ opt.data = str(opt.data)
+ opt.project = str(opt.project)
+
+ optimizer_id = os.getenv("COMET_OPTIMIZER_ID")
+ if optimizer_id is None:
+ with open(opt.comet_optimizer_config) as f:
+ optimizer_config = json.load(f)
+ optimizer = comet_ml.Optimizer(optimizer_config)
+ else:
+ optimizer = comet_ml.Optimizer(optimizer_id)
+
+ opt.comet_optimizer_id = optimizer.id
+ status = optimizer.status()
+
+ opt.comet_optimizer_objective = status["spec"]["objective"]
+ opt.comet_optimizer_metric = status["spec"]["metric"]
+
+ logger.info("COMET INFO: Starting Hyperparameter Sweep")
+ for parameter in optimizer.get_parameters():
+ run(parameter["parameters"], opt)
diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/comet/optimizer_config.json b/Transfer Learning/Accident_Classifier/utils/loggers/comet/optimizer_config.json
new file mode 100644
index 00000000..0218f162
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/loggers/comet/optimizer_config.json
@@ -0,0 +1,135 @@
+{
+ "algorithm": "random",
+ "parameters": {
+ "anchor_t": {
+ "type": "discrete",
+ "values": [2, 8]
+ },
+ "batch_size": {
+ "type": "discrete",
+ "values": [16, 32, 64]
+ },
+ "box": {
+ "type": "discrete",
+ "values": [0.02, 0.2]
+ },
+ "cls": {
+ "type": "discrete",
+ "values": [0.2]
+ },
+ "cls_pw": {
+ "type": "discrete",
+ "values": [0.5]
+ },
+ "copy_paste": {
+ "type": "discrete",
+ "values": [1]
+ },
+ "degrees": {
+ "type": "discrete",
+ "values": [0, 45]
+ },
+ "epochs": {
+ "type": "discrete",
+ "values": [5]
+ },
+ "fl_gamma": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "fliplr": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "flipud": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "hsv_h": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "hsv_s": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "hsv_v": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "iou_t": {
+ "type": "discrete",
+ "values": [0.7]
+ },
+ "lr0": {
+ "type": "discrete",
+ "values": [1e-5, 0.1]
+ },
+ "lrf": {
+ "type": "discrete",
+ "values": [0.01, 1]
+ },
+ "mixup": {
+ "type": "discrete",
+ "values": [1]
+ },
+ "momentum": {
+ "type": "discrete",
+ "values": [0.6]
+ },
+ "mosaic": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "obj": {
+ "type": "discrete",
+ "values": [0.2]
+ },
+ "obj_pw": {
+ "type": "discrete",
+ "values": [0.5]
+ },
+ "optimizer": {
+ "type": "categorical",
+ "values": ["SGD", "Adam", "AdamW"]
+ },
+ "perspective": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "scale": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "shear": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "translate": {
+ "type": "discrete",
+ "values": [0]
+ },
+ "warmup_bias_lr": {
+ "type": "discrete",
+ "values": [0, 0.2]
+ },
+ "warmup_epochs": {
+ "type": "discrete",
+ "values": [5]
+ },
+ "warmup_momentum": {
+ "type": "discrete",
+ "values": [0, 0.95]
+ },
+ "weight_decay": {
+ "type": "discrete",
+ "values": [0, 0.001]
+ }
+ },
+ "spec": {
+ "maxCombo": 0,
+ "metric": "metrics/mAP_0.5",
+ "objective": "maximize"
+ },
+ "trials": 1
+}
diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/wandb/__init__.py b/Transfer Learning/Accident_Classifier/utils/loggers/wandb/__init__.py
new file mode 100644
index 00000000..e69de29b
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diff --git a/Transfer Learning/Accident_Classifier/utils/loggers/wandb/wandb_utils.py b/Transfer Learning/Accident_Classifier/utils/loggers/wandb/wandb_utils.py
new file mode 100644
index 00000000..9883d573
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/loggers/wandb/wandb_utils.py
@@ -0,0 +1,210 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+# WARNING ⚠️ wandb is deprecated and will be removed in future release.
+# See supported integrations at https://github.com/ultralytics/yolov5#integrations
+
+import logging
+import os
+import sys
+from contextlib import contextmanager
+from pathlib import Path
+
+from utils.general import LOGGER, colorstr
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[3] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+RANK = int(os.getenv("RANK", -1))
+DEPRECATION_WARNING = (
+ f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. "
+ f'See supported integrations at https://github.com/ultralytics/yolov5#integrations.'
+)
+
+try:
+ import wandb
+
+ assert hasattr(wandb, "__version__") # verify package import not local dir
+ LOGGER.warning(DEPRECATION_WARNING)
+except (ImportError, AssertionError):
+ wandb = None
+
+
+class WandbLogger:
+ """
+ Log training runs, datasets, models, and predictions to Weights & Biases.
+
+ This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system
+ configuration and metrics, model metrics, and basic data metrics and analyses.
+
+ By providing additional command line arguments to train.py, datasets, models and predictions can also be logged.
+
+ For more on how this logger is used, see the Weights & Biases documentation:
+ https://docs.wandb.com/guides/integrations/yolov5
+ """
+
+ def __init__(self, opt, run_id=None, job_type="Training"):
+ """
+ - Initialize WandbLogger instance
+ - Upload dataset if opt.upload_dataset is True
+ - Setup training processes if job_type is 'Training'.
+
+ Arguments:
+ opt (namespace) -- Commandline arguments for this run
+ run_id (str) -- Run ID of W&B run to be resumed
+ job_type (str) -- To set the job_type for this run
+
+ """
+ # Pre-training routine --
+ self.job_type = job_type
+ self.wandb, self.wandb_run = wandb, wandb.run if wandb else None
+ self.val_artifact, self.train_artifact = None, None
+ self.train_artifact_path, self.val_artifact_path = None, None
+ self.result_artifact = None
+ self.val_table, self.result_table = None, None
+ self.max_imgs_to_log = 16
+ self.data_dict = None
+ if self.wandb:
+ self.wandb_run = wandb.run or wandb.init(
+ config=opt,
+ resume="allow",
+ project="YOLOv5" if opt.project == "runs/train" else Path(opt.project).stem,
+ entity=opt.entity,
+ name=opt.name if opt.name != "exp" else None,
+ job_type=job_type,
+ id=run_id,
+ allow_val_change=True,
+ )
+
+ if self.wandb_run and self.job_type == "Training":
+ if isinstance(opt.data, dict):
+ # This means another dataset manager has already processed the dataset info (e.g. ClearML)
+ # and they will have stored the already processed dict in opt.data
+ self.data_dict = opt.data
+ self.setup_training(opt)
+
+ def setup_training(self, opt):
+ """
+ Setup the necessary processes for training YOLO models:
+ - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX
+ - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded
+ - Setup log_dict, initialize bbox_interval.
+
+ Arguments:
+ opt (namespace) -- commandline arguments for this run
+
+ """
+ self.log_dict, self.current_epoch = {}, 0
+ self.bbox_interval = opt.bbox_interval
+ if isinstance(opt.resume, str):
+ model_dir, _ = self.download_model_artifact(opt)
+ if model_dir:
+ self.weights = Path(model_dir) / "last.pt"
+ config = self.wandb_run.config
+ opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = (
+ str(self.weights),
+ config.save_period,
+ config.batch_size,
+ config.bbox_interval,
+ config.epochs,
+ config.hyp,
+ config.imgsz,
+ )
+
+ if opt.bbox_interval == -1:
+ self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1
+ if opt.evolve or opt.noplots:
+ self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval
+
+ def log_model(self, path, opt, epoch, fitness_score, best_model=False):
+ """
+ Log the model checkpoint as W&B artifact.
+
+ Arguments:
+ path (Path) -- Path of directory containing the checkpoints
+ opt (namespace) -- Command line arguments for this run
+ epoch (int) -- Current epoch number
+ fitness_score (float) -- fitness score for current epoch
+ best_model (boolean) -- Boolean representing if the current checkpoint is the best yet.
+ """
+ model_artifact = wandb.Artifact(
+ f"run_{wandb.run.id}_model",
+ type="model",
+ metadata={
+ "original_url": str(path),
+ "epochs_trained": epoch + 1,
+ "save period": opt.save_period,
+ "project": opt.project,
+ "total_epochs": opt.epochs,
+ "fitness_score": fitness_score,
+ },
+ )
+ model_artifact.add_file(str(path / "last.pt"), name="last.pt")
+ wandb.log_artifact(
+ model_artifact,
+ aliases=[
+ "latest",
+ "last",
+ f"epoch {str(self.current_epoch)}",
+ "best" if best_model else "",
+ ],
+ )
+ LOGGER.info(f"Saving model artifact on epoch {epoch + 1}")
+
+ def val_one_image(self, pred, predn, path, names, im):
+ """Evaluates model prediction for a single image, returning metrics and visualizations."""
+ pass
+
+ def log(self, log_dict):
+ """
+ Save the metrics to the logging dictionary.
+
+ Arguments:
+ log_dict (Dict) -- metrics/media to be logged in current step
+ """
+ if self.wandb_run:
+ for key, value in log_dict.items():
+ self.log_dict[key] = value
+
+ def end_epoch(self):
+ """
+ Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict.
+
+ Arguments:
+ best_result (boolean): Boolean representing if the result of this evaluation is best or not
+ """
+ if self.wandb_run:
+ with all_logging_disabled():
+ try:
+ wandb.log(self.log_dict)
+ except BaseException as e:
+ LOGGER.info(
+ f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}"
+ )
+ self.wandb_run.finish()
+ self.wandb_run = None
+ self.log_dict = {}
+
+ def finish_run(self):
+ """Log metrics if any and finish the current W&B run."""
+ if self.wandb_run:
+ if self.log_dict:
+ with all_logging_disabled():
+ wandb.log(self.log_dict)
+ wandb.run.finish()
+ LOGGER.warning(DEPRECATION_WARNING)
+
+
+@contextmanager
+def all_logging_disabled(highest_level=logging.CRITICAL):
+ """Source - https://gist.github.com/simon-weber/7853144
+ A context manager that will prevent any logging messages triggered during the body from being processed.
+ :param highest_level: the maximum logging level in use.
+ This would only need to be changed if a custom level greater than CRITICAL is defined.
+ """
+ previous_level = logging.root.manager.disable
+ logging.disable(highest_level)
+ try:
+ yield
+ finally:
+ logging.disable(previous_level)
diff --git a/Transfer Learning/Accident_Classifier/utils/loss.py b/Transfer Learning/Accident_Classifier/utils/loss.py
new file mode 100644
index 00000000..fd5912f4
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/loss.py
@@ -0,0 +1,259 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Loss functions."""
+
+import torch
+import torch.nn as nn
+
+from utils.metrics import bbox_iou
+from utils.torch_utils import de_parallel
+
+
+def smooth_BCE(eps=0.1):
+ """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441."""
+ return 1.0 - 0.5 * eps, 0.5 * eps
+
+
+class BCEBlurWithLogitsLoss(nn.Module):
+ """Modified BCEWithLogitsLoss to reduce missing label effects in YOLOv5 training with optional alpha smoothing."""
+
+ def __init__(self, alpha=0.05):
+ """Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing
+ parameter.
+ """
+ super().__init__()
+ self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none") # must be nn.BCEWithLogitsLoss()
+ self.alpha = alpha
+
+ def forward(self, pred, true):
+ """Computes modified BCE loss for YOLOv5 with reduced missing label effects, taking pred and true tensors,
+ returns mean loss.
+ """
+ loss = self.loss_fcn(pred, true)
+ pred = torch.sigmoid(pred) # prob from logits
+ dx = pred - true # reduce only missing label effects
+ # dx = (pred - true).abs() # reduce missing label and false label effects
+ alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4))
+ loss *= alpha_factor
+ return loss.mean()
+
+
+class FocalLoss(nn.Module):
+ """Applies focal loss to address class imbalance by modifying BCEWithLogitsLoss with gamma and alpha parameters."""
+
+ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
+ """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to
+ 'none'.
+ """
+ super().__init__()
+ self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
+ self.gamma = gamma
+ self.alpha = alpha
+ self.reduction = loss_fcn.reduction
+ self.loss_fcn.reduction = "none" # required to apply FL to each element
+
+ def forward(self, pred, true):
+ """Calculates the focal loss between predicted and true labels using a modified BCEWithLogitsLoss."""
+ loss = self.loss_fcn(pred, true)
+ # p_t = torch.exp(-loss)
+ # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability
+
+ # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
+ pred_prob = torch.sigmoid(pred) # prob from logits
+ p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
+ alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
+ modulating_factor = (1.0 - p_t) ** self.gamma
+ loss *= alpha_factor * modulating_factor
+
+ if self.reduction == "mean":
+ return loss.mean()
+ elif self.reduction == "sum":
+ return loss.sum()
+ else: # 'none'
+ return loss
+
+
+class QFocalLoss(nn.Module):
+ """Implements Quality Focal Loss to address class imbalance by modulating loss based on prediction confidence."""
+
+ def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
+ """Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'."""
+ super().__init__()
+ self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss()
+ self.gamma = gamma
+ self.alpha = alpha
+ self.reduction = loss_fcn.reduction
+ self.loss_fcn.reduction = "none" # required to apply FL to each element
+
+ def forward(self, pred, true):
+ """Computes the focal loss between `pred` and `true` using BCEWithLogitsLoss, adjusting for imbalance with
+ `gamma` and `alpha`.
+ """
+ loss = self.loss_fcn(pred, true)
+
+ pred_prob = torch.sigmoid(pred) # prob from logits
+ alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
+ modulating_factor = torch.abs(true - pred_prob) ** self.gamma
+ loss *= alpha_factor * modulating_factor
+
+ if self.reduction == "mean":
+ return loss.mean()
+ elif self.reduction == "sum":
+ return loss.sum()
+ else: # 'none'
+ return loss
+
+
+class ComputeLoss:
+ """Computes the total loss for YOLOv5 model predictions, including classification, box, and objectness losses."""
+
+ sort_obj_iou = False
+
+ # Compute losses
+ def __init__(self, model, autobalance=False):
+ """Initializes ComputeLoss with model and autobalance option, autobalances losses if True."""
+ device = next(model.parameters()).device # get model device
+ h = model.hyp # hyperparameters
+
+ # Define criteria
+ BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device))
+ BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device))
+
+ # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+ self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets
+
+ # Focal loss
+ g = h["fl_gamma"] # focal loss gamma
+ if g > 0:
+ BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+ m = de_parallel(model).model[-1] # Detect() module
+ self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
+ self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
+ self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
+ self.na = m.na # number of anchors
+ self.nc = m.nc # number of classes
+ self.nl = m.nl # number of layers
+ self.anchors = m.anchors
+ self.device = device
+
+ def __call__(self, p, targets): # predictions, targets
+ """Performs forward pass, calculating class, box, and object loss for given predictions and targets."""
+ lcls = torch.zeros(1, device=self.device) # class loss
+ lbox = torch.zeros(1, device=self.device) # box loss
+ lobj = torch.zeros(1, device=self.device) # object loss
+ tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets
+
+ # Losses
+ for i, pi in enumerate(p): # layer index, layer predictions
+ b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
+ tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
+
+ n = b.shape[0] # number of targets
+ if n:
+ # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0
+ pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions
+
+ # Regression
+ pxy = pxy.sigmoid() * 2 - 0.5
+ pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
+ pbox = torch.cat((pxy, pwh), 1) # predicted box
+ iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
+ lbox += (1.0 - iou).mean() # iou loss
+
+ # Objectness
+ iou = iou.detach().clamp(0).type(tobj.dtype)
+ if self.sort_obj_iou:
+ j = iou.argsort()
+ b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
+ if self.gr < 1:
+ iou = (1.0 - self.gr) + self.gr * iou
+ tobj[b, a, gj, gi] = iou # iou ratio
+
+ # Classification
+ if self.nc > 1: # cls loss (only if multiple classes)
+ t = torch.full_like(pcls, self.cn, device=self.device) # targets
+ t[range(n), tcls[i]] = self.cp
+ lcls += self.BCEcls(pcls, t) # BCE
+
+ # Append targets to text file
+ # with open('targets.txt', 'a') as file:
+ # [file.write('%11.5g ' * 4 % tuple(x) + '\n') for x in torch.cat((txy[i], twh[i]), 1)]
+
+ obji = self.BCEobj(pi[..., 4], tobj)
+ lobj += obji * self.balance[i] # obj loss
+ if self.autobalance:
+ self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+
+ if self.autobalance:
+ self.balance = [x / self.balance[self.ssi] for x in self.balance]
+ lbox *= self.hyp["box"]
+ lobj *= self.hyp["obj"]
+ lcls *= self.hyp["cls"]
+ bs = tobj.shape[0] # batch size
+
+ return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach()
+
+ def build_targets(self, p, targets):
+ """Prepares model targets from input targets (image,class,x,y,w,h) for loss computation, returning class, box,
+ indices, and anchors.
+ """
+ na, nt = self.na, targets.shape[0] # number of anchors, targets
+ tcls, tbox, indices, anch = [], [], [], []
+ gain = torch.ones(7, device=self.device) # normalized to gridspace gain
+ ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
+ targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices
+
+ g = 0.5 # bias
+ off = (
+ torch.tensor(
+ [
+ [0, 0],
+ [1, 0],
+ [0, 1],
+ [-1, 0],
+ [0, -1], # j,k,l,m
+ # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
+ ],
+ device=self.device,
+ ).float()
+ * g
+ ) # offsets
+
+ for i in range(self.nl):
+ anchors, shape = self.anchors[i], p[i].shape
+ gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
+
+ # Match targets to anchors
+ t = targets * gain # shape(3,n,7)
+ if nt:
+ # Matches
+ r = t[..., 4:6] / anchors[:, None] # wh ratio
+ j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare
+ # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
+ t = t[j] # filter
+
+ # Offsets
+ gxy = t[:, 2:4] # grid xy
+ gxi = gain[[2, 3]] - gxy # inverse
+ j, k = ((gxy % 1 < g) & (gxy > 1)).T
+ l, m = ((gxi % 1 < g) & (gxi > 1)).T
+ j = torch.stack((torch.ones_like(j), j, k, l, m))
+ t = t.repeat((5, 1, 1))[j]
+ offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
+ else:
+ t = targets[0]
+ offsets = 0
+
+ # Define
+ bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
+ a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class
+ gij = (gxy - offsets).long()
+ gi, gj = gij.T # grid indices
+
+ # Append
+ indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
+ tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
+ anch.append(anchors[a]) # anchors
+ tcls.append(c) # class
+
+ return tcls, tbox, indices, anch
diff --git a/Transfer Learning/Accident_Classifier/utils/metrics.py b/Transfer Learning/Accident_Classifier/utils/metrics.py
new file mode 100644
index 00000000..e8dc9df4
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/metrics.py
@@ -0,0 +1,381 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Model validation metrics."""
+
+import math
+import warnings
+from pathlib import Path
+
+import matplotlib.pyplot as plt
+import numpy as np
+import torch
+
+from utils import TryExcept, threaded
+
+
+def fitness(x):
+ """Calculates fitness of a model using weighted sum of metrics P, R, mAP@0.5, mAP@0.5:0.95."""
+ w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95]
+ return (x[:, :4] * w).sum(1)
+
+
+def smooth(y, f=0.05):
+ """Applies box filter smoothing to array `y` with fraction `f`, yielding a smoothed array."""
+ nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
+ p = np.ones(nf // 2) # ones padding
+ yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
+ return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed
+
+
+def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names=(), eps=1e-16, prefix=""):
+ """
+ Compute the average precision, given the recall and precision curves.
+
+ Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
+ # Arguments
+ tp: True positives (nparray, nx1 or nx10).
+ conf: Objectness value from 0-1 (nparray).
+ pred_cls: Predicted object classes (nparray).
+ target_cls: True object classes (nparray).
+ plot: Plot precision-recall curve at mAP@0.5
+ save_dir: Plot save directory
+ # Returns
+ The average precision as computed in py-faster-rcnn.
+ """
+ # Sort by objectness
+ i = np.argsort(-conf)
+ tp, conf, pred_cls = tp[i], conf[i], pred_cls[i]
+
+ # Find unique classes
+ unique_classes, nt = np.unique(target_cls, return_counts=True)
+ nc = unique_classes.shape[0] # number of classes, number of detections
+
+ # Create Precision-Recall curve and compute AP for each class
+ px, py = np.linspace(0, 1, 1000), [] # for plotting
+ ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000))
+ for ci, c in enumerate(unique_classes):
+ i = pred_cls == c
+ n_l = nt[ci] # number of labels
+ n_p = i.sum() # number of predictions
+ if n_p == 0 or n_l == 0:
+ continue
+
+ # Accumulate FPs and TPs
+ fpc = (1 - tp[i]).cumsum(0)
+ tpc = tp[i].cumsum(0)
+
+ # Recall
+ recall = tpc / (n_l + eps) # recall curve
+ r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases
+
+ # Precision
+ precision = tpc / (tpc + fpc) # precision curve
+ p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
+
+ # AP from recall-precision curve
+ for j in range(tp.shape[1]):
+ ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j])
+ if plot and j == 0:
+ py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5
+
+ # Compute F1 (harmonic mean of precision and recall)
+ f1 = 2 * p * r / (p + r + eps)
+ names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
+ names = dict(enumerate(names)) # to dict
+ if plot:
+ plot_pr_curve(px, py, ap, Path(save_dir) / f"{prefix}PR_curve.png", names)
+ plot_mc_curve(px, f1, Path(save_dir) / f"{prefix}F1_curve.png", names, ylabel="F1")
+ plot_mc_curve(px, p, Path(save_dir) / f"{prefix}P_curve.png", names, ylabel="Precision")
+ plot_mc_curve(px, r, Path(save_dir) / f"{prefix}R_curve.png", names, ylabel="Recall")
+
+ i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
+ p, r, f1 = p[:, i], r[:, i], f1[:, i]
+ tp = (r * nt).round() # true positives
+ fp = (tp / (p + eps) - tp).round() # false positives
+ return tp, fp, p, r, f1, ap, unique_classes.astype(int)
+
+
+def compute_ap(recall, precision):
+ """Compute the average precision, given the recall and precision curves
+ # Arguments
+ recall: The recall curve (list)
+ precision: The precision curve (list)
+ # Returns
+ Average precision, precision curve, recall curve.
+ """
+ # Append sentinel values to beginning and end
+ mrec = np.concatenate(([0.0], recall, [1.0]))
+ mpre = np.concatenate(([1.0], precision, [0.0]))
+
+ # Compute the precision envelope
+ mpre = np.flip(np.maximum.accumulate(np.flip(mpre)))
+
+ # Integrate area under curve
+ method = "interp" # methods: 'continuous', 'interp'
+ if method == "interp":
+ x = np.linspace(0, 1, 101) # 101-point interp (COCO)
+ ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate
+ else: # 'continuous'
+ i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes
+ ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve
+
+ return ap, mpre, mrec
+
+
+class ConfusionMatrix:
+ """Generates and visualizes a confusion matrix for evaluating object detection classification performance."""
+
+ def __init__(self, nc, conf=0.25, iou_thres=0.45):
+ """Initializes ConfusionMatrix with given number of classes, confidence, and IoU threshold."""
+ self.matrix = np.zeros((nc + 1, nc + 1))
+ self.nc = nc # number of classes
+ self.conf = conf
+ self.iou_thres = iou_thres
+
+ def process_batch(self, detections, labels):
+ """
+ Return intersection-over-union (Jaccard index) of boxes.
+
+ Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+
+ Arguments:
+ detections (Array[N, 6]), x1, y1, x2, y2, conf, class
+ labels (Array[M, 5]), class, x1, y1, x2, y2
+ Returns:
+ None, updates confusion matrix accordingly
+ """
+ if detections is None:
+ gt_classes = labels.int()
+ for gc in gt_classes:
+ self.matrix[self.nc, gc] += 1 # background FN
+ return
+
+ detections = detections[detections[:, 4] > self.conf]
+ gt_classes = labels[:, 0].int()
+ detection_classes = detections[:, 5].int()
+ iou = box_iou(labels[:, 1:], detections[:, :4])
+
+ x = torch.where(iou > self.iou_thres)
+ if x[0].shape[0]:
+ matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy()
+ if x[0].shape[0] > 1:
+ matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+ matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+ else:
+ matches = np.zeros((0, 3))
+
+ n = matches.shape[0] > 0
+ m0, m1, _ = matches.transpose().astype(int)
+ for i, gc in enumerate(gt_classes):
+ j = m0 == i
+ if n and sum(j) == 1:
+ self.matrix[detection_classes[m1[j]], gc] += 1 # correct
+ else:
+ self.matrix[self.nc, gc] += 1 # true background
+
+ if n:
+ for i, dc in enumerate(detection_classes):
+ if not any(m1 == i):
+ self.matrix[dc, self.nc] += 1 # predicted background
+
+ def tp_fp(self):
+ """Calculates true positives (tp) and false positives (fp) excluding the background class from the confusion
+ matrix.
+ """
+ tp = self.matrix.diagonal() # true positives
+ fp = self.matrix.sum(1) - tp # false positives
+ # fn = self.matrix.sum(0) - tp # false negatives (missed detections)
+ return tp[:-1], fp[:-1] # remove background class
+
+ @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure")
+ def plot(self, normalize=True, save_dir="", names=()):
+ """Plots confusion matrix using seaborn, optional normalization; can save plot to specified directory."""
+ import seaborn as sn
+
+ array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns
+ array[array < 0.005] = np.nan # don't annotate (would appear as 0.00)
+
+ fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True)
+ nc, nn = self.nc, len(names) # number of classes, names
+ sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size
+ labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels
+ ticklabels = (names + ["background"]) if labels else "auto"
+ with warnings.catch_warnings():
+ warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered
+ sn.heatmap(
+ array,
+ ax=ax,
+ annot=nc < 30,
+ annot_kws={"size": 8},
+ cmap="Blues",
+ fmt=".2f",
+ square=True,
+ vmin=0.0,
+ xticklabels=ticklabels,
+ yticklabels=ticklabels,
+ ).set_facecolor((1, 1, 1))
+ ax.set_xlabel("True")
+ ax.set_ylabel("Predicted")
+ ax.set_title("Confusion Matrix")
+ fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250)
+ plt.close(fig)
+
+ def print(self):
+ """Prints the confusion matrix row-wise, with each class and its predictions separated by spaces."""
+ for i in range(self.nc + 1):
+ print(" ".join(map(str, self.matrix[i])))
+
+
+def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7):
+ """
+ Calculates IoU, GIoU, DIoU, or CIoU between two boxes, supporting xywh/xyxy formats.
+
+ Input shapes are box1(1,4) to box2(n,4).
+ """
+ # Get the coordinates of bounding boxes
+ if xywh: # transform from xywh to xyxy
+ (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1)
+ w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2
+ b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_
+ b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_
+ else: # x1, y1, x2, y2 = box1
+ b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
+ b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
+ w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps)
+ w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps)
+
+ # Intersection area
+ inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * (
+ b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)
+ ).clamp(0)
+
+ # Union Area
+ union = w1 * h1 + w2 * h2 - inter + eps
+
+ # IoU
+ iou = inter / union
+ if CIoU or DIoU or GIoU:
+ cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width
+ ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height
+ if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1
+ c2 = cw**2 + ch**2 + eps # convex diagonal squared
+ rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2
+ if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
+ v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
+ with torch.no_grad():
+ alpha = v / (v - iou + (1 + eps))
+ return iou - (rho2 / c2 + v * alpha) # CIoU
+ return iou - rho2 / c2 # DIoU
+ c_area = cw * ch + eps # convex area
+ return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf
+ return iou # IoU
+
+
+def box_iou(box1, box2, eps=1e-7):
+ # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py
+ """
+ Return intersection-over-union (Jaccard index) of boxes.
+
+ Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
+
+ Arguments:
+ box1 (Tensor[N, 4])
+ box2 (Tensor[M, 4])
+
+ Returns:
+ iou (Tensor[N, M]): the NxM matrix containing the pairwise
+ IoU values for every element in boxes1 and boxes2
+ """
+ # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
+ (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2)
+ inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
+
+ # IoU = inter / (area1 + area2 - inter)
+ return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps)
+
+
+def bbox_ioa(box1, box2, eps=1e-7):
+ """
+ Returns the intersection over box2 area given box1, box2.
+
+ Boxes are x1y1x2y2
+ box1: np.array of shape(4)
+ box2: np.array of shape(nx4)
+ returns: np.array of shape(n)
+ """
+ # Get the coordinates of bounding boxes
+ b1_x1, b1_y1, b1_x2, b1_y2 = box1
+ b2_x1, b2_y1, b2_x2, b2_y2 = box2.T
+
+ # Intersection area
+ inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * (
+ np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1)
+ ).clip(0)
+
+ # box2 area
+ box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps
+
+ # Intersection over box2 area
+ return inter_area / box2_area
+
+
+def wh_iou(wh1, wh2, eps=1e-7):
+ """Calculates the Intersection over Union (IoU) for two sets of widths and heights; `wh1` and `wh2` should be nx2
+ and mx2 tensors.
+ """
+ wh1 = wh1[:, None] # [N,1,2]
+ wh2 = wh2[None] # [1,M,2]
+ inter = torch.min(wh1, wh2).prod(2) # [N,M]
+ return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter)
+
+
+# Plots ----------------------------------------------------------------------------------------------------------------
+
+
+@threaded
+def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()):
+ """Plots precision-recall curve, optionally per class, saving to `save_dir`; `px`, `py` are lists, `ap` is Nx2
+ array, `names` optional.
+ """
+ fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+ py = np.stack(py, axis=1)
+
+ if 0 < len(names) < 21: # display per-class legend if < 21 classes
+ for i, y in enumerate(py.T):
+ ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision)
+ else:
+ ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision)
+
+ ax.plot(px, py.mean(1), linewidth=3, color="blue", label=f"all classes {ap[:, 0].mean():.3f} mAP@0.5")
+ ax.set_xlabel("Recall")
+ ax.set_ylabel("Precision")
+ ax.set_xlim(0, 1)
+ ax.set_ylim(0, 1)
+ ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+ ax.set_title("Precision-Recall Curve")
+ fig.savefig(save_dir, dpi=250)
+ plt.close(fig)
+
+
+@threaded
+def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric"):
+ """Plots a metric-confidence curve for model predictions, supporting per-class visualization and smoothing."""
+ fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True)
+
+ if 0 < len(names) < 21: # display per-class legend if < 21 classes
+ for i, y in enumerate(py):
+ ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric)
+ else:
+ ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric)
+
+ y = smooth(py.mean(0), 0.05)
+ ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}")
+ ax.set_xlabel(xlabel)
+ ax.set_ylabel(ylabel)
+ ax.set_xlim(0, 1)
+ ax.set_ylim(0, 1)
+ ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
+ ax.set_title(f"{ylabel}-Confidence Curve")
+ fig.savefig(save_dir, dpi=250)
+ plt.close(fig)
diff --git a/Transfer Learning/Accident_Classifier/utils/plots.py b/Transfer Learning/Accident_Classifier/utils/plots.py
new file mode 100644
index 00000000..e899ea4c
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/plots.py
@@ -0,0 +1,517 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Plotting utils."""
+
+import contextlib
+import math
+import os
+from copy import copy
+from pathlib import Path
+
+import cv2
+import matplotlib
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sn
+import torch
+from PIL import Image, ImageDraw
+from scipy.ndimage.filters import gaussian_filter1d
+from ultralytics.utils.plotting import Annotator
+
+from utils import TryExcept, threaded
+from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh
+from utils.metrics import fitness
+
+# Settings
+RANK = int(os.getenv("RANK", -1))
+matplotlib.rc("font", **{"size": 11})
+matplotlib.use("Agg") # for writing to files only
+
+
+class Colors:
+ """Provides an RGB color palette derived from Ultralytics color scheme for visualization tasks."""
+
+ def __init__(self):
+ """
+ Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB.
+
+ Colors derived from `hex = matplotlib.colors.TABLEAU_COLORS.values()`.
+ """
+ hexs = (
+ "FF3838",
+ "FF9D97",
+ "FF701F",
+ "FFB21D",
+ "CFD231",
+ "48F90A",
+ "92CC17",
+ "3DDB86",
+ "1A9334",
+ "00D4BB",
+ "2C99A8",
+ "00C2FF",
+ "344593",
+ "6473FF",
+ "0018EC",
+ "8438FF",
+ "520085",
+ "CB38FF",
+ "FF95C8",
+ "FF37C7",
+ )
+ self.palette = [self.hex2rgb(f"#{c}") for c in hexs]
+ self.n = len(self.palette)
+
+ def __call__(self, i, bgr=False):
+ """Returns color from palette by index `i`, in BGR format if `bgr=True`, else RGB; `i` is an integer index."""
+ c = self.palette[int(i) % self.n]
+ return (c[2], c[1], c[0]) if bgr else c
+
+ @staticmethod
+ def hex2rgb(h):
+ """Converts hexadecimal color `h` to an RGB tuple (PIL-compatible) with order (R, G, B)."""
+ return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4))
+
+
+colors = Colors() # create instance for 'from utils.plots import colors'
+
+
+def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detect/exp")):
+ """
+ x: Features to be visualized
+ module_type: Module type
+ stage: Module stage within model
+ n: Maximum number of feature maps to plot
+ save_dir: Directory to save results.
+ """
+ if ("Detect" not in module_type) and (
+ "Segment" not in module_type
+ ): # 'Detect' for Object Detect task,'Segment' for Segment task
+ batch, channels, height, width = x.shape # batch, channels, height, width
+ if height > 1 and width > 1:
+ f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename
+
+ blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels
+ n = min(n, channels) # number of plots
+ fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols
+ ax = ax.ravel()
+ plt.subplots_adjust(wspace=0.05, hspace=0.05)
+ for i in range(n):
+ ax[i].imshow(blocks[i].squeeze()) # cmap='gray'
+ ax[i].axis("off")
+
+ LOGGER.info(f"Saving {f}... ({n}/{channels})")
+ plt.savefig(f, dpi=300, bbox_inches="tight")
+ plt.close()
+ np.save(str(f.with_suffix(".npy")), x[0].cpu().numpy()) # npy save
+
+
+def hist2d(x, y, n=100):
+ """
+ Generates a logarithmic 2D histogram, useful for visualizing label or evolution distributions.
+
+ Used in used in labels.png and evolve.png.
+ """
+ xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n)
+ hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges))
+ xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1)
+ yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1)
+ return np.log(hist[xidx, yidx])
+
+
+def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5):
+ """Applies a low-pass Butterworth filter to `data` with specified `cutoff`, `fs`, and `order`."""
+ from scipy.signal import butter, filtfilt
+
+ # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy
+ def butter_lowpass(cutoff, fs, order):
+ """Applies a low-pass Butterworth filter to a signal with specified cutoff frequency, sample rate, and filter
+ order.
+ """
+ nyq = 0.5 * fs
+ normal_cutoff = cutoff / nyq
+ return butter(order, normal_cutoff, btype="low", analog=False)
+
+ b, a = butter_lowpass(cutoff, fs, order=order)
+ return filtfilt(b, a, data) # forward-backward filter
+
+
+def output_to_target(output, max_det=300):
+ """Converts YOLOv5 model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, limiting detections
+ to `max_det`.
+ """
+ targets = []
+ for i, o in enumerate(output):
+ box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1)
+ j = torch.full((conf.shape[0], 1), i)
+ targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1))
+ return torch.cat(targets, 0).numpy()
+
+
+@threaded
+def plot_images(images, targets, paths=None, fname="images.jpg", names=None):
+ """Plots an image grid with labels from YOLOv5 predictions or targets, saving to `fname`."""
+ if isinstance(images, torch.Tensor):
+ images = images.cpu().float().numpy()
+ if isinstance(targets, torch.Tensor):
+ targets = targets.cpu().numpy()
+
+ max_size = 1920 # max image size
+ max_subplots = 16 # max image subplots, i.e. 4x4
+ bs, _, h, w = images.shape # batch size, _, height, width
+ bs = min(bs, max_subplots) # limit plot images
+ ns = np.ceil(bs**0.5) # number of subplots (square)
+ if np.max(images[0]) <= 1:
+ images *= 255 # de-normalise (optional)
+
+ # Build Image
+ mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
+ for i, im in enumerate(images):
+ if i == max_subplots: # if last batch has fewer images than we expect
+ break
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ im = im.transpose(1, 2, 0)
+ mosaic[y : y + h, x : x + w, :] = im
+
+ # Resize (optional)
+ scale = max_size / ns / max(h, w)
+ if scale < 1:
+ h = math.ceil(scale * h)
+ w = math.ceil(scale * w)
+ mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+ # Annotate
+ fs = int((h + w) * ns * 0.01) # font size
+ annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
+ for i in range(i + 1):
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
+ if paths:
+ annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
+ if len(targets) > 0:
+ ti = targets[targets[:, 0] == i] # image targets
+ boxes = xywh2xyxy(ti[:, 2:6]).T
+ classes = ti[:, 1].astype("int")
+ labels = ti.shape[1] == 6 # labels if no conf column
+ conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
+
+ if boxes.shape[1]:
+ if boxes.max() <= 1.01: # if normalized with tolerance 0.01
+ boxes[[0, 2]] *= w # scale to pixels
+ boxes[[1, 3]] *= h
+ elif scale < 1: # absolute coords need scale if image scales
+ boxes *= scale
+ boxes[[0, 2]] += x
+ boxes[[1, 3]] += y
+ for j, box in enumerate(boxes.T.tolist()):
+ cls = classes[j]
+ color = colors(cls)
+ cls = names[cls] if names else cls
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}"
+ annotator.box_label(box, label, color=color)
+ annotator.im.save(fname) # save
+
+
+def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""):
+ """Plots learning rate schedule for given optimizer and scheduler, saving plot to `save_dir`."""
+ optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals
+ y = []
+ for _ in range(epochs):
+ scheduler.step()
+ y.append(optimizer.param_groups[0]["lr"])
+ plt.plot(y, ".-", label="LR")
+ plt.xlabel("epoch")
+ plt.ylabel("LR")
+ plt.grid()
+ plt.xlim(0, epochs)
+ plt.ylim(0)
+ plt.savefig(Path(save_dir) / "LR.png", dpi=200)
+ plt.close()
+
+
+def plot_val_txt():
+ """
+ Plots 2D and 1D histograms of bounding box centers from 'val.txt' using matplotlib, saving as 'hist2d.png' and
+ 'hist1d.png'.
+
+ Example: from utils.plots import *; plot_val()
+ """
+ x = np.loadtxt("val.txt", dtype=np.float32)
+ box = xyxy2xywh(x[:, :4])
+ cx, cy = box[:, 0], box[:, 1]
+
+ fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True)
+ ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0)
+ ax.set_aspect("equal")
+ plt.savefig("hist2d.png", dpi=300)
+
+ fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True)
+ ax[0].hist(cx, bins=600)
+ ax[1].hist(cy, bins=600)
+ plt.savefig("hist1d.png", dpi=200)
+
+
+def plot_targets_txt():
+ """
+ Plots histograms of object detection targets from 'targets.txt', saving the figure as 'targets.jpg'.
+
+ Example: from utils.plots import *; plot_targets_txt()
+ """
+ x = np.loadtxt("targets.txt", dtype=np.float32).T
+ s = ["x targets", "y targets", "width targets", "height targets"]
+ fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)
+ ax = ax.ravel()
+ for i in range(4):
+ ax[i].hist(x[i], bins=100, label=f"{x[i].mean():.3g} +/- {x[i].std():.3g}")
+ ax[i].legend()
+ ax[i].set_title(s[i])
+ plt.savefig("targets.jpg", dpi=200)
+
+
+def plot_val_study(file="", dir="", x=None):
+ """
+ Plots validation study results from 'study*.txt' files in a directory or a specific file, comparing model
+ performance and speed.
+
+ Example: from utils.plots import *; plot_val_study()
+ """
+ save_dir = Path(file).parent if file else Path(dir)
+ plot2 = False # plot additional results
+ if plot2:
+ ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
+
+ fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
+ # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
+ for f in sorted(save_dir.glob("study*.txt")):
+ y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
+ x = np.arange(y.shape[1]) if x is None else np.array(x)
+ if plot2:
+ s = ["P", "R", "mAP@.5", "mAP@.5:.95", "t_preprocess (ms/img)", "t_inference (ms/img)", "t_NMS (ms/img)"]
+ for i in range(7):
+ ax[i].plot(x, y[i], ".-", linewidth=2, markersize=8)
+ ax[i].set_title(s[i])
+
+ j = y[3].argmax() + 1
+ ax2.plot(
+ y[5, 1:j],
+ y[3, 1:j] * 1e2,
+ ".-",
+ linewidth=2,
+ markersize=8,
+ label=f.stem.replace("study_coco_", "").replace("yolo", "YOLO"),
+ )
+
+ ax2.plot(
+ 1e3 / np.array([209, 140, 97, 58, 35, 18]),
+ [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
+ "k.-",
+ linewidth=2,
+ markersize=8,
+ alpha=0.25,
+ label="EfficientDet",
+ )
+
+ ax2.grid(alpha=0.2)
+ ax2.set_yticks(np.arange(20, 60, 5))
+ ax2.set_xlim(0, 57)
+ ax2.set_ylim(25, 55)
+ ax2.set_xlabel("GPU Speed (ms/img)")
+ ax2.set_ylabel("COCO AP val")
+ ax2.legend(loc="lower right")
+ f = save_dir / "study.png"
+ print(f"Saving {f}...")
+ plt.savefig(f, dpi=300)
+
+
+@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395
+def plot_labels(labels, names=(), save_dir=Path("")):
+ """Plots dataset labels, saving correlogram and label images, handles classes, and visualizes bounding boxes."""
+ LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ")
+ c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes
+ nc = int(c.max() + 1) # number of classes
+ x = pd.DataFrame(b.transpose(), columns=["x", "y", "width", "height"])
+
+ # seaborn correlogram
+ sn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9))
+ plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200)
+ plt.close()
+
+ # matplotlib labels
+ matplotlib.use("svg") # faster
+ ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel()
+ y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8)
+ with contextlib.suppress(Exception): # color histogram bars by class
+ [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195
+ ax[0].set_ylabel("instances")
+ if 0 < len(names) < 30:
+ ax[0].set_xticks(range(len(names)))
+ ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10)
+ else:
+ ax[0].set_xlabel("classes")
+ sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9)
+ sn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9)
+
+ # rectangles
+ labels[:, 1:3] = 0.5 # center
+ labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000
+ img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255)
+ for cls, *box in labels[:1000]:
+ ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot
+ ax[1].imshow(img)
+ ax[1].axis("off")
+
+ for a in [0, 1, 2, 3]:
+ for s in ["top", "right", "left", "bottom"]:
+ ax[a].spines[s].set_visible(False)
+
+ plt.savefig(save_dir / "labels.jpg", dpi=200)
+ matplotlib.use("Agg")
+ plt.close()
+
+
+def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")):
+ """Displays a grid of images with optional labels and predictions, saving to a file."""
+ from utils.augmentations import denormalize
+
+ names = names or [f"class{i}" for i in range(1000)]
+ blocks = torch.chunk(
+ denormalize(im.clone()).cpu().float(), len(im), dim=0
+ ) # select batch index 0, block by channels
+ n = min(len(blocks), nmax) # number of plots
+ m = min(8, round(n**0.5)) # 8 x 8 default
+ fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols
+ ax = ax.ravel() if m > 1 else [ax]
+ # plt.subplots_adjust(wspace=0.05, hspace=0.05)
+ for i in range(n):
+ ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0))
+ ax[i].axis("off")
+ if labels is not None:
+ s = names[labels[i]] + (f"—{names[pred[i]]}" if pred is not None else "")
+ ax[i].set_title(s, fontsize=8, verticalalignment="top")
+ plt.savefig(f, dpi=300, bbox_inches="tight")
+ plt.close()
+ if verbose:
+ LOGGER.info(f"Saving {f}")
+ if labels is not None:
+ LOGGER.info("True: " + " ".join(f"{names[i]:3s}" for i in labels[:nmax]))
+ if pred is not None:
+ LOGGER.info("Predicted:" + " ".join(f"{names[i]:3s}" for i in pred[:nmax]))
+ return f
+
+
+def plot_evolve(evolve_csv="path/to/evolve.csv"):
+ """
+ Plots hyperparameter evolution results from a given CSV, saving the plot and displaying best results.
+
+ Example: from utils.plots import *; plot_evolve()
+ """
+ evolve_csv = Path(evolve_csv)
+ data = pd.read_csv(evolve_csv)
+ keys = [x.strip() for x in data.columns]
+ x = data.values
+ f = fitness(x)
+ j = np.argmax(f) # max fitness index
+ plt.figure(figsize=(10, 12), tight_layout=True)
+ matplotlib.rc("font", **{"size": 8})
+ print(f"Best results from row {j} of {evolve_csv}:")
+ for i, k in enumerate(keys[7:]):
+ v = x[:, 7 + i]
+ mu = v[j] # best single result
+ plt.subplot(6, 5, i + 1)
+ plt.scatter(v, f, c=hist2d(v, f, 20), cmap="viridis", alpha=0.8, edgecolors="none")
+ plt.plot(mu, f.max(), "k+", markersize=15)
+ plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9}) # limit to 40 characters
+ if i % 5 != 0:
+ plt.yticks([])
+ print(f"{k:>15}: {mu:.3g}")
+ f = evolve_csv.with_suffix(".png") # filename
+ plt.savefig(f, dpi=200)
+ plt.close()
+ print(f"Saved {f}")
+
+
+def plot_results(file="path/to/results.csv", dir=""):
+ """
+ Plots training results from a 'results.csv' file; accepts file path and directory as arguments.
+
+ Example: from utils.plots import *; plot_results('path/to/results.csv')
+ """
+ save_dir = Path(file).parent if file else Path(dir)
+ fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True)
+ ax = ax.ravel()
+ files = list(save_dir.glob("results*.csv"))
+ assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
+ for f in files:
+ try:
+ data = pd.read_csv(f)
+ s = [x.strip() for x in data.columns]
+ x = data.values[:, 0]
+ for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]):
+ y = data.values[:, j].astype("float")
+ # y[y == 0] = np.nan # don't show zero values
+ ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=8) # actual results
+ ax[i].plot(x, gaussian_filter1d(y, sigma=3), ":", label="smooth", linewidth=2) # smoothing line
+ ax[i].set_title(s[j], fontsize=12)
+ # if j in [8, 9, 10]: # share train and val loss y axes
+ # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+ except Exception as e:
+ LOGGER.info(f"Warning: Plotting error for {f}: {e}")
+ ax[1].legend()
+ fig.savefig(save_dir / "results.png", dpi=200)
+ plt.close()
+
+
+def profile_idetection(start=0, stop=0, labels=(), save_dir=""):
+ """
+ Plots per-image iDetection logs, comparing metrics like storage and performance over time.
+
+ Example: from utils.plots import *; profile_idetection()
+ """
+ ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel()
+ s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"]
+ files = list(Path(save_dir).glob("frames*.txt"))
+ for fi, f in enumerate(files):
+ try:
+ results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows
+ n = results.shape[1] # number of rows
+ x = np.arange(start, min(stop, n) if stop else n)
+ results = results[:, x]
+ t = results[0] - results[0].min() # set t0=0s
+ results[0] = x
+ for i, a in enumerate(ax):
+ if i < len(results):
+ label = labels[fi] if len(labels) else f.stem.replace("frames_", "")
+ a.plot(t, results[i], marker=".", label=label, linewidth=1, markersize=5)
+ a.set_title(s[i])
+ a.set_xlabel("time (s)")
+ # if fi == len(files) - 1:
+ # a.set_ylim(bottom=0)
+ for side in ["top", "right"]:
+ a.spines[side].set_visible(False)
+ else:
+ a.remove()
+ except Exception as e:
+ print(f"Warning: Plotting error for {f}; {e}")
+ ax[1].legend()
+ plt.savefig(Path(save_dir) / "idetection_profile.png", dpi=200)
+
+
+def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True):
+ """Crops and saves an image from bounding box `xyxy`, applied with `gain` and `pad`, optionally squares and adjusts
+ for BGR.
+ """
+ xyxy = torch.tensor(xyxy).view(-1, 4)
+ b = xyxy2xywh(xyxy) # boxes
+ if square:
+ b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square
+ b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad
+ xyxy = xywh2xyxy(b).long()
+ clip_boxes(xyxy, im.shape)
+ crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)]
+ if save:
+ file.parent.mkdir(parents=True, exist_ok=True) # make directory
+ f = str(increment_path(file).with_suffix(".jpg"))
+ # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue
+ Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB
+ return crop
diff --git a/Transfer Learning/Accident_Classifier/utils/segment/__init__.py b/Transfer Learning/Accident_Classifier/utils/segment/__init__.py
new file mode 100644
index 00000000..e69de29b
diff --git a/Transfer Learning/Accident_Classifier/utils/segment/augmentations.py b/Transfer Learning/Accident_Classifier/utils/segment/augmentations.py
new file mode 100644
index 00000000..2e1dca11
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/segment/augmentations.py
@@ -0,0 +1,100 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Image augmentation functions."""
+
+import math
+import random
+
+import cv2
+import numpy as np
+
+from ..augmentations import box_candidates
+from ..general import resample_segments, segment2box
+
+
+def mixup(im, labels, segments, im2, labels2, segments2):
+ """
+ Applies MixUp augmentation blending two images, labels, and segments with a random ratio.
+
+ See https://arxiv.org/pdf/1710.09412.pdf
+ """
+ r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0
+ im = (im * r + im2 * (1 - r)).astype(np.uint8)
+ labels = np.concatenate((labels, labels2), 0)
+ segments = np.concatenate((segments, segments2), 0)
+ return im, labels, segments
+
+
+def random_perspective(
+ im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0)
+):
+ # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10))
+ # targets = [cls, xyxy]
+ """Applies random perspective, rotation, scale, shear, and translation augmentations to an image and targets."""
+ height = im.shape[0] + border[0] * 2 # shape(h,w,c)
+ width = im.shape[1] + border[1] * 2
+
+ # Center
+ C = np.eye(3)
+ C[0, 2] = -im.shape[1] / 2 # x translation (pixels)
+ C[1, 2] = -im.shape[0] / 2 # y translation (pixels)
+
+ # Perspective
+ P = np.eye(3)
+ P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y)
+ P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x)
+
+ # Rotation and Scale
+ R = np.eye(3)
+ a = random.uniform(-degrees, degrees)
+ # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations
+ s = random.uniform(1 - scale, 1 + scale)
+ # s = 2 ** random.uniform(-scale, scale)
+ R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s)
+
+ # Shear
+ S = np.eye(3)
+ S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg)
+ S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg)
+
+ # Translation
+ T = np.eye(3)
+ T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels)
+ T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels)
+
+ # Combined rotation matrix
+ M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT
+ if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed
+ if perspective:
+ im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114))
+ else: # affine
+ im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114))
+
+ # Visualize
+ # import matplotlib.pyplot as plt
+ # ax = plt.subplots(1, 2, figsize=(12, 6))[1].ravel()
+ # ax[0].imshow(im[:, :, ::-1]) # base
+ # ax[1].imshow(im2[:, :, ::-1]) # warped
+
+ # Transform label coordinates
+ n = len(targets)
+ new_segments = []
+ if n:
+ new = np.zeros((n, 4))
+ segments = resample_segments(segments) # upsample
+ for i, segment in enumerate(segments):
+ xy = np.ones((len(segment), 3))
+ xy[:, :2] = segment
+ xy = xy @ M.T # transform
+ xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine
+
+ # clip
+ new[i] = segment2box(xy, width, height)
+ new_segments.append(xy)
+
+ # filter candidates
+ i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01)
+ targets = targets[i]
+ targets[:, 1:5] = new[i]
+ new_segments = np.array(new_segments)[i]
+
+ return im, targets, new_segments
diff --git a/Transfer Learning/Accident_Classifier/utils/segment/dataloaders.py b/Transfer Learning/Accident_Classifier/utils/segment/dataloaders.py
new file mode 100644
index 00000000..5f5666c3
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/segment/dataloaders.py
@@ -0,0 +1,366 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Dataloaders."""
+
+import os
+import random
+
+import cv2
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+
+from ..augmentations import augment_hsv, copy_paste, letterbox
+from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, SmartDistributedSampler, seed_worker
+from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn
+from ..torch_utils import torch_distributed_zero_first
+from .augmentations import mixup, random_perspective
+
+RANK = int(os.getenv("RANK", -1))
+
+
+def create_dataloader(
+ path,
+ imgsz,
+ batch_size,
+ stride,
+ single_cls=False,
+ hyp=None,
+ augment=False,
+ cache=False,
+ pad=0.0,
+ rect=False,
+ rank=-1,
+ workers=8,
+ image_weights=False,
+ quad=False,
+ prefix="",
+ shuffle=False,
+ mask_downsample_ratio=1,
+ overlap_mask=False,
+ seed=0,
+):
+ """Creates a dataloader for training, validating, or testing YOLO models with various dataset options."""
+ if rect and shuffle:
+ LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False")
+ shuffle = False
+ with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
+ dataset = LoadImagesAndLabelsAndMasks(
+ path,
+ imgsz,
+ batch_size,
+ augment=augment, # augmentation
+ hyp=hyp, # hyperparameters
+ rect=rect, # rectangular batches
+ cache_images=cache,
+ single_cls=single_cls,
+ stride=int(stride),
+ pad=pad,
+ image_weights=image_weights,
+ prefix=prefix,
+ downsample_ratio=mask_downsample_ratio,
+ overlap=overlap_mask,
+ rank=rank,
+ )
+
+ batch_size = min(batch_size, len(dataset))
+ nd = torch.cuda.device_count() # number of CUDA devices
+ nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers
+ sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle)
+ loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates
+ generator = torch.Generator()
+ generator.manual_seed(6148914691236517205 + seed + RANK)
+ return loader(
+ dataset,
+ batch_size=batch_size,
+ shuffle=shuffle and sampler is None,
+ num_workers=nw,
+ sampler=sampler,
+ pin_memory=True,
+ collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn,
+ worker_init_fn=seed_worker,
+ generator=generator,
+ ), dataset
+
+
+class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing
+ """Loads images, labels, and segmentation masks for training and testing YOLO models with augmentation support."""
+
+ def __init__(
+ self,
+ path,
+ img_size=640,
+ batch_size=16,
+ augment=False,
+ hyp=None,
+ rect=False,
+ image_weights=False,
+ cache_images=False,
+ single_cls=False,
+ stride=32,
+ pad=0,
+ min_items=0,
+ prefix="",
+ downsample_ratio=1,
+ overlap=False,
+ rank=-1,
+ seed=0,
+ ):
+ """Initializes the dataset with image, label, and mask loading capabilities for training/testing."""
+ super().__init__(
+ path,
+ img_size,
+ batch_size,
+ augment,
+ hyp,
+ rect,
+ image_weights,
+ cache_images,
+ single_cls,
+ stride,
+ pad,
+ min_items,
+ prefix,
+ rank,
+ seed,
+ )
+ self.downsample_ratio = downsample_ratio
+ self.overlap = overlap
+
+ def __getitem__(self, index):
+ """Returns a transformed item from the dataset at the specified index, handling indexing and image weighting."""
+ index = self.indices[index] # linear, shuffled, or image_weights
+
+ hyp = self.hyp
+ mosaic = self.mosaic and random.random() < hyp["mosaic"]
+ masks = []
+ if mosaic:
+ # Load mosaic
+ img, labels, segments = self.load_mosaic(index)
+ shapes = None
+
+ # MixUp augmentation
+ if random.random() < hyp["mixup"]:
+ img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1)))
+
+ else:
+ # Load image
+ img, (h0, w0), (h, w) = self.load_image(index)
+
+ # Letterbox
+ shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape
+ img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment)
+ shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling
+
+ labels = self.labels[index].copy()
+ # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy
+ segments = self.segments[index].copy()
+ if len(segments):
+ for i_s in range(len(segments)):
+ segments[i_s] = xyn2xy(
+ segments[i_s],
+ ratio[0] * w,
+ ratio[1] * h,
+ padw=pad[0],
+ padh=pad[1],
+ )
+ if labels.size: # normalized xywh to pixel xyxy format
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1])
+
+ if self.augment:
+ img, labels, segments = random_perspective(
+ img,
+ labels,
+ segments=segments,
+ degrees=hyp["degrees"],
+ translate=hyp["translate"],
+ scale=hyp["scale"],
+ shear=hyp["shear"],
+ perspective=hyp["perspective"],
+ )
+
+ nl = len(labels) # number of labels
+ if nl:
+ labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3)
+ if self.overlap:
+ masks, sorted_idx = polygons2masks_overlap(
+ img.shape[:2], segments, downsample_ratio=self.downsample_ratio
+ )
+ masks = masks[None] # (640, 640) -> (1, 640, 640)
+ labels = labels[sorted_idx]
+ else:
+ masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio)
+
+ masks = (
+ torch.from_numpy(masks)
+ if len(masks)
+ else torch.zeros(
+ 1 if self.overlap else nl, img.shape[0] // self.downsample_ratio, img.shape[1] // self.downsample_ratio
+ )
+ )
+ # TODO: albumentations support
+ if self.augment:
+ # Albumentations
+ # there are some augmentation that won't change boxes and masks,
+ # so just be it for now.
+ img, labels = self.albumentations(img, labels)
+ nl = len(labels) # update after albumentations
+
+ # HSV color-space
+ augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"])
+
+ # Flip up-down
+ if random.random() < hyp["flipud"]:
+ img = np.flipud(img)
+ if nl:
+ labels[:, 2] = 1 - labels[:, 2]
+ masks = torch.flip(masks, dims=[1])
+
+ # Flip left-right
+ if random.random() < hyp["fliplr"]:
+ img = np.fliplr(img)
+ if nl:
+ labels[:, 1] = 1 - labels[:, 1]
+ masks = torch.flip(masks, dims=[2])
+
+ # Cutouts # labels = cutout(img, labels, p=0.5)
+
+ labels_out = torch.zeros((nl, 6))
+ if nl:
+ labels_out[:, 1:] = torch.from_numpy(labels)
+
+ # Convert
+ img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB
+ img = np.ascontiguousarray(img)
+
+ return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks)
+
+ def load_mosaic(self, index):
+ """Loads 1 image + 3 random images into a 4-image YOLOv5 mosaic, adjusting labels and segments accordingly."""
+ labels4, segments4 = [], []
+ s = self.img_size
+ yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y
+
+ # 3 additional image indices
+ indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices
+ for i, index in enumerate(indices):
+ # Load image
+ img, _, (h, w) = self.load_image(index)
+
+ # place img in img4
+ if i == 0: # top left
+ img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles
+ x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image)
+ elif i == 1: # top right
+ x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc
+ x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h
+ elif i == 2: # bottom left
+ x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h)
+ elif i == 3: # bottom right
+ x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h)
+ x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h)
+
+ img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax]
+ padw = x1a - x1b
+ padh = y1a - y1b
+
+ labels, segments = self.labels[index].copy(), self.segments[index].copy()
+
+ if labels.size:
+ labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format
+ segments = [xyn2xy(x, w, h, padw, padh) for x in segments]
+ labels4.append(labels)
+ segments4.extend(segments)
+
+ # Concat/clip labels
+ labels4 = np.concatenate(labels4, 0)
+ for x in (labels4[:, 1:], *segments4):
+ np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective()
+ # img4, labels4 = replicate(img4, labels4) # replicate
+
+ # Augment
+ img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"])
+ img4, labels4, segments4 = random_perspective(
+ img4,
+ labels4,
+ segments4,
+ degrees=self.hyp["degrees"],
+ translate=self.hyp["translate"],
+ scale=self.hyp["scale"],
+ shear=self.hyp["shear"],
+ perspective=self.hyp["perspective"],
+ border=self.mosaic_border,
+ ) # border to remove
+ return img4, labels4, segments4
+
+ @staticmethod
+ def collate_fn(batch):
+ """Custom collation function for DataLoader, batches images, labels, paths, shapes, and segmentation masks."""
+ img, label, path, shapes, masks = zip(*batch) # transposed
+ batched_masks = torch.cat(masks, 0)
+ for i, l in enumerate(label):
+ l[:, 0] = i # add target image index for build_targets()
+ return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks
+
+
+def polygon2mask(img_size, polygons, color=1, downsample_ratio=1):
+ """
+ Args:
+ img_size (tuple): The image size.
+ polygons (np.ndarray): [N, M], N is the number of polygons,
+ M is the number of points(Be divided by 2).
+ """
+ mask = np.zeros(img_size, dtype=np.uint8)
+ polygons = np.asarray(polygons)
+ polygons = polygons.astype(np.int32)
+ shape = polygons.shape
+ polygons = polygons.reshape(shape[0], -1, 2)
+ cv2.fillPoly(mask, polygons, color=color)
+ nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio)
+ # NOTE: fillPoly firstly then resize is trying the keep the same way
+ # of loss calculation when mask-ratio=1.
+ mask = cv2.resize(mask, (nw, nh))
+ return mask
+
+
+def polygons2masks(img_size, polygons, color, downsample_ratio=1):
+ """
+ Args:
+ img_size (tuple): The image size.
+ polygons (list[np.ndarray]): each polygon is [N, M],
+ N is the number of polygons,
+ M is the number of points(Be divided by 2).
+ """
+ masks = []
+ for si in range(len(polygons)):
+ mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio)
+ masks.append(mask)
+ return np.array(masks)
+
+
+def polygons2masks_overlap(img_size, segments, downsample_ratio=1):
+ """Return a (640, 640) overlap mask."""
+ masks = np.zeros(
+ (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio),
+ dtype=np.int32 if len(segments) > 255 else np.uint8,
+ )
+ areas = []
+ ms = []
+ for si in range(len(segments)):
+ mask = polygon2mask(
+ img_size,
+ [segments[si].reshape(-1)],
+ downsample_ratio=downsample_ratio,
+ color=1,
+ )
+ ms.append(mask)
+ areas.append(mask.sum())
+ areas = np.asarray(areas)
+ index = np.argsort(-areas)
+ ms = np.array(ms)[index]
+ for i in range(len(segments)):
+ mask = ms[i] * (i + 1)
+ masks = masks + mask
+ masks = np.clip(masks, a_min=0, a_max=i + 1)
+ return masks, index
diff --git a/Transfer Learning/Accident_Classifier/utils/segment/general.py b/Transfer Learning/Accident_Classifier/utils/segment/general.py
new file mode 100644
index 00000000..c9dfaaab
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/segment/general.py
@@ -0,0 +1,160 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+import cv2
+import numpy as np
+import torch
+import torch.nn.functional as F
+
+
+def crop_mask(masks, boxes):
+ """
+ "Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong).
+
+ Args:
+ - masks should be a size [n, h, w] tensor of masks
+ - boxes should be a size [n, 4] tensor of bbox coords in relative point form
+ """
+ n, h, w = masks.shape
+ x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n)
+ r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1)
+ c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1)
+
+ return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2))
+
+
+def process_mask_upsample(protos, masks_in, bboxes, shape):
+ """
+ Crop after upsample.
+ protos: [mask_dim, mask_h, mask_w]
+ masks_in: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape: input_image_size, (h, w).
+
+ return: h, w, n
+ """
+ c, mh, mw = protos.shape # CHW
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
+ masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW
+ masks = crop_mask(masks, bboxes) # CHW
+ return masks.gt_(0.5)
+
+
+def process_mask(protos, masks_in, bboxes, shape, upsample=False):
+ """
+ Crop before upsample.
+ proto_out: [mask_dim, mask_h, mask_w]
+ out_masks: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape:input_image_size, (h, w).
+
+ return: h, w, n
+ """
+ c, mh, mw = protos.shape # CHW
+ ih, iw = shape
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW
+
+ downsampled_bboxes = bboxes.clone()
+ downsampled_bboxes[:, 0] *= mw / iw
+ downsampled_bboxes[:, 2] *= mw / iw
+ downsampled_bboxes[:, 3] *= mh / ih
+ downsampled_bboxes[:, 1] *= mh / ih
+
+ masks = crop_mask(masks, downsampled_bboxes) # CHW
+ if upsample:
+ masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW
+ return masks.gt_(0.5)
+
+
+def process_mask_native(protos, masks_in, bboxes, shape):
+ """
+ Crop after upsample.
+ protos: [mask_dim, mask_h, mask_w]
+ masks_in: [n, mask_dim], n is number of masks after nms
+ bboxes: [n, 4], n is number of masks after nms
+ shape: input_image_size, (h, w).
+
+ return: h, w, n
+ """
+ c, mh, mw = protos.shape # CHW
+ masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw)
+ gain = min(mh / shape[0], mw / shape[1]) # gain = old / new
+ pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding
+ top, left = int(pad[1]), int(pad[0]) # y, x
+ bottom, right = int(mh - pad[1]), int(mw - pad[0])
+ masks = masks[:, top:bottom, left:right]
+
+ masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW
+ masks = crop_mask(masks, bboxes) # CHW
+ return masks.gt_(0.5)
+
+
+def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
+ """
+ img1_shape: model input shape, [h, w]
+ img0_shape: origin pic shape, [h, w, 3]
+ masks: [h, w, num].
+ """
+ # Rescale coordinates (xyxy) from im1_shape to im0_shape
+ if ratio_pad is None: # calculate from im0_shape
+ gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new
+ pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding
+ else:
+ pad = ratio_pad[1]
+ top, left = int(pad[1]), int(pad[0]) # y, x
+ bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0])
+
+ if len(masks.shape) < 2:
+ raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}')
+ masks = masks[top:bottom, left:right]
+ # masks = masks.permute(2, 0, 1).contiguous()
+ # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0]
+ # masks = masks.permute(1, 2, 0).contiguous()
+ masks = cv2.resize(masks, (im0_shape[1], im0_shape[0]))
+
+ if len(masks.shape) == 2:
+ masks = masks[:, :, None]
+ return masks
+
+
+def mask_iou(mask1, mask2, eps=1e-7):
+ """
+ mask1: [N, n] m1 means number of predicted objects
+ mask2: [M, n] m2 means number of gt objects
+ Note: n means image_w x image_h.
+
+ return: masks iou, [N, M]
+ """
+ intersection = torch.matmul(mask1, mask2.t()).clamp(0)
+ union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection
+ return intersection / (union + eps)
+
+
+def masks_iou(mask1, mask2, eps=1e-7):
+ """
+ mask1: [N, n] m1 means number of predicted objects
+ mask2: [N, n] m2 means number of gt objects
+ Note: n means image_w x image_h.
+
+ return: masks iou, (N, )
+ """
+ intersection = (mask1 * mask2).sum(1).clamp(0) # (N, )
+ union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection
+ return intersection / (union + eps)
+
+
+def masks2segments(masks, strategy="largest"):
+ """Converts binary (n,160,160) masks to polygon segments with options for concatenation or selecting the largest
+ segment.
+ """
+ segments = []
+ for x in masks.int().cpu().numpy().astype("uint8"):
+ c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0]
+ if c:
+ if strategy == "concat": # concatenate all segments
+ c = np.concatenate([x.reshape(-1, 2) for x in c])
+ elif strategy == "largest": # select largest segment
+ c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2)
+ else:
+ c = np.zeros((0, 2)) # no segments found
+ segments.append(c.astype("float32"))
+ return segments
diff --git a/Transfer Learning/Accident_Classifier/utils/segment/loss.py b/Transfer Learning/Accident_Classifier/utils/segment/loss.py
new file mode 100644
index 00000000..b3e76ae0
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/segment/loss.py
@@ -0,0 +1,198 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from ..general import xywh2xyxy
+from ..loss import FocalLoss, smooth_BCE
+from ..metrics import bbox_iou
+from ..torch_utils import de_parallel
+from .general import crop_mask
+
+
+class ComputeLoss:
+ """Computes the YOLOv5 model's loss components including classification, objectness, box, and mask losses."""
+
+ def __init__(self, model, autobalance=False, overlap=False):
+ """Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap
+ handling.
+ """
+ self.sort_obj_iou = False
+ self.overlap = overlap
+ device = next(model.parameters()).device # get model device
+ h = model.hyp # hyperparameters
+
+ # Define criteria
+ BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device))
+ BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device))
+
+ # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
+ self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets
+
+ # Focal loss
+ g = h["fl_gamma"] # focal loss gamma
+ if g > 0:
+ BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g)
+
+ m = de_parallel(model).model[-1] # Detect() module
+ self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7
+ self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index
+ self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance
+ self.na = m.na # number of anchors
+ self.nc = m.nc # number of classes
+ self.nl = m.nl # number of layers
+ self.nm = m.nm # number of masks
+ self.anchors = m.anchors
+ self.device = device
+
+ def __call__(self, preds, targets, masks): # predictions, targets, model
+ """Evaluates YOLOv5 model's loss for given predictions, targets, and masks; returns total loss components."""
+ p, proto = preds
+ bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width
+ lcls = torch.zeros(1, device=self.device)
+ lbox = torch.zeros(1, device=self.device)
+ lobj = torch.zeros(1, device=self.device)
+ lseg = torch.zeros(1, device=self.device)
+ tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets
+
+ # Losses
+ for i, pi in enumerate(p): # layer index, layer predictions
+ b, a, gj, gi = indices[i] # image, anchor, gridy, gridx
+ tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj
+
+ n = b.shape[0] # number of targets
+ if n:
+ pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions
+
+ # Box regression
+ pxy = pxy.sigmoid() * 2 - 0.5
+ pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i]
+ pbox = torch.cat((pxy, pwh), 1) # predicted box
+ iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target)
+ lbox += (1.0 - iou).mean() # iou loss
+
+ # Objectness
+ iou = iou.detach().clamp(0).type(tobj.dtype)
+ if self.sort_obj_iou:
+ j = iou.argsort()
+ b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j]
+ if self.gr < 1:
+ iou = (1.0 - self.gr) + self.gr * iou
+ tobj[b, a, gj, gi] = iou # iou ratio
+
+ # Classification
+ if self.nc > 1: # cls loss (only if multiple classes)
+ t = torch.full_like(pcls, self.cn, device=self.device) # targets
+ t[range(n), tcls[i]] = self.cp
+ lcls += self.BCEcls(pcls, t) # BCE
+
+ # Mask regression
+ if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample
+ masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0]
+ marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized
+ mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device))
+ for bi in b.unique():
+ j = b == bi # matching index
+ if self.overlap:
+ mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0)
+ else:
+ mask_gti = masks[tidxs[i]][j]
+ lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j])
+
+ obji = self.BCEobj(pi[..., 4], tobj)
+ lobj += obji * self.balance[i] # obj loss
+ if self.autobalance:
+ self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item()
+
+ if self.autobalance:
+ self.balance = [x / self.balance[self.ssi] for x in self.balance]
+ lbox *= self.hyp["box"]
+ lobj *= self.hyp["obj"]
+ lcls *= self.hyp["cls"]
+ lseg *= self.hyp["box"] / bs
+
+ loss = lbox + lobj + lcls + lseg
+ return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach()
+
+ def single_mask_loss(self, gt_mask, pred, proto, xyxy, area):
+ """Calculates and normalizes single mask loss for YOLOv5 between predicted and ground truth masks."""
+ pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80)
+ loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none")
+ return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean()
+
+ def build_targets(self, p, targets):
+ """Prepares YOLOv5 targets for loss computation; inputs targets (image, class, x, y, w, h), output target
+ classes/boxes.
+ """
+ na, nt = self.na, targets.shape[0] # number of anchors, targets
+ tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], []
+ gain = torch.ones(8, device=self.device) # normalized to gridspace gain
+ ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt)
+ if self.overlap:
+ batch = p[0].shape[0]
+ ti = []
+ for i in range(batch):
+ num = (targets[:, 0] == i).sum() # find number of targets of each image
+ ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num)
+ ti = torch.cat(ti, 1) # (na, nt)
+ else:
+ ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1)
+ targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices
+
+ g = 0.5 # bias
+ off = (
+ torch.tensor(
+ [
+ [0, 0],
+ [1, 0],
+ [0, 1],
+ [-1, 0],
+ [0, -1], # j,k,l,m
+ # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm
+ ],
+ device=self.device,
+ ).float()
+ * g
+ ) # offsets
+
+ for i in range(self.nl):
+ anchors, shape = self.anchors[i], p[i].shape
+ gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain
+
+ # Match targets to anchors
+ t = targets * gain # shape(3,n,7)
+ if nt:
+ # Matches
+ r = t[..., 4:6] / anchors[:, None] # wh ratio
+ j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare
+ # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2))
+ t = t[j] # filter
+
+ # Offsets
+ gxy = t[:, 2:4] # grid xy
+ gxi = gain[[2, 3]] - gxy # inverse
+ j, k = ((gxy % 1 < g) & (gxy > 1)).T
+ l, m = ((gxi % 1 < g) & (gxi > 1)).T
+ j = torch.stack((torch.ones_like(j), j, k, l, m))
+ t = t.repeat((5, 1, 1))[j]
+ offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j]
+ else:
+ t = targets[0]
+ offsets = 0
+
+ # Define
+ bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors
+ (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class
+ gij = (gxy - offsets).long()
+ gi, gj = gij.T # grid indices
+
+ # Append
+ indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid
+ tbox.append(torch.cat((gxy - gij, gwh), 1)) # box
+ anch.append(anchors[a]) # anchors
+ tcls.append(c) # class
+ tidxs.append(tidx)
+ xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized
+
+ return tcls, tbox, indices, anch, tidxs, xywhn
diff --git a/Transfer Learning/Accident_Classifier/utils/segment/metrics.py b/Transfer Learning/Accident_Classifier/utils/segment/metrics.py
new file mode 100644
index 00000000..091b5b16
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/segment/metrics.py
@@ -0,0 +1,225 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Model validation metrics."""
+
+import numpy as np
+
+from ..metrics import ap_per_class
+
+
+def fitness(x):
+ """Evaluates model fitness by a weighted sum of 8 metrics, `x`: [N,8] array, weights: [0.1, 0.9] for mAP and F1."""
+ w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9]
+ return (x[:, :8] * w).sum(1)
+
+
+def ap_per_class_box_and_mask(
+ tp_m,
+ tp_b,
+ conf,
+ pred_cls,
+ target_cls,
+ plot=False,
+ save_dir=".",
+ names=(),
+):
+ """
+ Args:
+ tp_b: tp of boxes.
+ tp_m: tp of masks.
+ other arguments see `func: ap_per_class`.
+ """
+ results_boxes = ap_per_class(
+ tp_b, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Box"
+ )[2:]
+ results_masks = ap_per_class(
+ tp_m, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Mask"
+ )[2:]
+
+ return {
+ "boxes": {
+ "p": results_boxes[0],
+ "r": results_boxes[1],
+ "ap": results_boxes[3],
+ "f1": results_boxes[2],
+ "ap_class": results_boxes[4],
+ },
+ "masks": {
+ "p": results_masks[0],
+ "r": results_masks[1],
+ "ap": results_masks[3],
+ "f1": results_masks[2],
+ "ap_class": results_masks[4],
+ },
+ }
+
+
+class Metric:
+ """Computes performance metrics like precision, recall, F1 score, and average precision for model evaluation."""
+
+ def __init__(self) -> None:
+ """Initializes performance metric attributes for precision, recall, F1 score, average precision, and class
+ indices.
+ """
+ self.p = [] # (nc, )
+ self.r = [] # (nc, )
+ self.f1 = [] # (nc, )
+ self.all_ap = [] # (nc, 10)
+ self.ap_class_index = [] # (nc, )
+
+ @property
+ def ap50(self):
+ """
+ AP@0.5 of all classes.
+
+ Return:
+ (nc, ) or [].
+ """
+ return self.all_ap[:, 0] if len(self.all_ap) else []
+
+ @property
+ def ap(self):
+ """AP@0.5:0.95
+ Return:
+ (nc, ) or [].
+ """
+ return self.all_ap.mean(1) if len(self.all_ap) else []
+
+ @property
+ def mp(self):
+ """
+ Mean precision of all classes.
+
+ Return:
+ float.
+ """
+ return self.p.mean() if len(self.p) else 0.0
+
+ @property
+ def mr(self):
+ """
+ Mean recall of all classes.
+
+ Return:
+ float.
+ """
+ return self.r.mean() if len(self.r) else 0.0
+
+ @property
+ def map50(self):
+ """
+ Mean AP@0.5 of all classes.
+
+ Return:
+ float.
+ """
+ return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0
+
+ @property
+ def map(self):
+ """
+ Mean AP@0.5:0.95 of all classes.
+
+ Return:
+ float.
+ """
+ return self.all_ap.mean() if len(self.all_ap) else 0.0
+
+ def mean_results(self):
+ """Mean of results, return mp, mr, map50, map."""
+ return (self.mp, self.mr, self.map50, self.map)
+
+ def class_result(self, i):
+ """Class-aware result, return p[i], r[i], ap50[i], ap[i]."""
+ return (self.p[i], self.r[i], self.ap50[i], self.ap[i])
+
+ def get_maps(self, nc):
+ """Calculates and returns mean Average Precision (mAP) for each class given number of classes `nc`."""
+ maps = np.zeros(nc) + self.map
+ for i, c in enumerate(self.ap_class_index):
+ maps[c] = self.ap[i]
+ return maps
+
+ def update(self, results):
+ """
+ Args:
+ results: tuple(p, r, ap, f1, ap_class).
+ """
+ p, r, all_ap, f1, ap_class_index = results
+ self.p = p
+ self.r = r
+ self.all_ap = all_ap
+ self.f1 = f1
+ self.ap_class_index = ap_class_index
+
+
+class Metrics:
+ """Metric for boxes and masks."""
+
+ def __init__(self) -> None:
+ """Initializes Metric objects for bounding boxes and masks to compute performance metrics in the Metrics
+ class.
+ """
+ self.metric_box = Metric()
+ self.metric_mask = Metric()
+
+ def update(self, results):
+ """
+ Args:
+ results: Dict{'boxes': Dict{}, 'masks': Dict{}}.
+ """
+ self.metric_box.update(list(results["boxes"].values()))
+ self.metric_mask.update(list(results["masks"].values()))
+
+ def mean_results(self):
+ """Computes and returns the mean results for both box and mask metrics by summing their individual means."""
+ return self.metric_box.mean_results() + self.metric_mask.mean_results()
+
+ def class_result(self, i):
+ """Returns the sum of box and mask metric results for a specified class index `i`."""
+ return self.metric_box.class_result(i) + self.metric_mask.class_result(i)
+
+ def get_maps(self, nc):
+ """Calculates and returns the sum of mean average precisions (mAPs) for both box and mask metrics for `nc`
+ classes.
+ """
+ return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc)
+
+ @property
+ def ap_class_index(self):
+ """Returns the class index for average precision, shared by both box and mask metrics."""
+ return self.metric_box.ap_class_index
+
+
+KEYS = [
+ "train/box_loss",
+ "train/seg_loss", # train loss
+ "train/obj_loss",
+ "train/cls_loss",
+ "metrics/precision(B)",
+ "metrics/recall(B)",
+ "metrics/mAP_0.5(B)",
+ "metrics/mAP_0.5:0.95(B)", # metrics
+ "metrics/precision(M)",
+ "metrics/recall(M)",
+ "metrics/mAP_0.5(M)",
+ "metrics/mAP_0.5:0.95(M)", # metrics
+ "val/box_loss",
+ "val/seg_loss", # val loss
+ "val/obj_loss",
+ "val/cls_loss",
+ "x/lr0",
+ "x/lr1",
+ "x/lr2",
+]
+
+BEST_KEYS = [
+ "best/epoch",
+ "best/precision(B)",
+ "best/recall(B)",
+ "best/mAP_0.5(B)",
+ "best/mAP_0.5:0.95(B)",
+ "best/precision(M)",
+ "best/recall(M)",
+ "best/mAP_0.5(M)",
+ "best/mAP_0.5:0.95(M)",
+]
diff --git a/Transfer Learning/Accident_Classifier/utils/segment/plots.py b/Transfer Learning/Accident_Classifier/utils/segment/plots.py
new file mode 100644
index 00000000..f5b81711
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/segment/plots.py
@@ -0,0 +1,152 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+
+import contextlib
+import math
+from pathlib import Path
+
+import cv2
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import torch
+
+from .. import threaded
+from ..general import xywh2xyxy
+from ..plots import Annotator, colors
+
+
+@threaded
+def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None):
+ """Plots a grid of images, their labels, and masks with optional resizing and annotations, saving to fname."""
+ if isinstance(images, torch.Tensor):
+ images = images.cpu().float().numpy()
+ if isinstance(targets, torch.Tensor):
+ targets = targets.cpu().numpy()
+ if isinstance(masks, torch.Tensor):
+ masks = masks.cpu().numpy().astype(int)
+
+ max_size = 1920 # max image size
+ max_subplots = 16 # max image subplots, i.e. 4x4
+ bs, _, h, w = images.shape # batch size, _, height, width
+ bs = min(bs, max_subplots) # limit plot images
+ ns = np.ceil(bs**0.5) # number of subplots (square)
+ if np.max(images[0]) <= 1:
+ images *= 255 # de-normalise (optional)
+
+ # Build Image
+ mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init
+ for i, im in enumerate(images):
+ if i == max_subplots: # if last batch has fewer images than we expect
+ break
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ im = im.transpose(1, 2, 0)
+ mosaic[y : y + h, x : x + w, :] = im
+
+ # Resize (optional)
+ scale = max_size / ns / max(h, w)
+ if scale < 1:
+ h = math.ceil(scale * h)
+ w = math.ceil(scale * w)
+ mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h)))
+
+ # Annotate
+ fs = int((h + w) * ns * 0.01) # font size
+ annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names)
+ for i in range(i + 1):
+ x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin
+ annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders
+ if paths:
+ annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames
+ if len(targets) > 0:
+ idx = targets[:, 0] == i
+ ti = targets[idx] # image targets
+
+ boxes = xywh2xyxy(ti[:, 2:6]).T
+ classes = ti[:, 1].astype("int")
+ labels = ti.shape[1] == 6 # labels if no conf column
+ conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred)
+
+ if boxes.shape[1]:
+ if boxes.max() <= 1.01: # if normalized with tolerance 0.01
+ boxes[[0, 2]] *= w # scale to pixels
+ boxes[[1, 3]] *= h
+ elif scale < 1: # absolute coords need scale if image scales
+ boxes *= scale
+ boxes[[0, 2]] += x
+ boxes[[1, 3]] += y
+ for j, box in enumerate(boxes.T.tolist()):
+ cls = classes[j]
+ color = colors(cls)
+ cls = names[cls] if names else cls
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}"
+ annotator.box_label(box, label, color=color)
+
+ # Plot masks
+ if len(masks):
+ if masks.max() > 1.0: # mean that masks are overlap
+ image_masks = masks[[i]] # (1, 640, 640)
+ nl = len(ti)
+ index = np.arange(nl).reshape(nl, 1, 1) + 1
+ image_masks = np.repeat(image_masks, nl, axis=0)
+ image_masks = np.where(image_masks == index, 1.0, 0.0)
+ else:
+ image_masks = masks[idx]
+
+ im = np.asarray(annotator.im).copy()
+ for j, box in enumerate(boxes.T.tolist()):
+ if labels or conf[j] > 0.25: # 0.25 conf thresh
+ color = colors(classes[j])
+ mh, mw = image_masks[j].shape
+ if mh != h or mw != w:
+ mask = image_masks[j].astype(np.uint8)
+ mask = cv2.resize(mask, (w, h))
+ mask = mask.astype(bool)
+ else:
+ mask = image_masks[j].astype(bool)
+ with contextlib.suppress(Exception):
+ im[y : y + h, x : x + w, :][mask] = (
+ im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6
+ )
+ annotator.fromarray(im)
+ annotator.im.save(fname) # save
+
+
+def plot_results_with_masks(file="path/to/results.csv", dir="", best=True):
+ """
+ Plots training results from CSV files, plotting best or last result highlights based on `best` parameter.
+
+ Example: from utils.plots import *; plot_results('path/to/results.csv')
+ """
+ save_dir = Path(file).parent if file else Path(dir)
+ fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True)
+ ax = ax.ravel()
+ files = list(save_dir.glob("results*.csv"))
+ assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot."
+ for f in files:
+ try:
+ data = pd.read_csv(f)
+ index = np.argmax(
+ 0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11]
+ )
+ s = [x.strip() for x in data.columns]
+ x = data.values[:, 0]
+ for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]):
+ y = data.values[:, j]
+ # y[y == 0] = np.nan # don't show zero values
+ ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2)
+ if best:
+ # best
+ ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3)
+ ax[i].set_title(s[j] + f"\n{round(y[index], 5)}")
+ else:
+ # last
+ ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3)
+ ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}")
+ # if j in [8, 9, 10]: # share train and val loss y axes
+ # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5])
+ except Exception as e:
+ print(f"Warning: Plotting error for {f}: {e}")
+ ax[1].legend()
+ fig.savefig(save_dir / "results.png", dpi=200)
+ plt.close()
diff --git a/Transfer Learning/Accident_Classifier/utils/torch_utils.py b/Transfer Learning/Accident_Classifier/utils/torch_utils.py
new file mode 100644
index 00000000..8bf6585b
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/torch_utils.py
@@ -0,0 +1,482 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""PyTorch utils."""
+
+import math
+import os
+import platform
+import subprocess
+import time
+import warnings
+from contextlib import contextmanager
+from copy import deepcopy
+from pathlib import Path
+
+import torch
+import torch.distributed as dist
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.nn.parallel import DistributedDataParallel as DDP
+
+from utils.general import LOGGER, check_version, colorstr, file_date, git_describe
+
+LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html
+RANK = int(os.getenv("RANK", -1))
+WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1))
+
+try:
+ import thop # for FLOPs computation
+except ImportError:
+ thop = None
+
+# Suppress PyTorch warnings
+warnings.filterwarnings("ignore", message="User provided device_type of 'cuda', but CUDA is not available. Disabling")
+warnings.filterwarnings("ignore", category=UserWarning)
+
+
+def smart_inference_mode(torch_1_9=check_version(torch.__version__, "1.9.0")):
+ """Applies torch.inference_mode() if torch>=1.9.0, else torch.no_grad() as a decorator for functions."""
+
+ def decorate(fn):
+ """Applies torch.inference_mode() if torch>=1.9.0, else torch.no_grad() to the decorated function."""
+ return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn)
+
+ return decorate
+
+
+def smartCrossEntropyLoss(label_smoothing=0.0):
+ """Returns a CrossEntropyLoss with optional label smoothing for torch>=1.10.0; warns if smoothing on lower
+ versions.
+ """
+ if check_version(torch.__version__, "1.10.0"):
+ return nn.CrossEntropyLoss(label_smoothing=label_smoothing)
+ if label_smoothing > 0:
+ LOGGER.warning(f"WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0")
+ return nn.CrossEntropyLoss()
+
+
+def smart_DDP(model):
+ """Initializes DistributedDataParallel (DDP) for model training, respecting torch version constraints."""
+ assert not check_version(torch.__version__, "1.12.0", pinned=True), (
+ "torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. "
+ "Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395"
+ )
+ if check_version(torch.__version__, "1.11.0"):
+ return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True)
+ else:
+ return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
+
+
+def reshape_classifier_output(model, n=1000):
+ """Reshapes last layer of model to match class count 'n', supporting Classify, Linear, Sequential types."""
+ from models.common import Classify
+
+ name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module
+ if isinstance(m, Classify): # YOLOv5 Classify() head
+ if m.linear.out_features != n:
+ m.linear = nn.Linear(m.linear.in_features, n)
+ elif isinstance(m, nn.Linear): # ResNet, EfficientNet
+ if m.out_features != n:
+ setattr(model, name, nn.Linear(m.in_features, n))
+ elif isinstance(m, nn.Sequential):
+ types = [type(x) for x in m]
+ if nn.Linear in types:
+ i = len(types) - 1 - types[::-1].index(nn.Linear) # last nn.Linear index
+ if m[i].out_features != n:
+ m[i] = nn.Linear(m[i].in_features, n)
+ elif nn.Conv2d in types:
+ i = len(types) - 1 - types[::-1].index(nn.Conv2d) # last nn.Conv2d index
+ if m[i].out_channels != n:
+ m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None)
+
+
+@contextmanager
+def torch_distributed_zero_first(local_rank: int):
+ """Context manager ensuring ordered operations in distributed training by making all processes wait for the leading
+ process.
+ """
+ if local_rank not in [-1, 0]:
+ dist.barrier(device_ids=[local_rank])
+ yield
+ if local_rank == 0:
+ dist.barrier(device_ids=[0])
+
+
+def device_count():
+ """Returns the number of available CUDA devices; works on Linux and Windows by invoking `nvidia-smi`."""
+ assert platform.system() in ("Linux", "Windows"), "device_count() only supported on Linux or Windows"
+ try:
+ cmd = "nvidia-smi -L | wc -l" if platform.system() == "Linux" else 'nvidia-smi -L | find /c /v ""' # Windows
+ return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1])
+ except Exception:
+ return 0
+
+
+def select_device(device="", batch_size=0, newline=True):
+ """Selects computing device (CPU, CUDA GPU, MPS) for YOLOv5 model deployment, logging device info."""
+ s = f"YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} "
+ device = str(device).strip().lower().replace("cuda:", "").replace("none", "") # to string, 'cuda:0' to '0'
+ cpu = device == "cpu"
+ mps = device == "mps" # Apple Metal Performance Shaders (MPS)
+ if cpu or mps:
+ os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False
+ elif device: # non-cpu device requested
+ os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available()
+ assert torch.cuda.is_available() and torch.cuda.device_count() >= len(
+ device.replace(",", "")
+ ), f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)"
+
+ if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available
+ devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7
+ n = len(devices) # device count
+ if n > 1 and batch_size > 0: # check batch_size is divisible by device_count
+ assert batch_size % n == 0, f"batch-size {batch_size} not multiple of GPU count {n}"
+ space = " " * (len(s) + 1)
+ for i, d in enumerate(devices):
+ p = torch.cuda.get_device_properties(i)
+ s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
+ arg = "cuda:0"
+ elif mps and getattr(torch, "has_mps", False) and torch.backends.mps.is_available(): # prefer MPS if available
+ s += "MPS\n"
+ arg = "mps"
+ else: # revert to CPU
+ s += "CPU\n"
+ arg = "cpu"
+
+ if not newline:
+ s = s.rstrip()
+ LOGGER.info(s)
+ return torch.device(arg)
+
+
+def time_sync():
+ """Synchronizes PyTorch for accurate timing, leveraging CUDA if available, and returns the current time."""
+ if torch.cuda.is_available():
+ torch.cuda.synchronize()
+ return time.time()
+
+
+def profile(input, ops, n=10, device=None):
+ """YOLOv5 speed/memory/FLOPs profiler
+ Usage:
+ input = torch.randn(16, 3, 640, 640)
+ m1 = lambda x: x * torch.sigmoid(x)
+ m2 = nn.SiLU()
+ profile(input, [m1, m2], n=100) # profile over 100 iterations.
+ """
+ results = []
+ if not isinstance(device, torch.device):
+ device = select_device(device)
+ print(
+ f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}"
+ f"{'input':>24s}{'output':>24s}"
+ )
+
+ for x in input if isinstance(input, list) else [input]:
+ x = x.to(device)
+ x.requires_grad = True
+ for m in ops if isinstance(ops, list) else [ops]:
+ m = m.to(device) if hasattr(m, "to") else m # device
+ m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m
+ tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward
+ try:
+ flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1e9 * 2 # GFLOPs
+ except Exception:
+ flops = 0
+
+ try:
+ for _ in range(n):
+ t[0] = time_sync()
+ y = m(x)
+ t[1] = time_sync()
+ try:
+ _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward()
+ t[2] = time_sync()
+ except Exception: # no backward method
+ # print(e) # for debug
+ t[2] = float("nan")
+ tf += (t[1] - t[0]) * 1000 / n # ms per op forward
+ tb += (t[2] - t[1]) * 1000 / n # ms per op backward
+ mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0 # (GB)
+ s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else "list" for x in (x, y)) # shapes
+ p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters
+ print(f"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}")
+ results.append([p, flops, mem, tf, tb, s_in, s_out])
+ except Exception as e:
+ print(e)
+ results.append(None)
+ torch.cuda.empty_cache()
+ return results
+
+
+def is_parallel(model):
+ """Checks if the model is using Data Parallelism (DP) or Distributed Data Parallelism (DDP)."""
+ return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
+
+
+def de_parallel(model):
+ """Returns a single-GPU model by removing Data Parallelism (DP) or Distributed Data Parallelism (DDP) if applied."""
+ return model.module if is_parallel(model) else model
+
+
+def initialize_weights(model):
+ """Initializes weights of Conv2d, BatchNorm2d, and activations (Hardswish, LeakyReLU, ReLU, ReLU6, SiLU) in the
+ model.
+ """
+ for m in model.modules():
+ t = type(m)
+ if t is nn.Conv2d:
+ pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+ elif t is nn.BatchNorm2d:
+ m.eps = 1e-3
+ m.momentum = 0.03
+ elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]:
+ m.inplace = True
+
+
+def find_modules(model, mclass=nn.Conv2d):
+ """Finds and returns list of layer indices in `model.module_list` matching the specified `mclass`."""
+ return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)]
+
+
+def sparsity(model):
+ """Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total
+ parameters.
+ """
+ a, b = 0, 0
+ for p in model.parameters():
+ a += p.numel()
+ b += (p == 0).sum()
+ return b / a
+
+
+def prune(model, amount=0.3):
+ """Prunes Conv2d layers in a model to a specified sparsity using L1 unstructured pruning."""
+ import torch.nn.utils.prune as prune
+
+ for name, m in model.named_modules():
+ if isinstance(m, nn.Conv2d):
+ prune.l1_unstructured(m, name="weight", amount=amount) # prune
+ prune.remove(m, "weight") # make permanent
+ LOGGER.info(f"Model pruned to {sparsity(model):.3g} global sparsity")
+
+
+def fuse_conv_and_bn(conv, bn):
+ """
+ Fuses Conv2d and BatchNorm2d layers into a single Conv2d layer.
+
+ See https://tehnokv.com/posts/fusing-batchnorm-and-conv/.
+ """
+ fusedconv = (
+ nn.Conv2d(
+ conv.in_channels,
+ conv.out_channels,
+ kernel_size=conv.kernel_size,
+ stride=conv.stride,
+ padding=conv.padding,
+ dilation=conv.dilation,
+ groups=conv.groups,
+ bias=True,
+ )
+ .requires_grad_(False)
+ .to(conv.weight.device)
+ )
+
+ # Prepare filters
+ w_conv = conv.weight.clone().view(conv.out_channels, -1)
+ w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var)))
+ fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape))
+
+ # Prepare spatial bias
+ b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias
+ b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps))
+ fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn)
+
+ return fusedconv
+
+
+def model_info(model, verbose=False, imgsz=640):
+ """
+ Prints model summary including layers, parameters, gradients, and FLOPs; imgsz may be int or list.
+
+ Example: img_size=640 or img_size=[640, 320]
+ """
+ n_p = sum(x.numel() for x in model.parameters()) # number parameters
+ n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients
+ if verbose:
+ print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}")
+ for i, (name, p) in enumerate(model.named_parameters()):
+ name = name.replace("module_list.", "")
+ print(
+ "%5g %40s %9s %12g %20s %10.3g %10.3g"
+ % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std())
+ )
+
+ try: # FLOPs
+ p = next(model.parameters())
+ stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 # max stride
+ im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format
+ flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1e9 * 2 # stride GFLOPs
+ imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float
+ fs = f", {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs" # 640x640 GFLOPs
+ except Exception:
+ fs = ""
+
+ name = Path(model.yaml_file).stem.replace("yolov5", "YOLOv5") if hasattr(model, "yaml_file") else "Model"
+ LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}")
+
+
+def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416)
+ """Scales an image tensor `img` of shape (bs,3,y,x) by `ratio`, optionally maintaining the original shape, padded to
+ multiples of `gs`.
+ """
+ if ratio == 1.0:
+ return img
+ h, w = img.shape[2:]
+ s = (int(h * ratio), int(w * ratio)) # new size
+ img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize
+ if not same_shape: # pad/crop img
+ h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w))
+ return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean
+
+
+def copy_attr(a, b, include=(), exclude=()):
+ """Copies attributes from object b to a, optionally filtering with include and exclude lists."""
+ for k, v in b.__dict__.items():
+ if (len(include) and k not in include) or k.startswith("_") or k in exclude:
+ continue
+ else:
+ setattr(a, k, v)
+
+
+def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5):
+ """
+ Initializes YOLOv5 smart optimizer with 3 parameter groups for different decay configurations.
+
+ Groups are 0) weights with decay, 1) weights no decay, 2) biases no decay.
+ """
+ g = [], [], [] # optimizer parameter groups
+ bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d()
+ for v in model.modules():
+ for p_name, p in v.named_parameters(recurse=0):
+ if p_name == "bias": # bias (no decay)
+ g[2].append(p)
+ elif p_name == "weight" and isinstance(v, bn): # weight (no decay)
+ g[1].append(p)
+ else:
+ g[0].append(p) # weight (with decay)
+
+ if name == "Adam":
+ optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum
+ elif name == "AdamW":
+ optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0)
+ elif name == "RMSProp":
+ optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum)
+ elif name == "SGD":
+ optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True)
+ else:
+ raise NotImplementedError(f"Optimizer {name} not implemented.")
+
+ optimizer.add_param_group({"params": g[0], "weight_decay": decay}) # add g0 with weight_decay
+ optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights)
+ LOGGER.info(
+ f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups "
+ f'{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias'
+ )
+ return optimizer
+
+
+def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs):
+ """YOLOv5 torch.hub.load() wrapper with smart error handling, adjusting torch arguments for compatibility."""
+ if check_version(torch.__version__, "1.9.1"):
+ kwargs["skip_validation"] = True # validation causes GitHub API rate limit errors
+ if check_version(torch.__version__, "1.12.0"):
+ kwargs["trust_repo"] = True # argument required starting in torch 0.12
+ try:
+ return torch.hub.load(repo, model, **kwargs)
+ except Exception:
+ return torch.hub.load(repo, model, force_reload=True, **kwargs)
+
+
+def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, resume=True):
+ """Resumes training from a checkpoint, updating optimizer, ema, and epochs, with optional resume verification."""
+ best_fitness = 0.0
+ start_epoch = ckpt["epoch"] + 1
+ if ckpt["optimizer"] is not None:
+ optimizer.load_state_dict(ckpt["optimizer"]) # optimizer
+ best_fitness = ckpt["best_fitness"]
+ if ema and ckpt.get("ema"):
+ ema.ema.load_state_dict(ckpt["ema"].float().state_dict()) # EMA
+ ema.updates = ckpt["updates"]
+ if resume:
+ assert start_epoch > 0, (
+ f"{weights} training to {epochs} epochs is finished, nothing to resume.\n"
+ f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'"
+ )
+ LOGGER.info(f"Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs")
+ if epochs < start_epoch:
+ LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
+ epochs += ckpt["epoch"] # finetune additional epochs
+ return best_fitness, start_epoch, epochs
+
+
+class EarlyStopping:
+ """Implements early stopping to halt training when no improvement is observed for a specified number of epochs."""
+
+ def __init__(self, patience=30):
+ """Initializes simple early stopping mechanism for YOLOv5, with adjustable patience for non-improving epochs."""
+ self.best_fitness = 0.0 # i.e. mAP
+ self.best_epoch = 0
+ self.patience = patience or float("inf") # epochs to wait after fitness stops improving to stop
+ self.possible_stop = False # possible stop may occur next epoch
+
+ def __call__(self, epoch, fitness):
+ """Evaluates if training should stop based on fitness improvement and patience, returning a boolean."""
+ if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training
+ self.best_epoch = epoch
+ self.best_fitness = fitness
+ delta = epoch - self.best_epoch # epochs without improvement
+ self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch
+ stop = delta >= self.patience # stop training if patience exceeded
+ if stop:
+ LOGGER.info(
+ f"Stopping training early as no improvement observed in last {self.patience} epochs. "
+ f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n"
+ f"To update EarlyStopping(patience={self.patience}) pass a new patience value, "
+ f"i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping."
+ )
+ return stop
+
+
+class ModelEMA:
+ """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
+ Keeps a moving average of everything in the model state_dict (parameters and buffers)
+ For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage.
+ """
+
+ def __init__(self, model, decay=0.9999, tau=2000, updates=0):
+ """Initializes EMA with model parameters, decay rate, tau for decay adjustment, and update count; sets model to
+ evaluation mode.
+ """
+ self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA
+ self.updates = updates # number of EMA updates
+ self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs)
+ for p in self.ema.parameters():
+ p.requires_grad_(False)
+
+ def update(self, model):
+ """Updates the Exponential Moving Average (EMA) parameters based on the current model's parameters."""
+ self.updates += 1
+ d = self.decay(self.updates)
+
+ msd = de_parallel(model).state_dict() # model state_dict
+ for k, v in self.ema.state_dict().items():
+ if v.dtype.is_floating_point: # true for FP16 and FP32
+ v *= d
+ v += (1 - d) * msd[k].detach()
+ # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32'
+
+ def update_attr(self, model, include=(), exclude=("process_group", "reducer")):
+ """Updates EMA attributes by copying specified attributes from model to EMA, excluding certain attributes by
+ default.
+ """
+ copy_attr(self.ema, model, include, exclude)
diff --git a/Transfer Learning/Accident_Classifier/utils/triton.py b/Transfer Learning/Accident_Classifier/utils/triton.py
new file mode 100644
index 00000000..3230ecd8
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/utils/triton.py
@@ -0,0 +1,90 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""Utils to interact with the Triton Inference Server."""
+
+import typing
+from urllib.parse import urlparse
+
+import torch
+
+
+class TritonRemoteModel:
+ """
+ A wrapper over a model served by the Triton Inference Server.
+
+ It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as
+ outputs.
+ """
+
+ def __init__(self, url: str):
+ """
+ Keyword Arguments:
+ url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000.
+ """
+ parsed_url = urlparse(url)
+ if parsed_url.scheme == "grpc":
+ from tritonclient.grpc import InferenceServerClient, InferInput
+
+ self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client
+ model_repository = self.client.get_model_repository_index()
+ self.model_name = model_repository.models[0].name
+ self.metadata = self.client.get_model_metadata(self.model_name, as_json=True)
+
+ def create_input_placeholders() -> typing.List[InferInput]:
+ return [
+ InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"]
+ ]
+
+ else:
+ from tritonclient.http import InferenceServerClient, InferInput
+
+ self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client
+ model_repository = self.client.get_model_repository_index()
+ self.model_name = model_repository[0]["name"]
+ self.metadata = self.client.get_model_metadata(self.model_name)
+
+ def create_input_placeholders() -> typing.List[InferInput]:
+ return [
+ InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"]
+ ]
+
+ self._create_input_placeholders_fn = create_input_placeholders
+
+ @property
+ def runtime(self):
+ """Returns the model runtime."""
+ return self.metadata.get("backend", self.metadata.get("platform"))
+
+ def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]:
+ """
+ Invokes the model.
+
+ Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of
+ the model. kwargs are matched with the model input names.
+ """
+ inputs = self._create_inputs(*args, **kwargs)
+ response = self.client.infer(model_name=self.model_name, inputs=inputs)
+ result = []
+ for output in self.metadata["outputs"]:
+ tensor = torch.as_tensor(response.as_numpy(output["name"]))
+ result.append(tensor)
+ return result[0] if len(result) == 1 else result
+
+ def _create_inputs(self, *args, **kwargs):
+ """Creates input tensors from args or kwargs, not both; raises error if none or both are provided."""
+ args_len, kwargs_len = len(args), len(kwargs)
+ if not args_len and not kwargs_len:
+ raise RuntimeError("No inputs provided.")
+ if args_len and kwargs_len:
+ raise RuntimeError("Cannot specify args and kwargs at the same time")
+
+ placeholders = self._create_input_placeholders_fn()
+ if args_len:
+ if args_len != len(placeholders):
+ raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.")
+ for input, value in zip(placeholders, args):
+ input.set_data_from_numpy(value.cpu().numpy())
+ else:
+ for input in placeholders:
+ value = kwargs[input.name]
+ input.set_data_from_numpy(value.cpu().numpy())
+ return placeholders
diff --git a/Transfer Learning/Accident_Classifier/val.py b/Transfer Learning/Accident_Classifier/val.py
new file mode 100644
index 00000000..b8db6122
--- /dev/null
+++ b/Transfer Learning/Accident_Classifier/val.py
@@ -0,0 +1,604 @@
+# Ultralytics YOLOv5 🚀, AGPL-3.0 license
+"""
+Validate a trained YOLOv5 detection model on a detection dataset.
+
+Usage:
+ $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
+
+Usage - formats:
+ $ python val.py --weights yolov5s.pt # PyTorch
+ yolov5s.torchscript # TorchScript
+ yolov5s.onnx # ONNX Runtime or OpenCV DNN with --dnn
+ yolov5s_openvino_model # OpenVINO
+ yolov5s.engine # TensorRT
+ yolov5s.mlpackage # CoreML (macOS-only)
+ yolov5s_saved_model # TensorFlow SavedModel
+ yolov5s.pb # TensorFlow GraphDef
+ yolov5s.tflite # TensorFlow Lite
+ yolov5s_edgetpu.tflite # TensorFlow Edge TPU
+ yolov5s_paddle_model # PaddlePaddle
+"""
+
+import argparse
+import json
+import os
+import subprocess
+import sys
+from pathlib import Path
+
+import numpy as np
+import torch
+from tqdm import tqdm
+
+FILE = Path(__file__).resolve()
+ROOT = FILE.parents[0] # YOLOv5 root directory
+if str(ROOT) not in sys.path:
+ sys.path.append(str(ROOT)) # add ROOT to PATH
+ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative
+
+from models.common import DetectMultiBackend
+from utils.callbacks import Callbacks
+from utils.dataloaders import create_dataloader
+from utils.general import (
+ LOGGER,
+ TQDM_BAR_FORMAT,
+ Profile,
+ check_dataset,
+ check_img_size,
+ check_requirements,
+ check_yaml,
+ coco80_to_coco91_class,
+ colorstr,
+ increment_path,
+ non_max_suppression,
+ print_args,
+ scale_boxes,
+ xywh2xyxy,
+ xyxy2xywh,
+)
+from utils.metrics import ConfusionMatrix, ap_per_class, box_iou
+from utils.plots import output_to_target, plot_images, plot_val_study
+from utils.torch_utils import select_device, smart_inference_mode
+
+
+def save_one_txt(predn, save_conf, shape, file):
+ """
+ Saves one detection result to a txt file in normalized xywh format, optionally including confidence.
+
+ Args:
+ predn (torch.Tensor): Predicted bounding boxes and associated confidence scores and classes in xyxy format, tensor
+ of shape (N, 6) where N is the number of detections.
+ save_conf (bool): If True, saves the confidence scores along with the bounding box coordinates.
+ shape (tuple): Shape of the original image as (height, width).
+ file (str | Path): File path where the result will be saved.
+
+ Returns:
+ None
+
+ Notes:
+ The xyxy bounding box format represents the coordinates (xmin, ymin, xmax, ymax).
+ The xywh format represents the coordinates (center_x, center_y, width, height) and is normalized by the width and
+ height of the image.
+
+ Example:
+ ```python
+ predn = torch.tensor([[10, 20, 30, 40, 0.9, 1]]) # example prediction
+ save_one_txt(predn, save_conf=True, shape=(640, 480), file="output.txt")
+ ```
+ """
+ gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
+ for *xyxy, conf, cls in predn.tolist():
+ xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
+ line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
+ with open(file, "a") as f:
+ f.write(("%g " * len(line)).rstrip() % line + "\n")
+
+
+def save_one_json(predn, jdict, path, class_map):
+ """
+ Saves a single JSON detection result, including image ID, category ID, bounding box, and confidence score.
+
+ Args:
+ predn (torch.Tensor): Predicted detections in xyxy format with shape (n, 6) where n is the number of detections.
+ The tensor should contain [x_min, y_min, x_max, y_max, confidence, class_id] for each detection.
+ jdict (list[dict]): List to collect JSON formatted detection results.
+ path (pathlib.Path): Path object of the image file, used to extract image_id.
+ class_map (dict[int, int]): Mapping from model class indices to dataset-specific category IDs.
+
+ Returns:
+ None: Appends detection results as dictionaries to `jdict` list in-place.
+
+ Example:
+ ```python
+ predn = torch.tensor([[100, 50, 200, 150, 0.9, 0], [50, 30, 100, 80, 0.8, 1]])
+ jdict = []
+ path = Path("42.jpg")
+ class_map = {0: 18, 1: 19}
+ save_one_json(predn, jdict, path, class_map)
+ ```
+ This will append to `jdict`:
+ ```
+ [
+ {'image_id': 42, 'category_id': 18, 'bbox': [125.0, 75.0, 100.0, 100.0], 'score': 0.9},
+ {'image_id': 42, 'category_id': 19, 'bbox': [75.0, 55.0, 50.0, 50.0], 'score': 0.8}
+ ]
+ ```
+
+ Notes:
+ The `bbox` values are formatted as [x, y, width, height], where x and y represent the top-left corner of the box.
+ """
+ image_id = int(path.stem) if path.stem.isnumeric() else path.stem
+ box = xyxy2xywh(predn[:, :4]) # xywh
+ box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
+ for p, b in zip(predn.tolist(), box.tolist()):
+ jdict.append(
+ {
+ "image_id": image_id,
+ "category_id": class_map[int(p[5])],
+ "bbox": [round(x, 3) for x in b],
+ "score": round(p[4], 5),
+ }
+ )
+
+
+def process_batch(detections, labels, iouv):
+ """
+ Return a correct prediction matrix given detections and labels at various IoU thresholds.
+
+ Args:
+ detections (np.ndarray): Array of shape (N, 6) where each row corresponds to a detection with format
+ [x1, y1, x2, y2, conf, class].
+ labels (np.ndarray): Array of shape (M, 5) where each row corresponds to a ground truth label with format
+ [class, x1, y1, x2, y2].
+ iouv (np.ndarray): Array of IoU thresholds to evaluate at.
+
+ Returns:
+ correct (np.ndarray): A binary array of shape (N, len(iouv)) indicating whether each detection is a true positive
+ for each IoU threshold. There are 10 IoU levels used in the evaluation.
+
+ Example:
+ ```python
+ detections = np.array([[50, 50, 200, 200, 0.9, 1], [30, 30, 150, 150, 0.7, 0]])
+ labels = np.array([[1, 50, 50, 200, 200]])
+ iouv = np.linspace(0.5, 0.95, 10)
+ correct = process_batch(detections, labels, iouv)
+ ```
+
+ Notes:
+ - This function is used as part of the evaluation pipeline for object detection models.
+ - IoU (Intersection over Union) is a common evaluation metric for object detection performance.
+ """
+ correct = np.zeros((detections.shape[0], iouv.shape[0])).astype(bool)
+ iou = box_iou(labels[:, 1:], detections[:, :4])
+ correct_class = labels[:, 0:1] == detections[:, 5]
+ for i in range(len(iouv)):
+ x = torch.where((iou >= iouv[i]) & correct_class) # IoU > threshold and classes match
+ if x[0].shape[0]:
+ matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
+ if x[0].shape[0] > 1:
+ matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
+ # matches = matches[matches[:, 2].argsort()[::-1]]
+ matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
+ correct[matches[:, 1].astype(int), i] = True
+ return torch.tensor(correct, dtype=torch.bool, device=iouv.device)
+
+
+@smart_inference_mode()
+def run(
+ data,
+ weights=None, # model.pt path(s)
+ batch_size=32, # batch size
+ imgsz=640, # inference size (pixels)
+ conf_thres=0.001, # confidence threshold
+ iou_thres=0.6, # NMS IoU threshold
+ max_det=300, # maximum detections per image
+ task="val", # train, val, test, speed or study
+ device="", # cuda device, i.e. 0 or 0,1,2,3 or cpu
+ workers=8, # max dataloader workers (per RANK in DDP mode)
+ single_cls=False, # treat as single-class dataset
+ augment=False, # augmented inference
+ verbose=False, # verbose output
+ save_txt=False, # save results to *.txt
+ save_hybrid=False, # save label+prediction hybrid results to *.txt
+ save_conf=False, # save confidences in --save-txt labels
+ save_json=False, # save a COCO-JSON results file
+ project=ROOT / "runs/val", # save to project/name
+ name="exp", # save to project/name
+ exist_ok=False, # existing project/name ok, do not increment
+ half=True, # use FP16 half-precision inference
+ dnn=False, # use OpenCV DNN for ONNX inference
+ model=None,
+ dataloader=None,
+ save_dir=Path(""),
+ plots=True,
+ callbacks=Callbacks(),
+ compute_loss=None,
+):
+ """
+ Evaluates a YOLOv5 model on a dataset and logs performance metrics.
+
+ Args:
+ data (str | dict): Path to a dataset YAML file or a dataset dictionary.
+ weights (str | list[str], optional): Path to the model weights file(s). Supports various formats including PyTorch,
+ TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow SavedModel, TensorFlow GraphDef, TensorFlow Lite,
+ TensorFlow Edge TPU, and PaddlePaddle.
+ batch_size (int, optional): Batch size for inference. Default is 32.
+ imgsz (int, optional): Input image size (pixels). Default is 640.
+ conf_thres (float, optional): Confidence threshold for object detection. Default is 0.001.
+ iou_thres (float, optional): IoU threshold for Non-Maximum Suppression (NMS). Default is 0.6.
+ max_det (int, optional): Maximum number of detections per image. Default is 300.
+ task (str, optional): Task type - 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.
+ device (str, optional): Device to use for computation, e.g., '0' or '0,1,2,3' for CUDA or 'cpu' for CPU. Default is ''.
+ workers (int, optional): Number of dataloader workers. Default is 8.
+ single_cls (bool, optional): Treat dataset as a single class. Default is False.
+ augment (bool, optional): Enable augmented inference. Default is False.
+ verbose (bool, optional): Enable verbose output. Default is False.
+ save_txt (bool, optional): Save results to *.txt files. Default is False.
+ save_hybrid (bool, optional): Save label and prediction hybrid results to *.txt files. Default is False.
+ save_conf (bool, optional): Save confidences in --save-txt labels. Default is False.
+ save_json (bool, optional): Save a COCO-JSON results file. Default is False.
+ project (str | Path, optional): Directory to save results. Default is ROOT/'runs/val'.
+ name (str, optional): Name of the run. Default is 'exp'.
+ exist_ok (bool, optional): Overwrite existing project/name without incrementing. Default is False.
+ half (bool, optional): Use FP16 half-precision inference. Default is True.
+ dnn (bool, optional): Use OpenCV DNN for ONNX inference. Default is False.
+ model (torch.nn.Module, optional): Model object for training. Default is None.
+ dataloader (torch.utils.data.DataLoader, optional): Dataloader object. Default is None.
+ save_dir (Path, optional): Directory to save results. Default is Path('').
+ plots (bool, optional): Plot validation images and metrics. Default is True.
+ callbacks (utils.callbacks.Callbacks, optional): Callbacks for logging and monitoring. Default is Callbacks().
+ compute_loss (function, optional): Loss function for training. Default is None.
+
+ Returns:
+ dict: Contains performance metrics including precision, recall, mAP50, and mAP50-95.
+ """
+ # Initialize/load model and set device
+ training = model is not None
+ if training: # called by train.py
+ device, pt, jit, engine = next(model.parameters()).device, True, False, False # get model device, PyTorch model
+ half &= device.type != "cpu" # half precision only supported on CUDA
+ model.half() if half else model.float()
+ else: # called directly
+ device = select_device(device, batch_size=batch_size)
+
+ # Directories
+ save_dir = increment_path(Path(project) / name, exist_ok=exist_ok) # increment run
+ (save_dir / "labels" if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
+
+ # Load model
+ model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data, fp16=half)
+ stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
+ imgsz = check_img_size(imgsz, s=stride) # check image size
+ half = model.fp16 # FP16 supported on limited backends with CUDA
+ if engine:
+ batch_size = model.batch_size
+ else:
+ device = model.device
+ if not (pt or jit):
+ batch_size = 1 # export.py models default to batch-size 1
+ LOGGER.info(f"Forcing --batch-size 1 square inference (1,3,{imgsz},{imgsz}) for non-PyTorch models")
+
+ # Data
+ data = check_dataset(data) # check
+
+ # Configure
+ model.eval()
+ cuda = device.type != "cpu"
+ is_coco = isinstance(data.get("val"), str) and data["val"].endswith(f"coco{os.sep}val2017.txt") # COCO dataset
+ nc = 1 if single_cls else int(data["nc"]) # number of classes
+ iouv = torch.linspace(0.5, 0.95, 10, device=device) # iou vector for mAP@0.5:0.95
+ niou = iouv.numel()
+
+ # Dataloader
+ if not training:
+ if pt and not single_cls: # check --weights are trained on --data
+ ncm = model.model.nc
+ assert ncm == nc, (
+ f"{weights} ({ncm} classes) trained on different --data than what you passed ({nc} "
+ f"classes). Pass correct combination of --weights and --data that are trained together."
+ )
+ model.warmup(imgsz=(1 if pt else batch_size, 3, imgsz, imgsz)) # warmup
+ pad, rect = (0.0, False) if task == "speed" else (0.5, pt) # square inference for benchmarks
+ task = task if task in ("train", "val", "test") else "val" # path to train/val/test images
+ dataloader = create_dataloader(
+ data[task],
+ imgsz,
+ batch_size,
+ stride,
+ single_cls,
+ pad=pad,
+ rect=rect,
+ workers=workers,
+ prefix=colorstr(f"{task}: "),
+ )[0]
+
+ seen = 0
+ confusion_matrix = ConfusionMatrix(nc=nc)
+ names = model.names if hasattr(model, "names") else model.module.names # get class names
+ if isinstance(names, (list, tuple)): # old format
+ names = dict(enumerate(names))
+ class_map = coco80_to_coco91_class() if is_coco else list(range(1000))
+ s = ("%22s" + "%11s" * 6) % ("Class", "Images", "Instances", "P", "R", "mAP50", "mAP50-95")
+ tp, fp, p, r, f1, mp, mr, map50, ap50, map = 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0
+ dt = Profile(device=device), Profile(device=device), Profile(device=device) # profiling times
+ loss = torch.zeros(3, device=device)
+ jdict, stats, ap, ap_class = [], [], [], []
+ callbacks.run("on_val_start")
+ pbar = tqdm(dataloader, desc=s, bar_format=TQDM_BAR_FORMAT) # progress bar
+ for batch_i, (im, targets, paths, shapes) in enumerate(pbar):
+ callbacks.run("on_val_batch_start")
+ with dt[0]:
+ if cuda:
+ im = im.to(device, non_blocking=True)
+ targets = targets.to(device)
+ im = im.half() if half else im.float() # uint8 to fp16/32
+ im /= 255 # 0 - 255 to 0.0 - 1.0
+ nb, _, height, width = im.shape # batch size, channels, height, width
+
+ # Inference
+ with dt[1]:
+ preds, train_out = model(im) if compute_loss else (model(im, augment=augment), None)
+
+ # Loss
+ if compute_loss:
+ loss += compute_loss(train_out, targets)[1] # box, obj, cls
+
+ # NMS
+ targets[:, 2:] *= torch.tensor((width, height, width, height), device=device) # to pixels
+ lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
+ with dt[2]:
+ preds = non_max_suppression(
+ preds, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls, max_det=max_det
+ )
+
+ # Metrics
+ for si, pred in enumerate(preds):
+ labels = targets[targets[:, 0] == si, 1:]
+ nl, npr = labels.shape[0], pred.shape[0] # number of labels, predictions
+ path, shape = Path(paths[si]), shapes[si][0]
+ correct = torch.zeros(npr, niou, dtype=torch.bool, device=device) # init
+ seen += 1
+
+ if npr == 0:
+ if nl:
+ stats.append((correct, *torch.zeros((2, 0), device=device), labels[:, 0]))
+ if plots:
+ confusion_matrix.process_batch(detections=None, labels=labels[:, 0])
+ continue
+
+ # Predictions
+ if single_cls:
+ pred[:, 5] = 0
+ predn = pred.clone()
+ scale_boxes(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) # native-space pred
+
+ # Evaluate
+ if nl:
+ tbox = xywh2xyxy(labels[:, 1:5]) # target boxes
+ scale_boxes(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels
+ labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels
+ correct = process_batch(predn, labelsn, iouv)
+ if plots:
+ confusion_matrix.process_batch(predn, labelsn)
+ stats.append((correct, pred[:, 4], pred[:, 5], labels[:, 0])) # (correct, conf, pcls, tcls)
+
+ # Save/log
+ if save_txt:
+ (save_dir / "labels").mkdir(parents=True, exist_ok=True)
+ save_one_txt(predn, save_conf, shape, file=save_dir / "labels" / f"{path.stem}.txt")
+ if save_json:
+ save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary
+ callbacks.run("on_val_image_end", pred, predn, path, names, im[si])
+
+ # Plot images
+ if plots and batch_i < 3:
+ plot_images(im, targets, paths, save_dir / f"val_batch{batch_i}_labels.jpg", names) # labels
+ plot_images(im, output_to_target(preds), paths, save_dir / f"val_batch{batch_i}_pred.jpg", names) # pred
+
+ callbacks.run("on_val_batch_end", batch_i, im, targets, paths, shapes, preds)
+
+ # Compute metrics
+ stats = [torch.cat(x, 0).cpu().numpy() for x in zip(*stats)] # to numpy
+ if len(stats) and stats[0].any():
+ tp, fp, p, r, f1, ap, ap_class = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names)
+ ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95
+ mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
+ nt = np.bincount(stats[3].astype(int), minlength=nc) # number of targets per class
+
+ # Print results
+ pf = "%22s" + "%11i" * 2 + "%11.3g" * 4 # print format
+ LOGGER.info(pf % ("all", seen, nt.sum(), mp, mr, map50, map))
+ if nt.sum() == 0:
+ LOGGER.warning(f"WARNING ⚠️ no labels found in {task} set, can not compute metrics without labels")
+
+ # Print results per class
+ if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
+ for i, c in enumerate(ap_class):
+ LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
+
+ # Print speeds
+ t = tuple(x.t / seen * 1e3 for x in dt) # speeds per image
+ if not training:
+ shape = (batch_size, 3, imgsz, imgsz)
+ LOGGER.info(f"Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}" % t)
+
+ # Plots
+ if plots:
+ confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
+ callbacks.run("on_val_end", nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix)
+
+ # Save JSON
+ if save_json and len(jdict):
+ w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights
+ anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations
+ if not os.path.exists(anno_json):
+ anno_json = os.path.join(data["path"], "annotations", "instances_val2017.json")
+ pred_json = str(save_dir / f"{w}_predictions.json") # predictions
+ LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...")
+ with open(pred_json, "w") as f:
+ json.dump(jdict, f)
+
+ try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
+ check_requirements("pycocotools>=2.0.6")
+ from pycocotools.coco import COCO
+ from pycocotools.cocoeval import COCOeval
+
+ anno = COCO(anno_json) # init annotations api
+ pred = anno.loadRes(pred_json) # init predictions api
+ eval = COCOeval(anno, pred, "bbox")
+ if is_coco:
+ eval.params.imgIds = [int(Path(x).stem) for x in dataloader.dataset.im_files] # image IDs to evaluate
+ eval.evaluate()
+ eval.accumulate()
+ eval.summarize()
+ map, map50 = eval.stats[:2] # update results (mAP@0.5:0.95, mAP@0.5)
+ except Exception as e:
+ LOGGER.info(f"pycocotools unable to run: {e}")
+
+ # Return results
+ model.float() # for training
+ if not training:
+ s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ""
+ LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
+ maps = np.zeros(nc) + map
+ for i, c in enumerate(ap_class):
+ maps[c] = ap[i]
+ return (mp, mr, map50, map, *(loss.cpu() / len(dataloader)).tolist()), maps, t
+
+
+def parse_opt():
+ """
+ Parse command-line options for configuring YOLOv5 model inference.
+
+ Args:
+ data (str, optional): Path to the dataset YAML file. Default is 'data/coco128.yaml'.
+ weights (list[str], optional): List of paths to model weight files. Default is 'yolov5s.pt'.
+ batch_size (int, optional): Batch size for inference. Default is 32.
+ imgsz (int, optional): Inference image size in pixels. Default is 640.
+ conf_thres (float, optional): Confidence threshold for predictions. Default is 0.001.
+ iou_thres (float, optional): IoU threshold for Non-Max Suppression (NMS). Default is 0.6.
+ max_det (int, optional): Maximum number of detections per image. Default is 300.
+ task (str, optional): Task type - options are 'train', 'val', 'test', 'speed', or 'study'. Default is 'val'.
+ device (str, optional): Device to run the model on. e.g., '0' or '0,1,2,3' or 'cpu'. Default is empty to let the system choose automatically.
+ workers (int, optional): Maximum number of dataloader workers per rank in DDP mode. Default is 8.
+ single_cls (bool, optional): If set, treats the dataset as a single-class dataset. Default is False.
+ augment (bool, optional): If set, performs augmented inference. Default is False.
+ verbose (bool, optional): If set, reports mAP by class. Default is False.
+ save_txt (bool, optional): If set, saves results to *.txt files. Default is False.
+ save_hybrid (bool, optional): If set, saves label+prediction hybrid results to *.txt files. Default is False.
+ save_conf (bool, optional): If set, saves confidences in --save-txt labels. Default is False.
+ save_json (bool, optional): If set, saves results to a COCO-JSON file. Default is False.
+ project (str, optional): Project directory to save results to. Default is 'runs/val'.
+ name (str, optional): Name of the directory to save results to. Default is 'exp'.
+ exist_ok (bool, optional): If set, existing directory will not be incremented. Default is False.
+ half (bool, optional): If set, uses FP16 half-precision inference. Default is False.
+ dnn (bool, optional): If set, uses OpenCV DNN for ONNX inference. Default is False.
+
+ Returns:
+ argparse.Namespace: Parsed command-line options.
+
+ Notes:
+ - The '--data' parameter is checked to ensure it ends with 'coco.yaml' if '--save-json' is set.
+ - The '--save-txt' option is set to True if '--save-hybrid' is enabled.
+ - Args are printed using `print_args` to facilitate debugging.
+
+ Example:
+ To validate a trained YOLOv5 model on a COCO dataset:
+ ```python
+ $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
+ ```
+ Different model formats could be used instead of `yolov5s.pt`:
+ ```python
+ $ python val.py --weights yolov5s.pt yolov5s.torchscript yolov5s.onnx yolov5s_openvino_model yolov5s.engine
+ ```
+ Additional options include saving results in different formats, selecting devices, and more.
+ """
+ parser = argparse.ArgumentParser()
+ parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path")
+ parser.add_argument("--weights", nargs="+", type=str, default=ROOT / "yolov5s.pt", help="model path(s)")
+ parser.add_argument("--batch-size", type=int, default=32, help="batch size")
+ parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="inference size (pixels)")
+ parser.add_argument("--conf-thres", type=float, default=0.001, help="confidence threshold")
+ parser.add_argument("--iou-thres", type=float, default=0.6, help="NMS IoU threshold")
+ parser.add_argument("--max-det", type=int, default=300, help="maximum detections per image")
+ parser.add_argument("--task", default="val", help="train, val, test, speed or study")
+ parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu")
+ parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)")
+ parser.add_argument("--single-cls", action="store_true", help="treat as single-class dataset")
+ parser.add_argument("--augment", action="store_true", help="augmented inference")
+ parser.add_argument("--verbose", action="store_true", help="report mAP by class")
+ parser.add_argument("--save-txt", action="store_true", help="save results to *.txt")
+ parser.add_argument("--save-hybrid", action="store_true", help="save label+prediction hybrid results to *.txt")
+ parser.add_argument("--save-conf", action="store_true", help="save confidences in --save-txt labels")
+ parser.add_argument("--save-json", action="store_true", help="save a COCO-JSON results file")
+ parser.add_argument("--project", default=ROOT / "runs/val", help="save to project/name")
+ parser.add_argument("--name", default="exp", help="save to project/name")
+ parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment")
+ parser.add_argument("--half", action="store_true", help="use FP16 half-precision inference")
+ parser.add_argument("--dnn", action="store_true", help="use OpenCV DNN for ONNX inference")
+ opt = parser.parse_args()
+ opt.data = check_yaml(opt.data) # check YAML
+ opt.save_json |= opt.data.endswith("coco.yaml")
+ opt.save_txt |= opt.save_hybrid
+ print_args(vars(opt))
+ return opt
+
+
+def main(opt):
+ """
+ Executes YOLOv5 tasks like training, validation, testing, speed, and study benchmarks based on provided options.
+
+ Args:
+ opt (argparse.Namespace): Parsed command-line options.
+ This includes values for parameters like 'data', 'weights', 'batch_size', 'imgsz', 'conf_thres',
+ 'iou_thres', 'max_det', 'task', 'device', 'workers', 'single_cls', 'augment', 'verbose', 'save_txt',
+ 'save_hybrid', 'save_conf', 'save_json', 'project', 'name', 'exist_ok', 'half', and 'dnn', essential
+ for configuring the YOLOv5 tasks.
+
+ Returns:
+ None
+
+ Examples:
+ To validate a trained YOLOv5 model on the COCO dataset with a specific weights file, use:
+ ```python
+ $ python val.py --weights yolov5s.pt --data coco128.yaml --img 640
+ ```
+ """
+ check_requirements(ROOT / "requirements.txt", exclude=("tensorboard", "thop"))
+
+ if opt.task in ("train", "val", "test"): # run normally
+ if opt.conf_thres > 0.001: # https://github.com/ultralytics/yolov5/issues/1466
+ LOGGER.info(f"WARNING ⚠️ confidence threshold {opt.conf_thres} > 0.001 produces invalid results")
+ if opt.save_hybrid:
+ LOGGER.info("WARNING ⚠️ --save-hybrid will return high mAP from hybrid labels, not from predictions alone")
+ run(**vars(opt))
+
+ else:
+ weights = opt.weights if isinstance(opt.weights, list) else [opt.weights]
+ opt.half = torch.cuda.is_available() and opt.device != "cpu" # FP16 for fastest results
+ if opt.task == "speed": # speed benchmarks
+ # python val.py --task speed --data coco.yaml --batch 1 --weights yolov5n.pt yolov5s.pt...
+ opt.conf_thres, opt.iou_thres, opt.save_json = 0.25, 0.45, False
+ for opt.weights in weights:
+ run(**vars(opt), plots=False)
+
+ elif opt.task == "study": # speed vs mAP benchmarks
+ # python val.py --task study --data coco.yaml --iou 0.7 --weights yolov5n.pt yolov5s.pt...
+ for opt.weights in weights:
+ f = f"study_{Path(opt.data).stem}_{Path(opt.weights).stem}.txt" # filename to save to
+ x, y = list(range(256, 1536 + 128, 128)), [] # x axis (image sizes), y axis
+ for opt.imgsz in x: # img-size
+ LOGGER.info(f"\nRunning {f} --imgsz {opt.imgsz}...")
+ r, _, t = run(**vars(opt), plots=False)
+ y.append(r + t) # results and times
+ np.savetxt(f, y, fmt="%10.4g") # save
+ subprocess.run(["zip", "-r", "study.zip", "study_*.txt"])
+ plot_val_study(x=x) # plot
+ else:
+ raise NotImplementedError(f'--task {opt.task} not in ("train", "val", "test", "speed", "study")')
+
+
+if __name__ == "__main__":
+ opt = parse_opt()
+ main(opt)
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