101 KiB
Vendored
101 KiB
Vendored
This YOLOv5 🚀 notebook by Ultralytics presents simple train, validate and predict examples to help start your AI adventure.
See GitHub for community support or contact us for professional support.
Setup
Clone GitHub repository, install dependencies and check PyTorch and GPU.
!git clone https://github.com/ultralytics/yolov5 # clone
%cd yolov5
%pip install -qr requirements.txt # install
import torch
import utils
display = utils.notebook_init() # checksYOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB)
Setup complete ✅ (2 CPUs, 12.7 GB RAM, 22.6/78.2 GB disk)
1. Predict
classify/predict.py runs YOLOv5 Classifcation inference on a variety of sources, downloading models automatically from the latest YOLOv5 release, and saving results to runs/predict-cls. Example inference sources are:
python classify/predict.py --source 0 # webcam
img.jpg # image
vid.mp4 # video
screen # screenshot
path/ # directory
'path/*.jpg' # glob
'https://youtu.be/Zgi9g1ksQHc' # YouTube
'rtsp://example.com/media.mp4' # RTSP, RTMP, HTTP stream
!python classify/predict.py --weights yolov5s-cls.pt --img 224 --source data/images
# display.Image(filename='runs/predict-cls/exp/zidane.jpg', width=600)[34m[1mclassify/predict: [0mweights=['yolov5s-cls.pt'], source=data/images, data=data/coco128.yaml, imgsz=[224, 224], device=, view_img=False, save_txt=False, nosave=False, augment=False, visualize=False, update=False, project=runs/predict-cls, name=exp, exist_ok=False, half=False, dnn=False, vid_stride=1 YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB) Downloading https://github.com/ultralytics/yolov5/releases/download/v7.0/yolov5s-cls.pt to yolov5s-cls.pt... 100% 10.5M/10.5M [00:00<00:00, 12.3MB/s] Fusing layers... Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs image 1/2 /content/yolov5/data/images/bus.jpg: 224x224 minibus 0.39, police van 0.24, amphibious vehicle 0.05, recreational vehicle 0.04, trolleybus 0.03, 3.9ms image 2/2 /content/yolov5/data/images/zidane.jpg: 224x224 suit 0.38, bow tie 0.19, bridegroom 0.18, rugby ball 0.04, stage 0.02, 4.6ms Speed: 0.3ms pre-process, 4.3ms inference, 1.5ms NMS per image at shape (1, 3, 224, 224) Results saved to [1mruns/predict-cls/exp[0m
2. Validate
Validate a model's accuracy on the Imagenet dataset's val or test splits. Models are downloaded automatically from the latest YOLOv5 release. To show results by class use the --verbose flag.
# Download Imagenet val (6.3G, 50000 images)
!bash data/scripts/get_imagenet.sh --val--2022-11-22 19:53:40-- https://image-net.org/data/ILSVRC/2012/ILSVRC2012_img_val.tar Resolving image-net.org (image-net.org)... 171.64.68.16 Connecting to image-net.org (image-net.org)|171.64.68.16|:443... connected. HTTP request sent, awaiting response... 200 OK Length: 6744924160 (6.3G) [application/x-tar] Saving to: ‘ILSVRC2012_img_val.tar’ ILSVRC2012_img_val. 100%[===================>] 6.28G 16.1MB/s in 10m 52s 2022-11-22 20:04:32 (9.87 MB/s) - ‘ILSVRC2012_img_val.tar’ saved [6744924160/6744924160]
# Validate YOLOv5s on Imagenet val
!python classify/val.py --weights yolov5s-cls.pt --data ../datasets/imagenet --img 224 --half[34m[1mclassify/val: [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 YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB) Fusing layers... Model summary: 117 layers, 5447688 parameters, 0 gradients, 11.4 GFLOPs validating: 100% 391/391 [04:57<00:00, 1.31it/s] Class Images top1_acc top5_acc all 50000 0.715 0.902 tench 50 0.94 0.98 goldfish 50 0.88 0.92 great white shark 50 0.78 0.96 tiger shark 50 0.68 0.96 hammerhead shark 50 0.82 0.92 electric ray 50 0.76 0.9 stingray 50 0.7 0.9 cock 50 0.78 0.92 hen 50 0.84 0.96 ostrich 50 0.98 1 brambling 50 0.9 0.96 goldfinch 50 0.92 0.98 house finch 50 0.88 0.96 junco 50 0.94 0.98 indigo bunting 50 0.86 0.88 American robin 50 0.9 0.96 bulbul 50 0.84 0.96 jay 50 0.9 0.96 magpie 50 0.84 0.96 chickadee 50 0.9 1 American dipper 50 0.82 0.92 kite 50 0.76 0.94 bald eagle 50 0.92 1 vulture 50 0.96 1 great grey owl 50 0.94 0.98 fire salamander 50 0.96 0.98 smooth newt 50 0.58 0.94 newt 50 0.74 0.9 spotted salamander 50 0.86 0.94 axolotl 50 0.86 0.96 American bullfrog 50 0.78 0.92 tree frog 50 0.84 0.96 tailed frog 50 0.48 0.8 loggerhead sea turtle 50 0.68 0.94 leatherback sea turtle 50 0.5 0.8 mud turtle 50 0.64 0.84 terrapin 50 0.52 0.98 box turtle 50 0.84 0.98 banded gecko 50 0.7 0.88 green iguana 50 0.76 0.94 Carolina anole 50 0.58 0.96 desert grassland whiptail lizard 50 0.82 0.94 agama 50 0.74 0.92 frilled-necked lizard 50 0.84 0.86 alligator lizard 50 0.58 0.78 Gila monster 50 0.72 0.8 European green lizard 50 0.42 0.9 chameleon 50 0.76 0.84 Komodo dragon 50 0.86 0.96 Nile crocodile 50 0.7 0.84 American alligator 50 0.76 0.96 triceratops 50 0.9 0.94 worm snake 50 0.76 0.88 ring-necked snake 50 0.8 0.92 eastern hog-nosed snake 50 0.58 0.88 smooth green snake 50 0.6 0.94 kingsnake 50 0.82 0.9 garter snake 50 0.88 0.94 water snake 50 0.7 0.94 vine snake 50 0.66 0.76 night snake 50 0.34 0.82 boa constrictor 50 0.8 0.96 African rock python 50 0.48 0.76 Indian cobra 50 0.82 0.94 green mamba 50 0.54 0.86 sea snake 50 0.62 0.9 Saharan horned viper 50 0.56 0.86 eastern diamondback rattlesnake 50 0.6 0.86 sidewinder 50 0.28 0.86 trilobite 50 0.98 0.98 harvestman 50 0.86 0.94 scorpion 50 0.86 0.94 yellow garden spider 50 0.92 0.96 barn spider 50 0.38 0.98 European garden spider 50 0.62 0.98 southern black widow 50 0.88 0.94 tarantula 50 0.94 1 wolf spider 50 0.82 0.92 tick 50 0.74 0.84 centipede 50 0.68 0.82 black grouse 50 0.88 0.98 ptarmigan 50 0.78 0.94 ruffed grouse 50 0.88 1 prairie grouse 50 0.92 1 peacock 50 0.88 0.9 quail 50 0.9 0.94 partridge 50 0.74 0.96 grey parrot 50 0.9 0.96 macaw 50 0.88 0.98 sulphur-crested cockatoo 50 0.86 0.92 lorikeet 50 0.96 1 coucal 50 0.82 0.88 bee eater 50 0.96 0.98 hornbill 50 0.9 0.96 hummingbird 50 0.88 0.96 jacamar 50 0.92 0.94 toucan 50 0.84 0.94 duck 50 0.76 0.94 red-breasted merganser 50 0.86 0.96 goose 50 0.74 0.96 black swan 50 0.94 0.98 tusker 50 0.54 0.92 echidna 50 0.98 1 platypus 50 0.72 0.84 wallaby 50 0.78 0.88 koala 50 0.84 0.92 wombat 50 0.78 0.84 jellyfish 50 0.88 0.96 sea anemone 50 0.72 0.9 brain coral 50 0.88 0.96 flatworm 50 0.8 0.98 nematode 50 0.86 0.9 conch 50 0.74 0.88 snail 50 0.78 0.88 slug 50 0.74 0.82 sea slug 50 0.88 0.98 chiton 50 0.88 0.98 chambered nautilus 50 0.88 0.92 Dungeness crab 50 0.78 0.94 rock crab 50 0.68 0.86 fiddler crab 50 0.64 0.86 red king crab 50 0.76 0.96 American lobster 50 0.78 0.96 spiny lobster 50 0.74 0.88 crayfish 50 0.56 0.86 hermit crab 50 0.78 0.96 isopod 50 0.66 0.78 white stork 50 0.88 0.96 black stork 50 0.84 0.98 spoonbill 50 0.96 1 flamingo 50 0.94 1 little blue heron 50 0.92 0.98 great egret 50 0.9 0.96 bittern 50 0.86 0.94 crane (bird) 50 0.62 0.9 limpkin 50 0.98 1 common gallinule 50 0.92 0.96 American coot 50 0.9 0.98 bustard 50 0.92 0.96 ruddy turnstone 50 0.94 1 dunlin 50 0.86 0.94 common redshank 50 0.9 0.96 dowitcher 50 0.84 0.96 oystercatcher 50 0.86 0.94 pelican 50 0.92 0.96 king penguin 50 0.88 0.96 albatross 50 0.9 1 grey whale 50 0.84 0.92 killer whale 50 0.92 1 dugong 50 0.84 0.96 sea lion 50 0.82 0.92 Chihuahua 50 0.66 0.84 Japanese Chin 50 0.72 0.98 Maltese 50 0.76 0.94 Pekingese 50 0.84 0.94 Shih Tzu 50 0.74 0.96 King Charles Spaniel 50 0.88 0.98 Papillon 50 0.86 0.94 toy terrier 50 0.48 0.94 Rhodesian Ridgeback 50 0.76 0.98 Afghan Hound 50 0.84 1 Basset Hound 50 0.8 0.92 Beagle 50 0.82 0.96 Bloodhound 50 0.48 0.72 Bluetick Coonhound 50 0.86 0.94 Black and Tan Coonhound 50 0.54 0.8 Treeing Walker Coonhound 50 0.66 0.98 English foxhound 50 0.32 0.84 Redbone Coonhound 50 0.62 0.94 borzoi 50 0.92 1 Irish Wolfhound 50 0.48 0.88 Italian Greyhound 50 0.76 0.98 Whippet 50 0.74 0.92 Ibizan Hound 50 0.6 0.86 Norwegian Elkhound 50 0.88 0.98 Otterhound 50 0.62 0.9 Saluki 50 0.72 0.92 Scottish Deerhound 50 0.86 0.98 Weimaraner 50 0.88 0.94 Staffordshire Bull Terrier 50 0.66 0.98 American Staffordshire Terrier 50 0.64 0.92 Bedlington Terrier 50 0.9 0.92 Border Terrier 50 0.86 0.92 Kerry Blue Terrier 50 0.78 0.98 Irish Terrier 50 0.7 0.96 Norfolk Terrier 50 0.68 0.9 Norwich Terrier 50 0.72 1 Yorkshire Terrier 50 0.66 0.9 Wire Fox Terrier 50 0.64 0.98 Lakeland Terrier 50 0.74 0.92 Sealyham Terrier 50 0.76 0.9 Airedale Terrier 50 0.82 0.92 Cairn Terrier 50 0.76 0.9 Australian Terrier 50 0.48 0.84 Dandie Dinmont Terrier 50 0.82 0.92 Boston Terrier 50 0.92 1 Miniature Schnauzer 50 0.68 0.9 Giant Schnauzer 50 0.72 0.98 Standard Schnauzer 50 0.74 1 Scottish Terrier 50 0.76 0.96 Tibetan Terrier 50 0.48 1 Australian Silky Terrier 50 0.66 0.96 Soft-coated Wheaten Terrier 50 0.74 0.96 West Highland White Terrier 50 0.88 0.96 Lhasa Apso 50 0.68 0.96 Flat-Coated Retriever 50 0.72 0.94 Curly-coated Retriever 50 0.82 0.94 Golden Retriever 50 0.86 0.94 Labrador Retriever 50 0.82 0.94 Chesapeake Bay Retriever 50 0.76 0.96 German Shorthaired Pointer 50 0.8 0.96 Vizsla 50 0.68 0.96 English Setter 50 0.7 1 Irish Setter 50 0.8 0.9 Gordon Setter 50 0.84 0.92 Brittany 50 0.84 0.96 Clumber Spaniel 50 0.92 0.96 English Springer Spaniel 50 0.88 1 Welsh Springer Spaniel 50 0.92 1 Cocker Spaniels 50 0.7 0.94 Sussex Spaniel 50 0.72 0.92 Irish Water Spaniel 50 0.88 0.98 Kuvasz 50 0.66 0.9 Schipperke 50 0.9 0.98 Groenendael 50 0.8 0.94 Malinois 50 0.86 0.98 Briard 50 0.52 0.8 Australian Kelpie 50 0.6 0.88 Komondor 50 0.88 0.94 Old English Sheepdog 50 0.94 0.98 Shetland Sheepdog 50 0.74 0.9 collie 50 0.6 0.96 Border Collie 50 0.74 0.96 Bouvier des Flandres 50 0.78 0.94 Rottweiler 50 0.88 0.96 German Shepherd Dog 50 0.8 0.98 Dobermann 50 0.68 0.96 Miniature Pinscher 50 0.76 0.88 Greater Swiss Mountain Dog 50 0.68 0.94 Bernese Mountain Dog 50 0.96 1 Appenzeller Sennenhund 50 0.22 1 Entlebucher Sennenhund 50 0.64 0.98 Boxer 50 0.7 0.92 Bullmastiff 50 0.78 0.98 Tibetan Mastiff 50 0.88 0.96 French Bulldog 50 0.84 0.94 Great Dane 50 0.54 0.9 St. Bernard 50 0.92 1 husky 50 0.46 0.98 Alaskan Malamute 50 0.76 0.96 Siberian Husky 50 0.46 0.98 Dalmatian 50 0.94 0.98 Affenpinscher 50 0.78 0.9 Basenji 50 0.92 0.94 pug 50 0.94 0.98 Leonberger 50 1 1 Newfoundland 50 0.78 0.96 Pyrenean Mountain Dog 50 0.78 0.96 Samoyed 50 0.96 1 Pomeranian 50 0.98 1 Chow Chow 50 0.9 0.96 Keeshond 50 0.88 0.94 Griffon Bruxellois 50 0.84 0.98 Pembroke Welsh Corgi 50 0.82 0.94 Cardigan Welsh Corgi 50 0.66 0.98 Toy Poodle 50 0.52 0.88 Miniature Poodle 50 0.52 0.92 Standard Poodle 50 0.8 1 Mexican hairless dog 50 0.88 0.98 grey wolf 50 0.82 0.92 Alaskan tundra wolf 50 0.78 0.98 red wolf 50 0.48 0.9 coyote 50 0.64 0.86 dingo 50 0.76 0.88 dhole 50 0.9 0.98 African wild dog 50 0.98 1 hyena 50 0.88 0.96 red fox 50 0.54 0.92 kit fox 50 0.72 0.98 Arctic fox 50 0.94 1 grey fox 50 0.7 0.94 tabby cat 50 0.54 0.92 tiger cat 50 0.22 0.94 Persian cat 50 0.9 0.98 Siamese cat 50 0.96 1 Egyptian Mau 50 0.54 0.8 cougar 50 0.9 1 lynx 50 0.72 0.88 leopard 50 0.78 0.98 snow leopard 50 0.9 0.98 jaguar 50 0.7 0.94 lion 50 0.9 0.98 tiger 50 0.92 0.98 cheetah 50 0.94 0.98 brown bear 50 0.94 0.98 American black bear 50 0.8 1 polar bear 50 0.84 0.96 sloth bear 50 0.72 0.92 mongoose 50 0.7 0.92 meerkat 50 0.82 0.92 tiger beetle 50 0.92 0.94 ladybug 50 0.86 0.94 ground beetle 50 0.64 0.94 longhorn beetle 50 0.62 0.88 leaf beetle 50 0.64 0.98 dung beetle 50 0.86 0.98 rhinoceros beetle 50 0.86 0.94 weevil 50 0.9 1 fly 50 0.78 0.94 bee 50 0.68 0.94 ant 50 0.68 0.78 grasshopper 50 0.5 0.92 cricket 50 0.64 0.92 stick insect 50 0.64 0.92 cockroach 50 0.72 0.8 mantis 50 0.64 0.86 cicada 50 0.9 0.96 leafhopper 50 0.88 0.94 lacewing 50 0.78 0.92 dragonfly 50 0.82 0.98 damselfly 50 0.82 1 red admiral 50 0.94 0.96 ringlet 50 0.86 0.98 monarch butterfly 50 0.9 0.92 small white 50 0.9 1 sulphur butterfly 50 0.92 1 gossamer-winged butterfly 50 0.88 1 starfish 50 0.88 0.92 sea urchin 50 0.84 0.94 sea cucumber 50 0.66 0.84 cottontail rabbit 50 0.72 0.94 hare 50 0.84 0.96 Angora rabbit 50 0.94 0.98 hamster 50 0.96 1 porcupine 50 0.88 0.98 fox squirrel 50 0.76 0.94 marmot 50 0.92 0.96 beaver 50 0.78 0.94 guinea pig 50 0.78 0.94 common sorrel 50 0.96 0.98 zebra 50 0.94 0.96 pig 50 0.5 0.76 wild boar 50 0.84 0.96 warthog 50 0.84 0.96 hippopotamus 50 0.88 0.96 ox 50 0.48 0.94 water buffalo 50 0.78 0.94 bison 50 0.88 0.96 ram 50 0.58 0.92 bighorn sheep 50 0.66 1 Alpine ibex 50 0.92 0.98 hartebeest 50 0.94 1 impala 50 0.82 0.96 gazelle 50 0.7 0.96 dromedary 50 0.9 1 llama 50 0.82 0.94 weasel 50 0.44 0.92 mink 50 0.78 0.96 European polecat 50 0.46 0.9 black-footed ferret 50 0.68 0.96 otter 50 0.66 0.88 skunk 50 0.96 0.96 badger 50 0.86 0.92 armadillo 50 0.88 0.9 three-toed sloth 50 0.96 1 orangutan 50 0.78 0.92 gorilla 50 0.82 0.94 chimpanzee 50 0.84 0.94 gibbon 50 0.76 0.86 siamang 50 0.68 0.94 guenon 50 0.8 0.94 patas monkey 50 0.62 0.82 baboon 50 0.9 0.98 macaque 50 0.8 0.86 langur 50 0.6 0.82 black-and-white colobus 50 0.86 0.9 proboscis monkey 50 1 1 marmoset 50 0.74 0.98 white-headed capuchin 50 0.72 0.9 howler monkey 50 0.86 0.94 titi 50 0.5 0.9 Geoffroy's spider monkey 50 0.42 0.8 common squirrel monkey 50 0.76 0.92 ring-tailed lemur 50 0.72 0.94 indri 50 0.9 0.96 Asian elephant 50 0.58 0.92 African bush elephant 50 0.7 0.98 red panda 50 0.94 0.94 giant panda 50 0.94 0.98 snoek 50 0.74 0.9 eel 50 0.6 0.84 coho salmon 50 0.84 0.96 rock beauty 50 0.88 0.98 clownfish 50 0.78 0.98 sturgeon 50 0.68 0.94 garfish 50 0.62 0.8 lionfish 50 0.96 0.96 pufferfish 50 0.88 0.96 abacus 50 0.74 0.88 abaya 50 0.84 0.92 academic gown 50 0.42 0.86 accordion 50 0.8 0.9 acoustic guitar 50 0.5 0.76 aircraft carrier 50 0.8 0.96 airliner 50 0.92 1 airship 50 0.76 0.82 altar 50 0.64 0.98 ambulance 50 0.88 0.98 amphibious vehicle 50 0.64 0.94 analog clock 50 0.52 0.92 apiary 50 0.82 0.96 apron 50 0.7 0.84 waste container 50 0.4 0.8 assault rifle 50 0.42 0.84 backpack 50 0.34 0.64 bakery 50 0.4 0.68 balance beam 50 0.8 0.98 balloon 50 0.86 0.96 ballpoint pen 50 0.52 0.96 Band-Aid 50 0.7 0.9 banjo 50 0.84 1 baluster 50 0.68 0.94 barbell 50 0.56 0.9 barber chair 50 0.7 0.92 barbershop 50 0.54 0.86 barn 50 0.96 0.96 barometer 50 0.84 0.98 barrel 50 0.56 0.88 wheelbarrow 50 0.66 0.88 baseball 50 0.74 0.98 basketball 50 0.88 0.98 bassinet 50 0.66 0.92 bassoon 50 0.74 0.98 swimming cap 50 0.62 0.88 bath towel 50 0.54 0.78 bathtub 50 0.4 0.88 station wagon 50 0.66 0.84 lighthouse 50 0.78 0.94 beaker 50 0.52 0.68 military cap 50 0.84 0.96 beer bottle 50 0.66 0.88 beer glass 50 0.6 0.84 bell-cot 50 0.56 0.96 bib 50 0.58 0.82 tandem bicycle 50 0.86 0.96 bikini 50 0.56 0.88 ring binder 50 0.64 0.84 binoculars 50 0.54 0.78 birdhouse 50 0.86 0.94 boathouse 50 0.74 0.92 bobsleigh 50 0.92 0.96 bolo tie 50 0.8 0.94 poke bonnet 50 0.64 0.86 bookcase 50 0.66 0.92 bookstore 50 0.62 0.88 bottle cap 50 0.58 0.7 bow 50 0.72 0.86 bow tie 50 0.7 0.9 brass 50 0.92 0.96 bra 50 0.5 0.7 breakwater 50 0.62 0.86 breastplate 50 0.4 0.9 broom 50 0.6 0.86 bucket 50 0.66 0.8 buckle 50 0.5 0.68 bulletproof vest 50 0.5 0.78 high-speed train 50 0.94 0.96 butcher shop 50 0.74 0.94 taxicab 50 0.64 0.86 cauldron 50 0.44 0.66 candle 50 0.48 0.74 cannon 50 0.88 0.94 canoe 50 0.94 1 can opener 50 0.66 0.86 cardigan 50 0.68 0.8 car mirror 50 0.94 0.96 carousel 50 0.94 0.98 tool kit 50 0.56 0.78 carton 50 0.42 0.7 car wheel 50 0.38 0.74 automated teller machine 50 0.76 0.94 cassette 50 0.52 0.8 cassette player 50 0.28 0.9 castle 50 0.78 0.88 catamaran 50 0.78 1 CD player 50 0.52 0.82 cello 50 0.82 1 mobile phone 50 0.68 0.86 chain 50 0.38 0.66 chain-link fence 50 0.7 0.84 chain mail 50 0.64 0.9 chainsaw 50 0.84 0.92 chest 50 0.68 0.92 chiffonier 50 0.26 0.64 chime 50 0.62 0.84 china cabinet 50 0.82 0.96 Christmas stocking 50 0.92 0.94 church 50 0.62 0.9 movie theater 50 0.58 0.88 cleaver 50 0.32 0.62 cliff dwelling 50 0.88 1 cloak 50 0.32 0.64 clogs 50 0.58 0.88 cocktail shaker 50 0.62 0.7 coffee mug 50 0.44 0.72 coffeemaker 50 0.64 0.92 coil 50 0.66 0.84 combination lock 50 0.64 0.84 computer keyboard 50 0.7 0.82 confectionery store 50 0.54 0.86 container ship 50 0.82 0.98 convertible 50 0.78 0.98 corkscrew 50 0.82 0.92 cornet 50 0.46 0.88 cowboy boot 50 0.64 0.8 cowboy hat 50 0.64 0.82 cradle 50 0.38 0.8 crane (machine) 50 0.78 0.94 crash helmet 50 0.92 0.96 crate 50 0.52 0.82 infant bed 50 0.74 1 Crock Pot 50 0.78 0.9 croquet ball 50 0.9 0.96 crutch 50 0.46 0.7 cuirass 50 0.54 0.86 dam 50 0.74 0.92 desk 50 0.6 0.86 desktop computer 50 0.54 0.94 rotary dial telephone 50 0.88 0.94 diaper 50 0.68 0.84 digital clock 50 0.54 0.76 digital watch 50 0.58 0.86 dining table 50 0.76 0.9 dishcloth 50 0.94 1 dishwasher 50 0.44 0.78 disc brake 50 0.98 1 dock 50 0.54 0.94 dog sled 50 0.84 1 dome 50 0.72 0.92 doormat 50 0.56 0.82 drilling rig 50 0.84 0.96 drum 50 0.38 0.68 drumstick 50 0.56 0.72 dumbbell 50 0.62 0.9 Dutch oven 50 0.7 0.84 electric fan 50 0.82 0.86 electric guitar 50 0.62 0.84 electric locomotive 50 0.92 0.98 entertainment center 50 0.9 0.98 envelope 50 0.44 0.86 espresso machine 50 0.72 0.94 face powder 50 0.7 0.92 feather boa 50 0.7 0.84 filing cabinet 50 0.88 0.98 fireboat 50 0.94 0.98 fire engine 50 0.84 0.9 fire screen sheet 50 0.62 0.76 flagpole 50 0.74 0.88 flute 50 0.36 0.72 folding chair 50 0.62 0.84 football helmet 50 0.86 0.94 forklift 50 0.8 0.92 fountain 50 0.84 0.94 fountain pen 50 0.76 0.92 four-poster bed 50 0.78 0.94 freight car 50 0.96 1 French horn 50 0.76 0.92 frying pan 50 0.36 0.78 fur coat 50 0.84 0.96 garbage truck 50 0.9 0.98 gas mask 50 0.84 0.92 gas pump 50 0.9 0.98 goblet 50 0.68 0.82 go-kart 50 0.9 1 golf ball 50 0.84 0.9 golf cart 50 0.78 0.86 gondola 50 0.98 0.98 gong 50 0.74 0.92 gown 50 0.62 0.96 grand piano 50 0.7 0.96 greenhouse 50 0.8 0.98 grille 50 0.72 0.9 grocery store 50 0.66 0.94 guillotine 50 0.86 0.92 barrette 50 0.52 0.66 hair spray 50 0.5 0.74 half-track 50 0.78 0.9 hammer 50 0.56 0.76 hamper 50 0.64 0.84 hair dryer 50 0.56 0.74 hand-held computer 50 0.42 0.86 handkerchief 50 0.78 0.94 hard disk drive 50 0.76 0.84 harmonica 50 0.7 0.88 harp 50 0.88 0.96 harvester 50 0.78 1 hatchet 50 0.54 0.74 holster 50 0.66 0.84 home theater 50 0.64 0.94 honeycomb 50 0.56 0.88 hook 50 0.3 0.6 hoop skirt 50 0.64 0.86 horizontal bar 50 0.68 0.98 horse-drawn vehicle 50 0.88 0.94 hourglass 50 0.88 0.96 iPod 50 0.76 0.94 clothes iron 50 0.82 0.88 jack-o'-lantern 50 0.98 0.98 jeans 50 0.68 0.84 jeep 50 0.72 0.9 T-shirt 50 0.72 0.96 jigsaw puzzle 50 0.84 0.94 pulled rickshaw 50 0.86 0.94 joystick 50 0.8 0.9 kimono 50 0.84 0.96 knee pad 50 0.62 0.88 knot 50 0.66 0.8 lab coat 50 0.8 0.96 ladle 50 0.36 0.64 lampshade 50 0.48 0.84 laptop computer 50 0.26 0.88 lawn mower 50 0.78 0.96 lens cap 50 0.46 0.72 paper knife 50 0.26 0.5 library 50 0.54 0.9 lifeboat 50 0.92 0.98 lighter 50 0.56 0.78 limousine 50 0.76 0.92 ocean liner 50 0.88 0.94 lipstick 50 0.74 0.9 slip-on shoe 50 0.74 0.92 lotion 50 0.5 0.86 speaker 50 0.52 0.68 loupe 50 0.32 0.52 sawmill 50 0.72 0.9 magnetic compass 50 0.52 0.82 mail bag 50 0.68 0.92 mailbox 50 0.82 0.92 tights 50 0.22 0.94 tank suit 50 0.24 0.9 manhole cover 50 0.96 0.98 maraca 50 0.74 0.9 marimba 50 0.84 0.94 mask 50 0.44 0.82 match 50 0.66 0.9 maypole 50 0.96 1 maze 50 0.8 0.96 measuring cup 50 0.54 0.76 medicine chest 50 0.6 0.84 megalith 50 0.8 0.92 microphone 50 0.52 0.7 microwave oven 50 0.48 0.72 military uniform 50 0.62 0.84 milk can 50 0.68 0.82 minibus 50 0.7 1 miniskirt 50 0.46 0.76 minivan 50 0.38 0.8 missile 50 0.4 0.84 mitten 50 0.76 0.88 mixing bowl 50 0.8 0.92 mobile home 50 0.54 0.78 Model T 50 0.92 0.96 modem 50 0.58 0.86 monastery 50 0.44 0.9 monitor 50 0.4 0.86 moped 50 0.56 0.94 mortar 50 0.68 0.94 square academic cap 50 0.5 0.84 mosque 50 0.9 1 mosquito net 50 0.9 0.98 scooter 50 0.9 0.98 mountain bike 50 0.78 0.96 tent 50 0.88 0.96 computer mouse 50 0.42 0.82 mousetrap 50 0.76 0.88 moving van 50 0.4 0.72 muzzle 50 0.5 0.72 nail 50 0.68 0.74 neck brace 50 0.56 0.68 necklace 50 0.86 1 nipple 50 0.7 0.88 notebook computer 50 0.34 0.84 obelisk 50 0.8 0.92 oboe 50 0.6 0.84 ocarina 50 0.8 0.86 odometer 50 0.96 1 oil filter 50 0.58 0.82 organ 50 0.82 0.9 oscilloscope 50 0.9 0.96 overskirt 50 0.2 0.7 bullock cart 50 0.7 0.94 oxygen mask 50 0.46 0.84 packet 50 0.5 0.78 paddle 50 0.56 0.94 paddle wheel 50 0.86 0.96 padlock 50 0.74 0.78 paintbrush 50 0.62 0.8 pajamas 50 0.56 0.92 palace 50 0.64 0.96 pan flute 50 0.84 0.86 paper towel 50 0.66 0.84 parachute 50 0.92 0.94 parallel bars 50 0.62 0.96 park bench 50 0.74 0.9 parking meter 50 0.84 0.92 passenger car 50 0.5 0.82 patio 50 0.58 0.84 payphone 50 0.74 0.92 pedestal 50 0.52 0.9 pencil case 50 0.64 0.92 pencil sharpener 50 0.52 0.78 perfume 50 0.7 0.9 Petri dish 50 0.6 0.8 photocopier 50 0.88 0.98 plectrum 50 0.7 0.84 Pickelhaube 50 0.72 0.86 picket fence 50 0.84 0.94 pickup truck 50 0.64 0.92 pier 50 0.52 0.82 piggy bank 50 0.82 0.94 pill bottle 50 0.76 0.86 pillow 50 0.76 0.9 ping-pong ball 50 0.84 0.88 pinwheel 50 0.76 0.88 pirate ship 50 0.76 0.94 pitcher 50 0.46 0.84 hand plane 50 0.84 0.94 planetarium 50 0.88 0.98 plastic bag 50 0.36 0.62 plate rack 50 0.52 0.78 plow 50 0.78 0.88 plunger 50 0.42 0.7 Polaroid camera 50 0.84 0.92 pole 50 0.38 0.74 police van 50 0.76 0.94 poncho 50 0.58 0.86 billiard table 50 0.8 0.88 soda bottle 50 0.56 0.94 pot 50 0.78 0.92 potter's wheel 50 0.9 0.94 power drill 50 0.42 0.72 prayer rug 50 0.7 0.86 printer 50 0.54 0.86 prison 50 0.7 0.9 projectile 50 0.28 0.9 projector 50 0.62 0.84 hockey puck 50 0.92 0.96 punching bag 50 0.6 0.68 purse 50 0.42 0.78 quill 50 0.68 0.84 quilt 50 0.64 0.9 race car 50 0.72 0.92 racket 50 0.72 0.9 radiator 50 0.66 0.76 radio 50 0.64 0.92 radio telescope 50 0.9 0.96 rain barrel 50 0.8 0.98 recreational vehicle 50 0.84 0.94 reel 50 0.72 0.82 reflex camera 50 0.72 0.92 refrigerator 50 0.7 0.9 remote control 50 0.7 0.88 restaurant 50 0.5 0.66 revolver 50 0.82 1 rifle 50 0.38 0.7 rocking chair 50 0.62 0.84 rotisserie 50 0.88 0.92 eraser 50 0.54 0.76 rugby ball 50 0.86 0.94 ruler 50 0.68 0.86 running shoe 50 0.78 0.94 safe 50 0.82 0.92 safety pin 50 0.4 0.62 salt shaker 50 0.66 0.9 sandal 50 0.66 0.86 sarong 50 0.64 0.86 saxophone 50 0.66 0.88 scabbard 50 0.76 0.92 weighing scale 50 0.58 0.78 school bus 50 0.92 1 schooner 50 0.84 1 scoreboard 50 0.9 0.96 CRT screen 50 0.14 0.7 screw 50 0.9 0.98 screwdriver 50 0.3 0.58 seat belt 50 0.88 0.94 sewing machine 50 0.76 0.9 shield 50 0.56 0.82 shoe store 50 0.78 0.96 shoji 50 0.8 0.92 shopping basket 50 0.52 0.88 shopping cart 50 0.76 0.92 shovel 50 0.62 0.84 shower cap 50 0.7 0.84 shower curtain 50 0.64 0.82 ski 50 0.74 0.92 ski mask 50 0.72 0.88 sleeping bag 50 0.68 0.8 slide rule 50 0.72 0.88 sliding door 50 0.44 0.78 slot machine 50 0.94 0.98 snorkel 50 0.86 0.98 snowmobile 50 0.88 1 snowplow 50 0.84 0.98 soap dispenser 50 0.56 0.86 soccer ball 50 0.86 0.96 sock 50 0.62 0.76 solar thermal collector 50 0.72 0.96 sombrero 50 0.6 0.84 soup bowl 50 0.56 0.94 space bar 50 0.34 0.88 space heater 50 0.52 0.74 space shuttle 50 0.82 0.96 spatula 50 0.3 0.6 motorboat 50 0.86 1 spider web 50 0.7 0.9 spindle 50 0.86 0.98 sports car 50 0.6 0.94 spotlight 50 0.26 0.6 stage 50 0.68 0.86 steam locomotive 50 0.94 1 through arch bridge 50 0.84 0.96 steel drum 50 0.82 0.9 stethoscope 50 0.6 0.82 scarf 50 0.5 0.92 stone wall 50 0.76 0.9 stopwatch 50 0.58 0.9 stove 50 0.46 0.74 strainer 50 0.64 0.84 tram 50 0.88 0.96 stretcher 50 0.6 0.8 couch 50 0.8 0.96 stupa 50 0.88 0.88 submarine 50 0.72 0.92 suit 50 0.4 0.78 sundial 50 0.58 0.74 sunglass 50 0.14 0.58 sunglasses 50 0.28 0.58 sunscreen 50 0.32 0.7 suspension bridge 50 0.6 0.94 mop 50 0.74 0.92 sweatshirt 50 0.28 0.66 swimsuit 50 0.52 0.82 swing 50 0.76 0.84 switch 50 0.56 0.76 syringe 50 0.62 0.82 table lamp 50 0.6 0.88 tank 50 0.8 0.96 tape player 50 0.46 0.76 teapot 50 0.84 1 teddy bear 50 0.82 0.94 television 50 0.6 0.9 tennis ball 50 0.7 0.94 thatched roof 50 0.88 0.9 front curtain 50 0.8 0.92 thimble 50 0.6 0.8 threshing machine 50 0.56 0.88 throne 50 0.72 0.82 tile roof 50 0.72 0.94 toaster 50 0.66 0.84 tobacco shop 50 0.42 0.7 toilet seat 50 0.62 0.88 torch 50 0.64 0.84 totem pole 50 0.92 0.98 tow truck 50 0.62 0.88 toy store 50 0.6 0.94 tractor 50 0.76 0.98 semi-trailer truck 50 0.78 0.92 tray 50 0.46 0.64 trench coat 50 0.54 0.72 tricycle 50 0.72 0.94 trimaran 50 0.7 0.98 tripod 50 0.58 0.86 triumphal arch 50 0.92 0.98 trolleybus 50 0.9 1 trombone 50 0.54 0.88 tub 50 0.24 0.82 turnstile 50 0.84 0.94 typewriter keyboard 50 0.68 0.98 umbrella 50 0.52 0.7 unicycle 50 0.74 0.96 upright piano 50 0.76 0.9 vacuum cleaner 50 0.62 0.9 vase 50 0.5 0.78 vault 50 0.76 0.92 velvet 50 0.2 0.42 vending machine 50 0.9 1 vestment 50 0.54 0.82 viaduct 50 0.78 0.86 violin 50 0.68 0.78 volleyball 50 0.86 1 waffle iron 50 0.72 0.88 wall clock 50 0.54 0.88 wallet 50 0.52 0.9 wardrobe 50 0.68 0.88 military aircraft 50 0.9 0.98 sink 50 0.72 0.96 washing machine 50 0.78 0.94 water bottle 50 0.54 0.74 water jug 50 0.22 0.74 water tower 50 0.9 0.96 whiskey jug 50 0.64 0.74 whistle 50 0.72 0.84 wig 50 0.84 0.9 window screen 50 0.68 0.8 window shade 50 0.52 0.76 Windsor tie 50 0.22 0.66 wine bottle 50 0.42 0.82 wing 50 0.54 0.96 wok 50 0.46 0.82 wooden spoon 50 0.58 0.8 wool 50 0.32 0.82 split-rail fence 50 0.74 0.9 shipwreck 50 0.84 0.96 yawl 50 0.78 0.96 yurt 50 0.84 1 website 50 0.98 1 comic book 50 0.62 0.9 crossword 50 0.84 0.88 traffic sign 50 0.78 0.9 traffic light 50 0.8 0.94 dust jacket 50 0.72 0.94 menu 50 0.82 0.96 plate 50 0.44 0.88 guacamole 50 0.8 0.92 consomme 50 0.54 0.88 hot pot 50 0.86 0.98 trifle 50 0.92 0.98 ice cream 50 0.68 0.94 ice pop 50 0.62 0.84 baguette 50 0.62 0.88 bagel 50 0.64 0.92 pretzel 50 0.72 0.88 cheeseburger 50 0.9 1 hot dog 50 0.74 0.94 mashed potato 50 0.74 0.9 cabbage 50 0.84 0.96 broccoli 50 0.9 0.96 cauliflower 50 0.82 1 zucchini 50 0.74 0.9 spaghetti squash 50 0.8 0.96 acorn squash 50 0.82 0.96 butternut squash 50 0.7 0.94 cucumber 50 0.6 0.96 artichoke 50 0.84 0.94 bell pepper 50 0.84 0.98 cardoon 50 0.88 0.94 mushroom 50 0.38 0.92 Granny Smith 50 0.9 0.96 strawberry 50 0.6 0.88 orange 50 0.7 0.92 lemon 50 0.78 0.98 fig 50 0.82 0.96 pineapple 50 0.86 0.96 banana 50 0.84 0.96 jackfruit 50 0.9 0.98 custard apple 50 0.86 0.96 pomegranate 50 0.82 0.98 hay 50 0.8 0.92 carbonara 50 0.88 0.94 chocolate syrup 50 0.46 0.84 dough 50 0.4 0.6 meatloaf 50 0.58 0.84 pizza 50 0.84 0.96 pot pie 50 0.68 0.9 burrito 50 0.8 0.98 red wine 50 0.54 0.82 espresso 50 0.64 0.88 cup 50 0.38 0.7 eggnog 50 0.38 0.7 alp 50 0.54 0.88 bubble 50 0.8 0.96 cliff 50 0.64 1 coral reef 50 0.72 0.96 geyser 50 0.94 1 lakeshore 50 0.54 0.88 promontory 50 0.58 0.94 shoal 50 0.6 0.96 seashore 50 0.44 0.78 valley 50 0.72 0.94 volcano 50 0.78 0.96 baseball player 50 0.72 0.94 bridegroom 50 0.72 0.88 scuba diver 50 0.8 1 rapeseed 50 0.94 0.98 daisy 50 0.96 0.98 yellow lady's slipper 50 1 1 corn 50 0.4 0.88 acorn 50 0.92 0.98 rose hip 50 0.92 0.98 horse chestnut seed 50 0.94 0.98 coral fungus 50 0.96 0.96 agaric 50 0.82 0.94 gyromitra 50 0.98 1 stinkhorn mushroom 50 0.8 0.94 earth star 50 0.98 1 hen-of-the-woods 50 0.8 0.96 bolete 50 0.74 0.94 ear 50 0.48 0.94 toilet paper 50 0.36 0.68 Speed: 0.1ms pre-process, 0.3ms inference, 0.0ms post-process per image at shape (1, 3, 224, 224) Results saved to [1mruns/val-cls/exp[0m
3. Train
Train a YOLOv5s Classification model on the Imagenette dataset with --data imagenet, starting from pretrained --pretrained yolov5s-cls.pt.
- Pretrained Models are downloaded automatically from the latest YOLOv5 release
- Training Results are saved to
runs/train-cls/with incrementing run directories, i.e.runs/train-cls/exp2,runs/train-cls/exp3etc.
A Mosaic Dataloader is used for training which combines 4 images into 1 mosaic.
Train on Custom Data with Roboflow 🌟 NEW
Roboflow enables you to easily organize, label, and prepare a high quality dataset with your own custom data. Roboflow also makes it easy to establish an active learning pipeline, collaborate with your team on dataset improvement, and integrate directly into your model building workflow with the roboflow pip package.
- Custom Training Example: https://blog.roboflow.com/train-yolov5-classification-custom-data/
- Custom Training Notebook:
#@title Select YOLOv5 🚀 logger {run: 'auto'}
logger = 'TensorBoard' #@param ['TensorBoard', 'Comet', 'ClearML']
if logger == 'TensorBoard':
%load_ext tensorboard
%tensorboard --logdir runs/train
elif logger == 'Comet':
%pip install -q comet_ml
import comet_ml; comet_ml.init()
elif logger == 'ClearML':
import clearml; clearml.browser_login()# Train YOLOv5s Classification on Imagenette160 for 3 epochs
!python classify/train.py --model yolov5s-cls.pt --data imagenette160 --epochs 5 --img 224 --cache[34m[1mclassify/train: [0mmodel=yolov5s-cls.pt, data=imagenette160, epochs=5, batch_size=64, imgsz=224, nosave=False, cache=ram, device=, workers=8, project=runs/train-cls, name=exp, exist_ok=False, pretrained=True, optimizer=Adam, lr0=0.001, decay=5e-05, label_smoothing=0.1, cutoff=None, dropout=None, verbose=False, seed=0, local_rank=-1 [34m[1mgithub: [0mup to date with https://github.com/ultralytics/yolov5 ✅ YOLOv5 🚀 v7.0-3-g61ebf5e Python-3.7.15 torch-1.12.1+cu113 CUDA:0 (Tesla T4, 15110MiB) [34m[1mTensorBoard: [0mStart with 'tensorboard --logdir runs/train-cls', view at http://localhost:6006/ Dataset not found ⚠️, missing path /content/datasets/imagenette160, attempting download... Downloading https://github.com/ultralytics/yolov5/releases/download/v1.0/imagenette160.zip to /content/datasets/imagenette160.zip... 100% 103M/103M [00:00<00:00, 347MB/s] Unzipping /content/datasets/imagenette160.zip... Dataset download success ✅ (3.3s), saved to [1m/content/datasets/imagenette160[0m [34m[1malbumentations: [0mRandomResizedCrop(p=1.0, height=224, width=224, scale=(0.08, 1.0), ratio=(0.75, 1.3333333333333333), interpolation=1), HorizontalFlip(p=0.5), ColorJitter(p=0.5, brightness=[0.6, 1.4], contrast=[0.6, 1.4], saturation=[0.6, 1.4], hue=[0, 0]), Normalize(p=1.0, mean=(0.485, 0.456, 0.406), std=(0.229, 0.224, 0.225), max_pixel_value=255.0), ToTensorV2(always_apply=True, p=1.0, transpose_mask=False) Model summary: 149 layers, 4185290 parameters, 4185290 gradients, 10.5 GFLOPs [34m[1moptimizer:[0m Adam(lr=0.001) with parameter groups 32 weight(decay=0.0), 33 weight(decay=5e-05), 33 bias Image sizes 224 train, 224 test Using 1 dataloader workers Logging results to [1mruns/train-cls/exp[0m Starting yolov5s-cls.pt training on imagenette160 dataset with 10 classes for 5 epochs... Epoch GPU_mem train_loss val_loss top1_acc top5_acc 1/5 1.47G 1.05 0.974 0.828 0.975: 100% 148/148 [00:38<00:00, 3.82it/s] 2/5 1.73G 0.895 0.766 0.911 0.994: 100% 148/148 [00:36<00:00, 4.03it/s] 3/5 1.73G 0.82 0.704 0.934 0.996: 100% 148/148 [00:35<00:00, 4.20it/s] 4/5 1.73G 0.766 0.664 0.951 0.998: 100% 148/148 [00:36<00:00, 4.05it/s] 5/5 1.73G 0.724 0.634 0.959 0.997: 100% 148/148 [00:37<00:00, 3.94it/s] Training complete (0.052 hours) Results saved to [1mruns/train-cls/exp[0m Predict: python classify/predict.py --weights runs/train-cls/exp/weights/best.pt --source im.jpg Validate: python classify/val.py --weights runs/train-cls/exp/weights/best.pt --data /content/datasets/imagenette160 Export: python export.py --weights runs/train-cls/exp/weights/best.pt --include onnx PyTorch Hub: model = torch.hub.load('ultralytics/yolov5', 'custom', 'runs/train-cls/exp/weights/best.pt') Visualize: https://netron.app
4. Visualize
Comet Logging and Visualization 🌟 NEW
Comet is now fully integrated with YOLOv5. Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with Comet Custom Panels! 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 is easy:
pip install comet_ml # 1. install
export COMET_API_KEY=<Your API Key> # 2. paste API key
python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt # 3. train
To learn more about all of the supported Comet features for this integration, check out the Comet Tutorial. If you'd like to learn more about Comet, head over to our documentation. Get started by trying out the Comet Colab Notebook:
ClearML Logging and Automation 🌟 NEW
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):
pip install clearml- run
clearml-initto connect to a ClearML server (deploy your own open-source server, or use our free hosted server)
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).
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 for details!
Local Logging
Training results are automatically logged with Tensorboard and CSV loggers to runs/train, with a new experiment directory created for each new training as runs/train/exp2, runs/train/exp3, etc.
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.
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
- Notebooks with free GPU:
- Google Cloud Deep Learning VM. See GCP Quickstart Guide
- Amazon Deep Learning AMI. See AWS Quickstart Guide
- Docker Image. See Docker Quickstart Guide
Status
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (val.py), inference (detect.py) and export (export.py) on macOS, Windows, and Ubuntu every 24 hours and on every commit.
Appendix
Additional content below.
# YOLOv5 PyTorch HUB Inference (DetectionModels only)
import torch
model = torch.hub.load('ultralytics/yolov5', 'yolov5s') # yolov5n - yolov5x6 or custom
im = 'https://ultralytics.com/images/zidane.jpg' # file, Path, PIL.Image, OpenCV, nparray, list
results = model(im) # inference
results.print() # or .show(), .save(), .crop(), .pandas(), etc.