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- # Ultralytics YOLO 🚀, AGPL-3.0 license
- # COCO 2017 dataset http://cocodataset.org by Microsoft
- # Example usage: yolo train data=coco.yaml
- # parent
- # ├── ultralytics
- # └── 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 ultralytics.utils.downloads import download
- from pathlib import Path
- # Download labels
- segments = True # segment or box labels
- dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
- 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)
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