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- # Ultralytics YOLO 🚀, AGPL-3.0 license
- # COCO 2017 dataset http://cocodataset.org by Microsoft
- # Example usage: yolo train data=coco-pose.yaml
- # parent
- # ├── ultralytics
- # └── datasets
- # └── coco-pose ← 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-pose # 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
- # Keypoints
- kpt_shape: [17, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
- flip_idx: [0, 2, 1, 4, 3, 6, 5, 8, 7, 10, 9, 12, 11, 14, 13, 16, 15]
- # Classes
- names:
- 0: person
- # Download script/URL (optional)
- download: |
- from ultralytics.utils.downloads import download
- from pathlib import Path
- # Download labels
- dir = Path(yaml['path']) # dataset root dir
- url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
- urls = [url + 'coco2017labels-pose.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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