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ultralytics/cfg/datasets/coco-pose.yaml

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+# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license
+
+# COCO 2017 Keypoints dataset https://cocodataset.org by Microsoft
+# Documentation: https://docs.ultralytics.com/datasets/pose/coco/
+# 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: coco-pose # dataset root dir
+train: train2017.txt # train images (relative to 'path') 56599 images
+val: val2017.txt # val images (relative to 'path') 2346 images
+test: test-dev2017.txt # 20288 of 40670 images, submit to https://codalab.lisn.upsaclay.fr/competitions/7403
+
+# 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 pathlib import Path
+
+  from ultralytics.utils.downloads import download
+
+  # Download labels
+  dir = Path(yaml["path"])  # dataset root dir
+  url = "https://github.com/ultralytics/assets/releases/download/v0.0.0/"
+  urls = [f"{url}coco2017labels-pose.zip"]
+  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)