healthonrails/mouse_keypoints

YOLO Pose Dataset (ADPT-derived)

This dataset contains images and YOLO-format pose annotations for training Ultralytics YOLO pose models. The original images/labels are derived from the ADPT dataset (see source below). This release provides a YOLO-ready layout plus a simple data.yaml for immediate training and evaluation.

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Source / Attribution

Original dataset: ADPT (Tang Guoling et al.) Upstream repository: https://github.com/tangguoling/ADPT

This Hugging Face dataset is a repackaged and converted version intended for convenient use with Ultralytics YOLO. Please refer to the ADPT repository for upstream details, original licensing/terms, and citations.

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Dataset Structure

dataset/
├── images/
│   ├── train/
│   ├── val/
│   └── test/        # optional
├── labels/
│   ├── train/
│   ├── val/
│   └── test/        # optional
├── data.yaml
└── README.md

• images/** contains the image files (.jpg / .png)
• labels/** contains YOLO pose label files (.txt), one per image

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Annotation Format (YOLO Pose)

Each label line corresponds to one instance:

class x_center y_center width height x1 y1 v1 x2 y2 v2 ... xN yN vN

All coordinates are normalized to [0,1].

Visibility flag v:

• 0: not labeled
• 1: labeled but not visible
• 2: labeled and visible

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data.yaml (Example)

Update class names and keypoints to match your dataset:

path: .
train: images/train
val: images/val

nc: 1
names: ["object"]

kpt_shape: [K, 3]      # K = number of keypoints
flip_idx: []           # optional, set if left/right keypoints exist

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Train with Ultralytics YOLO

yolo pose train model=yolo11n-pose.pt data=data.yaml imgsz=640 epochs=300

Inference:

yolo pose predict model=best.pt source=images/val

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Notes

• This repo focuses on YOLO compatibility (folder layout + label format).
• If you need the original ADPT annotation format or metadata, use the upstream repo.

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License

The license of the original data is defined by ADPT. Please follow the upstream dataset’s license and attribution requirements:

https://github.com/tangguoling/ADPT

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Citation

If you use this dataset, please cite the original ADPT work (source dataset) and Annolid (tooling / conversion / workflow), as appropriate.

ADPT (Upstream Dataset / Method) @article{tang2025adpt, title = {Anti-drift pose tracker (ADPT), A transformer-based network for robust animal pose estimation cross-species}, author = {Tang, Guoling and Han, Yaning and Sun, Xing and Zhang, Ruonan and Han, Ming-Hu and Liu, Quanying and Wei, Pengfei}, journal = {eLife}, year = {2025}, month = {03}, doi = {10.7554/eLife.95709} } Annolid (Tooling / Conversion / Pipeline) @misc{yang2024annolid, title = {Annolid: Annotate, Segment, and Track Anything You Need}, author = {Chen Yang and Thomas A. Cleland}, year = {2024}, eprint = {2403.18690}, archivePrefix = {arXiv}, primaryClass = {cs.CV} }

@article{yang2023automated, title = {Automated Behavioral Analysis Using Instance Segmentation}, author = {Yang, Chen and Forest, Jeremy and Einhorn, Matthew and Cleland, Thomas A}, journal = {arXiv preprint arXiv:2312.07723}, year = {2023} }

@misc{yang2020annolid, author = {Chen Yang and Jeremy Forest and Matthew Einhorn and Thomas Cleland}, title = {Annolid: an instance segmentation-based multiple animal tracking and behavior analysis package}, howpublished = {\url{https://github.com/healthonrails/annolid}}, year = {2020} }