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README.md

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+# Simple CodeBase for Skeleton Action Detection Evaluation
+This is an repo for skeleton-based action detection, used for the evalution of the self-supervised models, e.g., [HiCLR](https://github.com/JHang2020/HiCLR), [PCM3](https://github.com/JHang2020/PCM3). 
+
+## Dataset
+You need to download the PKUMMD untrimmed dataset [here](https://www.icst.pku.edu.cn/struct/Projects/PKUMMD.html).
+
+## Test your model:
+You need to choose your model arch and the params, change the config.yaml and run:
+```
+python main.py detection_ds_linear_evaluation --config /mnt/netdisk/zhangjh/Code/skeleton_detection/detection/config/release/gcn_ntu60/linear_eval/detection.yaml
+```
+
+## Citation
+If you find this repository useful, please consider citing our paper:
+```
+@article{zhang2022s,
+    title={Hierarchical Consistent Contrastive Learning for Skeleton-Based Action Recognition with Growing Augmentations},
+    author={Zhang, Jiahang and Lin, Lilang and Liu, Jiaying},
+    journal={arXiv preprint arXiv:2211.13466},
+    year={2022},
+}
+```
+
+```
+@inproceedings{zhang2023prompted,
+  title={Prompted Contrast with Masked Motion Modeling: Towards Versatile 3D Action Representation Learning},
+  author={Zhang, Jiahang and Lin, Lilang and Liu, Jiaying},
+  booktitle={Proceedings of the 31st ACM International Conference on Multimedia},
+  pages={7175--7183},
+  year={2023}
+}
+```
+
+
+## Acknowledgement
+We sincerely thank the authors for releasing the code of their valuable works. Our code is built based on the PKUMMD.
+## Licence
+This project is licensed under the terms of the MIT license.