Both backbones share the same data preparation process. Please follow the instructions in VideoMAE-Action-Detection/DATASET.md to prepare the AVA dataset.

python -m torch.distributed.launch --nproc_per_node=8 train_net.py --config-file "config_files/config_file.yaml" --transfer --no-head --use-tfboard --skip-final-test

python -m torch.distributed.launch --nproc_per_node=8 test_net.py --config-file "config_files/config_file.yaml" MODEL.WEIGHT "path/to/model/weight"

This project is built upon AlphaAction and maskrcnn-benchmark. Thanks to the contributors of these great codebases. We also thankfully acknowledge the computing resource support of Tencent Corporation for this project.

If you find this project useful, please feel free to leave a star and cite our paper:

@article{chen2025cycleacr,
  title={Cycleacr: Cycle modeling of actor-context relations for video action detection},
  author={Chen, Lei and Tong, Zhan and Song, Yibing and Wu, Gangshan and Wang, Limin},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
  year={2025},
  publisher={IEEE}
}
@article{chen2023cycleacr,
  title={CycleACR: Cycle Modeling of Actor-Context Relations for Video Action Detection},
  author={Chen, Lei and Tong, Zhan and Song, Yibing and Wu, Gangshan and Wang, Limin},
  journal={arXiv preprint arXiv:2303.16118},
  year={2023}
}