tadtr

TadTR

End-to-end Temporal Action Detection with Transformer
Xiaolong Liu, Qimeng Wang, Yao Hu, Xu Tang, Shiwei Zhang, Song Bai, Xiang Bai

An Empirical Study of End-to-End Temporal Action Detection
Xiaolong Liu, Song Bai, Xiang Bai

Results and Models

THUMOS-14

Feature	mAP@0.3	mAP@0.4	mAP@0.5	mAP@0.6	mAP@0.7	ave. mAP	Config	Download
I3D	71.90	67.29	59.00	48.34	34.61	56.23	config	model | log

Backbone	mAP@0.3	mAP@0.4	mAP@0.5	mAP@0.6	mAP@0.7	ave. mAP	Config	Download
E2E-SlowFasR50	64.47	59.49	53.11	44.08	33.50	50.93	config	model | log

Train

You can use the following command to train a model.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py ${CONFIG_FILE} [optional arguments]

Example: train TadTR on THUMOS dataset.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/train.py configs/tadtr/thumos_i3d.py

For more details, you can refer to the Training part in the Usage.

Test

You can use the following command to test a model.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py ${CONFIG_FILE} --checkpoint ${CHECKPOINT_FILE} [optional arguments]

Example: test TadTR on THUMOS dataset.

torchrun --nnodes=1 --nproc_per_node=1 --rdzv_backend=c10d --rdzv_endpoint=localhost:0 tools/test.py configs/tadtr/thumos_i3d.py --checkpoint exps/thumos/tridet_i3d/gpu1_id0/checkpoint/epoch_14.pth

For more details, you can refer to the Test part in the Usage.

Citation

@article{liu2022end,
  title={End-to-end temporal action detection with transformer},
  author={Liu, Xiaolong and Wang, Qimeng and Hu, Yao and Tang, Xu and Zhang, Shiwei and Bai, Song and Bai, Xiang},
  journal={IEEE Transactions on Image Processing},
  volume={31},
  pages={5427--5441},
  year={2022},
  publisher={IEEE}
}

@inproceedings{liu2022empirical,
  title={An empirical study of end-to-end temporal action detection},
  author={Liu, Xiaolong and Bai, Song and Bai, Xiang},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={20010--20019},
  year={2022}
}