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DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition

Paper

DyGait: Exploiting Dynamic Representations for High-performance Gait Recognition

Ming Wang*, Xianda Guo*, BeiBei Lin, Tian Yang, Zheng Zhu, Lincheng Li, Shunli Zhang, Xin Yu.

Getting Started

1. Training

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./configs/Dygait_GREW.yaml --phase train

python -m torch.distributed.launch DDP launch instruction.
--nproc_per_node The number of gpus to use, and it must equal the length of CUDA_VISIBLE_DEVICES.
--cfgs The path to config file.
--phase Specified as train.

--log_to_file If specified, the terminal log will be written on disk simultaneously.

2. Test

Evaluate the trained model by

CUDA_VISIBLE_DEVICES=0,1,2,3,4,5,6,7 python -m torch.distributed.launch --nproc_per_node=8 lib/main.py --cfgs ./configs/Dygait_GREW.yaml --phase test

Acknowledgement

Citation

If this work is helpful for your research, please consider citing the following BibTeX entries.

@inproceedings{wang2023dygait,
  title={DyGait: Exploiting dynamic representations for high-performance gait recognition},
  author={Wang, Ming and Guo, Xianda and Lin, Beibei and Yang, Tian and Zhu, Zheng and Li, Lincheng and Zhang, Shunli and Yu, Xin},
  booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
  pages={13424--13433},
  year={2023}
}

Note: This code is only used for academic purposes, people cannot use this code for anything that might be considered commercial use.