HSTL

This is the code for the paper Hierarchical Spatio-Temporal Representation Learning for Gait Recognition.

Operating Environments

Hardware Environment

Our code is running on a server with 8 GeForce RTX 3090 GPUs and a CPU model Intel(R) Core(TM) i7-9800X @ 3.80GHz.

Software Environment

• pytorch = 1.10
• torchvision
• pyyaml
• tensorboard
• opencv-python
• tqdm

Checkpoints

• The checkpoints for CASIA-B link
• The checkpoints for OUMVLP link

Train and test

Train

Train a model by

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/htsl/hstl.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.

You can run commands in train.sh for training different models.

Test

Evaluate the trained model by

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 opengait/main.py --cfgs ./configs/htsl/hstl.yaml --phase test

• --phase Specified as test.
• --iter Specify a iteration checkpoint.

Tip: Other arguments are the same as train phase.

You can run commands in test.sh for testing different models.

Acknowledgement

• The codebase is based on OpenGait.

Citation

@InProceedings{Wang_2023_ICCV,
    author    = {Wang, Lei and Liu, Bo and Liang, Fangfang and Wang, Bincheng},
    title     = {Hierarchical Spatio-Temporal Representation Learning for Gait Recognition},
    booktitle = {Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)},
    month     = {October},
    year      = {2023},
    pages     = {19639-19649}
}