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


OpenGait is a flexible and extensible gait recognition project provided by the Shiqi Yu Group and supported in part by WATRIX.AI. Just the pre-beta version is released now, and more documentations as well as the reproduced methods will be offered as soon as possible.

Highlighted features:

  • Multiple Models Support: We reproduced several SOTA methods, and reached the same or even better performance.
  • DDP Support: The officially recommended Distributed Data Parallel (DDP) mode is used during the training and testing phases.
  • AMP Support: The Auto Mixed Precision (AMP) option is available.
  • Nice log: We use tensorboard and logging to log everything, which looks pretty.

Model Zoo

Model NM BG CL Configuration Input Size Inference Time Model Size
Baseline 96.3 92.2 77.6 baseline.yaml 64×44 12s 3.78M
GaitSet(AAAI2019) 95.8(95.0) 90.0(87.2) 75.4(70.4) gaitset.yaml 64×44 11s 2.59M
GaitPart(CVPR2020) 96.1(96.2) 90.7(91.5) 78.7(78.7) gaitpart.yaml 64×44 22s 1.20M
GLN*(ECCV2020) 96.1(95.6) 92.5(92.0) 80.4(77.2) gln_phase1.yaml, gln_phase2.yaml 128×88 14s 9.46M / 15.6214M
GaitGL(ICCV2021) 97.5(97.4) 95.1(94.5) 83.5(83.6) gaitgl.yaml 64×44 31s 3.10M

The results in the parentheses are mentioned in the papers


  • All the models were tested on CASIA-B (Rank@1, excluding identical-view cases).
  • The shown result of GLN is implemented without compact block.
  • Only 2 RTX6000 are used during the inference phase.
  • The results on OUMVLP will be released soon.
    It’s inference process just cost about 90 secs(Baseline & 8 RTX6000).

Get Started


  1. clone this repo.

    git clone
  2. Install dependenices:

    • pytorch >= 1.6
    • torchvision
    • pyyaml
    • tensorboard
    • opencv-python
    • tqdm

    Install dependenices by Anaconda:

    conda install tqdm pyyaml tensorboard opencv
    conda install pytorch==1.6.0 torchvision -c pytorch

    Or, Install dependenices by pip:

    pip install tqdm pyyaml tensorboard opencv-python
    pip install torch==1.6.0 torchvision==0.7.0

Prepare dataset

See prepare dataset.


Train a model by

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/ --cfgs ./config/baseline.yaml --phase train
  • python -m torch.distributed.launch Our implementation uses DistributedDataParallel.
  • --nproc_per_node The number of gpu to use, it must equal the length of CUDA_VISIBLE_DEVICES.
  • --cfgs The path of config file.
  • --phase Specified as train.
  • --iter You can specify a number of iterations or use restore_hint in the configuration file and resume training from there.
  • --log_to_file If specified, log will be written on disk simultaneously.

You can run commands in for training different models.


Use trained model to evaluate by

CUDA_VISIBLE_DEVICES=0,1 python -m torch.distributed.launch --nproc_per_node=2 lib/ --cfgs ./config/baseline.yaml --phase test
  • --phase Specified as test.
  • --iter You can specify a number of iterations or or use restore_hint in the configuration file and restore model from there.

Tip: Other arguments are the same as train phase.

You can run commands in for testing different models.


If you want customize your own model, see here.


  • Some models may not be compatible with AMP, you can disable it by setting enable_float16 False.
  • In DDP mode, zombie processes may occur when the program terminates abnormally. You can use this command kill $(ps aux | grep | grep -v grep | awk '{print $2}') to clear them.
  • We implemented the functionality of testing while training, but it slightly affected the results. None of our published models use this functionality. You can disable it by setting with_test False.


Open Gait Team (OGT)



GitHub - ShiqiYu/OpenGait at
A flexible and extensible framework for gait recognition. You can focus on designing your own models and comparing with state-of-the-arts easily with the help of OpenGait. - GitHub - ShiqiYu/OpenGa...