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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.

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OpenGait is a flexible and extensible gait recognition project provided by the Shiqi Yu Group and supported in part by WATRIX.AI.

What's New

Highlighted features

  • Mutiple Dataset supported: OpenGait supports four popular gait datasets: CASIA-B, OUMVLP, HID, and GREW.
  • Multiple Models Support: We reproduced several SOTA methods, and reached the same or even the better performance.
  • DDP Support: The officially recommended Distributed Data Parallel (DDP) mode is used during both 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.

Getting Started

Please see 0.get_started.md. We also provide the following tutorials for your reference:

Model Zoo

Results and models are available in the model zoo.

Authors:

Open Gait Team (OGT)

Acknowledgement

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

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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.

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  • Python 97.3%
  • Shell 2.7%