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
- [Jul 2022] Our paper "GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality" has been accepted by ECCV 2022.
- [Jun 2022] Paper "A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges" is available now.
- [Jun 2022] Paper "Learning Gait Representation from Massive Unlabelled Walking Sequences: A Benchmark" is available now. And the code will be released as soon as possible.
- [Mar 2022] More results on GREW are supported, and the model files are coming soon.
- [Mar 2022] Dataset GREW is supported in datasets/GREW.
- [Mar 2022] HID support is ready in datasets/HID.
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
tensorboardandloggingto 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)
- Chao Fan (樊超), [email protected]
- Chuanfu Shen (沈川福), [email protected]
- Junhao Liang (梁峻豪), [email protected]
Acknowledgement
- GLN: Saihui Hou (侯赛辉)
- GaitGL: Beibei Lin (林贝贝)
- GREW: GREW TEAM
Note: This code is only used for academic purposes, people cannot use this code for anything that might be considered commercial use.