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OpenGait is a flexible and extensible gait recognition project provided by the [Shiqi Yu Group](https://faculty.sustech.edu.cn/yusq/) and supported in part by [WATRIX.AI](http://www.watrix.ai). OpenGait is a flexible and extensible gait recognition project provided by the [Shiqi Yu Group](https://faculty.sustech.edu.cn/yusq/) and supported in part by [WATRIX.AI](http://www.watrix.ai).
## What's New ## What's New
- **[July 2023]** [SUSTech1K](datasets/SUSTech1K/README.md) is released and supported by OpenGait. - **[July 2023]** [SUSTech1K](https://lidargait.github.io) is released and supported in [datasets/SUSTech1K](./datasets/SUSTech1K).
- **[May 2023]** A real gait recognition system [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) provided by [Dongyang Jin](https://github.com/jdyjjj) is avaliable. - **[May 2023]** A real gait recognition system [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) provided by [Dongyang Jin](https://github.com/jdyjjj) is avaliable.
- [Apr 2023] [CASIA-E](datasets/CASIA-E/README.md) is supported by OpenGait. - [Apr 2023] [CASIA-E](datasets/CASIA-E/README.md) is supported by OpenGait.
- [Feb 2023] [HID 2023 competition](https://hid2023.iapr-tc4.org/) is open, welcome to participate. Additionally, tutorial for the competition has been updated in [datasets/HID/](./datasets/HID). - [Feb 2023] [HID 2023 competition](https://hid2023.iapr-tc4.org/) is open, welcome to participate. Additionally, tutorial for the competition has been updated in [datasets/HID/](./datasets/HID).
@@ -18,7 +18,7 @@ OpenGait is a flexible and extensible gait recognition project provided by the [
- [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW). - [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW).
## Our Publications ## Our Publications
- [**CVPR 2023**] LidarGait: Benchmarking 3D Gait Recognition with Point Clouds, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.pdf), [*Dataset and Code(Coming Soon)*](https://lidargait.github.io). - [**CVPR 2023**] LidarGait: Benchmarking 3D Gait Recognition with Point Clouds, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Shen_LidarGait_Benchmarking_3D_Gait_Recognition_With_Point_Clouds_CVPR_2023_paper.pdf), [*Dataset*](https://lidargait.github.io) and [*Code*](datasets/SUSTech1K/README.md).
- [**CVPR 2023 Highlight**] OpenGait: Revisiting Gait Recognition Toward Better Practicality, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf), [*Code*](configs/gaitbase). - [**CVPR 2023 Highlight**] OpenGait: Revisiting Gait Recognition Toward Better Practicality, [*Paper*](https://openaccess.thecvf.com/content/CVPR2023/papers/Fan_OpenGait_Revisiting_Gait_Recognition_Towards_Better_Practicality_CVPR_2023_paper.pdf), [*Code*](configs/gaitbase).
- [**ECCV 2022**] GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality, [*Paper*](), [*Code*](configs/gaitedge/README.md). - [**ECCV 2022**] GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality, [*Paper*](), [*Code*](configs/gaitedge/README.md).
@@ -31,7 +31,7 @@ The workflow of [All-in-One-Gait](https://github.com/jdyjjj/All-in-One-Gait) inv
See [here](https://github.com/jdyjjj/All-in-One-Gait) for details. See [here](https://github.com/jdyjjj/All-in-One-Gait) for details.
## Highlighted features ## Highlighted features
- **Mutiple Dataset supported**: [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp), [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html), [HID](http://hid2022.iapr-tc4.org/), [GREW](https://www.grew-benchmark.org), [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark), and [CASIA-E](https://www.scidb.cn/en/detail?dataSetId=57be0e918db743279baf44a38d013a06). - **Mutiple Dataset supported**: [CASIA-B](http://www.cbsr.ia.ac.cn/english/Gait%20Databases.asp), [OUMVLP](http://www.am.sanken.osaka-u.ac.jp/BiometricDB/GaitMVLP.html), [SUSTech1K](https://lidargait.github.io), [HID](http://hid2022.iapr-tc4.org/), [GREW](https://www.grew-benchmark.org), [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark), and [CASIA-E](https://www.scidb.cn/en/detail?dataSetId=57be0e918db743279baf44a38d013a06).
- **Multiple Models Support**: We reproduced several SOTA methods, and reached the same or even the better performance. - **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)`](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) mode is used during both the training and testing phases. - **DDP Support**: The officially recommended [`Distributed Data Parallel (DDP)`](https://pytorch.org/tutorials/intermediate/ddp_tutorial.html) mode is used during both the training and testing phases.
- **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available. - **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available.