Chao Fan
64 lines
4.2 KiB
Markdown
64 lines
4.2 KiB
Markdown
<img src="./assets/logo2.png" width = "320" height = "110" alt="logo" />
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<div align="center"><img src="./assets/nm.gif" width = "100" height = "100" alt="nm" /><img src="./assets/bg.gif" width = "100" height = "100" alt="bg" /><img src="./assets/cl.gif" width = "100" height = "100" alt="cl" /></div>
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------------------------------------------
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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).
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## What's New
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- **[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).
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- [Dec 2022] Dataset [Gait3D](https://github.com/Gait3D/Gait3D-Benchmark) is supported in [datasets/Gait3D](./datasets/Gait3D).
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- [Nov 2022] Paper "[OpenGait: Revisiting Gait Recognition Toward Better Practicality](https://arxiv.org/pdf/2211.06597.pdf)" is available now. And the code will be released as soon as possible.
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- [Jul 2022] Our paper "[GaitEdge: Beyond Plain End-to-end Gait Recognition for Better Practicality](configs/gaitedge/README.md)" has been accepted by ECCV 2022.
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- [Jun 2022] Paper "[A Comprehensive Survey on Deep Gait Recognition: Algorithms, Datasets and Challenges](https://arxiv.org/pdf/2206.13732.pdf)" is available now.
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- [Jun 2022] Paper "[Learning Gait Representation from Massive Unlabelled Walking Sequences: A Benchmark](https://arxiv.org/pdf/2206.13964.pdf)" is available now. And the code will be released as soon as possible.
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- [Mar 2022] Dataset [GREW](https://www.grew-benchmark.org) is supported in [datasets/GREW](./datasets/GREW).
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## Highlighted features
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- **Mutiple Dataset supported**: OpenGait supports four popular gait datasets: [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/), and [GREW](https://www.grew-benchmark.org).
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- **Multiple Models Support**: We reproduced several SOTA methods, and reached the same or even the better performance.
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- **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.
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- **AMP Support**: The [`Auto Mixed Precision (AMP)`](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html?highlight=amp) option is available.
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- **Nice log**: We use [`tensorboard`](https://pytorch.org/docs/stable/tensorboard.html) and `logging` to log everything, which looks pretty.
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## Getting Started
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Please see [0.get_started.md](docs/0.get_started.md). We also provide the following tutorials for your reference:
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- [Prepare dataset](docs/2.prepare_dataset.md)
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- [Detailed configuration](docs/3.detailed_config.md)
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- [Customize model](docs/4.how_to_create_your_model.md)
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- [Advanced usages](docs/5.advanced_usages.md)
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## Model Zoo
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Results and models are available in the [model zoo](docs/1.model_zoo.md).
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## Authors:
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**Open Gait Team (OGT)**
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- [Chao Fan (樊超)](https://chaofan996.github.io), 12131100@mail.sustech.edu.cn
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- [Chuanfu Shen (沈川福)](https://faculty.sustech.edu.cn/?p=95396&tagid=yusq&cat=2&iscss=1&snapid=1&orderby=date), 11950016@mail.sustech.edu.cn
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- [Junhao Liang (梁峻豪)](https://faculty.sustech.edu.cn/?p=95401&tagid=yusq&cat=2&iscss=1&snapid=1&orderby=date), 12132342@mail.sustech.edu.cn
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## Acknowledgement
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- GLN: [Saihui Hou (侯赛辉)](http://home.ustc.edu.cn/~saihui/index_english.html)
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- GaitGL: [Beibei Lin (林贝贝)](https://scholar.google.com/citations?user=KyvHam4AAAAJ&hl=en&oi=ao)
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- GREW: [GREW TEAM](https://www.grew-benchmark.org)
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## Citation
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```
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@misc{fan2022opengait,
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title={OpenGait: Revisiting Gait Recognition Toward Better Practicality},
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author={Chao Fan and Junhao Liang and Chuanfu Shen and Saihui Hou and Yongzhen Huang and Shiqi Yu},
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year={2022},
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eprint={2211.06597},
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archivePrefix={arXiv},
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primaryClass={cs.CV}
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}
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```
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**Note:**
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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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