mirror of https://github.com/JDAI-CV/fast-reid.git
70 lines
3.7 KiB
Markdown
70 lines
3.7 KiB
Markdown
<img src=".github/FastReID-Logo.png" width="300" >
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[](https://gitter.im/fast-reid/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge)
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Gitter: [fast-reid/community](https://gitter.im/fast-reid/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link)
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FastReID is a research platform that implements state-of-the-art re-identification algorithms. It is a groud-up rewrite of the previous version, [reid strong baseline](https://github.com/michuanhaohao/reid-strong-baseline).
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## What's New
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- [Apr 2021] Partial FC supported in [FastFace](projects/FastFace)!
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- [Jan 2021] TRT network definition APIs in [FastRT](projects/FastRT) has been released!
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Thanks for [Darren](https://github.com/TCHeish)'s contribution.
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- [Jan 2021] NAIC20(reid track) [1-st solution](projects/NAIC20) based on fastreid has been released！
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- [Jan 2021] FastReID V1.0 has been released！🎉
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Support many tasks beyond reid, such image retrieval and face recognition. See [release notes](https://github.com/JDAI-CV/fast-reid/releases/tag/v1.0.0).
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- [Oct 2020] Added the [Hyper-Parameter Optimization](projects/FastTune) based on fastreid. See `projects/FastTune`.
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- [Sep 2020] Added the [person attribute recognition](projects/FastAttr) based on fastreid. See `projects/FastAttr`.
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- [Sep 2020] Automatic Mixed Precision training is supported with `apex`. Set `cfg.SOLVER.FP16_ENABLED=True` to switch it on.
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- [Aug 2020] [Model Distillation](projects/FastDistill) is supported, thanks for [guan'an wang](https://github.com/wangguanan)'s contribution.
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- [Aug 2020] ONNX/TensorRT converter is supported.
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- [Jul 2020] Distributed training with multiple GPUs, it trains much faster.
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- Includes more features such as circle loss, abundant visualization methods and evaluation metrics, SoTA results on conventional, cross-domain, partial and vehicle re-id, testing on multi-datasets simultaneously, etc.
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- Can be used as a library to support [different projects](projects) on top of it. We'll open source more research projects in this way.
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- Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/).
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We write a [fastreid intro](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2020/05/29/fastreid.html)
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and [fastreid v1.0](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2021/04/28/fastreid-v1.html) about this toolbox.
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## Changelog
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Please refer to [changelog.md](CHANGELOG.md) for details and release history.
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## Installation
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See [INSTALL.md](INSTALL.md).
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## Quick Start
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The designed architecture follows this guide [PyTorch-Project-Template](https://github.com/L1aoXingyu/PyTorch-Project-Template), you can check each folder's purpose by yourself.
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See [GETTING_STARTED.md](GETTING_STARTED.md).
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Learn more at out [documentation](https://fast-reid.readthedocs.io/). And see [projects/](projects) for some projects that are build on top of fastreid.
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## Model Zoo and Baselines
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We provide a large set of baseline results and trained models available for download in the [Fastreid Model Zoo](MODEL_ZOO.md).
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## Deployment
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We provide some examples and scripts to convert fastreid model to Caffe, ONNX and TensorRT format in [Fastreid deploy](tools/deploy).
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## License
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Fastreid is released under the [Apache 2.0 license](LICENSE).
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## Citing FastReID
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If you use FastReID in your research or wish to refer to the baseline results published in the Model Zoo, please use the following BibTeX entry.
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```BibTeX
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@article{he2020fastreid,
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title={FastReID: A Pytorch Toolbox for General Instance Re-identification},
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author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao},
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journal={arXiv preprint arXiv:2006.02631},
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year={2020}
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}
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```
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