[](https://gitter.im/fast-reid/community?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge) Gitter: [fast-reid/community](https://gitter.im/fast-reid/community?utm_source=share-link&utm_medium=link&utm_campaign=share-link) Wechat: FastReID is a research platform that implements state-of-the-art re-identification algorithms. It is a ground-up rewrite of the previous version, [reid strong baseline](https://github.com/michuanhaohao/reid-strong-baseline). ## What's New - [Sep 2021] [DG-ReID](https://github.com/xiaomingzhid/sskd) is updated, you can check the [paper](https://arxiv.org/pdf/2108.05045.pdf). - [June 2021] [Contiguous parameters](https://github.com/PhilJd/contiguous_pytorch_params) is supported, now it can accelerate ~20%. - [May 2021] Vision Transformer backbone supported, see `configs/Market1501/bagtricks_vit.yml`. - [Apr 2021] Partial FC supported in [FastFace](projects/FastFace)! - [Jan 2021] TRT network definition APIs in [FastRT](projects/FastRT) has been released! Thanks for [Darren](https://github.com/TCHeish)'s contribution. - [Jan 2021] NAIC20(reid track) [1-st solution](projects/NAIC20) based on fastreid has been released！ - [Jan 2021] FastReID V1.0 has been released！🎉 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). - [Oct 2020] Added the [Hyper-Parameter Optimization](projects/FastTune) based on fastreid. See `projects/FastTune`. - [Sep 2020] Added the [person attribute recognition](projects/FastAttr) based on fastreid. See `projects/FastAttr`. - [Sep 2020] Automatic Mixed Precision training is supported with `apex`. Set `cfg.SOLVER.FP16_ENABLED=True` to switch it on. - [Aug 2020] [Model Distillation](projects/FastDistill) is supported, thanks for [guan'an wang](https://github.com/wangguanan)'s contribution. - [Aug 2020] ONNX/TensorRT converter is supported. - [Jul 2020] Distributed training with multiple GPUs, it trains much faster. - 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. - 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. - Remove [ignite](https://github.com/pytorch/ignite)(a high-level library) dependency and powered by [PyTorch](https://pytorch.org/). We write a [fastreid intro](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2020/05/29/fastreid.html) and [fastreid v1.0](https://l1aoxingyu.github.io/blogpages/reid/fastreid/2021/04/28/fastreid-v1.html) about this toolbox. ## Changelog Please refer to [changelog.md](CHANGELOG.md) for details and release history. ## Installation See [INSTALL.md](INSTALL.md). ## Quick Start 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. See [GETTING_STARTED.md](GETTING_STARTED.md). Learn more at out [documentation](https://fast-reid.readthedocs.io/). And see [projects/](projects) for some projects that are build on top of fastreid. ## Model Zoo and Baselines We provide a large set of baseline results and trained models available for download in the [Fastreid Model Zoo](MODEL_ZOO.md). ## Deployment We provide some examples and scripts to convert fastreid model to Caffe, ONNX and TensorRT format in [Fastreid deploy](tools/deploy). ## License Fastreid is released under the [Apache 2.0 license](LICENSE). ## Citing FastReID 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. ```BibTeX @article{he2020fastreid, title={FastReID: A Pytorch Toolbox for General Instance Re-identification}, author={He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao}, journal={arXiv preprint arXiv:2006.02631}, year={2020} } ```