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fastreid-1.4.0
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مشاهده بیشترتوضیحات
SOTA Re-identification Methods and Toolbox
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | fastreid-1.4.0 |
| نام | fastreid |
| نسخه کتابخانه | 1.4.0 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | He, Lingxiao and Liao, Xingyu and Liu, Wu and Liu, Xinchen and Cheng, Peng and Mei, Tao |
| ایمیل نویسنده | - |
| آدرس صفحه اصلی | https://github.com/JDAI-CV/fast-reid |
| آدرس اینترنتی | https://pypi.org/project/fastreid/ |
| مجوز | Apache 2.0 |
<img src=".github/FastReID-Logo.png" width="300" >
[](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:
<img src=".github/wechat_group.png" width="150" >
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}
}
```
نیازمندی
| مقدار | نام |
|---|---|
| >=1.7.3,<2.0.0 | faiss-cpu |
| >=4.7.0.68,<5.0.0.0 | opencv-python-headless |
| >=1.2.1,<2.0.0 | scikit-learn |
| >=0.9.0,<0.10.0 | tabulate |
| >=2.12.0,<3.0.0 | tensorboard |
| >=2.2.0,<3.0.0 | termcolor |
| ==1.13.1 | torch |
| ==0.14.1 | torchvision |
| >=0.1.8,<0.2.0 | yacs |
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8,<4.0 | Python |
نحوه نصب
نصب پکیج whl fastreid-1.4.0:
pip install fastreid-1.4.0.whl
نصب پکیج tar.gz fastreid-1.4.0:
pip install fastreid-1.4.0.tar.gz