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Gitter: fast-reid/community
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.
configs/Market1501/bagtricks_vit.yml
.projects/FastTune
.projects/FastAttr
.apex
. Set cfg.SOLVER.FP16_ENABLED=True
to switch it on.We write a fastreid intro
and fastreid v1.0 about this toolbox.
Please refer to changelog.md for details and release history.
See INSTALL.md.
The designed architecture follows this guide PyTorch-Project-Template, you can check each folder's purpose by yourself.
See GETTING_STARTED.md.
Learn more at out documentation. And see projects/ for some projects that are build on top of fastreid.
We provide a large set of baseline results and trained models available for download in the Fastreid Model Zoo.
We provide some examples and scripts to convert fastreid model to Caffe, ONNX and TensorRT format in Fastreid deploy.
Fastreid is released under the Apache 2.0 license.
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.
@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}
}
该算法把样本聚类和特征学习融合到一个端到端的网络框架中,提升模型的跨域能力。该模型在Market训练,DukeMTMC上测试能达到82.0%的准确率,在DukeMTMC上训练,Market上测试能达到92.2%的准确率。
Text Python C++ Protocol Buffer Markdown other
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