Introduction

This is code for contest NAIC ReID 2020.

After completing the installation, you can write your own config in configs folder and run

python ./tools/train_net.py --gpu-id 0 \
--config-file configs/PATH/TO/YOUR/CONFIG.yml

to train your model and run

python ./tools/submit.py --config-file configs/PATH/TO/YOUR/CONFIG.yml \
  --gpu-id 1 --test-permutation

to get the results.

The rest is left for you to explore.

Installation

See INSTALL.md.

Quick Start

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.

Model Zoo and Baselines

We provide a large set of baseline results and trained models available for download in the Fastreid Model Zoo.

Deployment

We provide some examples and scripts to convert fastreid model to Caffe, ONNX and TensorRT format in Fastreid deploy.

License

Fastreid is released under the Apache 2.0 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.

@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}
}