hrishikesh1213/group_reid

Learning Multi-Attention Context Graph for Group-Based Re-Identification

Introduction

This repo contains the codebase of our TPAMI 2020 paper: Learning Multi-Attention Context Graph for Group-Based Re-Identification.

Installation

We use python 3.7 and pytorch=1.0.1 torchvision=0.2.1

Data preparation

All experiments are done on CSG. Please download CUHK-SYSU.

Original dataset webpage: CUHK-SYSU Person Search Dataset or JDE dataset zoo

The group annotation files are in ./data

Usage

To train the model, please run

python main_group_gcn_siamese_part_half_fulltest_sink.py --data-root [data path] --max-epoch 300 --stepsize 100 --eval-step 300 --gpu-devices 0

An example can be found in run_train_test.sh

To evaluate the trained model, run

python main_group_gcn_siamese_part_half_fulltest_sink.py --data-root [data path] --evaluate True --gpu-devices 0 --pretrained-model [model path]

Performance

Task	Rank1	mAP
Group re-id	65.5%	67.0%
Person re-id	65.1%	64.2%

Link of the trained model: [Google]

Acknowledgements

Our code is developed based on Video-Person-ReID (https://github.com/jiyanggao/Video-Person-ReID).

Citation

If you find this repo useful in your project or research, please consider citing it:

@ARTICLE{9233968,
  author={Y. {Yan} and J. {Qin} and B. {Ni} and J. {Chen} and L. {Liu} and F. {Zhu} and W. -S. {Zheng} and X. {Yang} and L. {Shao}},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Learning Multi-Attention Context Graph for Group-Based Re-Identification}, 
  year={2020},
  doi={10.1109/TPAMI.2020.3032542}}
}