This repo contains the source code for our CVPR'19 work "Patch-based discriminative feature learning for unsupervised person re-identification"
Our implementation is based on Pytorch.
- Pytorch 0.4.1
- Python 3.6
- Data preparation
mkdir data
ln -s [PATH TO MSMT17_V1] ./data/MSMT17_V1
ln -s [PATH TO DUKE] ./data/DukeMTMC-reID
ln -s [PATH TO Market] ./data/Market- set the path of ImageNet pretrained models
ln -s [THE PATH OF IMAGENET PRE-TRAINED MODELS] imagenet_models- For pretraining the model
cd ./src/train
python supervised_train.py --gpu [CHOOSE WHICH GPU TO RUN] --exp-name [YOUR EXP NAME]- Optionally, you can download our pretrained model from google drive or baidu cloud (kvkz)
and place it in
./snapshot/MSMT17_PRE/
mkdir ./snapshot
mkdir ./snapshot/MSMT17_PRE
cp [PATH TO PRETRAINED MODEL] ./snapshot/MSMT17_PRE/
# it means the name of the experiment of pretraining is 'MSMT17_PRE' - For unsupervised training
cd ./src/unsupervised
# for market
python unsupervised_train.py --data MARKET --gpu [CHOOSE WHICH GPU TO RUN] \
--pre-name [THE EXP NAME OF PRE-TRIANED MODEL] --exp-name [YOUR EXP NAME] \
--batch-size 42 --scale 15 --lr 0.0001
# for duke
python unsupervised_train.py --data DUKE --gpu [CHOOSE WHICH GPU TO RUN] \
--pre-name [THE EXP NAME OF PRE-TRIANED MODEL] --exp-name [YOUR EXP NAME] \
--batch-size 40 --scale 5 --lr 0.0001
If you find our work helpful in your research, please kindly cite our paper:
Qize Yang, Hong-Xing Yu, Ancong Wu, Wei-Shi Zheng, "Patch-based discriminative feature learning for unsupervised person re-identification", In CVPR, 2019.
bib:
@inproceedings{yu2019unsupervised,
title={Patch-based discriminative feature learning for unsupervised person re-identification},
author={Yang, Qize and Yu, Hong-Xing and Wu, Ancong and Zheng, Wei-Shi},
year={2019},
booktitle={IEEE International Conference on Computer Vision and Pattern Recognition (CVPR)},
}
If you have any problem/question, please feel free to contact me at chitse.yang@gmail.com or open an issue. Thank you.