PyTorch implementation of RRID

This is the implementation of the paper "Relation Network for Person Re-identification (AAAI 2020)".

For more information, checkout the project site [website] and the paper [PDF].

Dependencies

Python 3.6
PyTorch >= 0.4.1
numpy
h5py

Datasets

Download the datasets into datasets folder, like ./datasets/market1501/

The market1501 dataset is only available now, and other datasets will be uploaded later

Market1501 [market1501]
DukeMTMC-ReID [dukemtmc]
CUHK03 labeled [cuhk03_labeled]
CUHK03 detected [cuhk03_detected]

Training

git clone https://github.com/cvlab-yonsei/projects
cd projects/RRID/code
python Train.py --gpus 0 1 # for training

You can freely define parameters with your own settings like

python Train.py --gpus 0 1 --dataset_path 'your_dataset_directory' --dataset_type market1501 --exp_dir 'your_log_directory'

Pre-trained model

Download pre-trained weights
Link: [weights]
Two GPUs are needed to implement the trained model
The version of pytorch must be 0.4.1 when you implement the model with these weights

Evaluation

Test the model with our pre-trained weights

python Evaluate.py --gpus 0 1 --pretrained_weights_dir pretrained_weights.pth

Test your own model

python Evaluate.py --exp dir log

Bibtex

@inproceedings{park2019relation,
  title={Relation Network for Person Re-identification},
  author={Park, Hyunjong and Ham, Bumsub},
  booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
  year={2020}
}

minygd / RRID