JSIA-ReID

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[AAAI2020] Cross-Modality Paired-Images Generation for RGB-InfraredPerson Re-Identification

JSIA-ReID 최신버젼 다운로드

최종 버전 다운로드 (.zip)

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# JSIA-ReID

This is the official implementation for JSIA-ReID. Please refer our paper for more details:

**[[Paper](https://www.sciencedirect.com/science/article/abs/pii/S0893608020301702)] [Neural Network 2020] Cross-Modality Paired-Images Generation and Augmentation for RGB-Infrared Person Re-Identification**

**[[Paper](https://github.com/wangguanan/JSIA-ReID/blob/master/materials/paper.pdf), [Poster](https://github.com/wangguanan/JSIA-ReID/blob/master/materials/final_poster.png?raw=true)] [AAAI2020] Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification**

![](https://github.com/wangguanan/JSIA-ReID/blob/master/materials/framework.png?raw=true)

## Bibtex

If you find the code useful, please consider citing our paper:
```
@InProceedings{wang2020cross,
  title={Cross-modality paired-images generation and augmentation for RGB-infrared person re-identification},
  author={Guan'an, Wang and Yang, Yang and Zhang, Tianzhu and Cheng, Jian and Hou, Zengguang and Tiwari, Prayag and Pandey, Hari Mohan and others},
  journal={Neural Networks},
  volume={128},
  pages={294--304},
  year={2020},
  publisher={Elsevier}
}

@InProceedings{wang2020crossmodality,
author = "Guan-An {Wang} and Tianzhu {Zhang} and Yang {Yang} and Jian {Cheng} and Jianlong {Chang} and Xu {Liang} and Zengguang {Hou}",
title = {Cross-Modality Paired-Images Generation for RGB-Infrared Person Re-Identification},
booktitle = {AAAI-20 AAAI Conference on Artificial Intelligence},
year = {2020}
}
```

## Dependencies
* [Anaconda (Python 3.7)](https://www.anaconda.com/download/)
* [PyTorch 1.1.0](http://pytorch.org/)
* GPU Memory >= 20G
* Memory >= 20G

## Dataset Preparation
* SYSU-MM01 Dataset [[link](https://github.com/wuancong/SYSU-MM01)]

## Train
```
# train, please replace sysu-mm01-path with your own path
python main.py --dataset_path sysu-mm01-path --mode train
```

## Test with Pre-trained Model
* pretrained model ([Google Drive](https://drive.google.com/drive/folders/1Q1qw3s04QEzY9G_ueZZ_lqzSvU9yk4zz?usp=sharing)， [Baidu Disk](https://pan.baidu.com/s/1np9jC2sTYo8mVoWNivZT8A)(pwd:656y)), please download all the 4 files into a folder. 
* test with the pre-trained model
```
# test with pretrained model, please replace sysu-mm01-path and pretrained-model-path with your own paths
python main.py --dataset_path sysu-mm01-path --mode test --pretrained_model_path pretrained-model-path --pretrained_model_epoch 649
```

## Experimental Results

* Settings: We trained our model with 2 GTX1080ti GPUs.

* Comparison with SOTA
![](https://raw.githubusercontent.com/wangguanan/JSIA-ReID/master/materials/results.png)

## License

This repo is released under the MIT License.

## Contacts
If you have any question about the project, please feel free to contact with me.

E-mail: guan.wang0706@gmail.com

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