📋 Implicit Multi-Spectral Transformer: An Lightweight and Effective Visible to Infrared Image Translation Model
Guangdong University of Technology, University of Macau, Huizhou University
In International Joint Conference on Neural Networks 2024 (IJCNN 2024)
git clone https://github.com/CXH-Research/IRFormer.git
cd IRFormer
pip install -r requirements.txt
Please first specify TRAIN_DIR, VAL_DIR and SAVE_DIR in section TRAINING in config.yml
For single GPU training:
python train.py
For multiple GPUs training:
accelerate config
accelerate launch train.py
If you have difficulties on the usage of accelerate, please refer to Accelerate.
Please first specify TRAIN_DIR, VAL_DIR and SAVE_DIR in section TESTING in traning.yml
python test.py
This work was supported in part by the Guangdong Provincial Key R&D Programme under Grant No.2023B1111050010 and No.2020B0101100001, in part by the Huizhou Daya Bay Science and Technology Planning Project under Grant No.2020020003.
If you find our work helpful for your research, please cite:
@inproceedings{DBLP:conf/ijcnn/ChenCZLZL24,
author = {Yijia Chen and
Pinghua Chen and
Xiangxin Zhou and
Yingtie Lei and
Ziyang Zhou and
Mingxian Li},
title = {Implicit Multi-Spectral Transformer: An Lightweight and Effective
Visible to Infrared Image Translation Model},
booktitle = {International Joint Conference on Neural Networks},
pages = {1--8},
year = {2024}
}