# SeAFusion

**Repository Path**: tju_hfut_sym/SeAFusion

## Basic Information

- **Project Name**: SeAFusion
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: MIT
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2022-02-09
- **Last Updated**: 2022-03-28

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README



#  SeAFusion

The code of "Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network"

The paper is now freely available for download through the following links: [https://authors.elsevier.com/c/1eMto5a7-Gls0k](https://authors.elsevier.com/c/1eMto5a7-Gls0k).

## To Train

Run ```**CUDA_VISIBLE_DEVICES=0 python train.py**``` to train your model.
The training data are selected from the MFNet dataset. For convenient training, users can download the training dataset from [here](https://pan.baidu.com/s/1xueuKYvYp7uPObzvywdgyA), in which the extraction code is: **bvfl**.

The MFNet dataset can be downloaded via the following link: [https://drive.google.com/drive/folders/18BQFWRfhXzSuMloUmtiBRFrr6NSrf8Fw](https://drive.google.com/drive/folders/18BQFWRfhXzSuMloUmtiBRFrr6NSrf8Fw).

The MFNet project address is: [https://www.mi.t.u-tokyo.ac.jp/static/projects/mil_multispectral/](https://www.mi.t.u-tokyo.ac.jp/static/projects/mil_multispectral/).
## To Test

Run ```**CUDA_VISIBLE_DEVICES=0 python test.py**``` to test the model.

## For quantitative evaluation
For quantitative assessments, please follow the instruction to modify and run **. /Evaluation/test_evaluation.m** .

## Recommended Environment

 - [ ] torch  1.7.1
 - [ ] torchvision 0.8.2
 - [ ] numpy 1.19.2
 - [ ] pillow  8.0.1

## If this work is helpful to you, please cite it as：
```
@article{TANG202228SeAFusion,
title = {Image fusion in the loop of high-level vision tasks: A semantic-aware real-time infrared and visible image fusion network},
journal = {Information Fusion},
volume = {82},
pages = {28-42},
year = {2022},
issn = {1566-2535}
}
```