GMNet

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GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation

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# GMNet
Code and result about GMNet(IEEE TIP) 
'GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation' [IEEE TIP](https://ieeexplore.ieee.org/document/9531449)
![image](https://user-images.githubusercontent.com/65142166/134632191-8cfbc175-27e8-43de-b72b-bd5bf8f7c9b9.png)

# Requirements
Python 3.6.5, Pytorch 1.8.1+, Cuda 10.2, TensorboardX 2.0, opencv-python

# Dataset and Evaluate tools

The MFNet datesets for RGB-T semantic segmentation could be found in [百度网盘](https://pan.baidu.com/s/1dpPoEnEz4lj1jm6aD7803A) 提取码：fgyx 
 (We also provide the label.)

# Result
Predict maps: [百度网盘](https://pan.baidu.com/s/1LPZ845jeJQ7ANeKp-6kFrA) 提取码：v1fy 
Pretrained model download:[百度网盘](https://pan.baidu.com/s/1RWXgDRAdH70Bx3yRULvgxg) 提取码：6hip

# Citation
@ARTICLE{9531449, 
 author={Zhou, Wujie and Liu, Jinfu and Lei, Jingsheng and Yu, Lu and Hwang, Jenq-Neng}, 
 journal={IEEE Transactions on Image Processing}, 
 title={GMNet: Graded-Feature Multilabel-Learning Network for RGB-Thermal Urban Scene Semantic Segmentation}, 
 year={2021}, 
 volume={30}, 
 number={}, 
 pages={7790-7802}, 
 doi={10.1109/TIP.2021.3109518}}

# Acknowledgement
The implement of this project is based on the code of ‘RTFNet: RGB-Thermal Fusion Network for Semantic Segmentation of Urban Scenes (IEEE RAL)’ proposed by Yuxiang Sun et all.

# Contact
Please drop me an email for further problems or discussion: tjuliujinfu@outlook.com or  wujiezhou@163.com (https://wujiezhou.github.io/)

GMNet

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