The Code for Paper "SRFFNet：Self-refine, Fusion, Feedback for Salient Object Detection".
by Shuang Wu, Guangjian Zhang
the Paper has been accepted by《Cognitive Computation》.
- Python 3.7
- Pytorch 1.7
- OpenCV 4.0
- Numpy 1.15
- TensorboardX
- Apex
git clone https://github.com/user-wu/SRFFNet.git
cd SRFFNet/
Download the following datasets and unzip them into data folder
Directory Structure
data --------------------------
|-DUTS -image/
| -mask/
| -test.txt
| -train.txt
--------------------------
|-DUT-OMRON -image/
| -mask/
| -test.txt
--------------------------
|-ECSSD -image/
| -mask/
| -test.txt
--------------------------
|-HKU-IS -image/
| -mask/
| -test.txt
--------------------------
|-PASCAL-S -image/
| -mask/
| -test.txt
--------------------------
- If you want to test the performance of SRFFNet, please download the model into out folder
- If you want to train your own model, please download the pretrained model into
resfolder
cd src/
python train.py
- ResNet-50 is used as the backbone of SRFFNet and DUTS-TR is used to train the model
- batch=32, lr=0.05, momen=0.9, decay=5e-4, epoch=32
- Warm-up and linear decay strategies are used to change the learning rate lr
- After training, the result models will be saved in out folder
cd src
python test.py
- After testing, saliency maps of PASCAL-S, ECSSD, HKU-IS, DUT-OMRON, DUTS-TE will be saved in eval/maps/ folder.
- Trained model: model
- Saliency maps for reference: saliency maps
- If you find this work is helpful, please cite our paper
@article{wu2023srffnet,
title={SRFFNet: Self-refine, Fusion and Feedback for Salient Object Detection},
author={Wu, Shuang and Zhang, Guangjian},
journal={Cognitive Computation},
pages={1--13},
year={2023},
publisher={Springer}
}
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