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》.

Prerequisites

• Python 3.7
• Pytorch 1.7
• OpenCV 4.0
• Numpy 1.15
• TensorboardX
• Apex

Test Demo

Fast Test Demo

Clone repository

git clone https://github.com/user-wu/SRFFNet.git
cd SRFFNet/

Download dataset

Download the following datasets and unzip them into data folder

• PASCAL-S
• ECSSD
• HKU-IS
• DUT-OMRON
• DUTS

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
      --------------------------

Download model

• 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 res folder

  Training

  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

  Testing

  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

Citation

• 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}
  }