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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

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}
}