bobleono / CTDNet

Code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection"

CTDNet

The PyTorch code for ACM MM2021 paper "Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection"

Requirements

• Python 3.6
• Pytorch 1.4+
• OpenCV 4.0
• Numpy
• TensorboardX
• Apex

Dataset

Download the SOD datasets and unzip them into data folder.

Train

cd src
python train.py

• We implement our method by PyTorch and conduct experiments on a NVIDIA 1080Ti GPU.
• We adopt pre-trained ResNet-18 and ResNet-50 as backbone networks, which are saved in res folder.
• We train our method on DUTS-TR and test our method on other datasets.
• After training, the trained models will be saved in out folder.

Test

cd src
python test.py

• After testing, saliency maps will be saved in eval folder.

Results

• CTDNet-18： saliency maps (提取码：b6ba)； trained model (提取码：ftmz）
• CTDNet-50： saliency maps (提取码：j1zq)； trained model (提取码：ehvv）

Evaluation

    cd eval
    matlab main

• We use MATLAB code to evaluate the performace of our method.

Citation

• If you find this work is helpful, please cite our paper

@inproceedings{zhao2021complementary,
  title={Complementary Trilateral Decoder for Fast and Accurate Salient Object Detection},
  author={Zhao, Zhirui and Xia, Changqun and Xie, Chenxi and Li, Jia},
  booktitle={Proceedings of the 29th ACM International Conference on Multimedia},
  pages={4967--4975},
  year={2021}
}

Reference

This project is based on the implementation of F3Net.