Awesome
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.