The pytorch code for our TIP2020 paper: Reverse Attention Based Residual Network for Salient Object Detection
We use ResNet50 as backbone and add the IoU loss[1] for better performance in this pytorch implementation. The original caffe version is here. We provide two versions with different training strategies.
Modify the pathes of datasets, then run:
Training: python3 train.py
Testing: python3 test.py
The codes are tested on Ubuntu 18.04 environment (Python3.6.9, PyTorch1.5.0, torchvision0.6.0, CUDA10.2, cuDNN7.6.5) with RTX 2080Ti GPU. We select several recent SOTA methods for comparisons. The evaluation code can be found here.
- v1: model Baidu(bc3k) Google; smaps Baidu(kp6t) Google
- v2: model Baidu(wbz1) Google; smaps Baidu(j57z) Google
@article{chen2020tip,
author={Chen, Shuhan and Tan, Xiuli and Wang, Ben and Lu, Huchuan and Hu, Xuelong and Fu, Yun},
journal={IEEE Transactions on Image Processing},
title={Reverse Attention-Based Residual Network for Salient Object Detection},
volume={29},
pages={3763-3776},
year={2020}
}
@inproceedings{chen2018eccv,
author={Chen, Shuhan and Tan, Xiuli and Wang, Ben and Hu, Xuelong},
booktitle={European Conference on Computer Vision},
title={Reverse Attention for Salient Object Detection},
year={2018}
}
This code is built on CPD[2] and F3Net[3]. We thank the authors for sharing their codes.
[1] Xuebin Qin, Zichen Zhang, Chenyang Huang, Chao Gao, Masood Dehghan, Martin Jagersand. BASNet: Boundary-Aware Salient Object Detection. In CVPR, 2019.
[2] Zhe Wu, Li Su, Qingming Huang. Cascaded Partial Decoder for Fast and Accurate Salient Object Detection. In CVPR, 2019.
[3] Jun Wei, Shuhui Wang, Qingming Huang. F3Net: Fusion, Feedback and Focus for Salient Object Detection. In AAAI, 2020.