Awesome
Evaluation on saliency object detection(Evaluate SOD)
A One-key fast evaluation on saliency object detection with GPU implementation including MAE, Max F-measure, S-measure, E-measure.
Code are reimplemented from the matlab version which are available from http://dpfan.net/
- GPU implementation with pytorch which can be easier embedding into eval code.
- One-key evaluation
Usage:
python main.py --root_dir 'your_dir' --save_dir 'your_dir' --methods 'DSS RAS' --dataset 'ECSSD SOD' (if --methods and --dataset is not set, using all methods and datasets.)
example:
python main.py --root_dir './' --save_dir './'
example root_dir:
.
├── gt
│ ├── ECSSD
│ │ ├── 0001.png
│ │ └── 0002.png
│ ├── PASCAL-S
│ │ ├── 1.png
│ │ └── 2.png
│ └── SOD
│ ├── 2092.png
│ └── 3096.png
└── pred
└── dss
├── ECSSD
│ ├── 0001.png
│ └── 0002.png
├── PASCAL-S
│ ├── 1.png
│ └── 2.png
└── SOD
├── 2092.png
└── 3096.png
If you find the code useful to your research, please cite the following papers.
@inproceedings{fan2018SOC,
title={Salient Objects in Clutter: Bringing Salient Object Detection to the Foreground},
author={Fan, Deng-Ping and Cheng, Ming-Ming and Liu, Jiang-Jiang and Gao, Shang-Hua and Hou, Qibin and Borji, Ali},
booktitle = {European Conference on Computer Vision (ECCV)},
year={2018},
organization={Springer}
}
@inproceedings{fan2017structure,
title={{Structure-measure: A New Way to Evaluate Foreground Maps}},
author={Fan, Deng-Ping and Cheng, Ming-Ming and Liu, Yun and Li, Tao and Borji, Ali},
booktitle={IEEE International Conference on Computer Vision (ICCV)},
pages = {4548-4557},
year={2017},
note={\url{http://dpfan.net/smeasure/}},
organization={IEEE}
}
@inproceedings{Fan2018Enhanced,
author={Fan, Deng-Ping and Gong, Cheng and Cao, Yang and Ren, Bo and Cheng, Ming-Ming and Borji, Ali},
title={{Enhanced-alignment Measure for Binary Foreground Map Evaluation}},
booktitle={International Joint Conference on Artificial Intelligence (IJCAI)},
pages={698--704},
note={\url{http://dpfan.net/e-measure/}},
year={2018}
}