PyTorch-Based Evaluation Tool for Co-Saliency Detection
Automatically evaluate 8 metrics and draw 4 types of curves
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Eval Co-SOD is an extended version of Evaluate-SOD for co-saliency detection task. It provides eight metrics and four curves:
The structure of root_dir
should be organized as follows:
.
├── gt
│ ├── dataset1
│ │ ├── accordion
│ │ │ ├── 51499.png
│ │ │ └── 186605.png
│ │ └── alarm clock
│ │ ├── 51499.png
│ │ └── 186605.png
│ ├── dataset2 ...
│ └── dataset3 ...
│
└── pred
└── method1
│ ├── dataset1
│ │ ├── accordion
│ │ │ ├── 51499.png
│ │ │ └── 186605.png
│ │ └── alarm clock
│ │ ├── 51499.png
│ │ └── 186605.png
│ ├── dataset2 ..
│ └── dataset3 ...
└──method2 ...
eval.sh
--methods method1+method2+method3 (Multiple items are connected with '+')
--datasets dataset1+dataset2+dataset3
--save_dir ./Result (Path to save results)
--root_dir ../SalMaps
sh eval.sh
plot_curve.sh
--methods method1+method2+method3 (Multiple items are connected with '+')
--datasets dataset1+dataset2+dataset3
--out_dir ./Result/Curves (Path to save results)
--res_dir ./Result/Detail
sh plot_curve.sh
If you find this tool is useful for your research, please cite the following papers.
@inproceedings{zhang2020gicd,
title={Gradient-Induced Co-Saliency Detection},
author={Zhang, Zhao and Jin, Wenda and Xu, Jun and Cheng, Ming-Ming},
booktitle={European Conference on Computer Vision (ECCV)},
year={2020}
}
@inproceedings{fan2020taking,
title={Taking a Deeper Look at the Co-salient Object Detection},
author={Fan, Deng-Ping and Lin, Zheng and Ji, Ge-Peng and Zhang, Dingwen and Fu, Huazhu and Cheng, Ming-Ming},
booktitle={IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
year={2020}
}
If you have any questions, feel free to contact me via zzhang🥳mail😲nankai😲edu😲cn