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:

• Metrics:
  • Mean Absolute Error (MAE)
  • Maximum F-measure (max-Fm)
  • Mean F-measure (mean-Fm)
  • Maximum E-measure (max-Em)
  • Mean E-measure (mean-Em)
  • S-measure (Sm)
  • Average Precision (AP)
  • Area Under Curve (AUC)
• Curves:
  • Precision-Recall (PR) curve
  • Receiver Operating Characteristic (ROC) curve
  • F-measure curve
  • E-measure curve

Prerequisites

• PyTorch >= 1.0

Usage

1. Prepare your data

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 ...

2. Evaluate on the 8 metrices

1. Configure 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

2. Run by

sh eval.sh

3. Draw the 4 types of curves

1. Configure 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

2. Run by

sh plot_curve.sh

Citation

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} 
} 

Contact

If you have any questions, feel free to contact me via zzhang🥳mail😲nankai😲edu😲cn