Awesome
<!-- PROJECT LOGO --> <br /> <p align="center"> <a href="http://zhaozhang.net/coca.html"> <img src="img/eval_co-sod.png" alt="Logo" width="210" height="100"> </a> <h3 align="center">PyTorch-Based Evaluation Tool for Co-Saliency Detection</h3> <p align="center"> Automatically evaluate 8 metrics and draw 4 types of curves <br /> <a href="http://zhaozhang.net/coca.html"><strong>⭐ Project Home »</strong></a> <br /> </p> </p>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
- 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
- Run by
sh eval.sh
3. Draw the 4 types of curves
- 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
- 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