https://github.com/zzhanghub/eval-co-sod
PyTorch-Based Evaluation Tool for Co-Saliency Detection
https://github.com/zzhanghub/eval-co-sod
auc average-precision co-saliency e-measure evaluation f-measure mae mean-absolute-error pr-curve python pytorch roc-curve s-measure salient-object-detection
Last synced: 7 months ago
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PyTorch-Based Evaluation Tool for Co-Saliency Detection
- Host: GitHub
- URL: https://github.com/zzhanghub/eval-co-sod
- Owner: zzhanghub
- Created: 2020-08-04T07:35:01.000Z (almost 6 years ago)
- Default Branch: master
- Last Pushed: 2020-12-12T08:31:17.000Z (over 5 years ago)
- Last Synced: 2024-05-29T09:01:15.317Z (about 2 years ago)
- Topics: auc, average-precision, co-saliency, e-measure, evaluation, f-measure, mae, mean-absolute-error, pr-curve, python, pytorch, roc-curve, s-measure, salient-object-detection
- Language: Python
- Homepage: http://zhaozhang.net/coca.html
- Size: 65.4 KB
- Stars: 92
- Watchers: 3
- Forks: 28
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
- awesome-segmentation-saliency-dataset - https://github.com/zzhanghub/eval-co-sod
README
PyTorch-Based Evaluation Tool for Co-Saliency Detection
Automatically evaluate 8 metrics and draw 4 types of curves
⭐ Project Home »
***
**Eval Co-SOD** is an extended version of [Evaluate-SOD](https://github.com/Hanqer/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`
```shell
--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`
```shell
--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`