https://github.com/haofanwang/score-cam

Official implementation of Score-CAM in PyTorch
https://github.com/haofanwang/score-cam

cam class-activation-maps cnn-visualization cnn-visualization-technique explainability grad-cam gradcam gradient-free heatmap pytorch saliency score-cam scorecam visual-explanations

Last synced: 7 days ago
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Official implementation of Score-CAM in PyTorch

• Host: GitHub
• URL: https://github.com/haofanwang/score-cam
• Owner: haofanwang
• License: mit
• Created: 2020-04-13T10:53:37.000Z (over 4 years ago)
• Default Branch: master
• Last Pushed: 2022-08-06T09:40:47.000Z (over 2 years ago)
• Last Synced: 2023-10-20T20:09:43.089Z (about 1 year ago)
• Topics: cam, class-activation-maps, cnn-visualization, cnn-visualization-technique, explainability, grad-cam, gradcam, gradient-free, heatmap, pytorch, saliency, score-cam, scorecam, visual-explanations
• Language: Python
• Homepage:
• Size: 2.22 MB
• Stars: 365
• Watchers: 6
• Forks: 63
• Open Issues: 4
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

README

        
## Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks

We develop a novel post-hoc visual explanation method called Score-CAM, which is the first gradient-free CAM-based visualization method that achieves better visual performance (**state-of-the-art**). 



Paper: [Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks](http://openaccess.thecvf.com/content_CVPRW_2020/papers/w1/Wang_Score-CAM_Score-Weighted_Visual_Explanations_for_Convolutional_Neural_Networks_CVPRW_2020_paper.pdf), appeared at IEEE [CVPR 2020 Workshop on Fair, Data Efficient and Trusted Computer Vision](https://fadetrcv.github.io). Our paper has been cited by **400**!

Demo: You can run an example via [Colab](https://colab.research.google.com/drive/1m1VAhKaO7Jns5qt5igfd7lSVZudoKmID?usp=sharing)

## Update

**`2021.12.16`**: A great MATLAB implementation from [Kenta Itakura](https://github.com/KentaItakura/Explainable-AI-interpreting-the-classification-using-score-CAM).

**`2021.4.03`**: A Pytorch implementation [jacobgil/pytorch-grad-cam](https://github.com/jacobgil/pytorch-grad-cam) (3.8K Stars).

**`2020.8.18`**: A PaddlePaddle implementation from [PaddlePaddle/InterpretDL](https://github.com/PaddlePaddle/InterpretDL).

**`2020.7.11`**: A Tensorflow implementation from [keisen/tf-keras-vis](https://github.com/keisen/tf-keras-vis).

**`2020.5.11`**: A Pytorch implementation from [utkuozbulak/pytorch-cnn-visualizations](https://github.com/utkuozbulak/pytorch-cnn-visualizations) (6.2K Stars).

**`2020.3.24`**: Merged into [frgfm/torch-cam](https://github.com/frgfm/torch-cam), a wonderful library that supports multiple CAM-based methods.

## Citation

If you find this work is helpful in your research, please cite our work:

```

@inproceedings{wang2020score,

  title={Score-CAM: Score-weighted visual explanations for convolutional neural networks},

  author={Wang, Haofan and Wang, Zifan and Du, Mengnan and Yang, Fan and Zhang, Zijian and Ding, Sirui and Mardziel, Piotr and Hu, Xia},

  booktitle={Proceedings of the IEEE/CVF conference on computer vision and pattern recognition workshops},

  pages={24--25},

  year={2020}

}

```

## Thanks

Utils are built on [flashtorch](https://github.com/MisaOgura/flashtorch), thanks for releasing this great work!

## Contact

If you have any questions, feel free to open an issue or directly contact me via: `[email protected]`.