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, appeared at IEEE CVPR 2020 Workshop on Fair, Data Efficient and Trusted Computer Vision. Our paper has been cited by 400!

Demo: You can run an example via Colab

Update

2021.12.16: A great MATLAB implementation from Kenta Itakura.

2021.4.03: A Pytorch implementation jacobgil/pytorch-grad-cam (3.8K Stars).

2020.8.18: A PaddlePaddle implementation from PaddlePaddle/InterpretDL.

2020.7.11: A Tensorflow implementation from keisen/tf-keras-vis.

2020.5.11: A Pytorch implementation from utkuozbulak/pytorch-cnn-visualizations (6.2K Stars).

2020.3.24: Merged into 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, thanks for releasing this great work!

Contact

If you have any questions, feel free to open an issue or directly contact me via: haofanwang.ai@gmail.com.