Score-Weighted Visual Explanations for Convolutional Neural Networks.

In this paper, we develop a novel post-hoc visual explanation method called Score-CAM based on class activation mapping. Score-CAM is a gradient-free visualization method, extended from Grad-CAM and Grad-CAM++. It achieves better visual performance and fairness for interpreting the decision making process.

Paper: Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks (Haofan Wang, Zifan Wang, Mengnan Du, Fan Yang, Zijian Zhang, Sirui Ding, Piotr Mardziel and Xia Hu.)


2020.4.13: First version of Score-CAM code has been released. More implementations will be added later.


    • [ ] Support for Colab notebook.
    • [ ] Support for faster version of Score-CAM.
    • [ ] Support for pre-trained model in Pytorch.
    • [ ] Support for self-defined model in Pytorch.
    • [ ] Add visualization result and quantitive evaluation.
    • [ ] Support for other tasks such as object localization task.

Implement Score-CAM into popular visualization tools.

It would be very appreciated for implementing Score-CAM for other popular projects, if any of you are interested.

Other implementations

Before we release the official code, some great researchers have implemented Score-CAM on different framework.
I am very grateful for the efforts made in their implementation.


torch-cam by frgfm

ScoreCAM by yiskw713

xdeep by datamllab


score-cam by matheushent


scam-net by andreysorokin

Score-CAM by tabayashi0117

Score-CAM-VGG16 by bunbunjp

Blog post


Demystifying Convolutional Neural Networks using ScoreCam



If you find this work or code is helpful in your research, please cite and star:

  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},
  journal={arXiv preprint arXiv:1910.01279v2},