Grad-CAM

Grad-CAM (Gradient-weighted Class Activation Mapping) generates class-discriminative heatmaps, pinpointing the exact image regions a Convolutional Neural Network (CNN) used to make its prediction.

This is Grad-CAM: your solution for robust CNN explainability. The technique generates a coarse localization heatmap by calculating the gradient of the target class score with respect to the final convolutional layer's feature maps. This process provides a critical visual explanation, highlighting the input regions (pixels) that drive the model's decision for a specific class. Its key advantage: Grad-CAM works across a wide range of CNN architectures (e.g., VGG, ResNet) without requiring any architectural changes or re-training, ensuring maximum compatibility and immediate deployment.

https://github.com/ramprs/grad-cam/