TorchCAM: class activation explorer

TorchCAM provides a minimal yet flexible way to explore the spatial importance of features on your PyTorch model outputs. Check out the live demo on HuggingFace Spaces 🤗

This project is meant for:

• ⚡ exploration: easily assess the influence of spatial features on your model’s outputs

• 👩‍🔬 research: quickly implement your own ideas for new CAM methods

CAM zoo

Activation-based methods

• CAM from “Learning Deep Features for Discriminative Localization”

• Score-CAM from “Score-CAM: Score-Weighted Visual Explanations for Convolutional Neural Networks”

• SS-CAM from “SS-CAM: Smoothed Score-CAM for Sharper Visual Feature Localization”

• IS-CAM from “IS-CAM: Integrated Score-CAM for axiomatic-based explanations”

Gradient-based methods

• Grad-CAM from “Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization”

• Grad-CAM++ from “Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks”

• Smooth Grad-CAM++ from “Smooth Grad-CAM++: An Enhanced Inference Level Visualization Technique for Deep Convolutional Neural Network Models”

• X-Grad-CAM from “Axiom-based Grad-CAM: Towards Accurate Visualization and Explanation of CNNs”

• Layer-CAM from “LayerCAM: Exploring Hierarchical Class Activation Maps for Localization”