pytorch-grad-cam and Grad-CAM
The pytorch-grad-cam library provides the core implementation that powers the Grad-CAM web demo, making them ecosystem siblings where one is the underlying technical framework and the other is a user-friendly interface for the same visualization technique.
About pytorch-grad-cam
jacobgil/pytorch-grad-cam
Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.
Implements 16+ attribution methods ranging from gradient-based approaches (GradCAM, GradCAM++) to perturbation-based techniques (AblationCAM, ScoreCAM) with batched inference for high performance. Built on PyTorch, it supports explainability across diverse architectures including CNNs, Vision Transformers, and multimodal models like CLIP, plus includes built-in metrics and smoothing algorithms to validate and refine explanation quality. Also works with medical imaging, embedding similarity tasks, and provides deep feature factorization for interpretable representation analysis.
About Grad-CAM
Cloud-CV/Grad-CAM
:rainbow: :camera: Gradient-weighted Class Activation Mapping (Grad-CAM) Demo
Related comparisons
Scores updated daily from GitHub, PyPI, and npm data. How scores work