pytorch-grad-cam and torch-cam
These are competitors offering overlapping implementations of multiple class activation mapping techniques, with pytorch-grad-cam being the more mature and widely-adopted option while torch-cam provides a broader algorithmic toolkit (supporting additional variants like Score-CAM and Layer-CAM) but with minimal adoption.
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 torch-cam
frgfm/torch-cam
Class activation maps for your PyTorch models (CAM, Grad-CAM, Grad-CAM++, Smooth Grad-CAM++, Score-CAM, SS-CAM, IS-CAM, XGrad-CAM, Layer-CAM)
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