jacobgil/pytorch-grad-cam

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.

12,682 stars and 58,294 monthly downloads. Used by 3 other packages. No commits in the last 6 months. Available on PyPI.