jacobgil/pytorch-grad-cam

Health v2 · maintenance only

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12958 stars · 1707 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch library providing state-of-the-art pixel attribution (saliency) methods like GradCAM, ScoreCAM, and AblationCAM for explainable AI in computer vision. It supports CNNs and Vision Transformers across classification, object detection, segmentation, and embedding-similarity tasks, with metrics for evaluating explanation trustworthiness.

Use cases

• visualize which image regions a CNN uses for its predictions
• explain vision transformer predictions with saliency maps
• debug why my image classifier misclassifies images
• generate class activation maps for object detection models
• benchmark new explainability methods for computer vision
• check if I can trust my model's explanations with faithfulness metrics

When to choose

• you need pixel attribution/saliency visualizations for PyTorch vision models
• you want a comprehensive, well-tested collection of CAM variants including for Vision Transformers
• you need explanation quality metrics and batch processing for research or production diagnostics

When to avoid

• you use TensorFlow or JAX instead of PyTorch
• you need explainability for NLP or tabular models rather than vision
• you want a GUI tool rather than a Python library

Facets

library · maturity active

machine-learning deep-learning data-visualization image-processing computer-vision deep-learning computer-vision machine-learning python cross-platform explainable-ai grad-cam interpretability pytorch saliency-maps vision-transformers xai class-activation-maps gpu

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