jacobgil/vit-explain

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

1098 stars · 108 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch library implementing explainability methods for Vision Transformers, including Attention Rollout and Gradient Attention Rollout. It provides both a Python API and a command-line script to visualize where a transformer model attends in an image.

Use cases

• visualize attention maps of vision transformers
• explain vision transformer classifications
• generate class-specific saliency masks for ViT models
• understand which image regions a DeiT model uses
• debug transformer attention head behavior
• create attention heatmap overlays for research papers

When to choose

• you need attention-based explainability for Vision Transformers in PyTorch
• you want class-specific attention visualizations with gradient rollout
• you want a quick CLI to generate attention heatmaps from images

When to avoid

• you need explainability for CNNs rather than transformers
• you need production-grade model interpretation tooling with broad model support
• you need actively maintained features like attention flow, which is unfinished

Facets

library · maturity maintenance

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision machine-learning artificial-intelligence python vision-transformer explainable-ai attention-rollout pytorch interpretability

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