meta-pytorch/captum

Health v2 · maintenance only

Adoption not part of the score

5693 stars · 563 forks observed · 2026-08-28

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

Captum is a model interpretability and understanding library for PyTorch, providing implementations of algorithms like Integrated Gradients, saliency maps, SmoothGrad, TCAV, and TracIn. It helps researchers and developers understand which features, examples, or concepts contribute to a model's predictions, and includes adversarial attack and counterfactual explanation capabilities.

Use cases

• explain why a PyTorch model made a prediction
• compute feature attributions for a neural network
• generate saliency maps for image classifiers
• identify which training examples influenced a model's output
• debug unexpected model outputs by finding important features
• run adversarial attacks to test model robustness
• benchmark new interpretability algorithms against existing ones

When to choose

• you use PyTorch and need explainability for vision, text, or multimodal models
• you want state-of-the-art attribution methods like Integrated Gradients with minimal code changes
• you are an interpretability researcher needing an extensible benchmarking framework

When to avoid

• your models are built with TensorFlow, JAX, or scikit-learn rather than PyTorch
• you need interpretability for non-neural models like gradient-boosted trees
• you only need simple model metrics rather than attribution or explanation methods

Facets

library · maturity active

machine-learning data-science developer-tools machine-learning deep-learning artificial-intelligence data-science python cross-platform interpretability explainable-ai feature-attribution integrated-gradients saliency-maps pytorch model-debugging adversarial-attacks gpu

Member repositories

For agents

markdown · JSON · MCP: product_card(name="meta-pytorch/captum")