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CAPTUM
Explain any PyTorch model's predictions with feature attribution
BSD-3-Clause
ABOUT
Deep learning models are black boxes — when a model makes a wrong prediction, engineers have no insight into why. Traditional debugging tools show loss curves and gradients but can't attribute decisions to specific input features. Captum (Latin for "understanding") applies a suite of attribution algorithms — Integrated Gradients, DeepLIFT, Gradient SHAP, Layer Conductance, and Neuron Conductance — to any PyTorch model. It shows which pixels mattered for an image classifier, which words drove a sentiment prediction, or which neurons fired for a specific output. Teams use it to validate model behavior, debug failures, and build trust with stakeholders.
INSTALL
pip install captumINTEGRATION GUIDE
1. Debug why an image classifier misidentified an object by visualizing pixel-level feature attribution
2. Verify that a medical diagnosis model relies on clinically relevant features, not background artifacts
3. Compare feature importance across model versions to detect regressions before deployment
4. Generate attribution maps as part of model documentation for regulatory compliance
5. Identify which neurons or layers are responsible for specific model behaviors
TAGS
pytorchinterpretabilityexplainabilityfeature-attributionml-debuggingpythonmodel-understanding