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captum.ai

★3.3
💬1921
💲Free

Captum is a PyTorch library that provides tools for model interpretability, allowing users to understand how PyTorch models make predictions across various modalities. It supports easy integration with existing PyTorch models and is extensible for research purposes.

💻
Platform
web
AttributionExplainable AIIntegrated GradientsModel interpretabilityPyTorchTextVision

What is captum.ai?

Captum is a PyTorch library designed for model interpretability across different modalities such as vision and text. It helps users understand and attribute the predictions of PyTorch models, supporting most PyTorch models and enabling easy implementation and benchmarking of new interpretability algorithms.

Core Technologies

  • PyTorch
  • Attribution
  • Integrated Gradients
  • Explainable AI (XAI)

Key Capabilities

  • Model interpretability across modalities
  • Integration with PyTorch models
  • Implementation of interpretability algorithms
  • Benchmarking of algorithms

Use Cases

  • Analyze feature importance in image classification models
  • Evaluate word influence in text classification models

Core Benefits

  • Understand model predictions across modalities
  • Easy integration with PyTorch models
  • Open-source and extensible
  • Evaluate convergence of attribution methods

Key Features

  • Multi-modal interpretability support
  • Built on PyTorch
  • Extensible and open source

How to Use

  1. 1
    Install Captum via conda or pip.
  2. 2
    Import necessary libraries and define your PyTorch model.
  3. 3
    Instantiate an interpretability algorithm.
  4. 4
    Apply the algorithm to input data and baseline.
  5. 5
    Obtain attributions and convergence delta.

Frequently Asked Questions

Q.How do I install Captum?

A.You can install Captum via conda (conda install captum -c pytorch) or pip (pip install captum).

Q.What types of models does Captum support?

A.Captum supports most types of PyTorch models and can be used with minimal modification to the original neural network.

Q.What is Integrated Gradients?

A.Integrated Gradients is an algorithm implemented in Captum that helps to attribute the prediction of a model to its input features.

Pros & Cons (Reserved)

✓ Pros

  • Supports various modalities (vision, text, etc.)
  • Easy to integrate with existing PyTorch models
  • Open-source and extensible for research
  • Provides tools for evaluating the convergence of attribution methods

✗ Cons

  • Requires familiarity with PyTorch
  • May require some modification to the original neural network
  • The example provided is very basic and may not cover all use cases

Alternatives

No alternatives found.