captum.ai

PyTorch-based library designed for interpreting models across various data modalities.

Visit Site

AI Developer Tools AI Models Open Source AI Models AI Research Tool

About captum.ai

Captum is an open-source PyTorch library that enables in-depth model interpretability. It offers a suite of tools to analyze and attribute predictions across multiple modalities, including vision and text. Compatible with most PyTorch models, Captum simplifies implementing and benchmarking advanced interpretability algorithms.

How to Use

Install Captum using pip or conda. Import relevant libraries such as torch, numpy, and IntegratedGradients from captum.attr. Prepare your trained PyTorch model, then instantiate an interpretability algorithm like IntegratedGradients. Apply it to your input data with baselines to generate attribution scores and assess model explanations.

Features

Open-source and highly customizable

Supports multimodal interpretability (vision, text, etc.)

Built specifically for PyTorch models

Use Cases

Identifying key features in image classification

Understanding word influence in NLP models

Best For

Data scientistsPyTorch developersAI researchersMachine learning engineersModel interpretability practitioners

Pros

Seamlessly integrates with existing PyTorch workflows

Extensible framework suitable for research and development

Includes tools for assessing attribution convergence

Supports multiple data modalities including vision and text

Cons

May need modifications to existing neural network architectures

Sample examples are basic and may require adaptation

Requires familiarity with PyTorch and interpretability concepts

Frequently Asked Questions

Find answers to common questions about captum.ai

How can I install Captum?

Install Captum easily via pip using 'pip install captum' or with conda through 'conda install captum -c pytorch'.

Which model types are compatible with Captum?

Captum works with most PyTorch models with minimal adjustments needed for integration.

What is the purpose of Integrated Gradients in Captum?

It helps attribute a model’s predictions to specific input features, enhancing interpretability.

Can I use Captum for multimodal data?

Yes, Captum supports interpretability across different data types, including vision and text.

Is Captum suitable for research purposes?

Absolutely, its open-source design makes it ideal for research and experimental development.