Captum
Explain PyTorch model decisions with attribution algorithms
Available On
Desktop
Target Audience
- PyTorch developers
- ML researchers
- AI ethics teams
- Model validation engineers
Hashtags
Overview
Captum is an open-source Python library that helps developers understand why their AI models make specific predictions. It provides model interpretability for PyTorch models through various attribution methods, working with both vision and text models without requiring major code changes. Essential for AI builders who need to debug models and meet regulatory requirements for explainable AI.
Key Features
Multi-Modal
Works with vision, text, and other data types
PyTorch Native
Integrates seamlessly with existing PyTorch models
Research Ready
Extensible platform for developing new algorithms
Use Cases
Debug model predictions
Compare interpretability methods
Develop new attribution algorithms
Pros & Cons
Pros
- Open-source and free to use
- Native integration with PyTorch ecosystem
- Supports multi-modal AI models
- Extensible architecture for researchers
Cons
- Requires PyTorch/Python expertise
- No graphical interface for non-coders
Frequently Asked Questions
How do I install Captum?
Install via conda (recommended) with 'conda install captum -c pytorch' or via pip with 'pip install captum'
Does Captum work with any PyTorch model?
Supports most PyTorch models with minimal modifications to original code
What's the main purpose of Captum?
Provides model interpretability through attribution algorithms to understand model decisions
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