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