This library provides advanced AI explainability methods for PyTorch computer vision models, targeting researchers and developers who need to understand model predictions. It offers a comprehensive suite of state-of-the-art techniques for diagnosing model behavior, benchmarking new methods, and visualizing feature attributions.

How It Works

The package implements a variety of pixel attribution methods, including GradCAM, HiResCAM, ScoreCAM, and AblationCAM, by weighting model activations based on gradients or output perturbations. It supports advanced use cases like object detection and semantic segmentation by allowing custom reshape_transform and model_target functions to adapt to diverse architectures and tasks beyond standard classification.

Quick Start & Requirements

• Install via pip: pip install grad-cam
• Requires PyTorch. GPU acceleration is recommended for performance.
• Documentation and advanced tutorials are available at https://jacobgil.github.io/pytorch-gradcam-book.

Highlighted Details

• Supports a wide range of methods including GradCAM, HiResCAM, ScoreCAM, AblationCAM, EigenCAM, and more.
• Tested on common CNNs (ResNet, VGG) and Vision Transformers (ViT, SwinT).
• Includes metrics for evaluating explanation trustworthiness and tuning performance.
• Offers smoothing techniques (aug_smooth, eigen_smooth) for cleaner visualizations.

Maintenance & Community

The project is actively maintained by Jacob Gildenblat and contributors. Community support channels are not explicitly mentioned in the README.

Licensing & Compatibility

The project is licensed under the MIT License, permitting commercial use and integration with closed-source projects.

Limitations & Caveats

Some methods like ScoreCAM and AblationCAM can be computationally intensive due to multiple forward passes, with batch size configurable for performance tuning. Adapting to highly custom architectures may require understanding the reshape_transform and model_target concepts.