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pytorch-grad-cam

12,164
1,660
Python

Project Description

Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classification, Object detection, Segmentation, Image similarity and more.

pytorch-grad-cam: Advanced AI Explainability for computer vision. Support for CNNs, Vision Transformers, Classificati

Project Title

pytorch-grad-cam — Advanced AI Explainability for Computer Vision with PyTorch

Overview

PyTorch-Grad-CAM is a Python package that offers state-of-the-art methods for explainable AI in computer vision. It supports a wide range of models and tasks, including CNNs, Vision Transformers, classification, object detection, and segmentation. The package is designed to diagnose model predictions and serve as a benchmark for new explainability methods.

Key Features

  • Comprehensive collection of Pixel Attribution methods for Computer Vision
  • Tested on many Common CNN Networks and Vision Transformers
  • Advanced use cases: Classification, Object Detection, Semantic Segmentation, Embedding-similarity
  • Includes smoothing methods to enhance CAMs appearance
  • High performance: full support for batches of images in all methods
  • Metrics for checking the trustworthiness of explanations and tuning for best performance

Use Cases

  • Researchers and developers diagnosing model predictions in production or development
  • Benchmarking algorithms and metrics for research of new explainability methods
  • Enhancing the interpretability of AI models in computer vision tasks

Advantages

  • Supports a wide range of models and computer vision tasks
  • Includes various advanced methods for generating pixel attributions
  • Provides metrics to evaluate the quality of explanations
  • High performance with support for batch processing

Limitations / Considerations

  • The project's license is currently unknown, which may affect its use in commercial applications
  • The complexity of the methods may require a steep learning curve for new users

Similar / Related Projects

  • LIME: A Python package for explaining the predictions of any classifier in a model-agnostic manner, but it is not specific to computer vision tasks.
  • SHAP: A game theoretic approach to explain the output of any machine learning model, also not limited to computer vision.
  • Captum: A PyTorch library for model interpretability, which offers a range of methods but may not be as specialized in computer vision as pytorch-grad-cam.

Basic Information


📊 Project Information

🏷️ Project Topics

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Project Information

Created on 5/31/2017
Updated on 9/15/2025