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pytorch-grad-cam is an open-source library that provides advanced explainable AI techniques for interpreting the predictions of deep learning models used in computer vision. The project implements Grad-CAM and several related visualization methods that highlight the regions of an image that most strongly influence a neural network’s decision. These visualization techniques allow developers and researchers to better understand how convolutional neural networks and transformer-based vision models make predictions. The library supports a wide variety of tasks including image classification, object detection, semantic segmentation, and similarity analysis. It also provides metrics and evaluation tools that help measure the reliability and quality of the generated explanations. By integrating easily with PyTorch models, the library allows developers to diagnose model errors, detect biases in datasets, and improve model transparency.
Features
- Implementation of Grad-CAM and other class activation map techniques
- Support for CNNs and vision transformer architectures
- Visualization tools for classification, detection, and segmentation models
- Batch processing support for generating explanations on multiple images
- Smoothing techniques for producing clearer attention heatmaps
- Evaluation metrics for validating explanation quality
Project Samples
