zhoubolei/CAM

Health v2 · maintenance only

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1883 stars · 461 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

Reference implementation of Class Activation Mapping (CAM), a CVPR'16 technique that visualizes which image regions a CNN attends to when predicting a class. It includes MATLAB/Caffe demo code with pre-trained GoogLeNet, VGG16, and AlexNet models plus a PyTorch script for generating heatmaps from modern networks.

Use cases

• visualize what regions of an image a CNN focuses on for its prediction
• generate class activation heatmaps from pretrained ResNet or DenseNet in PyTorch
• extract weakly-supervised object bounding boxes from image-level labels
• interpret and debug CNN classification decisions
• reproduce the CVPR'16 CAM paper experiments on ImageNet and Places

When to choose

• you need the original CAM method for research reproduction or citation
• you want heatmap visualization without modifying network architecture using GAP-based models
• you need pre-trained CAM models for Caffe on ImageNet or Places

When to avoid

• you need modern explainability methods like Grad-CAM with finer localization
• your project requires actively maintained tooling or recent framework support
• you work outside Python/PyTorch or MATLAB/Caffe ecosystems

Facets

library · maturity maintenance

machine-learning computer-vision image-processing data-visualization deep-learning computer-vision image-processing machine-learning python cross-platform class-activation-mapping cnn-visualization saliency-maps weakly-supervised-localization cvpr-2016 pytorch caffe matlab

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