utkuozbulak/pytorch-cnn-visualizations

Health v2 · maintenance only

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8233 stars · 1503 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-29, confidence not recorded

A PyTorch library implementing a wide range of convolutional neural network visualization and interpretability techniques, including Grad-CAM, guided backpropagation, saliency maps, Smooth Grad, Deep Dream, and integrated gradients. Each technique is provided as a standalone, well-commented Python file designed for learning and research.

Use cases

• visualize what a CNN layer has learned
• generate grad-cam heatmaps for image classification
• compute saliency maps for model interpretability
• run deep dream on pretrained networks
• learn how cnn visualization techniques work
• explain pytorch model predictions with class activation maps

When to choose

• you want reference implementations of many CNN visualization methods in one place
• you are studying or teaching interpretability techniques with readable per-technique code
• you need Grad-CAM, Smooth Grad, or integrated gradients on AlexNet/VGG-style models

When to avoid

• you need a maintained library compatible with recent PyTorch versions (code targets torch 0.4.1)
• you need production-grade explainability tooling with broad model support
• you work with models lacking a features/classifier layer split without modifying code

Facets

library · maturity maintenance

machine-learning deep-learning image-processing data-visualization deep-learning computer-vision machine-learning education python cross-platform pytorch cnn-visualization grad-cam saliency-maps explainability deep-dream smooth-grad guided-backpropagation interpretability

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