yosinski/deep-visualization-toolbox

Health v2 · maintenance only

Flags: no_releases

Adoption not part of the score

4051 stars · 928 forks observed · 2026-08-28

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

A GUI toolbox for visualizing and understanding deep neural networks, showing per-unit activations, backprop/deconv, and regularized-optimization visualizations of what individual neurons respond to. Bundled with CaffeNet, GoogLeNet, and SqueezeNet models and described in an ICML 2015 Deep Learning Workshop paper.

Use cases

• visualize what individual neurons in a CNN have learned
• run images through a network and inspect activations layer by layer
• generate synthetic images that maximally activate a neuron
• understand why a neural network classifies an image a certain way
• teach deep learning interpretability concepts with a live demo
• visualize network activations from a webcam feed

When to choose

• you use Caffe-based CNN models and want interactive neuron-level visualization
• you are studying or teaching neural network interpretability
• you want to reproduce the Yosinski et al. 2015 deep visualization experiments

When to avoid

• you work with PyTorch or TensorFlow models, since the toolbox is built around Caffe
• you need actively maintained tooling - the project has not seen releases since 2020
• you need modern interpretability methods like Grad-CAM, SHAP, or attention visualizations

Facets

application · maturity maintenance

machine-learning data-visualization deep-learning gui deep-learning machine-learning computer-vision data-visualization python neural-network-visualization caffe interpretability deconvnet research-tool linux macos desktop

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