Open a Colab notebook and visualize which image patterns cause a specific neuron in a pretrained model to activate, with no local install needed.
Run feature visualization experiments across dozens of pretrained vision models using the Lucid model zoo API to compare how different architectures represent concepts.
Study published Distill interpretability research interactively by running the companion Lucid notebooks alongside the articles.
Explore activation atlases to see a map of how a neural network internally organizes visual concepts like textures and objects.
| tensorflow/lucid | remitchell/python-scraping | jonkrohn/ml-foundations | |
|---|---|---|---|
| Stars | 4,705 | 4,708 | 4,710 |
| Language | Jupyter Notebook | Jupyter Notebook | Jupyter Notebook |
| Setup difficulty | easy | moderate | easy |
| Complexity | 3/5 | 2/5 | 1/5 |
| Audience | researcher | developer | researcher |
Figures from each repo's GitHub metadata at analysis time.
Requires TensorFlow 1.x only, add a Colab magic command to downgrade before importing, TensorFlow 2 is not supported.
Lucid is a research toolkit built by the TensorFlow team to help people understand what happens inside image-recognition AI models. The core question it addresses: when an AI model looks at a photo and identifies a cat, which parts of the model activated, and why? Lucid provides tools and ready-to-run notebooks for exploring those questions through visualization. The main way to use Lucid is through its collection of Jupyter notebooks, most of which run directly in Google Colab, a browser-based coding environment that requires no local installation. You open a link, run the cells, and see results. The notebooks cover several research areas: feature visualization (what patterns cause individual neurons to fire), the building blocks of interpretability (how spatial and channel-level signals combine to produce a prediction), differentiable image parameterizations (techniques for generating images that reveal model behavior), and activation atlases (maps of how a model organizes concepts internally). Lucid works with TensorFlow 1.x only. The project explicitly states it does not support TensorFlow 2, so users running in Colab need to add a magic command to switch to the older version before importing. This is research code, not a polished product. The maintainers are volunteers and cannot provide significant technical support. Beyond the notebooks, Lucid also includes a model zoo: a consistent API for working with dozens of pre-trained vision models so researchers can run the same visualization experiments across multiple models and compare results. The project is associated with the Distill publication, a research journal focused on clear explanations of machine learning ideas. Several notebook collections correspond directly to published Distill articles, meaning the notebooks are designed to be read alongside those papers. If you are a researcher or curious learner trying to understand how neural networks form internal representations, Lucid gives you hands-on tools to explore those ideas without needing to build anything from scratch.
A research toolkit from the TensorFlow team for visualizing what happens inside image-recognition AI models, run browser-based notebooks to see which patterns activate neurons and how models organize visual concepts.
Mainly Jupyter Notebook. The stack also includes Python, TensorFlow, Jupyter Notebook.
Use freely for any purpose, including commercial use, as long as you keep the copyright notice.
Setup difficulty is rated easy, with roughly 5min to a first successful run.
Mainly researcher.
This repo across BitVibe Labs
Verify against the repo before relying on details.