tensorflow/lucid

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.