cnn-explainer
Learning Convolutional Neural Networks with Interactive Visualization.
An interactive browser tool from Georgia Tech that animates how a convolutional neural network processes an image, explaining each step in plain English for learners with no ML background.
CNN Explainer is an interactive web tool that lets you see inside a convolutional neural network as it processes an image. A convolutional neural network, or CNN, is a type of AI model commonly used to recognize objects in photos. The project was created by researchers at Georgia Tech and Oregon State to help people with no machine learning background understand how these models actually work.
When you open the tool, you see a small pre-trained network displayed visually. You can upload an image, and the tool animates how the network transforms it step by step, layer by layer, until it arrives at a final classification. Each intermediate stage is shown as a grid of colored squares, and you can click on any of them to get an explanation of what that step is doing in plain terms.
The README is quite short and focuses mainly on how to run the project locally. You install dependencies with npm, start a development server, and then open it in a browser. The underlying model was trained on a small set of image categories and is included in the repository, so no external download is needed to get started.
The project is published as a research paper in IEEE Transactions on Visualization and Computer Graphics and is available under the MIT license. A live version is hosted publicly so you can try it without setting anything up. If you want to use the visualizer with your own trained model or different image categories, the repository links to relevant issues that explain how to do that.
Where it fits
- Explore how a convolutional neural network classifies images by watching each layer animate step by step in the browser.
- Upload your own image to see how the pre-trained CNN processes and classifies it, with plain-English explanations at each layer.
- Use as a teaching aid to explain neural networks to students or non-technical stakeholders without requiring any ML background.