Guide · Open source
How to use ConvNetJS: train neural networks in your browser
Updated · written with AI from the project's own documentation and checked against our catalogue data
ConvNetJS is an open-source JavaScript library from karpathy on GitHub for building and training deep learning models, including convolutional neural networks, directly in a web browser. It is aimed at people who want to see how these networks learn without installing a heavy machine learning stack. This guide explains what it is good for, how to get the code from the repository, and where it fits compared with other options.
ConvNetJSkarpathy/convnetjs · ★ 11.2k · MIT Install options & downloads Quick start
- 1Check prerequisites. Make sure you have a web browser and Git installed.
- 2Clone the repository. Run git clone https://github.com/karpathy/convnetjs in the folder where you want the project.
- 3Open the project files. Look through the cloned repository to find the library files and any examples it includes.
- 4Build a small network. Start with a simple ordinary neural network so you can check that training works.
- 5Try a convolutional network. Once the basics work, build a convolutional neural network and train it on a small dataset.
- 6Judge whether it fits. If you need large-scale training or a maintained production stack, compare it with TensorFlow.js, Brain.js or ml5.js.
What ConvNetJS does
ConvNetJS is described as a way to train ordinary neural networks and convolutional neural networks without installing heavy machine learning software. Everything runs in the browser, so the main requirement is a web browser.
People use it mainly for learning and experimenting with how these networks work. Its README describes it as deep learning in JavaScript.
- Trains ordinary (fully connected) and convolutional neural networks
- Runs in a web browser
- Written in JavaScript
- Licensed under MIT
What you need before you start
The source lists two prerequisites: a web browser, and Git to clone the repository. You do not need a Python environment or a GPU to start experimenting with the library.
The README excerpt available for this guide does not describe any other system requirements.
- A web browser
- Git, to clone the repository
Get the ConvNetJS code
The install method is clone-and-build: you copy the repository to your machine with Git and then build it. Run the following command in the folder where you want the project to live.
git clone https://github.com/karpathy/convnetjsLearn the basics and try training
Once the repository is on your machine, you can start working with the library to build a network and train it. ConvNetJS is well suited to small experiments where you want to watch training happen step by step in a browser.
The source excerpt does not include a full API walkthrough, so check the repository files for the exact usage of the library's classes and functions.
- Start with a small ordinary network before trying a convolutional one
- Keep datasets small, since the library is aimed at experiments rather than large jobs
Know the limits before you commit
ConvNetJS is not presented as a tool for large-scale training. The README does not claim performance suitable for big datasets or GPUs. Its maintenance activity is also limited: the project has not seen recent development, so treat it as a learning and experimentation library rather than a production toolkit.
If you need pretrained model hubs or a Python-based ecosystem, this project does not describe those features.
- Not suited to large datasets or GPU-scale training (not claimed by the README)
- Not an actively maintained production toolkit
- No pretrained model hub described
Compare ConvNetJS with similar projects
Comparable JavaScript options include TensorFlow.js, Brain.js and ml5.js. ConvNetJS is the one most focused on a minimal, from-scratch view of training networks in the browser.
Pick ConvNetJS if your goal is understanding and teaching. Pick a more actively developed library if you are building an application that must be maintained over time.
- TensorFlow.js
- Brain.js
- ml5.js
What people use it for
- Teaching students how convolutional neural networks are trained, using a visual in-browser demo
- Running a quick neural network experiment without installing a Python ML stack
- Giving a presentation that shows training happening live in a browser
- Exploring how a neural network behaves for a JavaScript developer who wants to learn deep learning
Frequently asked questions
Is ConvNetJS free to use?
Yes. ConvNetJS is licensed under the MIT license, as listed in the project details. You can use, copy and modify it under those terms.
How do I install ConvNetJS?
The install method is clone-and-build. Use Git to run git clone https://github.com/karpathy/convnetjs, then build from the cloned repository. The excerpt available here does not list further build steps.
What do I need to run ConvNetJS?
You need a web browser and Git to clone the repository. The source does not list other requirements.
Can ConvNetJS train large models or use a GPU?
The README does not claim performance suitable for large datasets or GPUs. It is best used for small experiments and learning.
What are the alternatives to ConvNetJS?
Comparable projects include TensorFlow.js, Brain.js and ml5.js. Choose based on whether you need learning-focused code or a more actively maintained library.
Is ConvNetJS still maintained?
The project has had little recent development activity, so it is not an actively maintained production toolkit. Check the repository's commit history before relying on it for anything long-term.
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Our verdict
ConvNetJS suits JavaScript developers, students and teachers who want to see neural networks train in a browser with no heavy setup. It is a poor choice for large-scale training, production systems, or anyone who needs pretrained models and an active Python ecosystem.
Install options & downloads