ml5.js is a friendly machine learning library for the web that makes neural networks approachable for artists, students and creative coders. Built on TensorFlow.js and born at NYU’s ITP program, it wraps powerful pre-trained models in a handful of simple JavaScript functions. The library has earned about 6,600 stars on GitHub and was developed through 2024.
Most ML tooling assumes Python, math fluency and GPU clusters. ml5.js assumes a web browser and curiosity. With a few lines of code you can classify images from a webcam, estimate body poses, detect faces, analyze sentiment, generate text, transfer artistic styles or identify sounds โ all running locally in the browser with no server. It pairs naturally with p5.js, the creative-coding library used in classrooms worldwide, and its examples and documentation are written for beginners rather than ML engineers. Under the hood it loads proven pre-trained models such as MobileNet, PoseNet and Word2Vec, so users get real results immediately. They can then fine-tune or train small custom models, for example with Teachable-Machine-style image classification workflows, still entirely on their own machine.
Free. ml5.js is an open-source community project; everything runs locally in the visitor’s browser, so there are no usage fees or paid tiers.
Artists, designers, students and educators who want to experiment with machine learning in web projects and interactive installations without first becoming ML engineers.