Breonna Taylor, George Floyd, Ahmaud Arbery, Tony McDade, Regis Korchinski-Paquet, Nina Pop, Monika Diamond, Yassin Mohamed, Finan H. Berhe, Sean Reed, Steven Demarco Taylor, Sandra Bland, David McAtee, Ariane McCree, Terrance Franklin, Miles Hall, Darius Tarver, William Green, Samuel David Mallard, Kwame Jones, De’von Bailey, Christopher Whitfield, Anthony Hill, De’Von Bailey, Eric Logan, Jamarion Robinson, Gregory Hill Jr, JaQuavion Slaton, Ryan Twyman, Brandon Webber, Jimmy Atchison, Willie McCoy, Emantic Fitzgerald Bradford J, D’ettrick Griffin, Jemel Roberson, DeAndre Ballard, Botham Shem Jean, Robert Lawrence White, Anthony Lamar Smith, Ramarley Graham, Manuel Loggins Jr, Trayvon Martin, Wendell Allen, Kendrec McDade, Larry Jackson Jr, Jonathan Ferrell, Jordan Baker, Victor White III, Dontre Hamilton, Eric Garner, John Crawford III, Michael Brown, Ezell Ford, Dante Parker, Kajieme Powell, Laquan McDonald, Akai Gurley, Tamir Rice, Rumain Brisbon, Jerame Reid, Charly Keunang, Tony Robinson, Walter Scott, Freddie Gray, Brendon Glenn, Samuel DuBose, Christian Taylor, Jamar Clark, Mario Woods, Quintonio LeGrier, Gregory Gunn, Akiel Denkins, Alton Sterling, Philando Castile, Terrence Sterling, Terence Crutcher, Keith Lamont Scott, Alfred Olango, Jordan Edwards, Stephon Clark, Danny Ray Thomas, DeJuan Guillory, Patrick Harmon, Jonathan Hart, Maurice Granton, Julius Johnson, Jamee Johnson, Michael Dean, and too many more to list here...
The ml5.js community stands in solidarity with Black Lives Matter.
Please consider donating to your local bail fund, Code Cooperative, Emergency Release Fund, Survived & Punished Mutual Aid Fund, Justice for Breonna Taylor, George Floyd Memorial Fund and Movement for Black Lives.
Friendly Machine Learning for the Web
A neighborly approach to creating and exploring artificial intelligence in the browser.
Get started with ml5.js
Why ml5.js?
ml5.js aims to make machine learning approachable for a broad audience of artists, creative coders, and students. The library provides access to machine learning algorithms and models in the browser, building on top of TensorFlow.js with no other external dependencies.
ml5.js is an open source, friendly high level interface to TensorFlow.js, a library for handling GPU-accelerated mathematical operations and memory management for machine learning algorithms.
ml5.js provides immediate access in the browser to pre-trained models for detecting human poses, generating text, styling an image with another, composing music, pitch detection, and common English language word relationships, and much more.
ml5.js aims to support broader public understanding of machine learning and foster deeper engagement with ethical computing, responsible data collection, and accessibility and diversity of people and perspectives in technology and the arts.
ml5.js provides an approachable API and examples to help you get started
// Step 1: Create an image classifier with MobileNet
const classifier = ml5.imageClassifier("MobileNet", onModelReady);
// Step 2: select an image
const img = document.querySelector("#myImage");
// Step 3: Make a prediction
let prediction = classifier.predict(img, gotResults);
// Step 4: Do something with the results!
function gotResults(err, results) {
console.log(results);
// all the amazing things you'll add
}Discover the creative possibilities of machine learning!
PoseNet is a machine learning model that allows for Real-time Human Pose Estimation.
YOLO (You only look once) is a state-of-the-art, real-time object detection and classification system.
pix2pix is image-to-image translation with conditional adversarial networks.
Classify the content of images with pre-trained models.
Generate new doodles with a neural network based on Google's Quick Draw.
Meet our team!
ml5.js is an open source project developed and maintained by NYU's Interactive Telecommunications/Interactive Media Arts program and by artists, designers, students, technologists, and developers from all over the world.
