Getting Started

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.

The library is supported by code examples, tutorials, and sample datasets with an emphasis on ethical computing. Bias in data, stereotypical harms, and responsible crowdsourcing are part of the documentation around data collection and usage.

ml5.js is heavily inspired by Processing and p5.js.

Setup

Reference the latest version of ml5.js using a script tag in an HTML file as below:

<!DOCTYPE html>
  <html>
    <head>
      <title>Getting Started with ml5.js</title>
      <script src="https://unpkg.com/ml5"></script>
    </head>

    <body>
      <script>

        // Your code will go here

      </script>
    </body>
  </html>

That's all! 💥

Creating a simple image classification example

Let's add something more to classify an image using the pre-trained MobileNet model.

<!DOCTYPE html>
<html>
  <head>
    <head>
      <title>Getting Started with ml5.js</title>
      <script src="https://unpkg.com/ml5"></script>
    </head>

  <body>
    <h1>Image classification using MobileNet</h1>
    <p>The MobileNet model labeled this as
    <span id="result">...</span> with a confidence of
    <span id="probability">...</span></p>
    <img src="https://ml5js.org/docs/assets/img/bird.jpg"
     crossorigin="anonymous" id="image" width="400">

    <script>
      // The image we want to classify
      const image = document.getElementById('image');
      // The result tag in the HTML
      const result = document.getElementById('result');
      // The probability tag in the HTML
      const probability = document.getElementById('probability');

      // Initialize the Image Classifier method with MobileNet
      const classifier = ml5.imageClassifier('MobileNet');

      // Make a prediction with the selected image
      // This will return an array with a default of 10 options with their probabilities
      classifier.predict(image, function(results) {
        result.innerText = results[0].className;
        probability.innerText = results[0].probability.toFixed(4);
      });

    </script>
  </body>
</html>

Open it in a web browser and you should see something like this (note it may take a few seconds for the model to load!):

💥🤖

imageClassifier() →