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Realtime predictions at the edge with roboflow.js
For most business applications, the Hosted API is suitable. But for many consumer applications and some enterprise use cases, having a server-hosted model is not workable (for example, if your users are bandwidth constrained or need lower latency than you can achieve using a remote API).
roboflow.js is have built a custom layer on top of Tensorflow.js to enable real-time inference via JavaScript using models trained on Roboflow.

Learning Resources

Try Your Model With a Webcam

Once you have a trained model, you can easily test it using your webcam using the "Try with Webcam" button.
The webcam demo is a sample app that is available for you to download and tinker with the "Get Code" link.
You can try out a webcam demo of a hand-detector model here (it is trained on the public EgoHands dataset).
Demo video of using roboflow.js to build a custom web app.

Interactive Replit Environment

We have published a "Getting Started" project on Repl.it with an accompanying tutorial showing how to deploy YOLOv8 models using our Repl.it template.

GitHub Template

The Roboflow homepage uses roboflow.js to power the COCO inference widget. The source code for this project is available on GitHub. The README contains instructions on how to use the repository template to deploy a model to the web using GitHub Pages.

Documentation

If you would like more details regarding specific functions in roboflow.js, check out our documentation page or click on any mention of a roboflow.js method in our guide below to be taken to the respective documentation.

Installation

To add roboflow.js to your project, simply add the script tag referencing our CDN to your page's <head> tag.
<script src="https://cdn.roboflow.com/0.2.26/roboflow.js"></script>

Initialization

You can obtain your publishable_key from the Roboflow API settings page. You can get it by going into your workspace, going into Settings, in the Roboflow API page, and getting your Publishable API Key.
Your model ID and version number are located in the URL of the dataset version page (where you started training and see your results).
Note: your publishable_key is used with roboflow.js, not your API key (which should remain secret).
You can use the roboflow.auth() and roboflow.load() functions, or chain them like roboflow.auth().load() to authenticate and load your model. roboflow.load() returns a Promise that you can use to access a loaded model object. If you plan to use your model in one place on the page, you can add a .then() statement directly after the .load() function like this:
roboflow.auth({
publishable_key: "<< YOUR PUBLISHABLE KEY >>"
}).load({
model: "<< YOUR MODEL ID >>",
version: 1 // <--- YOUR VERSION NUMBER
}).then(function(model) {
// model has loaded!
});
You can also define an async function that returns the model on which you can run inference:
async function getModel() {
var model = await roboflow
.auth({
publishable_key: API_KEY,
})
.load({
model: MODEL_NAME,
version: MODEL_VERSION,
});
return model;
}
var initialized_model = getModel();
initialized_model.then(function (model) {
/// use model.detect() to make a prediction (see "Getting Predictions" below)
});
This code lets you initialize your model in one place for use throughout your code.

Configuration

If you would like to customize and configure the way roboflow.js filters its predictions, there is a model.configure() method you can use to tune.

Getting Predictions

To get predictions back from your model object, use the model.detect() method. It takes an input image (which can be any <img>, <canvas>, or <video> element). It returns a promise that resolves with an array of predictions.
model.detect(video).then(function(predictions) {
console.log("Predictions:", predictions);
});
Last modified 2mo ago