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@tensorflow-models/blazeface
Advanced tools
Readme
Blazeface is a lightweight model that detects faces in images. Blazeface makes use of the Single Shot Detector architecture with a custom encoder. The model may serve as a first step for face-related computer vision applications, such as facial keypoint recognition.
More background information about the model, as well as its performance characteristics on different datasets, can be found here: https://drive.google.com/file/d/1f39lSzU5Oq-j_OXgS67KfN5wNsoeAZ4V/view
The model is designed for front-facing cameras on mobile devices, where faces in view tend to occupy a relatively large fraction of the canvas. Blazeface may struggle to identify far-away faces.
Check out our demo, which uses the model to predict facial bounding boxes from a live video stream.
This model is also available as part of MediaPipe, a framework for building multimodal applied ML pipelines.
Using yarn
:
$ yarn add @tensorflow-models/blazeface
Using npm
:
$ npm install @tensorflow-models/blazeface
Note that this package specifies @tensorflow/tfjs-core
and @tensorflow/tfjs-converter
as peer dependencies, so they will also need to be installed.
To import in npm:
import * as blazeface from '@tensorflow-models/blazeface';
or as a standalone script tag:
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-core"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow/tfjs-converter"></script>
<script src="https://cdn.jsdelivr.net/npm/@tensorflow-models/blazeface"></script>
Then:
async function main() {
// Load the model.
const model = await blazeface.load();
// Pass in an image or video to the model. The model returns an array of
// bounding boxes, probabilities, and landmarks, one for each detected face.
const returnTensors = false; // Pass in `true` to get tensors back, rather than values.
const predictions = await model.estimateFaces(document.querySelector("img"), returnTensors);
if (predictions.length > 0) {
/*
`predictions` is an array of objects describing each detected face, for example:
[
{
topLeft: [232.28, 145.26],
bottomRight: [449.75, 308.36],
probability: [0.998],
landmarks: [
[295.13, 177.64], // right eye
[382.32, 175.56], // left eye
[341.18, 205.03], // nose
[345.12, 250.61], // mouth
[252.76, 211.37], // right ear
[431.20, 204.93] // left ear
]
}
]
*/
for (let i = 0; i < predictions.length; i++) {
const start = predictions[i].topLeft;
const end = predictions[i].bottomRight;
const size = [end[0] - start[0], end[1] - start[1]];
// Render a rectangle over each detected face.
ctx.fillRect(start[0], start[1], size[0], size[1]);
}
}
}
main();
FAQs
Pretrained face detection model in TensorFlow.js
The npm package @tensorflow-models/blazeface receives a total of 6,568 weekly downloads. As such, @tensorflow-models/blazeface popularity was classified as popular.
We found that @tensorflow-models/blazeface demonstrated a healthy version release cadence and project activity because the last version was released less than a year ago. It has 9 open source maintainers collaborating on the project.
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