https://huggingface.co/jonathandinu/face-parsing with ONNX weights to be compatible with Transformers.js.
Usage (Transformers.js)
If you haven't already, you can install the Transformers.js JavaScript library from NPM using:
npm i @huggingface/transformers
Example: Face segmentation with Xenova/face-parsing.
import { pipeline } from '@huggingface/transformers';
const segmenter = await pipeline('image-segmentation', 'Xenova/face-parsing');
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/portrait-of-woman.jpg';
const output = await segmenter(url);
console.log(output)
// [
// {
// score: null,
// label: 'background',
// mask: RawImage { ... }
// },
// {
// score: null,
// label: 'skin.png',
// mask: RawImage { ... }
// },
// ...
// }
// ]
You can visualize the outputs with:
for (const l of output) {
l.mask.save(`${l.label}.png`);
}
Note: Having a separate repo for ONNX weights is intended to be a temporary solution until WebML gains more traction. If you would like to make your models web-ready, we recommend converting to ONNX using 🤗 Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).