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---
base_model: jonathandinu/face-parsing
library_name: transformers.js
pipeline_tag: image-segmentation
---
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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
```bash
npm i @xenova/transformers
```
**Example:** Face segmentation with `Xenova/face-parsing`.
```js
import { pipeline } from '@xenova/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:
```js
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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`). |