metadata
base_model: mattmdjaga/segformer_b2_clothes
library_name: transformers.js
pipeline_tag: image-segmentation
https://huggingface.co/mattmdjaga/segformer_b2_clothes 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 @xenova/transformers
Example: Clothes segmentation with Xenova/segformer_b2_clothes.
import { pipeline } from '@xenova/transformers';
const segmenter = await pipeline('image-segmentation', 'Xenova/segformer_b2_clothes');
const url = 'https://freerangestock.com/sample/139043/young-man-standing-and-leaning-on-car.jpg';
const output = await segmenter(url);
console.log(output)
// [
// {
// score: null,
// label: 'Background',
// mask: RawImage { ... }
// },
// {
// score: null,
// label: 'Hair',
// 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).