README.md

metadata

base_model: mattmdjaga/segformer_b0_clothes
library_name: transformers.js
pipeline_tag: image-segmentation

https://huggingface.co/mattmdjaga/segformer_b0_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 @huggingface/transformers

Example: Clothes segmentation with Xenova/segformer_b0_clothes.

import { pipeline } from '@huggingface/transformers';

const segmenter = await pipeline('image-segmentation', 'Xenova/segformer_b0_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).