Xenova/segformer-b5-finetuned-ade-640-640

https://huggingface.co/nvidia/segformer-b5-finetuned-ade-640-640 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: Image segmentation with Xenova/segformer-b5-finetuned-ade-640-640.

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

// Create an image segmentation pipeline
const segmenter = await pipeline('image-segmentation', 'Xenova/segformer-b5-finetuned-ade-640-640');

// Segment an image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/house.jpg';
const output = await segmenter(url);
console.log(output)
// [
//   {
//     score: null,
//     label: 'wall',
//     mask: RawImage { ... }
//   },
//   {
//     score: null,
//     label: 'building',
//     mask: RawImage { ... }
//   },
//   ...
// ]

You can visualize the outputs with:

for (const l of output) {
  l.mask.save(`${l.label}.png`);
}

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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).