Xenova/segformer-b5-finetuned-cityscapes-1024-1024

https://huggingface.co/nvidia/segformer-b5-finetuned-cityscapes-1024-1024 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: Image segmentation with Xenova/segformer-b5-finetuned-cityscapes-1024-1024.

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

// Create an image segmentation pipeline
const segmenter = await pipeline('image-segmentation', 'Xenova/segformer-b5-finetuned-cityscapes-1024-1024');

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