onnx-community/dinov2-with-registers-small-imagenet1k-1-layer

https://huggingface.co/facebook/dinov2-with-registers-small-imagenet1k-1-layer 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 classification w/ onnx-community/dinov2-with-registers-small-imagenet1k-1-layer.

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

// Create image classification pipeline
const classifier = await pipeline('image-classification', 'onnx-community/dinov2-with-registers-small-imagenet1k-1-layer');

// Classify an image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const output = await classifier(url);
console.log(output);
// [
//   { label: 'tabby, tabby cat', score: 0.8135351538658142 },
//   { label: 'tiger cat', score: 0.08967583626508713 },
//   { label: 'Egyptian cat', score: 0.06800546497106552 },
//   { label: 'radiator', score: 0.003501888597384095 },
//   { label: 'quilt, comforter, comfort, puff', score: 0.003408448537811637 },
// ]

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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).