https://huggingface.co/facebook/dinov2-base-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 @xenova/transformers
Example: Image classification w/ Xenova/dinov2-base-imagenet1k-1-layer.
import { pipeline } from '@xenova/transformers';
// Create image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/dinov2-base-imagenet1k-1-layer', { quantized: false });
// 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: 0.4475940465927124 }]
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).
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