import { pipeline } from '@huggingface/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).