Edit model card

https://huggingface.co/google/vit-base-patch16-224 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: Perform image classification with Xenova/vit-base-patch16-224

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

const classifier = await pipeline('image-classification', 'Xenova/vit-base-patch16-224')
const urls = [
    'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg',
    'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg',
];
const output = await classifier(urls)
// [
//   { label: 'tiger, Panthera tigris', score: 0.6074584722518921 },
//   { label: 'Egyptian cat', score: 0.8246098756790161 }
// ]

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 ๐Ÿค— Optimum and structuring your repo like this one (with ONNX weights located in a subfolder named onnx).

Downloads last month
6
Inference API
Drag image file here or click to browse from your device
Inference API (serverless) does not yet support transformers.js models for this pipeline type.

Space using Xenova/vit-base-patch16-224 1