README.md

---
base_model: facebook/dinov2-large-imagenet1k-1-layer
library_name: transformers.js
---

https://huggingface.co/facebook/dinov2-large-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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@xenova/transformers) using:
```bash
npm i @xenova/transformers
```

**Example:** Image classification w/ `Xenova/dinov2-base-imagenet1k-1-layer`.

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

// Create image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/dinov2-large-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: 0.5089695453643799 }]
```

---


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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).