library_name: transformers.js | |
https://huggingface.co/facebook/convnext-base-384-22k-1k 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:** Perform image classification with `Xenova/convnext-base-384-22k-1k`. | |
```js | |
import { pipeline } from '@xenova/transformers'; | |
// Create image classification pipeline | |
const classifier = await pipeline('image-classification', 'Xenova/convnext-base-384-22k-1k'); | |
// Classify an image | |
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg'; | |
const output = await classifier(url); | |
console.log(output) | |
``` | |
--- | |
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`). |