| | --- |
| | base_model: apple/mobilevit-small |
| | library_name: transformers.js |
| | --- |
| | |
| | https://huggingface.co/apple/mobilevit-small 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/@huggingface/transformers) using: |
| | ```bash |
| | npm i @huggingface/transformers |
| | ``` |
| |
|
| | **Example:** Perform image classification with `Xenova/mobilevit-small` |
| | ```js |
| | import { pipeline } from '@huggingface/transformers'; |
| | |
| | // Create an image classification pipeline |
| | const classifier = await pipeline('image-classification', 'Xenova/mobilevit-small', { |
| | dtype: "fp32", |
| | }); |
| | |
| | // 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); |
| | // [{ label: 'tiger, Panthera tigris', score: 0.7868736982345581 }] |
| | ``` |
| |
|
| | --- |
| |
|
| | 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`). |
