README.md

---
base_model: timm/fastvit_t8.apple_dist_in1k
library_name: transformers.js
license: other
pipeline_tag: image-classification
---

https://huggingface.co/timm/fastvit_t8.apple_dist_in1k 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/fastvit_t8.apple_dist_in1k`.
```js
import { pipeline } from '@xenova/transformers';

// Create an image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/fastvit_t8.apple_dist_in1k', {
  quantized: false
});

// Classify an image
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/tiger.jpg';
const output = await classifier(url, { topk: 5 });
console.log(output);
// [
//   { label: 'tiger, Panthera tigris', score: 0.7876936197280884 },
//   { label: 'tiger cat', score: 0.08878856152296066 },
//   { label: 'zebra', score: 0.0008800383075140417 },
//   { label: 'Appenzeller', score: 0.0008539424743503332 },
//   { label: 'jaguar, panther, Panthera onca, Felis onca', score: 0.0008008014992810786 }
// ]
```

---

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`).