Xenova/fastvit_t12.apple_dist_in1k

https://huggingface.co/timm/fastvit_t12.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 JavaScript library from NPM using:

npm i @xenova/transformers

Example: Perform image classification with Xenova/fastvit_t12.apple_dist_in1k.

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

// Create an image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/fastvit_t12.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: 0.7830049991607666 },
//   { label: 'tiger cat', score: 0.08560094237327576 },
//   { label: 'lynx, catamount', score: 0.0005580639117397368 },
//   { label: 'Bernese mountain dog', score: 0.0005574578535743058 },
//   { label: 'Appenzeller', score: 0.0005380114307627082 }
// ]

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