https://huggingface.co/timm/fastvit_t12.apple_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_in1k.
import { pipeline } from '@xenova/transformers';
// Create an image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/fastvit_t12.apple_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: 0.6649345755577087 },
// { label: 'tiger cat', score: 0.12454754114151001 },
// { label: 'lynx, catamount', score: 0.0010689536575227976 },
// { label: 'dhole, Cuon alpinus', score: 0.0010422508930787444 },
// { label: 'silky terrier, Sydney silky', score: 0.0009548701345920563 }
// ]
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).
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