https://huggingface.co/timm/fastvit_sa24.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_sa24.apple_dist_in1k
.
import { pipeline } from '@xenova/transformers';
// Create an image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/fastvit_sa24.apple_dist_in1k');
// 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: 0.6914318203926086 },
// { label: 'tiger cat', score: 0.051140956580638885 },
// { label: 'shower curtain', score: 0.04092452675104141 },
// { label: 'apron', score: 0.004144148901104927 },
// { label: 'pillow', score: 0.003142605535686016 }
// ]
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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Inference API (serverless) does not yet support transformers.js models for this pipeline type.