metadata
base_model: facebook/convnext-tiny-224
library_name: transformers.js
https://huggingface.co/facebook/convnext-tiny-224 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 @huggingface/transformers
Example: Perform image classification with Xenova/convnext-tiny-224.
import { pipeline } from '@huggingface/transformers';
// Create image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/convnext-tiny-224', {
dtype: "fp32" // Options: "fp32", "fp16", "q8", "q4"
});
// 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: 0.6153212785720825 }]
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).