https://huggingface.co/nvidia/mit-b2 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/mit-b2
.
import { pipeline } from '@xenova/transformers';
// Create image classification pipeline
const classifier = await pipeline('image-classification', 'Xenova/mit-b2');
// 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: 0.761507511138916 }]
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
).
- Downloads last month
- 7
Inference API (serverless) does not yet support transformers.js models for this pipeline type.
Model tree for Xenova/mit-b2
Base model
nvidia/mit-b2