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vit-base-patch16-224-in21k
Xenova/vit-base-patch16-224-in21k
vit-base-patch16-224-in21k is a image feature extraction model from Xenova. Use it for the image feature extraction task on the model card, and read the license before you ship it in a product. It is set up for transformers.js.
https://huggingface.co/google/vit-base-patch16-224-in21k with ONNX weights to be compatible with Transformers.js.
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.onnx848 MB · 100%
At a glance
- Task
- Image Feature Extraction
- Library
- transformers.js
- Model type
- vit
- Access
- Public
- Created
- May 3, 2023
- Updated
- Jul 10, 2025
- SHA
- 61fac553
Base models
Model card
From the Hugging Face model README
https://huggingface.co/google/vit-base-patch16-224-in21k 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 feature extraction.
import { pipeline } from '@huggingface/transformers';
const image_feature_extractor = await pipeline('image-feature-extraction', 'Xenova/vit-base-patch16-224-in21k');
const url = 'https://huggingface.co/datasets/huggingface/documentation-images/resolve/main/cats.png';
const features = await image_feature_extractor(url);
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).
- Task
- Image Feature Extraction
- Library
- transformers.js
- Type
- vit
- Created
- May 3, 2023
- Updated
- Jul 10, 2025