onnx-community/ijepa_vith16_1k

https://huggingface.co./jmtzt/ijepa_vith16_1k 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: Image feature extraction with onnx-community/ijepa_vith16_1k.

import { pipeline, cos_sim } from "@huggingface/transformers";

// Create an image feature extraction pipeline
const extractor = await pipeline(
  "image-feature-extraction",
  "onnx-community/ijepa_vith16_1k",
  { dtype: "q8" },
);

// Compute image embeddings
const url_1 = "http://images.cocodataset.org/val2017/000000039769.jpg"
const url_2 = "http://images.cocodataset.org/val2017/000000219578.jpg"
const output = await extractor([url_1, url_2]);
const pooled_output = output.mean(1); // Apply mean pooling

// Compute cosine similarity
const similarity = cos_sim(pooled_output[0].data, pooled_output[1].data);
console.log(similarity); // 0.5334921616321957

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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).