Ijepa Vith16 1k

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

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).

onnx/model_int8.onnx

onnx · 609 MB · SHA-256 7b75ad866418…d0a5 · Hugging Face

--- library_name: transformers.js base_model: jmtzt/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](https://huggingface.co/docs/transformers.js) JavaScript library from [NPM](https://www.npmjs.com/package/@huggingface/transformers) using: ```bash npm i @huggingface/transformers ``` **Example:** Image feature extraction with `onnx-community/ijepa_vith16_1k`. ```js 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 ``` --- 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](https://huggingface.co/docs/optimum/index) and structuring your repo like this one (with ONNX weights located in a subfolder named `onnx`).