Mobileclip S2

🚀 transformers.js

transformers.js 是一個與 ONNX 權重兼容的庫，基於 apple/ml-mobileclip 項目，可用於零樣本圖像分類等任務，為圖像特徵提取等工作提供支持。

🚀 快速開始

安裝

若你還未安裝 Transformers.js JavaScript 庫，可以通過以下命令從 NPM 進行安裝：

npm i @huggingface/transformers

💻 使用示例

基礎用法

以下是一個使用 transformers.js 執行零樣本圖像分類的示例：

import {
  AutoTokenizer,
  CLIPTextModelWithProjection,
  AutoProcessor,
  CLIPVisionModelWithProjection,
  RawImage,
  dot,
  softmax,
} from '@huggingface/transformers';

const model_id = 'Xenova/mobileclip_s2';

// Load tokenizer and text model
const tokenizer = await AutoTokenizer.from_pretrained(model_id);
const text_model = await CLIPTextModelWithProjection.from_pretrained(model_id);

// Load processor and vision model
const processor = await AutoProcessor.from_pretrained(model_id);
const vision_model = await CLIPVisionModelWithProjection.from_pretrained(model_id);

// Run tokenization
const texts = ['cats', 'dogs', 'birds'];
const text_inputs = tokenizer(texts, { padding: 'max_length', truncation: true });

// Compute text embeddings
const { text_embeds } = await text_model(text_inputs);
const normalized_text_embeds = text_embeds.normalize().tolist();

// Read image and run processor
const url = 'https://huggingface.co/datasets/Xenova/transformers.js-docs/resolve/main/cats.jpg';
const image = await RawImage.read(url);
const image_inputs = await processor(image);

// Compute vision embeddings
const { image_embeds } = await vision_model(image_inputs);
const normalized_image_embeds = image_embeds.normalize().tolist();

// Compute probabilities
const probabilities = normalized_image_embeds.map(
  x => softmax(normalized_text_embeds.map(y => 100 * dot(x, y)))
);
console.log(probabilities); // [[ 0.9999973851268408, 0.000002399646544186113, 2.1522661499262862e-7 ]]

📄 許可證

本項目採用 other 許可證。