https://github.com/apple/ml-mobileclip 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 zero-shot image classification.
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 ]]