unum-cloud/uform-vl-english-large-gpu-fp32

UForm

Multi-Modal Inference Library
For Semantic Search Applications

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UForm is a Multi-Modal Modal Inference package, designed to encode Multi-Lingual Texts, Images, and, soon, Audio, Video, and Documents, into a shared vector space!

This is model card of the English only model with:

• 12 layers BERT (6 layers for unimodal encoding and rest layers for multimodal encoding)
• ViT-L/14 (image resolution is 224x224)
• Multiple embedding sizes: 64, 256, 512, 768

If you need Multilingual model, check this.

Evaluation

The following metrics were obtained with multimodal re-ranking (text-to-image retrieval):

Dataset	Recall@1	Recall@5	Recall@10
Zero-Shot Flickr	0.693	0.875	0.923
Zero-Shot MS-COCO	0.382	0.617	0.728

ImageNet-Top1: 0.518
ImageNet-Top5: 0.756

Installation

pip install uform[onnx-gpu]

Usage

To load the model:

import uform
model, processor = uform.get_model_onnx('unum-cloud/uform-vl-english-large', device='gpu', dtype='fp32')

To encode data:

from PIL import Image

text = 'a small red panda in a zoo'
image = Image.open('red_panda.jpg')

image_data = processor.preprocess_image(image)
text_data = processor.preprocess_text(text)

image_features, image_embedding = model.encode_image(image_data, return_features=True)
text_features, text_embedding = model.encode_text(text_data, return_features=True)
score, joint_embedding = model.encode_multimodal(
    image_features=image_features,
    text_features=text_features,
    attention_mask=text_data['attention_mask'],
    return_scores=True
)

There are two options to calculate semantic compatibility between an image and a text: cosine similarity and Matching Score.

Cosine Similarity

Pros:

• Computationally cheap.
• Only unimodal embeddings are required, unimodal encoding is faster than joint encoding.
• Suitable for retrieval in large collections.

Cons:

• Takes into account only coarse-grained features.

Matching Score

Unlike cosine similarity, unimodal embedding are not enough. Joint embedding will be needed and the resulting score will belong to the [0, 1] range, 1 meaning the absolute match.

Pros:

• Joint embedding captures fine-grained features.
• Suitable for re-ranking – sorting retrieval result.

Cons:

• Resource-intensive.
• Not suitable for retrieval in large collections.