unum-cloud / uform-vl-english-large

UForm

Multi-Modal Inference Library
For Semantic Search Applications

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):

ImageNet-Top1: 0.518
ImageNet-Top5: 0.756

Installation

pip install uform[torch]

Usage

To load the model:

import uform

model, processor = uform.get_model('unum-cloud/uform-vl-english-large')

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)
joint_embedding = model.encode_multimodal(image=image_data, text=text_data)

To get features:

image_features, image_embedding = model.encode_image(image_data, return_features=True)
text_features, text_embedding = model.encode_text(text_data, return_features=True)

These features can later be used to produce joint multimodal encodings faster, as the first layers of the transformer can be skipped:

joint_embedding = model.encode_multimodal(
    image_features=image_features,
    text_features=text_features,
    attention_mask=text_data['attention_mask']
)

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

Cosine Similarity

import torch.nn.functional as F

similarity = F.cosine_similarity(image_embedding, text_embedding)

The similarity will belong to the [-1, 1] range, 1 meaning the absolute match.

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.

score = model.get_matching_scores(joint_embedding)

Pros :

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

Cons :

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