---
license: apache-2.0
pipeline_tag: feature-extraction
tags:
- clip
- vision
datasets:
- sbu_captions
- visual_genome
- ChristophSchuhmann/MS_COCO_2017_URL_TEXT
---
<h1 align="center">UForm</h1>
<h3 align="center">
Multi-Modal Inference Library<br/>
For Semantic Search Applications<br/>
</h3>

---

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:

* 4 layers BERT (2 layers for unimodal encoding and rest layers for multimodal encoding)
* ViT-B/16 (image resolution is 224x224)


If you need Multilingual model, check [this](https://huggingface.co/unum-cloud/uform-vl-multilingual).

## Evaluation

The following metrics were obtained with multimodal re-ranking:

| Dataset |  Recall@1 |  Recall@5 | Recall@10 |
| :-------- | ------: | --------: | --------: |
| Zero-Shot Flickr | 0.727 | 0.915 | 0.949 |
| MS-COCO (train split was in training data) | 0.510 | 0.761 | 0.838 |

## Installation

```bash
pip install uform
```

## Usage

To load the model:

```python
import uform

model = uform.get_model('unum-cloud/uform-vl-english')
```

To encode data:

```python
from PIL import Image

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

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

image_embedding = model.encode_image(image_data)
text_embedding = model.encode_text(text_data)
joint_embedding = model.encode_multimodal(image=image_data, text=text_data)
```

To get features:

```python
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:

```python
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](#cosine-similarity) and [Matching Score](#matching-score).

### Cosine Similarity

```python
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.

```python
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.