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# albef
# Image-Text Retrieval Embdding with ALBEF
*author: David Wang*
<br />
## Description
This operator extracts features for image or text with [ALBEF](https://arxiv.org/abs/2103.00020) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This research introduced a contrastive loss to ALign the image and text representations BEfore Fusing (ALBEF) them through cross-modal attention, which enables more grounded vision and language representation learning. This repo is an adaptation from [salesforce / ALBEF](https://github.com/salesforce/ALBEF)
<br />
## Code Example
Load an image from path './teddy.jpg' to generate an image embedding.
Read the text 'A teddybear on a skateboard in Times Square.' to generate an text embedding.
*Write the pipeline in simplified style*:
```python
import towhee
towhee.glob('./teddy.jpg') \
.image_decode() \
.image_text_embedding.albef(model_name='albef_4m', modality='image') \
.show()
towhee.dc(["A teddybear on a skateboard in Times Square."]) \
.image_text_embedding.albef(model_name='albef_4m', modality='text') \
.show()
```
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/vec2.png" alt="result2" style="height:20px;"/>
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
towhee.glob['path']('./teddy.jpg') \
.image_decode['path', 'img']() \
.image_text_embedding.albef['img', 'vec'](model_name='albef_4m', modality='image') \
.select['img', 'vec']() \
.show()
towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
.image_text_embedding.albef['text','vec'](model_name='albef_4m', modality='text') \
.select['text', 'vec']() \
.show()
```
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
<img src="https://towhee.io/image-text-embedding/clip/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>
<br />
## Factory Constructor
Create the operator via the following factory method
***albef(model_name, modality)***
**Parameters:**
***model_name:*** *str*
​ The model name of ALBEF. Supported model names:
- albef_4m
- albef_14m
***modality:*** *str*
​ Which modality(*image* or *text*) is used to generate the embedding.
<br />
## Interface
An image-text embedding operator takes a [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.
**Parameters:**
***data:*** *towhee.types.Image (a sub-class of numpy.ndarray)* or *str*
​ The data (image or text based on specified modality) to generate embedding.
**Returns:** *numpy.ndarray*
​ The data embedding extracted by model.

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