lightningdot/README.md

# Image-Text Retrieval Embdding with LightningDOT

*author: David Wang*


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## Description

This operator extracts features for image or text with [LightningDOT](https://arxiv.org/abs/2103.08784) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity.


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## 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.lightningdot(model_name='lightningdot_base', modality='image') \
      .show()

towhee.dc(["A teddybear on a skateboard in Times Square."]) \
      .image_text_embedding.lightningdot(model_name='lightningdot_base', modality='text') \
      .show()
```
<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/vec1.png" alt="result1" style="height:20px;"/>
<img src="https://towhee.io/image-text-embedding/lightningdot/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.lightningdot['img', 'vec'](model_name='lightningdot_base', modality='image') \
      .select['img', 'vec']() \
      .show()

towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
      .image_text_embedding.lightningdot['text','vec'](model_name='lightningdot_base', modality='text') \
      .select['text', 'vec']() \
      .show()
```
<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
<img src="https://towhee.io/image-text-embedding/lightningdot/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***lightningdot(model_name, modality)***

**Parameters:**


​   ***model_name:*** *str*

​   The model name of LightningDOT. Supported model names: 
- lightningdot_base
- lightningdot_coco_ft
- lightningdot_flickr_ft     

​   ***modality:*** *str*

​   Which modality(*image* or *text*) is used to generate the embedding. 

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## 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.