# Image-Text Retrieval Embdding with SLIP

*author: David Wang*


<br />



## Description

This operator extracts features for image or text with [SLIP](https://arxiv.org/abs/2112.12750), a multi-task learning framework for combining self-supervised learning and CLIP pre-training. This is an adaptation from [facebookresearch/SLIP](https://github.com/facebookresearch/SLIP).


<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('./moon.jpeg') \
      .image_decode() \
      .image_text_embedding.slip(model_name='slip_vit_small', modality='image') \
      .show()

towhee.dc(['moon in the night.']) \
      .image_text_embedding.slip(model_name='slip_vit_small', modality='text') \
      .show()
```
<img src="./vec1.png" alt="result1" style="height:20px;"/>
<img src="./vec2.png" alt="result2" style="height:20px;"/>

*Write a same pipeline with explicit inputs/outputs name specifications:*

```python
import towhee

towhee.glob['path']('./moon.jpeg') \
      .image_decode['path', 'img']() \
      .image_text_embedding.slip['img', 'vec'](model_name='slip_vit_small', modality='image') \
      .select['img', 'vec']() \
      .show()

towhee.dc['text'](['moon in the night.']) \
      .image_text_embedding.slip['text','vec'](model_name= 'slip_vit_small', modality='text') \
      .select['text', 'vec']() \
      .show()
```
<img src="./tabular1.png" alt="result1" style="height:60px;"/>
<img src="./tabular2.png" alt="result2" style="height:60px;"/>


<br />



## Factory Constructor

Create the operator via the following factory method

***slip(model_name, modality)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of SLIP. Supported model names: 
- slip_vit_small
- slip_vit_base
- slip_vit_large


​   ***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.