# Image-Text Retrieval Embdding with SLIP

*author: David Wang*


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## 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).


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## Code Example

Load an image from path './moon.jpg' to generate an image embedding. 

Read the text 'moon in the night.' to generate a text embedding. 

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

```python
from towhee.dc2 import pipe, ops, DataCollection

img_pipe = (
    pipe.input('url')
    .map('url', 'img', ops.image_decode.cv2_rgb())
    .map('img', 'vec', ops.image_text_embedding.slip(model_name='slip_vit_small', modality='image'))
    .output('img', 'vec')
)

text_pipe = (
    pipe.input('text')
    .map('text', 'vec', ops.image_text_embedding.slip(model_name='slip_vit_small', modality='text'))
    .output('text', 'vec')
)

DataCollection(img_pipe('./moon.jpg')).show()
DataCollection(text_pipe('moon in the night.')).show()

```
<img src="./tabular1.png" alt="result1" style="height:60px;"/>
<img src="./tabular2.png" alt="result2" style="height:60px;"/>


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## 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. 

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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.