# Image-Text Retrieval Embdding with CLIP
*author: David Wang*
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## Description
This operator extracts features for image or text with [CLIP ](https://arxiv.org/abs/2108.02927 ) which can genearte the embedding for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This operator is an adaptation from [openai/CLIP ](https://github.com/openai/CLIP ).
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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.cv2() \
.towhee.clip(name='ViT-B/32', modality='image') \
.show()
towhee.dc(["A teddybear on a skateboard in Times Square."]) \
.image_decode.cv2() \
.towhee.clip(name='ViT-B/32', modality='text') \
.show()
```
< img src = "https://towhee.io/towhee/clip/raw/branch/main/vec1.png" alt = "result1" style = "height:20px;" / >
< img src = "https://towhee.io/towhee/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.cv2['path', 'img']() \
.towhee.clip['data', 'vec'](name='ViT-B/32', modality='image') \
.select['data', 'vec']() \
.show()
towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
.towhee.clip['text','vec'](name='ViT-B/32', modality='text') \
.select['text', 'vec']() \
.show()
```
< img src = "https://towhee.io/towhee/clip/raw/branch/main/tabular1.png" alt = "result1" style = "height:60px;" / >
< img src = "https://towhee.io/towhee/clip/raw/branch/main/tabular2.png" alt = "result2" style = "height:60px;" / >
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## Factory Constructor
Create the operator via the following factory method
***clip(name, modality)***
**Parameters:**
** *name:*** *str*
The model name of CLIP. avaliable options are:
- RN50
- RN101
- RN50x4
- RN50x16
- RN50x64
- ViT-B/32
- ViT-B/64
- ViT-L/14
** *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 choosed modality) to generate the embedding.
**Returns:** *numpy.ndarray*
The data embedding extracted by model.
