# 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 './dog.jpg' to generate an image embedding.
Read the text 'a dog' to generate an text embedding.
*Write the pipeline in simplified style* :
```python
import towhee
towhee.glob('./dog.jpg') \
.image_decode.cv2() \
.towhee.clip(name='ViT-B/32', modality='image') \
.show()
towhee.dc(["a dog"]) \
.image_decode.cv2() \
.towhee.clip(name='ViT-B/32', modality='text') \
.show()
```
< img src = "https://towhee.io/image-embedding/dolg/raw/branch/main/result1.png" alt = "result1" style = "height:20px;" / >
*Write a same pipeline with explicit inputs/outputs name specifications:*
```python
import towhee
towhee.glob['path']('./dog.jpg') \
.image_decode.cv2['path', 'img']() \
.towhee.clip['data', 'vec'](name='ViT-B/32', modality='image') \
.select['data', 'vec']() \
.show()
towhee.dc(["a dog"]) \
.select['img', 'vec']() \
.towhee.clip['data', 'vec'](name='ViT-B/32', modality='image') \
.select['data', 'vec']() \
.show()
```
< img src = "https://towhee.io/image-embedding/dolg/raw/branch/main/result2.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.
** *modality:*** *str*
Which modality(*image* or *text* ) is used to generate the embedding.
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## Interface
An image embedding operator takes a [towhee image ](link/to/towhee/image/api/doc ) as input.
It uses the pre-trained model specified by model name to generate an image 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.
