# Image-Text Retrieval Embdding with BLIP

*author: David Wang*


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## Description

This operator extracts features for image or text with [BLIP](https://arxiv.org/abs/2201.12086) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity. This is a adaptation from [salesforce/BLIP](https://github.com/salesforce/BLIP).


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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 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.blip(model_name='blip_itm_base_coco', modality='image'))
    .output('img', 'vec')
)

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

DataCollection(img_pipe('./teddy.jpg')).show()
DataCollection(text_pipe('A teddybear on a skateboard in Times Square.')).show()

```
<img src="https://towhee.io/image-text-embedding/blip/raw/branch/main/tabular1.png" alt="result1" style="height:60px;"/>
<img src="https://towhee.io/image-text-embedding/blip/raw/branch/main/tabular2.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***blip(model_name, modality)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of BLIP. Supported model names: 
- blip_itm_base_coco
- blip_itm_large_coco
- blip_itm_base_flickr
- blip_itm_large_flickr


​   ***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.

***save_model(format='pytorch', path='default')***

Save model to local with specified format.

**Parameters:**

***format***: *str*

​	The format of saved model, defaults to 'pytorch'.

***path***: *str*

​	The path where model is saved to. By default, it will save model to the operator directory.


```python
from towhee import ops

op = ops.image_text_embedding.blip(model_name='blip_itm_base_coco', modality='image').get_op()
op.save_model('onnx', 'test.onnx')
```
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**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.	



**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.