# Image-Text Retrieval Embdding with CLIP

*author: David Wang*


<br />



## Description

This operator extracts features for image or text with [CLIP](https://arxiv.org/abs/2103.00020) which can generate embeddings for text and image by jointly training an image encoder and text encoder to maximize the cosine similarity.


<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('./teddy.jpg') \
      .image_decode() \
      .towhee.clip(model_name='clip_vit_b32', modality='image') \
      .show()

towhee.dc(["A teddybear on a skateboard in Times Square."]) \
      .towhee.clip(model_name='clip_vit_b32', 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['path', 'img']() \
      .towhee.clip['img', 'vec'](model_name='clip_vit_b32', modality='image') \
      .select['img', 'vec']() \
      .show()

towhee.dc['text'](["A teddybear on a skateboard in Times Square."]) \
      .towhee.clip['text','vec'](model_name='clip_vit_b32', 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;"/>


<br />



## Factory Constructor

Create the operator via the following factory method

***clip(model_name, modality)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of CLIP. Supported model names: 
- clip_resnet_r50      
- clip_resnet_r101
- clip_vit_b32 
- clip_vit_b16


​   ***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.






# More Resources

- [CLIP Object Detection: Merging AI Vision with Language Understanding - Zilliz blog](https://zilliz.com/learn/CLIP-object-detection-merge-AI-vision-with-language-understanding): CLIP Object Detection combines CLIP's text-image understanding with object detection tasks, allowing CLIP to locate and identify objects in images using texts.
- [Supercharged Semantic Similarity Search in Production - Zilliz blog](https://zilliz.com/learn/supercharged-semantic-similarity-search-in-production): Building a Blazing Fast, Highly Scalable Text-to-Image Search with CLIP embeddings and Milvus, the most advanced open-source vector database.
- [The guide to clip-vit-base-patch32 | OpenAI](https://zilliz.com/ai-models/clip-vit-base-patch32): clip-vit-base-patch32: a CLIP multimodal model variant by OpenAI for image and text embedding.
- [Image Embeddings for Enhanced Image Search - Zilliz blog](https://zilliz.com/learn/image-embeddings-for-enhanced-image-search): Image Embeddings are the core of modern computer vision algorithms. Understand their implementation and use cases and explore different image embedding models.
- [From Text to Image: Fundamentals of CLIP - Zilliz blog](https://zilliz.com/blog/fundamentals-of-clip): Search algorithms rely on semantic similarity to retrieve the most relevant results. With the CLIP model, the semantics of texts and images can be connected in a high-dimensional vector space. Read this simple introduction to see how CLIP can help you build a powerful text-to-image service.