# Video-Text Retrieval Embdding with CLIP4Clip

*author: Chen Zhang*


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## Description

This operator extracts features for video or text with [CLIP4Clip](https://arxiv.org/abs/2104.08860) which can generate embeddings for text and video by jointly training a video encoder and text encoder to maximize the cosine similarity.


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## Code Example

Read the text 'kids feeding and playing with the horse' to generate an text embedding. 

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

p = (
    pipe.input('text') \
        .map('text', 'vec', ops.video_text_embedding.clip4clip(model_name='clip_vit_b32', modality='text', device='cuda:1')) \
        .output('text', 'vec')
)

DataCollection(p('kids feeding and playing with the horse')).show()

```
![](text_emb_output.png)  

Load an video from path './demo_video.mp4' to generate an video embedding. 

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

p = (
    pipe.input('video_path') \
        .map('video_path', 'flame_gen', ops.video_decode.ffmpeg(sample_type='uniform_temporal_subsample', args={'num_samples': 12})) \
        .map('flame_gen', 'flame_list', lambda x: [y for y in x]) \
        .map('flame_list', 'vec', ops.video_text_embedding.clip4clip(model_name='clip_vit_b32', modality='video', device='cuda:2')) \
        .output('video_path', 'flame_list', 'vec')
)

DataCollection(p('./demo_video.mp4')).show()
```
![](video_emb_ouput.png)   

<br />



## Factory Constructor

Create the operator via the following factory method

***clip4clip(model_name, modality, weight_path)***

**Parameters:**

​   ***model_name:*** *str*

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


​   ***modality:*** *str*

​   Which modality(*video* or *text*) is used to generate the embedding. 

​   ***weight_path:*** *str*

​   pretrained model weights path.  

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## Interface

An video-text embedding operator takes a list of [towhee image](link/to/towhee/image/api/doc) or string as input and generate an embedding in ndarray.


**Parameters:**

​	***data:*** *List[towhee.types.Image]*  or *str*

​  The data (list of image(which is uniform subsampled from a video) or text based on specified modality) to generate embedding.	



**Returns:** *numpy.ndarray*

​   The data embedding extracted by model.






# More Resources

- [Vector Database Use Cases: Video Similarity Search - Zilliz](https://zilliz.com/vector-database-use-cases/video-similarity-search): Experience a 10x performance boost and unparalleled precision when your video similarity search system is powered by Zilliz Cloud.
- [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.
- [Tutorial: Diving into Text Embedding Models | Zilliz Webinar](https://zilliz.com/event/tutorial-text-embedding-models/success): Register for a free webinar diving into text embedding models in a presentation and tutorial
- [4 Steps to Building a Video Search System - Zilliz blog](https://zilliz.com/blog/building-video-search-system-with-milvus): Searching for videos by image with Milvus
- [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.