frozen-in-time/README.md

# Video-Text Retrieval Embedding with Frozen In Time

*author: Jinling Xu*

<br />

## Description

This operator extracts features for video or text with [Frozen In Time](https://arxiv.org/abs/2104.00650) 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

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

- Encode video (default):
```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': 4})) \
        .map('flame_gen', 'flame_list', lambda x: [y for y in x]) \
        .map('flame_list', 'vec', ops.video_text_embedding.frozen_in_time(model_name='frozen_in_time_base_16_244', modality='video', device='cpu')) \
        .output('video_path', 'flame_list', 'vec')
)

DataCollection(p('./demo_video.mp4')).show()

```
<img src="./video_emb_result.png" width="800px"/>

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

- Encode text:
```python
from towhee import pipe, ops, DataCollection

p = (
    pipe.input('text') \
        .map('text', 'vec', ops.video_text_embedding.frozen_in_time(model_name='frozen_in_time_base_16_244', modality='text', device='cpu')) \
        .output('text', 'vec')
)

DataCollection(p('kids feeding and playing with the horse')).show()
```
<img src="./text_emb_result.png" width="800px"/>


<br />



## Factory Constructor

Create the operator via the following factory method

***frozen_in_time(model_name, modality, weight_path)***

**Parameters:**

​   ***model_name:*** *str*

​   The model name of frozen in time. Supported model names: 
- frozen_in_time_base_16_244


​   ***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 VideoFrame](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  Towhee VideoFrame (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

- [What is BERT (Bidirectional Encoder Representations from Transformers)? - Zilliz blog](https://zilliz.com/learn/what-is-bert): Learn what Bidirectional Encoder Representations from Transformers (BERT) is and how it uses pre-training and fine-tuning to achieve its remarkable performance.
- [The guide to mistral-embed | Mistral AI](https://zilliz.com/ai-models/mistral-embed): mistral-embed: a specialized embedding model for text data with a context window of 8,000 tokens. Optimized for similarity retrieval and RAG applications.
- [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.
- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar/success): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
- [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
- [Tutorial: Diving into Text Embedding Models | Zilliz Webinar](https://zilliz.com/event/tutorial-text-embedding-models): Register for a free webinar diving into text embedding models in a presentation and tutorial
- [The guide to all-MiniLM-L12-v2 | Hugging Face](https://zilliz.com/ai-models/all-MiniLM-L12-v2): all-MiniLM-L12-v2: a text embedding model ideal for semantic search and RAG and fine-tuned based on Microsoft/MiniLM-L12-H384-uncased
- [Sparse and Dense Embeddings: A Guide for Effective Information Retrieval with Milvus | Zilliz Webinar](https://zilliz.com/event/sparse-and-dense-embeddings-webinar): Zilliz webinar covering what sparse and dense embeddings are and when you'd want to use one over the other.
- [Enhancing Information Retrieval with Sparse Embeddings | Zilliz Learn - Zilliz blog](https://zilliz.com/learn/enhancing-information-retrieval-learned-sparse-embeddings): Explore the inner workings, advantages, and practical applications of learned sparse embeddings with the Milvus vector database