# Image Embedding with data2vec

*author: David Wang*


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## Description

This operator extracts features for image with [data2vec](https://arxiv.org/abs/2202.03555). The core idea is to predict latent representations of the full input data based on a masked view of the input in a self-distillation setup using a standard Transformer architecture.

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## Code Example

Load an image from path './towhee.jpg' to generate an image embedding. 

*Write a pipeline with explicit inputs/outputs name specifications:*

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

p = (
    pipe.input('path')
        .map('path', 'img', ops.image_decode())
        .map('img', 'vec', ops.image_embedding.data2vec(model_name='facebook/data2vec-vision-base-ft1k'))
        .output('img', 'vec')
)

DataCollection(p('towhee.jpeg')).show()
```
<img src="./result2.png" alt="result2" style="height:60px;"/>


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## Factory Constructor

Create the operator via the following factory method

***data2vec(model_name='facebook/data2vec-vision-base')***

**Parameters:**


​  ***model_name***: *str*

The model name in string.
The default value is "facebook/data2vec-vision-base-ft1k".

Supported model name:
- facebook/data2vec-vision-base-ft1k
- facebook/data2vec-vision-large-ft1k

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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:**

​	***img:*** *towhee.types.Image (a sub-class of numpy.ndarray)* 

​	The decoded image data in towhee.types.Image (numpy.ndarray).



**Returns:** *numpy.ndarray*

​   The image embedding extracted by model.





# More Resources

- [What is a Transformer Model? An Engineer's Guide](https://zilliz.com/glossary/transformer-models): A transformer model is a neural network architecture. It's proficient in converting a particular type of input into a distinct output. Its core strength lies in its ability to handle inputs and outputs of different sequence length. It does this through encoding the input into a matrix with predefined dimensions and then combining that with another attention matrix to decode. This transformation unfolds through a sequence of collaborative layers, which deconstruct words into their corresponding numerical representations. At its heart, a transformer model is a bridge between disparate linguistic structures, employing sophisticated neural network configurations to decode and manipulate human language input. An example of a transformer model is GPT-3, which ingests human language and generates text output.
- [How to Get the Right Vector Embeddings - Zilliz blog](https://zilliz.com/blog/how-to-get-the-right-vector-embeddings): A comprehensive introduction to vector embeddings and how to generate them with popular open-source models.
- [Transforming Text: The Rise of Sentence Transformers in NLP - Zilliz blog](https://zilliz.com/learn/transforming-text-the-rise-of-sentence-transformers-in-nlp): Everything you need to know about the Transformers model, exploring its architecture, implementation, and limitations
- [What Are Vector Embeddings?](https://zilliz.com/glossary/vector-embeddings): Learn the definition of vector embeddings, how to create vector embeddings, and more.
- [What is Detection Transformers (DETR)?  - Zilliz blog](https://zilliz.com/learn/detection-transformers-detr-end-to-end-object-detection-with-transformers): DETR (DEtection TRansformer) is a deep learning model for end-to-end object detection using transformers.
- [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.
- [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
- [An Introduction to Vector Embeddings: What They Are and How to Use Them - Zilliz blog](https://zilliz.com/learn/everything-you-should-know-about-vector-embeddings): In this blog post, we will understand the concept of vector embeddings and explore its applications, best practices, and tools for working with embeddings.