# ViT Embedding Operator

Authors: kyle he

## Overview

The ViT(Vision Transformer) is a model for image classification that employs a Transformer-like architecture over patches of the image. This includes the use of Multi-Head Attention, Scaled Dot-Product Attention and other architectural features seen in the Transformer architecture traditionally used for NLP[1], which is trained on [imagenet dataset](https://image-net.org/download.php).

## Interface

```python
__init__(self, model_name: str = 'vit_large_patch16_224',
                 framework: str = 'pytorch', weights_path: str = None)
```

**Args:**

- model_name:
  - the model name for embedding
  - supported types: `str`, for example 'vit_large_patch16_224'
- framework:
  - the framework of the model
  - supported types: `str`, default is 'pytorch'
- weights_path:
  - the weights path
  - supported types: `str`, default is None, using pretrained weights

```python
__call__(self, img_path: str)
```

**Args:**

- img_path:
  - the input image path
  - supported types: `str`

**Returns:**

The Operator returns a tuple `Tuple[('embedding', numpy.ndarray)]` containing following fields:

- feature_vector:
  - the embedding of the image
  - data type: `numpy.ndarray`

## Requirements

You can get the required python package by [requirements.txt](./requirements.txt).

## How it works

The `towhee/vit-embedding` Operator implements the function of image embedding, which can add to the pipeline. For example, it's the key Operator named embedding_model within [vit-embedding](https://hub.towhee.io/towhee/vit-embedding) pipeline.

## Reference

[1].https://arxiv.org/abs/2010.11929