# Efficientnet Embedding Operator

Authors: kyle he

## Overview

EfficientNets are a family of image classification models, which achieve state-of-the-art accuracy, yet being an order-of-magnitude smaller and faster than previous models[1], which is trained on [imagenet dataset](https://image-net.org/download.php).

## Interface

```python
__init__(self, model_name: str = 'efficientnet-b7', framework: str = 'pytorch', weights_path: str = None)
```

**Args:**


- model_name:
  - the model name for embedding
  - supported types: `str`, for example 'efficientnet-b7'
- 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,  image: 'towhee.types.Image')
```

**Args:**

- image:
  - the input image
  - supported types: `towhee.types.Image`

**Returns:**

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

- feature_vector:
  - the embedding of the image
  - data type: `numpy.ndarray`
  - shape: (dim,)

## Requirements

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

## How it works

The `towhee/efficientnet-image-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 [image-embedding-efficientnetb5](https://hub.towhee.io/towhee/image-embedding-efficientnetb5) and [image-embedding-efficientnetb7](https://hub.towhee.io/towhee/image-embedding-efficientnetb7) pipeline.

## Reference

[1].https://github.com/lukemelas/EfficientNet-PyTorch#example-feature-extraction