# Template: Image Embedding Operator

Authors:

## **Overview**

> <font color=red>**Note:** this is just a **template**, not a runnable pipeline.</font>

This **class template for the image embedding operator** defines the image embedding functions, as well as the standard inputs and outputs. You can complete the operator by filling in the function(`__init__`, `__call__` ) in [image_embedding_operator_template.py](http://./image_embedding_operator_template.py) and update this README file. FYI, [image-embedding-resnet50](https://hub.towhee.io/towhee/image-embedding-resnet50) is based on this template.

This Operator generates feature vectors from "someone" model, which is trained on "someone" dataset.

## **Interface**

```python
__init__(self, model_name: str, framework: str = 'pytorch')
```

**Args:**

- model_name:
  - the model name for embedding
  - supported types: `str`, for example 'resnet50'

- framework:
  - the framework of the model
  - supported types: `str`, default is 'pytorch'

```python
__call__(self, img_tensor: torch.Tensor)
```

**Args:**

- img_tensor:
  - the input image tensor
  - supported types: `torch.Tensor`

**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`

## **Requirements**

You can get the required python package by [requirements.txt](https://zilliverse.feishu.cn/docs/requirements.txt).

## **How it works**

The `towhee/image-embedding-operator-template` 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-pipeline-template](https://hub.towhee.io/towhee/image-embedding-pipeline-template) pipeline.

## **Reference**