# Image Embedding with Timm

*author: Jael Gu, Filip*



## Desription

An image embedding operator implemented with pretrained models provided by [Timm](https://github.com/rwightman/pytorch-image-models).



```python
from towhee import ops
import numpy as np

img_encoder = ops.image_embedding.timm('resnet50')
fake_img = np.zeros((256, 256, 3))
image_embedding = img_encoder(fake_img)
```

## Factory Constructor

Create the operator via the following factory method

***ops.image_embedding.timm(model_name)***



## Interface

An image embedding operator takes an image in ndarray as input.
It uses the pre-trained model specified by model name to generate an image embedding in ndarray.


**Parameters:**

​	***img***: *numpy.ndarray*

​	The decoded image data in numpy.ndarray.



**Returns**: *numpy.ndarray*

​	The image embedding extracted by model.



## Code Example

Load an image from path './dog.jpg'
and use the pretrained ResNet50 model ('resnet50') to generate an image embedding. 

 *Write the pipeline in simplified style*:

```python
import towhee.DataCollection as dc

dc.glob(./dog.jpg)
  .image_decode()
  .image_embedding.timm('resnet50')
  .show()
```

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

```python
from towhee import DataCollection as dc

dc.glob['path'](./dog.jpg)
  .image_decode['path', 'img']()
  .image_embedding.timm['img', 'vec']('resnet50')
  .select('img')
  .show()
```