timm/README.md

# Image Embedding with Timm

*author: Jael Gu, Filip*

<br />

## Desription

An image embedding operator generates a vector given an image.
This operator extracts features for image with pretrained models provided by [Timm](https://github.com/rwightman/pytorch-image-models).
Timm is a deep-learning library developed by [Ross Wightman](https://twitter.com/wightmanr),
which maintains SOTA deep-learning models and tools in computer vision.

<br />

## Code Example

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

 *Write the pipeline in simplified style:*

```python
import towhee

towhee.glob('./towhee.jpg') \
      .image_decode() \
      .image_embedding.timm(model_name='resnet50') \
      .show()
```
<img src="./result1.png" height="50px"/>

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

```python
import towhee

towhee.glob['path']('./towhee.jpg') \
      .image_decode['path', 'img']() \
      .image_embedding.timm['img', 'vec'](model_name='resnet50') \
      .select['img', 'vec']() \
      .show()
```
<img src="./result2.png" height="150px"/>

<br />

## Factory Constructor

Create the operator via the following factory method

***image_embedding.timm(model_name='resnet34', num_classes=1000, skip_preprocess=False)***

**Parameters:**

***model_name:*** *str*

The model name in string. The default value is "resnet34".
Refer [Timm Docs](https://fastai.github.io/timmdocs/#List-Models-with-Pretrained-Weights) to get a full list of supported models.

***num_classes:*** *int*

The number of classes. The default value is 1000.
It is related to model and dataset.

***skip_preprocess:*** *bool*

The flag to control whether to skip image preprocess.
The default value is False.
If set to True, it will skip image preprocessing steps (transforms).
In this case, input image data must be prepared in advance in order to properly fit the model.

<br />

## Interface

An image embedding operator takes a towhee image 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 numpy.ndarray.



**Returns:** *numpy.ndarray*

The image embedding extracted by model.