# Image Embedding with Timm

*author: [Jael Gu](https://github.com/jaelgu), Filip*

<br />

## Description

An image embedding operator generates a vector given an image.
This operator extracts features for image with pre-trained 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),
who maintains SOTA deep-learning models and tools in computer vision.

<br />

## Code Example

Load an image from path './towhee.jpeg'
and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.

 *Write the pipeline in simplified style:*

```python
import towhee

towhee.glob('./towhee.jpeg') \
      .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.jpeg') \
      .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 to [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 pre-process.
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:**

***data:*** *towhee.types.Image*

The decoded image data in towhee Image (a subset of numpy.ndarray).


**Returns:** *numpy.ndarray*

 An image embedding generated by model, in shape of (feature_dim,).


<br />

## Towhee Serve

Models which is supported the towhee.serve.

**Model List**

 models | models | models | models
--------- | ---------- | ------------ | -----------
adv_inception_v3 | bat_resnext26ts | beit_base_patch16_224
 beit_base_patch16_224_in22k | beit_base_patch16_384 | beit_large_patch16_224
 beit_large_patch16_224_in22k | beit_large_patch16_384 | beit_large_patch16_512
 botnet26t_256 | cait_m36_384 | cait_m48_448
 cait_s24_224 | cait_s24_384 | cait_s36_384
 cait_xs24_384 | cait_xxs24_224 | cait_xxs24_384
 cait_xxs36_224 | cait_xxs36_384 | coat_lite_mini
 coat_lite_small | coat_lite_tiny | convit_base
 convit_small | convit_tiny | convmixer_768_32
 convmixer_1024_20_ks9_p14 | convmixer_1536_20 | convnext_base
 convnext_base_384_in22ft1k | convnext_base_in22ft1k | convnext_base_in22k
 convnext_large | convnext_large_384_in22ft1k | convnext_large_in22ft1k
 convnext_large_in22k | convnext_small | convnext_small_384_in22ft1k
 convnext_small_in22ft1k | convnext_small_in22k | convnext_tiny
 convnext_tiny_384_in22ft1k | convnext_tiny_hnf | convnext_tiny_in22ft1k
 convnext_tiny_in22k | convnext_xlarge_384_in22ft1k | convnext_xlarge_in22ft1k
 convnext_xlarge_in22k | cs3darknet_focus_l | cs3darknet_focus_m
 cs3darknet_l | cs3darknet_m | cspdarknet53
 cspresnet50 | cspresnext50 | darknet53
 deit3_base_patch16_224 | deit3_base_patch16_224_in21ft1k | deit3_base_patch16_384
 deit3_base_patch16_384_in21ft1k | deit3_huge_patch14_224 | deit3_huge_patch14_224_in21ft1k
 deit3_large_patch16_224 | deit3_large_patch16_224_in21ft1k | deit3_large_patch16_384
 deit3_large_patch16_384_in21ft1k | deit3_small_patch16_224 | deit3_small_patch16_224_in21ft1k
 deit3_small_patch16_384 | deit3_small_patch16_384_in21ft1k | deit_base_distilled_patch16_224
 deit_base_distilled_patch16_384 | deit_base_patch16_224 | deit_base_patch16_384
 deit_small_distilled_patch16_224 | deit_small_patch16_224 | deit_tiny_distilled_patch16_224
 deit_tiny_patch16_224 | densenet121 | densenet161
 densenet169 | densenet201 | densenetblur121d
 dla34 | dla46_c | dla46x_c