# 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
--------- | ---------- | ------------  
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
dla60 | dla60_res2net | dla60_res2next
 dla60x | dla60x_c | dla102
 dla102x | dla102x2 | dla169
 dm_nfnet_f0 | dm_nfnet_f1 | dm_nfnet_f2
 dm_nfnet_f3 | dm_nfnet_f4 | dm_nfnet_f5
 dm_nfnet_f6 | dpn68 | dpn68b
 dpn92 | dpn98 | dpn107
 dpn131 | eca_botnext26ts_256 | eca_halonext26ts
 eca_nfnet_l0 | eca_nfnet_l1 | eca_nfnet_l2
 eca_resnet33ts | eca_resnext26ts | ecaresnet26t
 ecaresnet50t | ecaresnet269d | edgenext_small
 edgenext_small_rw | edgenext_x_small | edgenext_xx_small
 efficientnet_b0 | efficientnet_b1 | efficientnet_b2
 efficientnet_b3 | efficientnet_b4 | efficientnet_el
 efficientnet_el_pruned | efficientnet_em | efficientnet_es
 efficientnet_es_pruned | efficientnet_lite0 | efficientnetv2_rw_m
 efficientnetv2_rw_s | efficientnetv2_rw_t | ens_adv_inception_resnet_v2
 ese_vovnet19b_dw | ese_vovnet39b | fbnetc_100
 fbnetv3_b | fbnetv3_d | fbnetv3_g
 gc_efficientnetv2_rw_t | gcresnet33ts | gcresnet50t
 gcresnext26ts | gcresnext50ts | gernet_l
 gernet_m | gernet_s | ghostnet_100
 gluon_inception_v3 | gluon_resnet18_v1b | gluon_resnet34_v1b
 gluon_resnet50_v1b | gluon_resnet50_v1c | gluon_resnet50_v1d
 gluon_resnet50_v1s | gluon_resnet101_v1b | gluon_resnet101_v1c
 gluon_resnet101_v1d | gluon_resnet101_v1s | gluon_resnet152_v1b
 gluon_resnet152_v1c | gluon_resnet152_v1d | gluon_resnet152_v1s
 gluon_resnext50_32x4d | gluon_resnext101_32x4d | gluon_resnext101_64x4d
 gluon_senet154 | gluon_seresnext50_32x4d | gluon_seresnext101_32x4d
 gluon_seresnext101_64x4d | gluon_xception65 | gmlp_s16_224
 halo2botnet50ts_25