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# Image Embedding with Timm
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*author: [Jael Gu](https://github.com/jaelgu), Filip*
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<br />
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## Description
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An image embedding operator generates a vector given an image.
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This operator extracts features for image with pre-trained models provided by [Timm](https://github.com/rwightman/pytorch-image-models).
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Timm is a deep-learning library developed by [Ross Wightman](https://twitter.com/wightmanr),
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who maintains SOTA deep-learning models and tools in computer vision.
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<br />
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## Code Example
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Load an image from path './towhee.jpeg'
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and use the pre-trained ResNet50 model ('resnet50') to generate an image embedding.
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*Write the pipeline in simplified style:*
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```python
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import towhee
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towhee.glob('./towhee.jpeg') \
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.image_decode() \
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.image_embedding.timm(model_name='resnet50') \
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.show()
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```
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<img src="./result1.png" height="50px"/>
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*Write a same pipeline with explicit inputs/outputs name specifications:*
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```python
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import towhee
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towhee.glob['path']('./towhee.jpeg') \
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.image_decode['path', 'img']() \
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.image_embedding.timm['img', 'vec'](model_name='resnet50') \
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.select['img', 'vec']() \
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.show()
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```
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<img src="./result2.png" height="150px"/>
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<br />
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## Factory Constructor
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Create the operator via the following factory method:
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***image_embedding.timm(model_name='resnet34', num_classes=1000, skip_preprocess=False)***
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**Parameters:**
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***model_name:*** *str*
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The model name in string. The default value is "resnet34".
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Refer to [Timm Docs](https://fastai.github.io/timmdocs/#List-Models-with-Pretrained-Weights) to get a full list of supported models.
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***num_classes:*** *int*
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The number of classes. The default value is 1000.
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It is related to model and dataset.
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***skip_preprocess:*** *bool*
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The flag to control whether to skip image pre-process.
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The default value is False.
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If set to True, it will skip image preprocessing steps (transforms).
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In this case, input image data must be prepared in advance in order to properly fit the model.
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<br />
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## Interface
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An image embedding operator takes a towhee image as input.
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It uses the pre-trained model specified by model name to generate an image embedding in ndarray.
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**Parameters:**
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***data:*** *towhee.types.Image*
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The decoded image data in towhee Image (a subset of numpy.ndarray).
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**Returns:** *numpy.ndarray*
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An image embedding generated by model, in shape of (feature_dim,).
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<br />
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## Towhee Serve
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Models which is supported the towhee.serve.
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**Model List**
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models | models | models
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--------- | ---------- | ------------
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adv_inception_v3 | bat_resnext26ts | beit_base_patch16_224
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beit_base_patch16_224_in22k | beit_base_patch16_384 | beit_large_patch16_224
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beit_large_patch16_224_in22k | beit_large_patch16_384 | beit_large_patch16_512
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botnet26t_256 | cait_m36_384 | cait_m48_448
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cait_s24_224 | cait_s24_384 | cait_s36_384
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cait_xs24_384 | cait_xxs24_224 | cait_xxs24_384
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cait_xxs36_224 | cait_xxs36_384 | coat_lite_mini
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coat_lite_small | coat_lite_tiny | convit_base
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convit_small | convit_tiny | convmixer_768_32
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convmixer_1024_20_ks9_p14 | convmixer_1536_20 | convnext_base
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convnext_base_384_in22ft1k | convnext_base_in22ft1k | convnext_base_in22k
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convnext_large | convnext_large_384_in22ft1k | convnext_large_in22ft1k
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convnext_large_in22k | convnext_small | convnext_small_384_in22ft1k
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convnext_small_in22ft1k | convnext_small_in22k | convnext_tiny
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convnext_tiny_384_in22ft1k | convnext_tiny_hnf | convnext_tiny_in22ft1k
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convnext_tiny_in22k | convnext_xlarge_384_in22ft1k | convnext_xlarge_in22ft1k
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convnext_xlarge_in22k | cs3darknet_focus_l | cs3darknet_focus_m
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cs3darknet_l | cs3darknet_m | cspdarknet53
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cspresnet50 | cspresnext50 | darknet53
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deit3_base_patch16_224 | deit3_base_patch16_224_in21ft1k | deit3_base_patch16_384
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deit3_base_patch16_384_in21ft1k | deit3_huge_patch14_224 | deit3_huge_patch14_224_in21ft1k
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deit3_large_patch16_224 | deit3_large_patch16_224_in21ft1k | deit3_large_patch16_384
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deit3_large_patch16_384_in21ft1k | deit3_small_patch16_224 | deit3_small_patch16_224_in21ft1k
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deit3_small_patch16_384 | deit3_small_patch16_384_in21ft1k | deit_base_distilled_patch16_224
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deit_base_distilled_patch16_384 | deit_base_patch16_224 | deit_base_patch16_384
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deit_small_distilled_patch16_224 | deit_small_patch16_224 | deit_tiny_distilled_patch16_224
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deit_tiny_patch16_224 | densenet121 | densenet161
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densenet169 | densenet201 | densenetblur121d
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dla34 | dla46_c | dla46x_c
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dla60 | dla60_res2net | dla60_res2next
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dla60x | dla60x_c | dla102
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dla102x | dla102x2 | dla169
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dm_nfnet_f0 | dm_nfnet_f1 | dm_nfnet_f2
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dm_nfnet_f3 | dm_nfnet_f4 | dm_nfnet_f5
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dm_nfnet_f6 | dpn68 | dpn68b
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dpn92 | dpn98 | dpn107
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dpn131 | eca_botnext26ts_256 | eca_halonext26ts
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eca_nfnet_l0 | eca_nfnet_l1 | eca_nfnet_l2
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eca_resnet33ts | eca_resnext26ts | ecaresnet26t
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ecaresnet50t | ecaresnet269d | edgenext_small
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edgenext_small_rw | edgenext_x_small | edgenext_xx_small
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efficientnet_b0 | efficientnet_b1 | efficientnet_b2
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efficientnet_b3 | efficientnet_b4 | efficientnet_el
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efficientnet_el_pruned | efficientnet_em | efficientnet_es
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efficientnet_es_pruned | efficientnet_lite0 | efficientnetv2_rw_m
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efficientnetv2_rw_s | efficientnetv2_rw_t | ens_adv_inception_resnet_v2
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ese_vovnet19b_dw | ese_vovnet39b | fbnetc_100
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fbnetv3_b | fbnetv3_d | fbnetv3_g
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gc_efficientnetv2_rw_t | gcresnet33ts | gcresnet50t
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gcresnext26ts | gcresnext50ts | gernet_l
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gernet_m | gernet_s | ghostnet_100
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gluon_inception_v3 | gluon_resnet18_v1b | gluon_resnet34_v1b
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gluon_resnet50_v1b | gluon_resnet50_v1c | gluon_resnet50_v1d
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gluon_resnet50_v1s | gluon_resnet101_v1b | gluon_resnet101_v1c
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gluon_resnet101_v1d | gluon_resnet101_v1s | gluon_resnet152_v1b
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gluon_resnet152_v1c | gluon_resnet152_v1d | gluon_resnet152_v1s
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gluon_resnext50_32x4d | gluon_resnext101_32x4d | gluon_resnext101_64x4d
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gluon_senet154 | gluon_seresnext50_32x4d | gluon_seresnext101_32x4d
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gluon_seresnext101_64x4d | gluon_xception65 | gmlp_s16_224
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halo2botnet50ts_25
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halo2botnet50ts_256 | halonet26t | halonet50ts
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haloregnetz_b | hardcorenas_a | hardcorenas_b
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hardcorenas_c | hardcorenas_d | hardcorenas_e
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hrnet_w18 | hrnet_w18_small | hrnet_w18_small_v2
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hrnet_w30 | hrnet_w32 | hrnet_w40
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hrnet_w44 | hrnet_w48 | hrnet_w64
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ig_resnext101_32x8d | ig_resnext101_32x16d | ig_resnext101_32x32d
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ig_resnext101_32x48d | inception_resnet_v2 | inception_v3
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inception_v4 | jx_nest_base | jx_nest_small
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jx_nest_tiny | lambda_resnet26rpt_256 | lambda_resnet26t
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lambda_resnet50ts | lamhalobotnet50ts_256 | lcnet_050
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lcnet_075 | lcnet_100 | legacy_seresnet18
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legacy_seresnet34 | legacy_seresnet50 | legacy_seresnet101
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legacy_seresnet152 | legacy_seresnext26_32x4d | levit_128
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levit_128s | levit_192 | levit_256
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levit_384 | mixer_b16_224 | mixer_b16_224_in21k
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mixer_b16_224_miil | mixer_b16_224_miil_in21k | mixer_l16_224
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mixer_l16_224_in21k | mixnet_l | mixnet_m
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mixnet_s | mixnet_xl | mnasnet_100
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mnasnet_small | mobilenetv2_050 | mobilenetv2_100
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mobilenetv2_110d | mobilenetv2_120d | mobilenetv2_140
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mobilenetv3_large_100 | mobilenetv3_large_100_miil | mobilenetv3_large_100_miil_in21k
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mobilenetv3_rw | mobilenetv3_small_050 | mobilenetv3_small_075
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mobilenetv3_small_100 | mobilevit_s | mobilevit_xs
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mobilevit_xxs | mobilevitv2_050 | mobilevitv2_075
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mobilevitv2_100 | mobilevitv2_125 | mobilevitv2_150
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mobilevitv2_150_384_in22ft1k | mobilevitv2_150_in22ft1k | mobilevitv2_175
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mobilevitv2_175_384_in22ft1k | mobilevitv2_175_in22ft1k | mobilevitv2_200
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mobilevitv2_200_384_in22ft1k | mobilevitv2_200_in22ft1k | nf_regnet_b1
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nf_resnet50 | nfnet_l0 | pit_b_224
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pit_b_distilled_224 | pit_s_224 | pit_s_distilled_224
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pit_ti_224 | pit_ti_distilled_224 | pit_xs_224
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