# Image Embedding with Timm *author: [Jael Gu](https://github.com/jaelgu), Filip*
## 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.
## 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() ``` *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() ```
## 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.
## 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,).
***save_model(format='pytorch', path='default')*** Save model to local with specified format. **Parameters:** ***format***: *str* ​ The format of saved model, defaults to 'pytorch'. ***path***: *str* ​ The path where model is saved to. By default, it will save model to the operator directory.
***supported_model_names(format=None)*** Get a list of all supported model names or supported model names for specified model format. **Parameters:** ***format***: *str* ​ The model format such as 'pytorch', 'torchscript'. ```python from towhee import ops op = ops.image_embedding.timm().get_op() full_list = op.supported_model_names() onnx_list = op.supported_model_names(format='onnx') print(f'Onnx-support/Total Models: {len(onnx_list)}/{len(full_list)}') ``` 2022-12-19 16:32:37,933 - 140704422594752 - timm_image.py-timm_image:88 - WARNING: The operator is initialized without specified model. Onnx-support/Total Models: 715/759
## 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 halo2botnet50ts_256 | halonet26t | halonet50ts haloregnetz_b | hardcorenas_a | hardcorenas_b hardcorenas_c | hardcorenas_d | hardcorenas_e hrnet_w18 | hrnet_w18_small | hrnet_w18_small_v2 hrnet_w30 | hrnet_w32 | hrnet_w40 hrnet_w44 | hrnet_w48 | hrnet_w64 ig_resnext101_32x8d | ig_resnext101_32x16d | ig_resnext101_32x32d ig_resnext101_32x48d | inception_resnet_v2 | inception_v3 inception_v4 | jx_nest_base | jx_nest_small jx_nest_tiny | lambda_resnet26rpt_256 | lambda_resnet26t lambda_resnet50ts | lamhalobotnet50ts_256 | lcnet_050 lcnet_075 | lcnet_100 | legacy_seresnet18 legacy_seresnet34 | legacy_seresnet50 | legacy_seresnet101 legacy_seresnet152 | legacy_seresnext26_32x4d | levit_128 levit_128s | levit_192 | levit_256 levit_384 | mixer_b16_224 | mixer_b16_224_in21k mixer_b16_224_miil | mixer_b16_224_miil_in21k | mixer_l16_224 mixer_l16_224_in21k | mixnet_l | mixnet_m mixnet_s | mixnet_xl | mnasnet_100 mnasnet_small | mobilenetv2_050 | mobilenetv2_100 mobilenetv2_110d | mobilenetv2_120d | mobilenetv2_140 mobilenetv3_large_100 | mobilenetv3_large_100_miil | mobilenetv3_large_100_miil_in21k mobilenetv3_rw | mobilenetv3_small_050 | mobilenetv3_small_075 mobilenetv3_small_100 | mobilevit_s | mobilevit_xs mobilevit_xxs | mobilevitv2_050 | mobilevitv2_075 mobilevitv2_100 | mobilevitv2_125 | mobilevitv2_150 mobilevitv2_150_384_in22ft1k | mobilevitv2_150_in22ft1k | mobilevitv2_175 mobilevitv2_175_384_in22ft1k | mobilevitv2_175_in22ft1k | mobilevitv2_200 mobilevitv2_200_384_in22ft1k | mobilevitv2_200_in22ft1k | nf_regnet_b1 nf_resnet50 | nfnet_l0 | pit_b_224 pit_b_distilled_224 | pit_s_224 | pit_s_distilled_224 pit_ti_224 | pit_ti_distilled_224 | pit_xs_224 pit_xs_distilled_224 | pnasnet5large | poolformer_m36 poolformer_m48 | poolformer_s12 | poolformer_s24 poolformer_s36 | regnetv_040 | regnetv_064 regnetx_002 | regnetx_004 | regnetx_006 regnetx_008 | regnetx_016 | regnetx_032 regnetx_040 | regnetx_064 | regnetx_080 regnetx_120 | regnetx_160 | regnetx_320 regnety_002 | regnety_004 | regnety_006 regnety_008 | regnety_016 | regnety_032 regnety_040 | regnety_064 | regnety_080 regnety_120 | regnety_160 | regnety_320 regnetz_040 | regnetz_040h | regnetz_b16 regnetz_c16 | regnetz_c16_evos | regnetz_d8 regnetz_d8_evos | regnetz_d32 | regnetz_e8 repvgg_a2 | repvgg_b0 | repvgg_b1 repvgg_b1g4 | repvgg_b2 | repvgg_b2g4 repvgg_b3 | repvgg_b3g4 | res2net50_14w_8s res2net50_26w_4s | res2net50_26w_6s | res2net50_26w_8s res2net50_48w_2s | res2net101_26w_4s | res2next50 resmlp_12_224 | resmlp_12_224_dino | resmlp_12_distilled_224 resmlp_24_224 | resmlp_24_224_dino | resmlp_24_distilled_224 resmlp_36_224 | resmlp_36_distilled_224 | resmlp_big_24_224 resmlp_big_24_224_in22ft1k | resnest14d | resnest26d resnest50d | resnest50d_1s4x24d | resnest50d_4s2x40d resnest101e | resnest200e | resnet10t resnet14t | resnet18 | resnet18d resnet26 | resnet26d | resnet26t resnet32ts | resnet33ts | resnet34 resnet34d | resnet50 | resnet50_gn resnet50d | resnet51q | resnet61q resnet101 | resnet101d | resnet152 resnet152d | resnet200d | resnetaa50 resnetblur50 | resnetrs50