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Updated 3 weeks ago


Image Embedding with Timm

author: Jael Gu, Filip


An image embedding operator generates a vector given an image. This operator extracts features for image with pre-trained models provided by Timm. Timm is a deep-learning library developed by Ross Wightman, 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 a pipeline with explicit inputs/outputs name specifications:

from towhee import pipe, ops, DataCollection

p = (
        .map('path', 'img', ops.image_decode())
        .map('img', 'vec', ops.image_embedding.timm(model_name='resnet50'))
        .output('img', 'vec')


Factory Constructor

Create the operator via the following factory method:

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


model_name: str

The model name in string. The default value is "resnet34". Refer to Timm Docs 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.


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.


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.


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.

from towhee import ops

op = ops.image_embedding.timm(model_name='resnet50').get_op()
op.save_model('onnx', 'test.onnx')


Get a list of all supported model names or supported model names for specified model format.


format: str

​ The model format such as 'pytorch', 'torchscript'.

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


To fine-tune this operator, please refer to this example guide.

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_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
junjie.jiang 6ff206bd86 opset_version to 14 112 Commits
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