Model Backbones

Histolytics' modular model implementations allows you to select from a variety of pre-trained backbone encoders. For example, foundation models like UNI, Virchow, or Prov-GigaPath can be used, given that you have been granted the permissions to use these models. In general, any backbone from the pytorch-image-models (timm) library can be used.

In [1]:

from huggingface_hub import login

# login to huggingface to load the weights
# NOTE: You need to have granted permission for the weights before you can run this
login()

from huggingface_hub import login # login to huggingface to load the weights # NOTE: You need to have granted permission for the weights before you can run this login()

VBox(children=(HTML(value='<center> <img\nsrc=https://huggingface.co/front/assets/huggingface_logo-noborder.sv…

Initializing panoptic Cellpose with UNI backbone¶

Note: Models like UNI and Virchow require some extra key-word arguments to be passed during the timm initialization. These can be passed in the model_kwargs dictionary that takes in arbitrary arguments needed for initializing the models. The encoder_kws is the argument inside the model_kwargs that is used to pass the extra key-word arguments to the timm backbone encoder.

Note: The enc_out_indices argument in the model_kwargs is used to specify which layers of the backbone encoder are passed to the panoptic segmentation model as the U-net skip connections. The indices correspond to the layers in the backbone encoder. By default, 4 layers is passed to the pixel decoders so four indices are specified in the list.

In [3]:

from histolytics.models.cellpose_panoptic import CellposePanoptic

cpose_panoptic = CellposePanoptic(
    n_nuc_classes=6,
    n_tissue_classes=6,
    enc_name="hf_hub:MahmoodLab/uni",
    enc_pretrain=True,
    model_kwargs={
        "encoder_kws": {"init_values": 1e-5, "dynamic_img_size": True},
        "enc_out_indices": (2, 4, 6, 8),  # using layers 2, 4, 6, 8 from UNI
    },
)

# Print only a summary of the model instead of the full output
print(str(cpose_panoptic.model)[:500] + "\n...")

from histolytics.models.cellpose_panoptic import CellposePanoptic cpose_panoptic = CellposePanoptic( n_nuc_classes=6, n_tissue_classes=6, enc_name="hf_hub:MahmoodLab/uni", enc_pretrain=True, model_kwargs={ "encoder_kws": {"init_values": 1e-5, "dynamic_img_size": True}, "enc_out_indices": (2, 4, 6, 8), # using layers 2, 4, 6, 8 from UNI }, ) # Print only a summary of the model instead of the full output print(str(cpose_panoptic.model)[:500] + "\n...")

CellPoseUnet(
  (hf_hub:MahmoodLab/uni): Encoder(
    (encoder): TimmEncoder(
      (encoder): VisionTransformer(
        (patch_embed): PatchEmbed(
          (proj): Conv2d(3, 1024, kernel_size=(16, 16), stride=(16, 16))
          (norm): Identity()
        )
        (pos_drop): Dropout(p=0.0, inplace=False)
        (patch_drop): Identity()
        (norm_pre): Identity()
        (blocks): Sequential(
          (0): Block(
            (norm1): LayerNorm((1024,), eps=1e-06, elementwise_affine=Tru
...

Initializing panoptic HoverNet with Virchow backbone¶

In [4]:

import torch
from timm.layers import SwiGLUPacked
from histolytics.models.hovernet_panoptic import HoverNetPanoptic

hnet_panoptic = HoverNetPanoptic(
    n_nuc_classes=6,
    n_tissue_classes=6,
    enc_name="hf-hub:paige-ai/Virchow",
    enc_pretrain=True,
    model_kwargs={
        "encoder_kws": {"mlp_layer": SwiGLUPacked, "act_layer": torch.nn.SiLU},
        "enc_out_indices": (2, 4, 6, 8),  # using layers 2, 4, 6, 8 from Virchow
    },
)

# Print only a summary of the model instead of the full output
print(str(hnet_panoptic.model)[:500] + "\n...")

import torch from timm.layers import SwiGLUPacked from histolytics.models.hovernet_panoptic import HoverNetPanoptic hnet_panoptic = HoverNetPanoptic( n_nuc_classes=6, n_tissue_classes=6, enc_name="hf-hub:paige-ai/Virchow", enc_pretrain=True, model_kwargs={ "encoder_kws": {"mlp_layer": SwiGLUPacked, "act_layer": torch.nn.SiLU}, "enc_out_indices": (2, 4, 6, 8), # using layers 2, 4, 6, 8 from Virchow }, ) # Print only a summary of the model instead of the full output print(str(hnet_panoptic.model)[:500] + "\n...")

HoverNetUnet(
  (hf-hub:paige-ai/Virchow): Encoder(
    (encoder): TimmEncoder(
      (encoder): VisionTransformer(
        (patch_embed): PatchEmbed(
          (proj): Conv2d(3, 1280, kernel_size=(14, 14), stride=(14, 14))
          (norm): Identity()
        )
        (pos_drop): Dropout(p=0.0, inplace=False)
        (patch_drop): Identity()
        (norm_pre): Identity()
        (blocks): Sequential(
          (0): Block(
            (norm1): LayerNorm((1280,), eps=1e-06, elementwise_affine=T
...

Initializing panoptic Stardist with Prov-GigaPath backbone¶

In [ ]:

from histolytics.models.stardist_panoptic import StarDistPanoptic

sdist_panoptic = StarDistPanoptic(
    n_nuc_classes=6,
    n_tissue_classes=6,
    enc_name="hf_hub:prov-gigapath/prov-gigapath",
    enc_pretrain=True,
    model_kwargs={
        "enc_out_indices": (2, 4, 6, 8),  # using layers 2, 4, 6, 8 from Prov-GigaPath
    },
)

# Print only a summary of the model instead of the full output
print(str(sdist_panoptic.model)[:500] + "\n...")

from histolytics.models.stardist_panoptic import StarDistPanoptic sdist_panoptic = StarDistPanoptic( n_nuc_classes=6, n_tissue_classes=6, enc_name="hf_hub:prov-gigapath/prov-gigapath", enc_pretrain=True, model_kwargs={ "enc_out_indices": (2, 4, 6, 8), # using layers 2, 4, 6, 8 from Prov-GigaPath }, ) # Print only a summary of the model instead of the full output print(str(sdist_panoptic.model)[:500] + "\n...")

StarDistUnet(
  (hf_hub:prov-gigapath/prov-gigapath): Encoder(
    (encoder): TimmEncoder(
      (encoder): VisionTransformer(
        (patch_embed): PatchEmbed(
          (proj): Conv2d(3, 1536, kernel_size=(16, 16), stride=(16, 16))
          (norm): Identity()
        )
        (pos_drop): Dropout(p=0.0, inplace=False)
        (patch_drop): Identity()
        (norm_pre): Identity()
        (blocks): Sequential(
          (0): Block(
            (norm1): LayerNorm((1536,), eps=1e-06, elementwi
...

Initializing panoptic CPPNet with SAM image encoder backbone¶

In [6]:

from histolytics.models.cppnet_panoptic import CPPNetPanoptic

cpp_panoptic = CPPNetPanoptic(
    n_nuc_classes=6,
    n_tissue_classes=6,
    enc_name="samvit_base_patch16",
    enc_pretrain=True,
    model_kwargs={
        "enc_out_indices": (2, 4, 6, 8),  # using layers 2, 4, 6, 8 from SAM
    },
)

# Print only a summary of the model instead of the full output
print(str(cpp_panoptic.model)[:500] + "\n...")

from histolytics.models.cppnet_panoptic import CPPNetPanoptic cpp_panoptic = CPPNetPanoptic( n_nuc_classes=6, n_tissue_classes=6, enc_name="samvit_base_patch16", enc_pretrain=True, model_kwargs={ "enc_out_indices": (2, 4, 6, 8), # using layers 2, 4, 6, 8 from SAM }, ) # Print only a summary of the model instead of the full output print(str(cpp_panoptic.model)[:500] + "\n...")

CPPNetUnet(
  (samvit_base_patch16): Encoder(
    (encoder): TimmEncoder(
      (encoder): VisionTransformerSAM(
        (patch_embed): PatchEmbed(
          (proj): Conv2d(3, 768, kernel_size=(16, 16), stride=(16, 16))
          (norm): Identity()
        )
        (pos_drop): Dropout(p=0.0, inplace=False)
        (patch_drop): Identity()
        (norm_pre): Identity()
        (blocks): Sequential(
          (0): Block(
            (norm1): LayerNorm((768,), eps=1e-05, elementwise_affine=True)

...

Initializing panoptic CellVit with the SAM huge backbone¶

In [7]:

from histolytics.models.cellvit_panoptic import CellVitPanoptic

cvit_panoptic = CellVitPanoptic(
    n_nuc_classes=6,
    n_tissue_classes=6,
    enc_name="samvit_huge_patch16",
    enc_pretrain=True,
    model_kwargs={
        "enc_out_indices": (2, 4, 6, 8),  # using layers 2, 4, 6, 8 from SAM Huge
    },
)

# Print only a summary of the model instead of the full output
print(str(cvit_panoptic.model)[:500] + "\n...")

from histolytics.models.cellvit_panoptic import CellVitPanoptic cvit_panoptic = CellVitPanoptic( n_nuc_classes=6, n_tissue_classes=6, enc_name="samvit_huge_patch16", enc_pretrain=True, model_kwargs={ "enc_out_indices": (2, 4, 6, 8), # using layers 2, 4, 6, 8 from SAM Huge }, ) # Print only a summary of the model instead of the full output print(str(cvit_panoptic.model)[:500] + "\n...")

CellVitSamUnet(
  (samvit_huge_patch16): Encoder(
    (encoder): TimmEncoder(
      (encoder): VisionTransformerSAM(
        (patch_embed): PatchEmbed(
          (proj): Conv2d(3, 1280, kernel_size=(16, 16), stride=(16, 16))
          (norm): Identity()
        )
        (pos_drop): Dropout(p=0.0, inplace=False)
        (patch_drop): Identity()
        (norm_pre): Identity()
        (blocks): Sequential(
          (0): Block(
            (norm1): LayerNorm((1280,), eps=1e-05, elementwise_affine=
...

To start finetuning your model with a pre-trained backbone, see the next chapter about finetuning

Available timm encoders¶

In general, any pre-trained model from the timm (pytorch-image-models) library can be used as long as the model has the forward_intermediates-method implemented. You can list the available timm encoders followingly:

In [8]:

import timm

timm.list_models()

import timm timm.list_models()

Out[8]:

['aimv2_1b_patch14_224',
 'aimv2_1b_patch14_336',
 'aimv2_1b_patch14_448',
 'aimv2_3b_patch14_224',
 'aimv2_3b_patch14_336',
 'aimv2_3b_patch14_448',
 'aimv2_huge_patch14_224',
 'aimv2_huge_patch14_336',
 'aimv2_huge_patch14_448',
 'aimv2_large_patch14_224',
 'aimv2_large_patch14_336',
 'aimv2_large_patch14_448',
 'bat_resnext26ts',
 'beit_base_patch16_224',
 'beit_base_patch16_384',
 'beit_large_patch16_224',
 'beit_large_patch16_384',
 'beit_large_patch16_512',
 'beitv2_base_patch16_224',
 'beitv2_large_patch16_224',
 'botnet26t_256',
 'botnet50ts_256',
 'caformer_b36',
 'caformer_m36',
 'caformer_s18',
 'caformer_s36',
 '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_medium',
 'coat_lite_medium_384',
 'coat_lite_mini',
 'coat_lite_small',
 'coat_lite_tiny',
 'coat_mini',
 'coat_small',
 'coat_tiny',
 'coatnet_0_224',
 'coatnet_0_rw_224',
 'coatnet_1_224',
 'coatnet_1_rw_224',
 'coatnet_2_224',
 'coatnet_2_rw_224',
 'coatnet_3_224',
 'coatnet_3_rw_224',
 'coatnet_4_224',
 'coatnet_5_224',
 'coatnet_bn_0_rw_224',
 'coatnet_nano_cc_224',
 'coatnet_nano_rw_224',
 'coatnet_pico_rw_224',
 'coatnet_rmlp_0_rw_224',
 'coatnet_rmlp_1_rw2_224',
 'coatnet_rmlp_1_rw_224',
 'coatnet_rmlp_2_rw_224',
 'coatnet_rmlp_2_rw_384',
 'coatnet_rmlp_3_rw_224',
 'coatnet_rmlp_nano_rw_224',
 'coatnext_nano_rw_224',
 'convformer_b36',
 'convformer_m36',
 'convformer_s18',
 'convformer_s36',
 'convit_base',
 'convit_small',
 'convit_tiny',
 'convmixer_768_32',
 'convmixer_1024_20_ks9_p14',
 'convmixer_1536_20',
 'convnext_atto',
 'convnext_atto_ols',
 'convnext_atto_rms',
 'convnext_base',
 'convnext_femto',
 'convnext_femto_ols',
 'convnext_large',
 'convnext_large_mlp',
 'convnext_nano',
 'convnext_nano_ols',
 'convnext_pico',
 'convnext_pico_ols',
 'convnext_small',
 'convnext_tiny',
 'convnext_tiny_hnf',
 'convnext_xlarge',
 'convnext_xxlarge',
 'convnext_zepto_rms',
 'convnext_zepto_rms_ols',
 'convnextv2_atto',
 'convnextv2_base',
 'convnextv2_femto',
 'convnextv2_huge',
 'convnextv2_large',
 'convnextv2_nano',
 'convnextv2_pico',
 'convnextv2_small',
 'convnextv2_tiny',
 'crossvit_9_240',
 'crossvit_9_dagger_240',
 'crossvit_15_240',
 'crossvit_15_dagger_240',
 'crossvit_15_dagger_408',
 'crossvit_18_240',
 'crossvit_18_dagger_240',
 'crossvit_18_dagger_408',
 'crossvit_base_240',
 'crossvit_small_240',
 'crossvit_tiny_240',
 'cs3darknet_focus_l',
 'cs3darknet_focus_m',
 'cs3darknet_focus_s',
 'cs3darknet_focus_x',
 'cs3darknet_l',
 'cs3darknet_m',
 'cs3darknet_s',
 'cs3darknet_x',
 'cs3edgenet_x',
 'cs3se_edgenet_x',
 'cs3sedarknet_l',
 'cs3sedarknet_x',
 'cs3sedarknet_xdw',
 'cspdarknet53',
 'cspresnet50',
 'cspresnet50d',
 'cspresnet50w',
 'cspresnext50',
 'darknet17',
 'darknet21',
 'darknet53',
 'darknetaa53',
 'davit_base',
 'davit_base_fl',
 'davit_giant',
 'davit_huge',
 'davit_huge_fl',
 'davit_large',
 'davit_small',
 'davit_tiny',
 'deit3_base_patch16_224',
 'deit3_base_patch16_384',
 'deit3_huge_patch14_224',
 'deit3_large_patch16_224',
 'deit3_large_patch16_384',
 'deit3_medium_patch16_224',
 'deit3_small_patch16_224',
 'deit3_small_patch16_384',
 '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',
 'densenet264d',
 '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',
 'dpn48b',
 'dpn68',
 'dpn68b',
 'dpn92',
 'dpn98',
 'dpn107',
 'dpn131',
 'eca_botnext26ts_256',
 'eca_halonext26ts',
 'eca_nfnet_l0',
 'eca_nfnet_l1',
 'eca_nfnet_l2',
 'eca_nfnet_l3',
 'eca_resnet33ts',
 'eca_resnext26ts',
 'eca_vovnet39b',
 'ecaresnet26t',
 'ecaresnet50d',
 'ecaresnet50d_pruned',
 'ecaresnet50t',
 'ecaresnet101d',
 'ecaresnet101d_pruned',
 'ecaresnet200d',
 'ecaresnet269d',
 'ecaresnetlight',
 'ecaresnext26t_32x4d',
 'ecaresnext50t_32x4d',
 'edgenext_base',
 'edgenext_small',
 'edgenext_small_rw',
 'edgenext_x_small',
 'edgenext_xx_small',
 'efficientformer_l1',
 'efficientformer_l3',
 'efficientformer_l7',
 'efficientformerv2_l',
 'efficientformerv2_s0',
 'efficientformerv2_s1',
 'efficientformerv2_s2',
 'efficientnet_b0',
 'efficientnet_b0_g8_gn',
 'efficientnet_b0_g16_evos',
 'efficientnet_b0_gn',
 'efficientnet_b1',
 'efficientnet_b1_pruned',
 'efficientnet_b2',
 'efficientnet_b2_pruned',
 'efficientnet_b3',
 'efficientnet_b3_g8_gn',
 'efficientnet_b3_gn',
 'efficientnet_b3_pruned',
 'efficientnet_b4',
 'efficientnet_b5',
 'efficientnet_b6',
 'efficientnet_b7',
 'efficientnet_b8',
 'efficientnet_blur_b0',
 'efficientnet_cc_b0_4e',
 'efficientnet_cc_b0_8e',
 'efficientnet_cc_b1_8e',
 'efficientnet_el',
 'efficientnet_el_pruned',
 'efficientnet_em',
 'efficientnet_es',
 'efficientnet_es_pruned',
 'efficientnet_h_b5',
 'efficientnet_l2',
 'efficientnet_lite0',
 'efficientnet_lite1',
 'efficientnet_lite2',
 'efficientnet_lite3',
 'efficientnet_lite4',
 'efficientnet_x_b3',
 'efficientnet_x_b5',
 'efficientnetv2_l',
 'efficientnetv2_m',
 'efficientnetv2_rw_m',
 'efficientnetv2_rw_s',
 'efficientnetv2_rw_t',
 'efficientnetv2_s',
 'efficientnetv2_xl',
 'efficientvit_b0',
 'efficientvit_b1',
 'efficientvit_b2',
 'efficientvit_b3',
 'efficientvit_l1',
 'efficientvit_l2',
 'efficientvit_l3',
 'efficientvit_m0',
 'efficientvit_m1',
 'efficientvit_m2',
 'efficientvit_m3',
 'efficientvit_m4',
 'efficientvit_m5',
 'ese_vovnet19b_dw',
 'ese_vovnet19b_slim',
 'ese_vovnet19b_slim_dw',
 'ese_vovnet39b',
 'ese_vovnet39b_evos',
 'ese_vovnet57b',
 'ese_vovnet99b',
 'eva02_base_patch14_224',
 'eva02_base_patch14_448',
 'eva02_base_patch16_clip_224',
 'eva02_enormous_patch14_clip_224',
 'eva02_large_patch14_224',
 'eva02_large_patch14_448',
 'eva02_large_patch14_clip_224',
 'eva02_large_patch14_clip_336',
 'eva02_small_patch14_224',
 'eva02_small_patch14_336',
 'eva02_tiny_patch14_224',
 'eva02_tiny_patch14_336',
 'eva_giant_patch14_224',
 'eva_giant_patch14_336',
 'eva_giant_patch14_560',
 'eva_giant_patch14_clip_224',
 'eva_large_patch14_196',
 'eva_large_patch14_336',
 'fastvit_ma36',
 'fastvit_mci0',
 'fastvit_mci1',
 'fastvit_mci2',
 'fastvit_s12',
 'fastvit_sa12',
 'fastvit_sa24',
 'fastvit_sa36',
 'fastvit_t8',
 'fastvit_t12',
 'fbnetc_100',
 'fbnetv3_b',
 'fbnetv3_d',
 'fbnetv3_g',
 'flexivit_base',
 'flexivit_large',
 'flexivit_small',
 'focalnet_base_lrf',
 'focalnet_base_srf',
 'focalnet_huge_fl3',
 'focalnet_huge_fl4',
 'focalnet_large_fl3',
 'focalnet_large_fl4',
 'focalnet_small_lrf',
 'focalnet_small_srf',
 'focalnet_tiny_lrf',
 'focalnet_tiny_srf',
 'focalnet_xlarge_fl3',
 'focalnet_xlarge_fl4',
 'gc_efficientnetv2_rw_t',
 'gcresnet33ts',
 'gcresnet50t',
 'gcresnext26ts',
 'gcresnext50ts',
 'gcvit_base',
 'gcvit_small',
 'gcvit_tiny',
 'gcvit_xtiny',
 'gcvit_xxtiny',
 'gernet_l',
 'gernet_m',
 'gernet_s',
 'ghostnet_050',
 'ghostnet_100',
 'ghostnet_130',
 'ghostnetv2_100',
 'ghostnetv2_130',
 'ghostnetv2_160',
 'gmixer_12_224',
 'gmixer_24_224',
 'gmlp_b16_224',
 'gmlp_s16_224',
 'gmlp_ti16_224',
 'halo2botnet50ts_256',
 'halonet26t',
 'halonet50ts',
 'halonet_h1',
 'haloregnetz_b',
 'hardcorenas_a',
 'hardcorenas_b',
 'hardcorenas_c',
 'hardcorenas_d',
 'hardcorenas_e',
 'hardcorenas_f',
 'hgnet_base',
 'hgnet_small',
 'hgnet_tiny',
 'hgnetv2_b0',
 'hgnetv2_b1',
 'hgnetv2_b2',
 'hgnetv2_b3',
 'hgnetv2_b4',
 'hgnetv2_b5',
 'hgnetv2_b6',
 'hiera_base_224',
 'hiera_base_abswin_256',
 'hiera_base_plus_224',
 'hiera_huge_224',
 'hiera_large_224',
 'hiera_small_224',
 'hiera_small_abswin_256',
 'hiera_tiny_224',
 'hieradet_small',
 'hrnet_w18',
 'hrnet_w18_small',
 'hrnet_w18_small_v2',
 'hrnet_w18_ssld',
 'hrnet_w30',
 'hrnet_w32',
 'hrnet_w40',
 'hrnet_w44',
 'hrnet_w48',
 'hrnet_w48_ssld',
 'hrnet_w64',
 'inception_next_atto',
 'inception_next_base',
 'inception_next_small',
 'inception_next_tiny',
 'inception_resnet_v2',
 'inception_v3',
 'inception_v4',
 'lambda_resnet26rpt_256',
 'lambda_resnet26t',
 'lambda_resnet50ts',
 'lamhalobotnet50ts_256',
 'lcnet_035',
 'lcnet_050',
 'lcnet_075',
 'lcnet_100',
 'lcnet_150',
 'legacy_senet154',
 'legacy_seresnet18',
 'legacy_seresnet34',
 'legacy_seresnet50',
 'legacy_seresnet101',
 'legacy_seresnet152',
 'legacy_seresnext26_32x4d',
 'legacy_seresnext50_32x4d',
 'legacy_seresnext101_32x4d',
 'legacy_xception',
 'levit_128',
 'levit_128s',
 'levit_192',
 'levit_256',
 'levit_256d',
 'levit_384',
 'levit_384_s8',
 'levit_512',
 'levit_512_s8',
 'levit_512d',
 'levit_conv_128',
 'levit_conv_128s',
 'levit_conv_192',
 'levit_conv_256',
 'levit_conv_256d',
 'levit_conv_384',
 'levit_conv_384_s8',
 'levit_conv_512',
 'levit_conv_512_s8',
 'levit_conv_512d',
 'mambaout_base',
 'mambaout_base_plus_rw',
 'mambaout_base_short_rw',
 'mambaout_base_tall_rw',
 'mambaout_base_wide_rw',
 'mambaout_femto',
 'mambaout_kobe',
 'mambaout_small',
 'mambaout_small_rw',
 'mambaout_tiny',
 'maxvit_base_tf_224',
 'maxvit_base_tf_384',
 'maxvit_base_tf_512',
 'maxvit_large_tf_224',
 'maxvit_large_tf_384',
 'maxvit_large_tf_512',
 'maxvit_nano_rw_256',
 'maxvit_pico_rw_256',
 'maxvit_rmlp_base_rw_224',
 'maxvit_rmlp_base_rw_384',
 'maxvit_rmlp_nano_rw_256',
 'maxvit_rmlp_pico_rw_256',
 'maxvit_rmlp_small_rw_224',
 'maxvit_rmlp_small_rw_256',
 'maxvit_rmlp_tiny_rw_256',
 'maxvit_small_tf_224',
 'maxvit_small_tf_384',
 'maxvit_small_tf_512',
 'maxvit_tiny_pm_256',
 'maxvit_tiny_rw_224',
 'maxvit_tiny_rw_256',
 'maxvit_tiny_tf_224',
 'maxvit_tiny_tf_384',
 'maxvit_tiny_tf_512',
 'maxvit_xlarge_tf_224',
 'maxvit_xlarge_tf_384',
 'maxvit_xlarge_tf_512',
 'maxxvit_rmlp_nano_rw_256',
 'maxxvit_rmlp_small_rw_256',
 'maxxvit_rmlp_tiny_rw_256',
 'maxxvitv2_nano_rw_256',
 'maxxvitv2_rmlp_base_rw_224',
 'maxxvitv2_rmlp_base_rw_384',
 'maxxvitv2_rmlp_large_rw_224',
 'mixer_b16_224',
 'mixer_b32_224',
 'mixer_l16_224',
 'mixer_l32_224',
 'mixer_s16_224',
 'mixer_s32_224',
 'mixnet_l',
 'mixnet_m',
 'mixnet_s',
 'mixnet_xl',
 'mixnet_xxl',
 'mnasnet_050',
 'mnasnet_075',
 'mnasnet_100',
 'mnasnet_140',
 'mnasnet_small',
 'mobilenet_edgetpu_100',
 'mobilenet_edgetpu_v2_l',
 'mobilenet_edgetpu_v2_m',
 'mobilenet_edgetpu_v2_s',
 'mobilenet_edgetpu_v2_xs',
 'mobilenetv1_100',
 'mobilenetv1_100h',
 'mobilenetv1_125',
 'mobilenetv2_035',
 'mobilenetv2_050',
 'mobilenetv2_075',
 'mobilenetv2_100',
 'mobilenetv2_110d',
 'mobilenetv2_120d',
 'mobilenetv2_140',
 'mobilenetv3_large_075',
 'mobilenetv3_large_100',
 'mobilenetv3_large_150d',
 'mobilenetv3_rw',
 'mobilenetv3_small_050',
 'mobilenetv3_small_075',
 'mobilenetv3_small_100',
 'mobilenetv4_conv_aa_large',
 'mobilenetv4_conv_aa_medium',
 'mobilenetv4_conv_blur_medium',
 'mobilenetv4_conv_large',
 'mobilenetv4_conv_medium',
 'mobilenetv4_conv_small',
 'mobilenetv4_conv_small_035',
 'mobilenetv4_conv_small_050',
 'mobilenetv4_hybrid_large',
 'mobilenetv4_hybrid_large_075',
 'mobilenetv4_hybrid_medium',
 'mobilenetv4_hybrid_medium_075',
 'mobileone_s0',
 'mobileone_s1',
 'mobileone_s2',
 'mobileone_s3',
 'mobileone_s4',
 'mobilevit_s',
 'mobilevit_xs',
 'mobilevit_xxs',
 'mobilevitv2_050',
 'mobilevitv2_075',
 'mobilevitv2_100',
 'mobilevitv2_125',
 'mobilevitv2_150',
 'mobilevitv2_175',
 'mobilevitv2_200',
 'mvitv2_base',
 'mvitv2_base_cls',
 'mvitv2_huge_cls',
 'mvitv2_large',
 'mvitv2_large_cls',
 'mvitv2_small',
 'mvitv2_small_cls',
 'mvitv2_tiny',
 'nasnetalarge',
 'nest_base',
 'nest_base_jx',
 'nest_small',
 'nest_small_jx',
 'nest_tiny',
 'nest_tiny_jx',
 'nextvit_base',
 'nextvit_large',
 'nextvit_small',
 'nf_ecaresnet26',
 'nf_ecaresnet50',
 'nf_ecaresnet101',
 'nf_regnet_b0',
 'nf_regnet_b1',
 'nf_regnet_b2',
 'nf_regnet_b3',
 'nf_regnet_b4',
 'nf_regnet_b5',
 'nf_resnet26',
 'nf_resnet50',
 'nf_resnet101',
 'nf_seresnet26',
 'nf_seresnet50',
 'nf_seresnet101',
 'nfnet_f0',
 'nfnet_f1',
 'nfnet_f2',
 'nfnet_f3',
 'nfnet_f4',
 'nfnet_f5',
 'nfnet_f6',
 'nfnet_f7',
 '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',
 'poolformerv2_m36',
 'poolformerv2_m48',
 'poolformerv2_s12',
 'poolformerv2_s24',
 'poolformerv2_s36',
 'pvt_v2_b0',
 'pvt_v2_b1',
 'pvt_v2_b2',
 'pvt_v2_b2_li',
 'pvt_v2_b3',
 'pvt_v2_b4',
 'pvt_v2_b5',
 'rdnet_base',
 'rdnet_large',
 'rdnet_small',
 'rdnet_tiny',
 'regnetv_040',
 'regnetv_064',
 'regnetx_002',
 'regnetx_004',
 'regnetx_004_tv',
 '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_008_tv',
 'regnety_016',
 'regnety_032',
 'regnety_040',
 'regnety_040_sgn',
 'regnety_064',
 'regnety_080',
 'regnety_080_tv',
 'regnety_120',
 'regnety_160',
 'regnety_320',
 'regnety_640',
 'regnety_1280',
 'regnety_2560',
 'regnetz_005',
 'regnetz_040',
 'regnetz_040_h',
 'regnetz_b16',
 'regnetz_b16_evos',
 'regnetz_c16',
 'regnetz_c16_evos',
 'regnetz_d8',
 'regnetz_d8_evos',
 'regnetz_d32',
 'regnetz_e8',
 'repghostnet_050',
 'repghostnet_058',
 'repghostnet_080',
 'repghostnet_100',
 'repghostnet_111',
 'repghostnet_130',
 'repghostnet_150',
 'repghostnet_200',
 'repvgg_a0',
 'repvgg_a1',
 'repvgg_a2',
 'repvgg_b0',
 'repvgg_b1',
 'repvgg_b1g4',
 'repvgg_b2',
 'repvgg_b2g4',
 'repvgg_b3',
 'repvgg_b3g4',
 'repvgg_d2se',
 'repvit_m0_9',
 'repvit_m1',
 'repvit_m1_0',
 'repvit_m1_1',
 'repvit_m1_5',
 'repvit_m2',
 'repvit_m2_3',
 'repvit_m3',
 'res2net50_14w_8s',
 'res2net50_26w_4s',
 'res2net50_26w_6s',
 'res2net50_26w_8s',
 'res2net50_48w_2s',
 'res2net50d',
 'res2net101_26w_4s',
 'res2net101d',
 'res2next50',
 'resmlp_12_224',
 'resmlp_24_224',
 'resmlp_36_224',
 'resmlp_big_24_224',
 'resnest14d',
 'resnest26d',
 'resnest50d',
 'resnest50d_1s4x24d',
 'resnest50d_4s2x40d',
 'resnest101e',
 'resnest200e',
 'resnest269e',
 'resnet10t',
 'resnet14t',
 'resnet18',
 'resnet18d',
 'resnet26',
 'resnet26d',
 'resnet26t',
 'resnet32ts',
 'resnet33ts',
 'resnet34',
 'resnet34d',
 'resnet50',
 'resnet50_clip',
 'resnet50_clip_gap',
 'resnet50_gn',
 'resnet50_mlp',
 'resnet50c',
 'resnet50d',
 'resnet50s',
 'resnet50t',
 'resnet50x4_clip',
 'resnet50x4_clip_gap',
 'resnet50x16_clip',
 'resnet50x16_clip_gap',
 'resnet50x64_clip',
 'resnet50x64_clip_gap',
 'resnet51q',
 'resnet61q',
 'resnet101',
 'resnet101_clip',
 'resnet101_clip_gap',
 'resnet101c',
 'resnet101d',
 'resnet101s',
 'resnet152',
 'resnet152c',
 'resnet152d',
 'resnet152s',
 'resnet200',
 'resnet200d',
 'resnetaa34d',
 'resnetaa50',
 'resnetaa50d',
 'resnetaa101d',
 'resnetblur18',
 'resnetblur50',
 'resnetblur50d',
 'resnetblur101d',
 'resnetrs50',
 'resnetrs101',
 'resnetrs152',
 'resnetrs200',
 'resnetrs270',
 'resnetrs350',
 'resnetrs420',
 'resnetv2_18',
 'resnetv2_18d',
 'resnetv2_34',
 'resnetv2_34d',
 'resnetv2_50',
 'resnetv2_50d',
 'resnetv2_50d_evos',
 'resnetv2_50d_frn',
 'resnetv2_50d_gn',
 'resnetv2_50t',
 'resnetv2_50x1_bit',
 'resnetv2_50x3_bit',
 'resnetv2_101',
 'resnetv2_101d',
 'resnetv2_101x1_bit',
 'resnetv2_101x3_bit',
 'resnetv2_152',
 'resnetv2_152d',
 'resnetv2_152x2_bit',
 'resnetv2_152x4_bit',
 'resnext26ts',
 'resnext50_32x4d',
 'resnext50d_32x4d',
 'resnext101_32x4d',
 'resnext101_32x8d',
 'resnext101_32x16d',
 'resnext101_32x32d',
 'resnext101_64x4d',
 'rexnet_100',
 'rexnet_130',
 'rexnet_150',
 'rexnet_200',
 'rexnet_300',
 'rexnetr_100',
 'rexnetr_130',
 'rexnetr_150',
 'rexnetr_200',
 'rexnetr_300',
 'sam2_hiera_base_plus',
 'sam2_hiera_large',
 'sam2_hiera_small',
 'sam2_hiera_tiny',
 'samvit_base_patch16',
 'samvit_base_patch16_224',
 'samvit_huge_patch16',
 'samvit_large_patch16',
 'sebotnet33ts_256',
 'sedarknet21',
 'sehalonet33ts',
 'selecsls42',
 'selecsls42b',
 'selecsls60',
 'selecsls60b',
 'selecsls84',
 'semnasnet_050',
 'semnasnet_075',
 'semnasnet_100',
 'semnasnet_140',
 'senet154',
 'sequencer2d_l',
 'sequencer2d_m',
 'sequencer2d_s',
 'seresnet18',
 'seresnet33ts',
 'seresnet34',
 'seresnet50',
 'seresnet50t',
 'seresnet101',
 'seresnet152',
 'seresnet152d',
 'seresnet200d',
 'seresnet269d',
 'seresnetaa50d',
 'seresnext26d_32x4d',
 'seresnext26t_32x4d',
 'seresnext26ts',
 'seresnext50_32x4d',
 'seresnext101_32x4d',
 'seresnext101_32x8d',
 'seresnext101_64x4d',
 'seresnext101d_32x8d',
 'seresnextaa101d_32x8d',
 'seresnextaa201d_32x8d',
 'skresnet18',
 'skresnet34',
 'skresnet50',
 'skresnet50d',
 'skresnext50_32x4d',
 'spnasnet_100',
 'swin_base_patch4_window7_224',
 'swin_base_patch4_window12_384',
 'swin_large_patch4_window7_224',
 'swin_large_patch4_window12_384',
 'swin_s3_base_224',
 'swin_s3_small_224',
 'swin_s3_tiny_224',
 'swin_small_patch4_window7_224',
 'swin_tiny_patch4_window7_224',
 'swinv2_base_window8_256',
 'swinv2_base_window12_192',
 'swinv2_base_window12to16_192to256',
 'swinv2_base_window12to24_192to384',
 'swinv2_base_window16_256',
 'swinv2_cr_base_224',
 'swinv2_cr_base_384',
 'swinv2_cr_base_ns_224',
 'swinv2_cr_giant_224',
 'swinv2_cr_giant_384',
 'swinv2_cr_huge_224',
 'swinv2_cr_huge_384',
 'swinv2_cr_large_224',
 'swinv2_cr_large_384',
 'swinv2_cr_small_224',
 'swinv2_cr_small_384',
 'swinv2_cr_small_ns_224',
 'swinv2_cr_small_ns_256',
 'swinv2_cr_tiny_224',
 'swinv2_cr_tiny_384',
 'swinv2_cr_tiny_ns_224',
 'swinv2_large_window12_192',
 'swinv2_large_window12to16_192to256',
 'swinv2_large_window12to24_192to384',
 'swinv2_small_window8_256',
 'swinv2_small_window16_256',
 'swinv2_tiny_window8_256',
 'swinv2_tiny_window16_256',
 'test_byobnet',
 'test_convnext',
 'test_convnext2',
 'test_convnext3',
 'test_efficientnet',
 'test_efficientnet_evos',
 'test_efficientnet_gn',
 'test_efficientnet_ln',
 'test_mambaout',
 'test_nfnet',
 'test_resnet',
 'test_vit',
 'test_vit2',
 'test_vit3',
 'test_vit4',
 'tf_efficientnet_b0',
 'tf_efficientnet_b1',
 'tf_efficientnet_b2',
 'tf_efficientnet_b3',
 'tf_efficientnet_b4',
 'tf_efficientnet_b5',
 'tf_efficientnet_b6',
 'tf_efficientnet_b7',
 'tf_efficientnet_b8',
 'tf_efficientnet_cc_b0_4e',
 'tf_efficientnet_cc_b0_8e',
 'tf_efficientnet_cc_b1_8e',
 'tf_efficientnet_el',
 'tf_efficientnet_em',
 'tf_efficientnet_es',
 'tf_efficientnet_l2',
 'tf_efficientnet_lite0',
 'tf_efficientnet_lite1',
 'tf_efficientnet_lite2',
 'tf_efficientnet_lite3',
 'tf_efficientnet_lite4',
 'tf_efficientnetv2_b0',
 'tf_efficientnetv2_b1',
 'tf_efficientnetv2_b2',
 'tf_efficientnetv2_b3',
 'tf_efficientnetv2_l',
 'tf_efficientnetv2_m',
 'tf_efficientnetv2_s',
 'tf_efficientnetv2_xl',
 'tf_mixnet_l',
 'tf_mixnet_m',
 'tf_mixnet_s',
 'tf_mobilenetv3_large_075',
 'tf_mobilenetv3_large_100',
 'tf_mobilenetv3_large_minimal_100',
 'tf_mobilenetv3_small_075',
 'tf_mobilenetv3_small_100',
 'tf_mobilenetv3_small_minimal_100',
 'tiny_vit_5m_224',
 'tiny_vit_11m_224',
 'tiny_vit_21m_224',
 'tiny_vit_21m_384',
 'tiny_vit_21m_512',
 'tinynet_a',
 'tinynet_b',
 'tinynet_c',
 'tinynet_d',
 'tinynet_e',
 'tnt_b_patch16_224',
 'tnt_s_patch16_224',
 'tresnet_l',
 'tresnet_m',
 'tresnet_v2_l',
 'tresnet_xl',
 'twins_pcpvt_base',
 'twins_pcpvt_large',
 'twins_pcpvt_small',
 'twins_svt_base',
 'twins_svt_large',
 'twins_svt_small',
 'vgg11',
 'vgg11_bn',
 'vgg13',
 'vgg13_bn',
 'vgg16',
 'vgg16_bn',
 'vgg19',
 'vgg19_bn',
 'visformer_small',
 'visformer_tiny',
 'vit_base_mci_224',
 'vit_base_patch8_224',
 'vit_base_patch14_dinov2',
 'vit_base_patch14_reg4_dinov2',
 'vit_base_patch16_18x2_224',
 'vit_base_patch16_224',
 'vit_base_patch16_224_miil',
 'vit_base_patch16_384',
 'vit_base_patch16_clip_224',
 'vit_base_patch16_clip_384',
 'vit_base_patch16_clip_quickgelu_224',
 'vit_base_patch16_gap_224',
 'vit_base_patch16_plus_240',
 'vit_base_patch16_plus_clip_240',
 'vit_base_patch16_reg4_gap_256',
 'vit_base_patch16_rope_reg1_gap_256',
 ...]

In [ ]: