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.
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.
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¶
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¶
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¶
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¶
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:
import timm
timm.list_models()
['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', ...]