feature_networks/constants.py

TORCHVISION = [
    "vgg11_bn",
    "vgg13_bn",
    "vgg16",
    "vgg16_bn",
    "vgg19_bn",
    "densenet121",
    "densenet169",
    "densenet201",
    "inception_v3",
    "resnet18",
    "resnet34",
    "resnet50",
    "resnet101",
    "resnet152",
    "shufflenet_v2_x0_5",
    "mobilenet_v2",
    "wide_resnet50_2",
    "mnasnet0_5",
    "mnasnet1_0",
    "ghostnet_100",
    "cspresnet50",
    "fbnetc_100",
    "spnasnet_100",
    "resnet50d",
    "resnet26",
    "resnet26d",
    "seresnet50",
    "resnetblur50",
    "resnetrs50",
    "tf_mixnet_s",
    "tf_mixnet_m",
    "tf_mixnet_l",
    "ese_vovnet19b_dw",
    "ese_vovnet39b",
    "res2next50",
    "gernet_s",
    "gernet_m",
    "repvgg_a2",
    "repvgg_b0",
    "repvgg_b1",
    "repvgg_b1g4",
    "revnet",
    "dm_nfnet_f1",
    "nfnet_l0",
]

REGNETS = [
    "regnetx_002",
    "regnetx_004",
    "regnetx_006",
    "regnetx_008",
    "regnetx_016",
    "regnetx_032",
    "regnetx_040",
    "regnetx_064",
    "regnety_002",
    "regnety_004",
    "regnety_006",
    "regnety_008",
    "regnety_016",
    "regnety_032",
    "regnety_040",
    "regnety_064",
]

EFFNETS_IMAGENET = [
    'tf_efficientnet_b0',
    'tf_efficientnet_b1',
    'tf_efficientnet_b2',
    'tf_efficientnet_b3',
    'tf_efficientnet_b4',
    'tf_efficientnet_b0_ns',
]

EFFNETS_INCEPTION = [
    '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',
    'efficientnet_b1',
    'efficientnet_b1_pruned',
    'efficientnet_b2_pruned',
    'efficientnet_b3_pruned',
]

EFFNETS = EFFNETS_IMAGENET + EFFNETS_INCEPTION

VITS_IMAGENET = [
    'deit_tiny_distilled_patch16_224',
    'deit_small_distilled_patch16_224',
    'deit_base_distilled_patch16_224',
]

VITS_INCEPTION = [
    'vit_base_patch16_224',
    'vit_large_patch16_224'
]

MAES = [
    'mae_vit_base_patch16',
    'mae_vit_large_patch16',
    'mae_vit_huge_patch14'
]

ST= ['swin_base_patch4_window7_224']

TNT = ['tnt_b_patch16_224']

VITS = VITS_IMAGENET + VITS_INCEPTION

CLIP = [
    'resnet50_clip'
]

ALL_MODELS = TORCHVISION + REGNETS + EFFNETS + VITS + CLIP + MAES + TNT

# Group according to input normalization

NORMALIZED_IMAGENET = TORCHVISION + REGNETS + EFFNETS_IMAGENET + VITS_IMAGENET

NORMALIZED_INCEPTION = EFFNETS_INCEPTION + VITS_INCEPTION + MAES + TNT + ST

NORMALIZED_CLIP = CLIP