Making large AI models cheaper, faster and more accessible
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import timm.models as tmm
import torchvision.models as tm
# input shape: (batch_size, 3, 224, 224)
tm_models = [
tm.alexnet,
tm.convnext_base,
tm.densenet121,
# tm.efficientnet_v2_s,
# tm.googlenet, # output bad case
# tm.inception_v3, # bad case
tm.mobilenet_v2,
tm.mobilenet_v3_small,
tm.mnasnet0_5,
tm.resnet18,
tm.regnet_x_16gf,
tm.resnext50_32x4d,
tm.shufflenet_v2_x0_5,
tm.squeezenet1_0,
# tm.swin_s, # fx bad case
tm.vgg11,
tm.vit_b_16,
tm.wide_resnet50_2,
]
tmm_models = [
tmm.beit_base_patch16_224,
tmm.beitv2_base_patch16_224,
tmm.cait_s24_224,
tmm.coat_lite_mini,
tmm.convit_base,
tmm.deit3_base_patch16_224,
tmm.dm_nfnet_f0,
tmm.eca_nfnet_l0,
tmm.efficientformer_l1,
# tmm.ese_vovnet19b_dw,
tmm.gmixer_12_224,
tmm.gmlp_b16_224,
# tmm.hardcorenas_a,
tmm.hrnet_w18_small,
tmm.inception_v3,
tmm.mixer_b16_224,
tmm.nf_ecaresnet101,
tmm.nf_regnet_b0,
# tmm.pit_b_224, # pretrained only
# tmm.regnetv_040,
# tmm.skresnet18,
# tmm.swin_base_patch4_window7_224, # fx bad case
# tmm.tnt_b_patch16_224, # bad case
tmm.vgg11,
tmm.vit_base_patch16_18x2_224,
tmm.wide_resnet50_2,
]