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import torch
import torchvision
from torch import nn
def create_effnetb2_model(num_classes:int=5,
seed:int=42):
weights=torchvision.models.EfficientNet_B2_Weights.DEFAULT
transforms=weights.transforms()
model = torchvision.models.efficientnet_b2(weights=weights)
for params in model.parameters():
params.requires_grad=False
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
model.classifier=nn.Sequential(
nn.Dropout(p=0.3, inplace=True),
nn.Linear(in_features=1408, out_features=num_classes, bias=True)
)
return model, transforms
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