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import torch
import torchvision
from torch import nn
def create_effnetb2_model(num_classes:int=3, seed:int=42):
weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
transforms = weights.transforms()
#setup pretrained model instance
model = torchvision.models.efficientnet_b2(weights=weights)
# free base layer in the model
for param in model.parameters():
param.requires_grad = False
torch.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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