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Upload model.py
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model.py
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import torchvision
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from torch import nn
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def create_effnetb2_model(num_classes: int):
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weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
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transforms = weights.transforms()
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model = torchvision.models.efficientnet_b2(weights=weights)
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# Freeze base model
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for param in model.parameters():
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param.requires_grad = False
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# Change classifier head
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model.classifier = nn.Sequential(
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nn.Dropout(p=0.3, inplace=True),
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nn.Linear(in_features=1408, out_features=num_classes),
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)
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return model, transforms
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