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