| import torch | |
| import torchvision | |
| from torch import nn | |
| def create_effnetb2_model(num_classes:int=3, | |
| seed:int=42): | |
| # Create Effnet pretrained model | |
| weights= torchvision.models.EfficientNet_B2_Weights.DEFAULT | |
| transforms= weights.transforms() | |
| model= torchvision.models.efficientnet_b2(weights=weights) | |
| # Freeze all layers in the base model | |
| for param in model.parameters(): | |
| param.requires_grad= False | |
| # Change the classifier layer | |
| torch.manual_seed(seed) | |
| model.classifier= nn.Sequential( | |
| nn.Dropout(p=0.3, inplace= True), | |
| nn.Linear(in_features=1408, out_features= num_classes) | |
| ) | |
| return model, transforms | |