| import torchvision | |
| from torch import nn | |
| def create_effnetb2_model(num_classes: int = 101): | |
| weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT | |
| # Get EffNetB2 transforms | |
| transforms = weights.transforms() | |
| # Setup pretrained model instance | |
| model = torchvision.models.efficientnet_b2(weights=weights) | |
| # Freeze the base layers in the model ( this will stop all layers from training) | |
| for param in model.parameters(): | |
| param.requires_grad = False | |
| 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 | |