model.py

import torch
import torchvision
import torch.nn as nn

def create_effnetb2_model(num_classes:int=3, seed:int=3):
  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
  transforms = weights.transforms()
  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
  
  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