model.py

import torch
import torchvision
from torch import nn

def create_effnetb2_model(num_classes:int=101,
                          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