model.py

import torch
import torchvision
from torch import nn

def create_effnetb2_model(num_classes:int=3, seed:int=42):

  weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT

  transforms = weights.transforms()

  #setup pretrained model instance

  model = torchvision.models.efficientnet_b2(weights=weights)

  # free base layer in the model

  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