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()
  model= torchvision.models.efficientnet_b2(weights=weights)
  for parm in model.parameters():
    parm.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))
  return model,transforms