model.py

import torch
import torchvision
from torch import nn

def create_effnetb2_model(seed: int=42 ,num_classes: int=3):
   weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
   effnet_transforms = weights.transforms()
   model = torchvision.models.efficientnet_b2(weights=weights)

   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 ,effnet_transforms