model.py

import torch
import torchvision

from torch import nn

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

    # 4. Freeze all layers in the base model
    for param in model.parameters():
        param.requires_grad = False

    # 5. change classifier head with random seed for reproducibility
    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