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 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)
    )
    return model,transforms