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)
    torch.cuda.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