model.py

import torch
import torchvision

from torch import nn


def create_effnetv2(num_classes:int=101,
                    seed:int=42):

    weights = torchvision.models.EfficientNet_V2_M_Weights.DEFAULT
    transforms = weights.transforms()
    model = torchvision.models.efficientnet_v2_m(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=1280, out_features=num_classes),
    )

    return model, transforms