model.py

import torch
import torchvision


def create_model(

    num_classes: int=120,

    seed: int=42):

  weights = torchvision.models.EfficientNet_V2_S_Weights.DEFAULT
  model = torchvision.models.efficientnet_v2_s(weights=weights).to("cpu")
  auto_transform = weights.transforms()

  for params in model.parameters():
    params.requires_grad = False

  model.classifier = torch.nn.Sequential(
      torch.nn.Dropout(p=0.3,inplace=True),
      torch.nn.Linear(1280,num_classes)
  )

  return model, auto_transform