model.py

import torch
import torchvision
from torch import nn
device = "cuda" if torch.cuda.is_available() else "cpu"
device

def create_effnetb2_model(num_classes=43,
                           seed: int=42):

  import torch
  from torch import nn
  import torchvision
  from torchvision import datasets
  from torchvision import transforms
  from torchvision.transforms import ToTensor





  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



  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