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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
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