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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
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