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8908af0 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 | import torchvision
from torch import nn
def create_effnetb2_model(num_classes: int = 101):
weights = torchvision.models.EfficientNet_B2_Weights.DEFAULT
# Get EffNetB2 transforms
transforms = weights.transforms()
# Setup pretrained model instance
model = torchvision.models.efficientnet_b2(weights=weights)
# Freeze the base layers in the model ( this will stop all layers from training)
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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