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import torch | |
import torchvision | |
from torch import nn | |
def create_efficientnet(output_shape: int): | |
weights = torchvision.models.EfficientNet_V2_L_Weights.IMAGENET1K_V1.DEFAULT | |
model = torchvision.models.efficientnet_v2_l(weights=weights) | |
for param in model.parameters(): | |
param.requires_grad = False | |
model.classifier = nn.Sequential( | |
nn.Dropout(p=0.10, inplace=True), | |
nn.Linear(in_features=1280, out_features=output_shape) | |
) | |
return model, weights.transforms() | |