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import torch
import torchvision
from torch import nn
def create_effnetb2_model(num_classes:int=3,
seed:int=42):
# 1, 2, 3 Create EffNetB2 pretrained weights, transforms and model
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)
)
return model,transforms
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