Upload model.py

model.py

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+
+import torch
+import torchvision
+
+from torch import nn
+
+def create_effnetb2_model(num_classes:int=101):
+    
+    # 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)
+    
+    # 4. Freeze all layers in the base model
+    for param in model.parameters():
+        param.requires_grad = False
+        
+    # 5. Change classifier head with random seed for reproducibility
+    model.classifier = nn.Sequential(
+        nn.Dropout(p=0.3, inplace=True),
+        nn.Linear(in_features=1408, out_features=num_classes)
+    )
+    
+    return model, transforms