feat: add dropout and weight decay to prevent overfitting

Co-authored-by: aider (gemini/gemini-2.5-pro-preview-05-06) <aider@aider.chat>

train.py

@@ -67,6 +67,7 @@ def train_model():
     NUM_EPOCHS = 10
     BATCH_SIZE = 32
     LEARNING_RATE = 0.001
+    WEIGHT_DECAY = 1e-5 # L2 regularization
 
     # --- Data Preparation ---
     # Define separate transforms for training (with augmentation) and validation (without)
@@ -123,7 +124,7 @@ def train_model():
 
     model = GarageDoorCNN(resize_dim=RESIZE_DIM).to(device)
     criterion = nn.CrossEntropyLoss()
-    optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE)
+    optimizer = optim.Adam(model.parameters(), lr=LEARNING_RATE, weight_decay=WEIGHT_DECAY)
 
     # --- Training Loop ---
     print("Starting training...")