diff --git a/image-inpainting/results/runtime_config.json b/image-inpainting/results/runtime_config.json
index 30d50ce..5dda256 100644
--- a/image-inpainting/results/runtime_config.json
+++ b/image-inpainting/results/runtime_config.json
@@ -1,12 +1,12 @@
 {
-  "learningrate": 0.005,
+  "learningrate": 0.0005,
   "weight_decay": 5e-05,
-  "n_updates": 35000,
+  "n_updates": 12000,
   "plot_at": 500,
   "early_stopping_patience": 20,
   "print_stats_at": 200,
-  "print_train_stats_at": 50,
-  "validate_at": 500,
+  "print_train_stats_at": 10,
+  "validate_at": 250,
   "commands": {
     "save_checkpoint": false,
     "run_test_validation": false,
diff --git a/image-inpainting/src/architecture.py b/image-inpainting/src/architecture.py
index 4ec81a3..f3a4f9d 100644
--- a/image-inpainting/src/architecture.py
+++ b/image-inpainting/src/architecture.py
@@ -241,11 +241,14 @@ class MyModel(nn.Module):
     def __init__(self, n_in_channels: int, base_channels: int = 64, dropout: float = 0.1):
         super().__init__()
         
+        # Separate mask processing for better feature extraction
         # Separate mask processing for better feature extraction
         self.mask_conv = nn.Sequential(
             nn.Conv2d(1, base_channels // 4, 3, padding=1),
+            nn.BatchNorm2d(base_channels // 4, momentum=0.1, eps=1e-5),
             nn.LeakyReLU(0.2, inplace=True),
             nn.Conv2d(base_channels // 4, base_channels // 4, 3, padding=1),
+            nn.BatchNorm2d(base_channels // 4, momentum=0.1, eps=1e-5),
             nn.LeakyReLU(0.2, inplace=True)
         )
         
diff --git a/image-inpainting/src/main.py b/image-inpainting/src/main.py
index 1dc4f37..df17262 100644
--- a/image-inpainting/src/main.py
+++ b/image-inpainting/src/main.py
@@ -17,24 +17,24 @@ if __name__ == '__main__':
 
     config_dict['seed'] = 42
     config_dict['testset_ratio'] = 0.1
-    config_dict['validset_ratio'] = 0.1
+    config_dict['validset_ratio'] = 0.05
     # Get the absolute path based on the script's location
     script_dir = os.path.dirname(os.path.abspath(__file__))
     project_root = os.path.dirname(script_dir)
     config_dict['results_path'] = os.path.join(project_root, "results")
     config_dict['data_path'] = os.path.join(project_root, "data", "dataset")
     config_dict['device'] = None
-    config_dict['learningrate'] = 5e-3  # Lower initial LR with warmup
+    config_dict['learningrate'] = 5e-4  # Lower initial LR with warmup
     config_dict['weight_decay'] = 5e-5  # Reduced for more capacity
-    config_dict['n_updates'] = 35000  # Extended training for better convergence
+    config_dict['n_updates'] = 12000  # Extended training for better convergence
     config_dict['batchsize'] = 64  # Reduced for larger model and mixed precision
     config_dict['early_stopping_patience'] = 20  # More patience for complex model
     config_dict['use_wandb'] = False
 
-    config_dict['print_train_stats_at'] = 50
+    config_dict['print_train_stats_at'] = 10
     config_dict['print_stats_at'] = 200
     config_dict['plot_at'] = 500
-    config_dict['validate_at'] = 500  # More frequent validation
+    config_dict['validate_at'] = 250  # More frequent validation
 
     network_config = {
         'n_in_channels': 4,
@@ -69,7 +69,7 @@ if __name__ == '__main__':
     print("  - save_checkpoint: Save model at current step")
     print("  - run_test_validation: Run validation on final test set")
     print("  - generate_predictions: Generate predictions on challenge testset")
-    print("\nChanges will be applied within 50 steps.")
+    print("\nChanges will be applied within 5 steps.")
     print("="*60)
     print()
 
diff --git a/image-inpainting/src/train.py b/image-inpainting/src/train.py
index 02d48d4..7beec8f 100644
--- a/image-inpainting/src/train.py
+++ b/image-inpainting/src/train.py
@@ -313,8 +313,8 @@ def train(seed, testset_ratio, validset_ratio, data_path, results_path, early_st
 
             input, target = input.to(device), target.to(device)
             
-            # Check for runtime config updates every 50 steps
-            if i % 50 == 0 and i > 0:
+            # Check for runtime config updates every 5 steps
+            if i % 5 == 0 and i > 0:
                 runtime_params = load_runtime_config(config_json_path, runtime_params)
                 n_updates = runtime_params['n_updates']
                 plot_at = runtime_params['plot_at']