diff --git a/src/victim/models.py b/src/victim/models.py
index 78eb361..1e926cb 100644
--- a/src/victim/models.py
+++ b/src/victim/models.py
@@ -11,14 +11,27 @@ class Classifier(Model):
     def _model(self) -> keras.Model:
         return keras.Sequential(
             [
-                keras.layers.Conv2D(32, (3, 3), activation="relu"),
-                keras.layers.Conv2D(32, (3, 3), activation="relu"),
+                keras.layers.Conv2D(32, (3, 3), activation="relu", padding="same"),
+                keras.layers.BatchNormalization(),
+                keras.layers.Conv2D(32, (3, 3), activation="relu", padding="same"),
+                keras.layers.BatchNormalization(),
                 keras.layers.MaxPooling2D((2, 2)),
-                keras.layers.Conv2D(64, (3, 3), activation="relu"),
-                keras.layers.Conv2D(64, (3, 3), activation="relu"),
+                keras.layers.Dropout(0.2),
+                keras.layers.Conv2D(64, (3, 3), activation="relu", padding="same"),
+                keras.layers.BatchNormalization(),
+                keras.layers.Conv2D(64, (3, 3), activation="relu", padding="same"),
+                keras.layers.BatchNormalization(),
                 keras.layers.MaxPooling2D((2, 2)),
+                keras.layers.Dropout(0.3),
+                keras.layers.Conv2D(128, (3, 3), activation="relu", padding="same"),
+                keras.layers.BatchNormalization(),
+                keras.layers.Conv2D(128, (3, 3), activation="relu", padding="same"),
+                keras.layers.BatchNormalization(),
+                keras.layers.MaxPooling2D((2, 2)),
+                keras.layers.Dropout(0.4),
                 keras.layers.Flatten(),
-                keras.layers.Dense(512, activation="relu"),
+                keras.layers.Dense(256, activation="relu"),
+                keras.layers.Dropout(0.5),
                 keras.layers.Dense(10, activation="softmax"),
             ],
         )