diff --git a/src/twomartens/masterthesis/data.py b/src/twomartens/masterthesis/data.py
index 78613c3..331bb24 100644
--- a/src/twomartens/masterthesis/data.py
+++ b/src/twomartens/masterthesis/data.py
@@ -23,7 +23,6 @@ Functions:
     load_scenenet_data(...): loads the SceneNet RGB-D data into a Tensorflow data set
     prepare_scenenet_data(...): prepares the SceneNet RGB-D data and returns it in Python format
 """
-import functools
 from typing import Callable, List, Mapping, Tuple
 from typing import Sequence
 
@@ -304,16 +303,22 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
                                                      width=resized_shape[1])
         ]
     
-    generator = functools.partial(
-        data_generator.generate,
+    generator = data_generator.generate(
         batch_size=batch_size,
         shuffle=shuffle,
         transformations=transformations,
         label_encoder=ssd_input_encoder.SSDInputEncoder(
-          img_height=resized_shape[0],
-          img_width=resized_shape[1],
-          n_classes=len(cats_to_classes),  # 80
-          predictor_sizes=predictor_sizes
+            img_height=resized_shape[0],
+            img_width=resized_shape[1],
+            n_classes=len(cats_to_classes),  # 80
+            predictor_sizes=predictor_sizes,
+            steps=[8, 16, 32, 64, 100, 300],
+            aspect_ratios_per_layer=[[1.0, 2.0, 0.5],
+                                     [1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
+                                     [1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
+                                     [1.0, 2.0, 0.5, 3.0, 1.0 / 3.0],
+                                     [1.0, 2.0, 0.5],
+                                     [1.0, 2.0, 0.5]]
         ),
         returns={'processed_images', 'encoded_labels'},
         keep_images_without_gt=False