diff --git a/src/twomartens/masterthesis/cli.py b/src/twomartens/masterthesis/cli.py
index d22c141..2666fde 100644
--- a/src/twomartens/masterthesis/cli.py
+++ b/src/twomartens/masterthesis/cli.py
@@ -345,7 +345,7 @@ def _ssd_train_get_generators(load_data: callable,
                   predictor_sizes=predictor_sizes,
                   batch_size=batch_size,
                   image_size=image_size,
-                  training=True, evaluation=False, augment=False,
+                  training=True, evaluation=True, augment=False,
                   nr_trajectories=nr_trajectories)
     
     val_generator, val_length = \
@@ -368,8 +368,9 @@ def _ssd_debug_save_images(args: argparse.Namespace, save_images_on_debug: bool,
         train_length -= batch_size
         train_images = train_data[0]
         train_labels = train_data[1]
+        train_labels_not_encoded = train_data[2]
         
-        save_images(train_images, train_labels, summary_path)
+        save_images(train_images, train_labels_not_encoded, summary_path)
     
     return train_length
 
diff --git a/src/twomartens/masterthesis/data.py b/src/twomartens/masterthesis/data.py
index 670ebfa..bdde2cb 100644
--- a/src/twomartens/masterthesis/data.py
+++ b/src/twomartens/masterthesis/data.py
@@ -320,6 +320,8 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
         ]
         
     returns = {'processed_images', 'encoded_labels'}
+    if training and evaluation:
+        returns = {'processed_images', 'encoded_labels', 'processed_labels'}
     
     if not training and evaluation:
         returns = {
diff --git a/src/twomartens/masterthesis/debug.py b/src/twomartens/masterthesis/debug.py
index 3b92ae5..68a6063 100644
--- a/src/twomartens/masterthesis/debug.py
+++ b/src/twomartens/masterthesis/debug.py
@@ -70,10 +70,10 @@ def save_ssd_train_images(images: np.ndarray, labels: np.ndarray,
         current_axis = pyplot.gca()
         
         for instance in instances:
-            xmin = (instance[-12] + instance[-8]) * image_size
-            ymin = (instance[-11] + instance[-7]) * image_size
-            xmax = (instance[-10] + instance[-6]) * image_size
-            ymax = (instance[-9] + instance[-5]) * image_size
+            xmin = (instance[-12]) * image_size
+            ymin = (instance[-11]) * image_size
+            xmax = (instance[-10]) * image_size
+            ymax = (instance[-9]) * image_size
             class_id = np.argmax(instance[:-12], axis=0)
             if class_id == 0:
                 continue