diff --git a/src/twomartens/masterthesis/data.py b/src/twomartens/masterthesis/data.py
index 05e42dd..de59c08 100644
--- a/src/twomartens/masterthesis/data.py
+++ b/src/twomartens/masterthesis/data.py
@@ -23,7 +23,7 @@ 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
 """
-from typing import Callable, List, Mapping, Tuple
+from typing import Callable, List, Mapping, Tuple, Optional
 from typing import Sequence
 
 import numpy as np
@@ -239,7 +239,8 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
                        resized_shape: Sequence[int],
                        training: bool,
                        evaluation: bool,
-                       augment: bool) -> Tuple[callable, int]:
+                       augment: bool,
+                       nr_trajectories: Optional[int] = None) -> Tuple[callable, int]:
     """
     Loads the SceneNet RGB-D data and returns a data set.
     
@@ -253,6 +254,7 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
         training: True if training data is desired
         evaluation: True if evaluation-ready data is desired
         augment: True if training data should be augmented
+        nr_trajectories: number of trajectories to consider
 
     Returns:
         scenenet data set generator
@@ -268,7 +270,10 @@ def load_scenenet_data(photo_paths: Sequence[Sequence[str]],
     cats_to_classes, _, _, _ = coco_utils.get_coco_category_maps(annotation_file_train)
     max_nr_labels = -1
     
-    for trajectory in trajectories:
+    for i, trajectory in enumerate(trajectories):
+        if nr_trajectories is not None and i >= nr_trajectories:
+            break
+        
         traj_image_paths, traj_instances = trajectory
         for image_path, frame_instances in zip(traj_image_paths, traj_instances):
             labels = []