diff --git a/src/twomartens/masterthesis/cli.py b/src/twomartens/masterthesis/cli.py
index 2061dcc..c6a1d72 100644
--- a/src/twomartens/masterthesis/cli.py
+++ b/src/twomartens/masterthesis/cli.py
@@ -353,9 +353,9 @@ def _ssd_evaluate(args: argparse.Namespace) -> None:
 
     true_positives, false_positives, \
         cum_true_positives, cum_false_positives, \
-        open_set_error = evaluate.match_predictions(predictions_per_class, labels,
-                                                    bounding_box_utils.iou,
-                                                    nr_classes, iou_threshold)
+        open_set_error, cumulative_open_set_error = evaluate.match_predictions(predictions_per_class, labels,
+                                                                               bounding_box_utils.iou,
+                                                                               nr_classes, iou_threshold)
     
     cum_precisions, cum_recalls = evaluate.get_precision_recall(number_gt_per_class,
                                                                 cum_true_positives,
@@ -375,7 +375,8 @@ def _ssd_evaluate(args: argparse.Namespace) -> None:
                                         f1_scores,
                                         average_precisions,
                                         mean_average_precision,
-                                        open_set_error)
+                                        open_set_error,
+                                        cumulative_open_set_error)
     
     _pickle(result_file, results)
 
@@ -829,7 +830,8 @@ def _ssd_evaluate_get_results(true_positives: Sequence[np.ndarray],
                               f1_scores: Sequence[np.ndarray],
                               average_precisions: Sequence[float],
                               mean_average_precision: float,
-                              open_set_error: int
+                              open_set_error: np.ndarray,
+                              cumulative_open_set_error: np.ndarray
                               ) -> Dict[str, Union[np.ndarray, float, int]]:
     results = {
         "true_positives":             true_positives,
@@ -841,7 +843,8 @@ def _ssd_evaluate_get_results(true_positives: Sequence[np.ndarray],
         "f1_scores":                  f1_scores,
         "mean_average_precisions":    average_precisions,
         "mean_average_precision":     mean_average_precision,
-        "open_set_error":             open_set_error
+        "open_set_error":             open_set_error,
+        "cumulative_open_set_error":  cumulative_open_set_error
     }
     
     return results
diff --git a/src/twomartens/masterthesis/evaluate.py b/src/twomartens/masterthesis/evaluate.py
index 6b45b7c..58a7823 100644
--- a/src/twomartens/masterthesis/evaluate.py
+++ b/src/twomartens/masterthesis/evaluate.py
@@ -99,7 +99,7 @@ def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, float, in
                       border_pixels: str = "include",
                       sorting_algorithm: str = "quicksort") -> Tuple[List[np.ndarray], List[np.ndarray],
                                                                      List[np.ndarray], List[np.ndarray],
-                                                                     int]:
+                                                                     np.ndarray, np.ndarray]:
     """
     Matches predictions to ground truth boxes.
     
@@ -124,13 +124,20 @@ def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, float, in
 
     Returns:
         true positives, false positives, cumulative true positives, and cumulative false positives for
-            each class, open set error as defined by Miller et al
+            each class, open set error as defined by Miller et al, cumulative open set error
     """
     true_positives = [[]]  # The false positives for each class, sorted by descending confidence.
     false_positives = [[]]  # The true positives for each class, sorted by descending confidence.
-    open_set_error = 0
     cumulative_true_positives = [[]]
     cumulative_false_positives = [[]]
+    most_predictions = -1
+
+    for class_id in range(1, nr_classes + 1):
+        nr_predictions = len(predictions[class_id])
+        if nr_predictions > most_predictions:
+            most_predictions = nr_predictions
+    
+    open_set_error = np.zeros(most_predictions, dtype=np.int)
     
     for class_id in range(1, nr_classes + 1):
         predictions_class = predictions[class_id]
@@ -189,7 +196,7 @@ def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, float, in
                 # If the image doesn't contain any objects of this class,
                 # the prediction becomes a false positive.
                 false_pos[i] = 1
-                open_set_error += 1
+                open_set_error[i] += 1
                 continue
 
             # Compute the IoU of this prediction with all ground truth boxes of the same class.
@@ -240,7 +247,12 @@ def match_predictions(predictions: Sequence[Sequence[Tuple[int, float, float, in
         cumulative_true_positives.append(cumulative_true_pos)
         cumulative_false_positives.append(cumulative_false_pos)
     
-    return true_positives, false_positives, cumulative_true_positives, cumulative_false_positives, open_set_error
+    cumulative_open_set_error = np.cumsum(open_set_error)
+    
+    return (
+        true_positives, false_positives, cumulative_true_positives, cumulative_false_positives,
+        open_set_error, cumulative_open_set_error
+    )
 
 
 def get_precision_recall(number_gt_per_class: np.ndarray,