diff --git a/src/twomartens/masterthesis/ssd.py b/src/twomartens/masterthesis/ssd.py
index 550c88a..738e7c7 100644
--- a/src/twomartens/masterthesis/ssd.py
+++ b/src/twomartens/masterthesis/ssd.py
@@ -424,7 +424,8 @@ def _apply_entropy_filtering(observations: Sequence[np.ndarray],
                              entropy_threshold: float,
                              confidence_threshold: float,
                              iou_threshold: float,
-                             nr_classes: int) -> List[np.ndarray]:
+                             nr_classes: int,
+                             use_nms: bool = True) -> List[np.ndarray]:
     final_observations = []
     batch_size = len(observations)
     for i in range(batch_size):
@@ -438,10 +439,13 @@ def _apply_entropy_filtering(observations: Sequence[np.ndarray],
             single_class = filtered_image_observations[:, [class_id, -5, -4, -3, -2]]
             threshold_met = single_class[single_class[:, 0] > confidence_threshold]
             if threshold_met.shape[0] > 0:
-                maxima = ssd_output_decoder._greedy_nms(threshold_met, iou_threshold=iou_threshold)
-                maxima_output = np.zeros((maxima.shape[0], maxima.shape[1] + 1))
+                if use_nms:
+                    maxima = ssd_output_decoder._greedy_nms(threshold_met, iou_threshold=iou_threshold)
+                    maxima_output = np.zeros((maxima.shape[0], maxima.shape[1] + 1))
+                else:
+                    maxima_output = np.zeros((threshold_met.shape[0], threshold_met.shape[1] + 1))
                 maxima_output[:, 0] = class_id
-                maxima_output[:, 1:] = maxima
+                maxima_output[:, 1:] = maxima if use_nms else threshold_met
                 final_image_observations.append(maxima_output)
         if final_image_observations:
             final_image_observations = np.concatenate(final_image_observations, axis=0)