diff --git a/src/twomartens/masterthesis/ssd.py b/src/twomartens/masterthesis/ssd.py
index afe2d62..9e5a4f4 100644
--- a/src/twomartens/masterthesis/ssd.py
+++ b/src/twomartens/masterthesis/ssd.py
@@ -214,6 +214,10 @@ def predict(generator: callable,
                       confidence_threshold=confidence_threshold,
                   ),
                   apply_entropy_threshold_func=_apply_entropy_threshold,
+                  apply_top_k_func=functools.partial(
+                      _apply_top_k,
+                      top_k=top_k
+                  ),
                   get_observations_func=_get_observations,
                   transform_func=functools.partial(
                       _transform_predictions,
@@ -300,7 +304,7 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
                   dropout_step: callable, vanilla_step: callable,
                   save_images: callable, decode_func: callable,
                   decode_func_dropout: callable, get_observations_func: callable,
-                  apply_entropy_threshold_func: callable,
+                  apply_entropy_threshold_func: callable, apply_top_k_func: callable,
                   transform_func: callable, save_func: callable,
                   use_entropy_threshold: bool, entropy_threshold_min: float,
                   entropy_threshold_max: float) -> None:
@@ -329,6 +333,7 @@ def _predict_loop(generator: Generator, use_dropout: bool, steps_per_epoch: int,
         for entropy_threshold in entropy_thresholds:
             if use_dropout:
                 decoded_predictions = apply_entropy_threshold_func(observations, entropy_threshold=entropy_threshold)
+                decoded_predictions = apply_top_k_func(decoded_predictions)
             else:
                 decoded_predictions = decode_func(predictions, entropy_threshold=entropy_threshold)
             if not saved_images_decoding:
@@ -421,6 +426,29 @@ def _apply_entropy_threshold(observations: Sequence[np.ndarray], entropy_thresho
     return final_observations
 
 
+def _apply_top_k(detections: Sequence[np.ndarray], top_k: float) -> List[np.ndarray]:
+    final_detections = []
+    batch_size = len(detections)
+    data_type = np.dtype([('image_id', np.int32),
+                          ('confidence', 'f4'),
+                          ('entropy', 'f4'),
+                          ('xmin', 'f4'),
+                          ('ymin', 'f4'),
+                          ('xmax', 'f4'),
+                          ('ymax', 'f4')])
+    for i in range(batch_size):
+        image_detections = detections[i]
+        image_detections_structured = np.array(image_detections, dtype=data_type)
+        descending_indices = np.argsort(-image_detections_structured['confidence'])
+        image_detections_sorted = np.asarray(image_detections_structured[descending_indices])
+        top_k_indices = np.argpartition(image_detections_sorted[:, 1],
+                                        kth=image_detections_sorted.shape[0] - top_k,
+                                        axis=0)[image_detections_sorted.shape[0] - top_k:]
+        final_detections.append(image_detections_sorted[top_k_indices])
+    
+    return final_detections
+
+
 def _transform_predictions(decoded_predictions: np.ndarray, inverse_transforms: Sequence[np.ndarray],
                            inverse_transform_func: callable) -> np.ndarray: