diff --git a/yolo/utils/bounding_box_utils.py b/yolo/utils/bounding_box_utils.py
index 12d95c5..1d714d3 100644
--- a/yolo/utils/bounding_box_utils.py
+++ b/yolo/utils/bounding_box_utils.py
@@ -222,12 +222,37 @@ def filter_duplicates(self, target_matrix: Tensor):
         return unique_indices[..., None]
 
     def __call__(self, target: Tensor, predict: Tuple[Tensor]) -> Tuple[Tensor, Tensor]:
-        """
-        1. For each anchor prediction, find the highest suitability targets
-        2. Select the targets
-        2. Noramlize the class probilities of targets
+        """Matches each target to the most suitable anchor.
+        1. For each anchor prediction, find the highest suitability targets.
+        2. Match target to the best anchor.
+        3. Noramlize the class probilities of targets.
+
+        Args:
+            target: The ground truth class and bounding box information
+                as tensor of size [batch x targets x 5].
+            predict: Tuple of predicted class and bounding box tensors.
+                Class tensor is of size [batch x anchors x class]
+                Bounding box tensor is of size [batch x anchors x 4].
+
+        Returns:
+            anchor_matched_targets: Tensor of size [batch x anchors x (class + 4)].
+                A tensor assigning each target/gt to the best fitting anchor.
+                The class probabilities are normalized.
+            valid_mask: Bool tensor of shape [batch x anchors].
+                True if a anchor has a target/gt assigned to it.
         """
         predict_cls, predict_bbox = predict
+
+        # return if target has no gt information.
+        n_targets = target.shape[1]
+        if n_targets == 0:
+            device = predict_bbox.device
+            align_cls = torch.zeros_like(predict_cls, device=device)
+            align_bbox = torch.zeros_like(predict_bbox, device=device)
+            valid_mask = torch.zeros(predict_cls.shape[:2], dtype=bool, device=device)
+            anchor_matched_targets = torch.cat([align_cls, align_bbox], dim=-1)
+            return anchor_matched_targets, valid_mask
+
         target_cls, target_bbox = target.split([1, 4], dim=-1)  # B x N x (C B) -> B x N x C, B x N x B
         target_cls = target_cls.long().clamp(0)
 
@@ -261,8 +286,8 @@ def __call__(self, target: Tensor, predict: Tuple[Tensor]) -> Tuple[Tensor, Tens
         normalize_term = (target_matrix / (max_target + 1e-9)) * max_iou
         normalize_term = normalize_term.permute(0, 2, 1).gather(2, unique_indices)
         align_cls = align_cls * normalize_term * valid_mask[:, :, None]
-
-        return torch.cat([align_cls, align_bbox], dim=-1), valid_mask.bool()
+        anchor_matched_targets = torch.cat([align_cls, align_bbox], dim=-1)
+        return anchor_matched_targets, valid_mask
 
 
 class Vec2Box: