diff --git a/utils/loss.py b/utils/loss.py
index 6504301ab6..17d195f3a1 100644
--- a/utils/loss.py
+++ b/utils/loss.py
@@ -1102,12 +1102,20 @@ def build_targets(self, p, targets, imgs):
                 matching_anchs[i].append(all_anch[layer_idx])
 
         for i in range(nl):
-            matching_bs[i] = torch.cat(matching_bs[i], dim=0)
-            matching_as[i] = torch.cat(matching_as[i], dim=0)
-            matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
-            matching_gis[i] = torch.cat(matching_gis[i], dim=0)
-            matching_targets[i] = torch.cat(matching_targets[i], dim=0)
-            matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+            if matching_targets[i] != []:
+                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
+                matching_as[i] = torch.cat(matching_as[i], dim=0)
+                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
+                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
+                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
+                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+            else:
+                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
 
         return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs       
 
@@ -1403,12 +1411,20 @@ def build_targets(self, p, targets, imgs):
                 matching_anchs[i].append(all_anch[layer_idx])
 
         for i in range(nl):
-            matching_bs[i] = torch.cat(matching_bs[i], dim=0)
-            matching_as[i] = torch.cat(matching_as[i], dim=0)
-            matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
-            matching_gis[i] = torch.cat(matching_gis[i], dim=0)
-            matching_targets[i] = torch.cat(matching_targets[i], dim=0)
-            matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+            if matching_targets[i] != []:
+                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
+                matching_as[i] = torch.cat(matching_as[i], dim=0)
+                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
+                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
+                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
+                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+            else:
+                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
 
         return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs
 
@@ -1548,12 +1564,20 @@ def build_targets2(self, p, targets, imgs):
                 matching_anchs[i].append(all_anch[layer_idx])
 
         for i in range(nl):
-            matching_bs[i] = torch.cat(matching_bs[i], dim=0)
-            matching_as[i] = torch.cat(matching_as[i], dim=0)
-            matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
-            matching_gis[i] = torch.cat(matching_gis[i], dim=0)
-            matching_targets[i] = torch.cat(matching_targets[i], dim=0)
-            matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+            if matching_targets[i] != []:
+                matching_bs[i] = torch.cat(matching_bs[i], dim=0)
+                matching_as[i] = torch.cat(matching_as[i], dim=0)
+                matching_gjs[i] = torch.cat(matching_gjs[i], dim=0)
+                matching_gis[i] = torch.cat(matching_gis[i], dim=0)
+                matching_targets[i] = torch.cat(matching_targets[i], dim=0)
+                matching_anchs[i] = torch.cat(matching_anchs[i], dim=0)
+            else:
+                matching_bs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_as[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_gjs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_gis[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_targets[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
+                matching_anchs[i] = torch.tensor([], device='cuda:0', dtype=torch.int64)
 
         return matching_bs, matching_as, matching_gjs, matching_gis, matching_targets, matching_anchs