diff --git a/docling_ibm_models/tableformer/data_management/tf_predictor.py b/docling_ibm_models/tableformer/data_management/tf_predictor.py
index 372d72b..74320d5 100644
--- a/docling_ibm_models/tableformer/data_management/tf_predictor.py
+++ b/docling_ibm_models/tableformer/data_management/tf_predictor.py
@@ -686,13 +686,20 @@ def predict_dummy(
                 )
 
                 if outputs_coord is not None:
-                    bbox_pred = u.box_cxcywh_to_xyxy(outputs_coord)
-                    prediction["bboxes"] = bbox_pred.tolist()
+                    if len(outputs_coord) == 0:
+                        prediction["bboxes"] = []
+                    else:
+                        bbox_pred = u.box_cxcywh_to_xyxy(outputs_coord)
+                        prediction["bboxes"] = bbox_pred.tolist()
                 else:
                     prediction["bboxes"] = []
+
                 if outputs_class is not None:
-                    result_class = torch.argmax(outputs_class, dim=1)
-                    prediction["classes"] = result_class.tolist()
+                    if len(outputs_class) == 0:
+                        prediction["classes"] = []
+                    else:
+                        result_class = torch.argmax(outputs_class, dim=1)
+                        prediction["classes"] = result_class.tolist()
                 else:
                     prediction["classes"] = []
                 if self._remove_padding:
@@ -807,13 +814,20 @@ def predict(
                 )
 
                 if outputs_coord is not None:
-                    bbox_pred = u.box_cxcywh_to_xyxy(outputs_coord)
-                    prediction["bboxes"] = bbox_pred.tolist()
+                    if len(outputs_coord) == 0:
+                        prediction["bboxes"] = []
+                    else:
+                        bbox_pred = u.box_cxcywh_to_xyxy(outputs_coord)
+                        prediction["bboxes"] = bbox_pred.tolist()
                 else:
                     prediction["bboxes"] = []
+
                 if outputs_class is not None:
-                    result_class = torch.argmax(outputs_class, dim=1)
-                    prediction["classes"] = result_class.tolist()
+                    if len(outputs_class) == 0:
+                        prediction["classes"] = []
+                    else:
+                        result_class = torch.argmax(outputs_class, dim=1)
+                        prediction["classes"] = result_class.tolist()
                 else:
                     prediction["classes"] = []
                 if self._remove_padding:
diff --git a/docling_ibm_models/tableformer/models/table04_rs/tablemodel04_rs.py b/docling_ibm_models/tableformer/models/table04_rs/tablemodel04_rs.py
index acfacbe..2e047d7 100644
--- a/docling_ibm_models/tableformer/models/table04_rs/tablemodel04_rs.py
+++ b/docling_ibm_models/tableformer/models/table04_rs/tablemodel04_rs.py
@@ -308,8 +308,12 @@ def predict(self, imgs, max_steps, k, return_attention=False):
 
         if len(outputs_coord1) > 0:
             outputs_coord1 = torch.stack(outputs_coord1)
+        else:
+            outputs_coord1 = torch.empty(0)
         if len(outputs_class1) > 0:
             outputs_class1 = torch.stack(outputs_class1)
+        else:
+            outputs_class1 = torch.empty(0)
 
         outputs_class = outputs_class1
         outputs_coord = outputs_coord1