provide another solution

src/otx/core/data/dataset/segmentation.py

@@ -98,7 +98,7 @@ def _extract_class_mask(item: DatasetItem, img_shape: tuple[int, int], ignore_in
         msg = "It is not currently support an ignore index which is more than 255."
         raise ValueError(msg, ignore_index)
 
-    # fill mask with background label if we have Polygon/Ellipse annotations
+    # fill mask with background label if we have Polygon/Ellipse/Bbox annotations
     fill_value = 0 if isinstance(item.annotations[0], (Ellipse, Polygon, Bbox, RotatedBbox)) else ignore_index
     class_mask = np.full(shape=img_shape[:2], fill_value=fill_value, dtype=np.uint8)
 
@@ -179,9 +179,9 @@ def __init__(
             to_tv_image,
         )
 
-        if self.has_polygons and "background" not in [label_name.lower() for label_name in self.label_info.label_names]:
+        if self.has_polygons:
             # insert background class at index 0 since polygons represent only objects
-            self.label_info.label_names.insert(0, "background")
+            self.label_info.label_names.insert(0, "otx_background_lbl")
 
         self.label_info = SegLabelInfo(
             label_names=self.label_info.label_names,

src/otx/core/model/base.py

@@ -1095,11 +1095,6 @@ def model_adapter_parameters(self) -> dict:
     def _set_label_info(self, label_info: LabelInfoTypes) -> None:
         """Set this model label information."""
         new_label_info = self._dispatch_label_info(label_info)
-
-        if self._label_info != new_label_info:
-            msg = "OVModel strictly does not allow overwrite label_info if they are different each other."
-            raise ValueError(msg)
-
         self._label_info = new_label_info
 
     def _create_label_info_from_ov_ir(self) -> LabelInfo:

src/otx/core/model/segmentation.py

@@ -5,6 +5,7 @@
 
 from __future__ import annotations
 
+import copy
 import json
 from abc import abstractmethod
 from collections.abc import Sequence
@@ -165,12 +166,20 @@ def _customize_outputs(
     @property
     def _export_parameters(self) -> TaskLevelExportParameters:
         """Defines parameters required to export a particular model implementation."""
+        if self.label_info.label_names[0] == "otx_background_lbl":
+            # remove otx background label for export
+            modified_label_info = copy.deepcopy(self.label_info)
+            modified_label_info.label_names.pop(0)
+        else:
+            modified_label_info = self.label_info
+
         return super()._export_parameters.wrap(
             model_type="Segmentation",
             task_type="segmentation",
             return_soft_prediction=True,
             soft_threshold=0.5,
             blur_strength=-1,
+            label_info=modified_label_info,
         )
 
     @property

src/otx/core/types/export.py

@@ -98,25 +98,17 @@ def to_metadata(self) -> dict[tuple[str, str], str]:
             dict[tuple[str, str], str]: It will be directly delivered to
             OpenVINO IR's `rt_info` or ONNX metadata slot.
         """
-        label_names = self.label_info.label_names
         if self.task_type == "instance_segmentation":
             # Instance segmentation needs to add empty label
             all_labels = "otx_empty_lbl "
             all_label_ids = "None "
-            for lbl in label_names:
-                all_labels += lbl.replace(" ", "_") + " "
-                all_label_ids += lbl.replace(" ", "_") + " "
-        elif self.task_type == "semantic_segmentation" and label_names[0].lower() == "background":
-            # Semantic segmentation needs to remove first background label
-            all_labels = ""
-            all_label_ids = ""
-            for lbl in label_names[1:]:
+            for lbl in self.label_info.label_names:
                 all_labels += lbl.replace(" ", "_") + " "
                 all_label_ids += lbl.replace(" ", "_") + " "
         else:
             all_labels = ""
             all_label_ids = ""
-            for lbl in label_names:
+            for lbl in self.label_info.label_names:
                 all_labels += lbl.replace(" ", "_") + " "
                 all_label_ids += lbl.replace(" ", "_") + " "
 

src/otx/engine/engine.py

@@ -5,6 +5,7 @@
 
 from __future__ import annotations
 
+import copy
 import csv
 import inspect
 import logging
@@ -370,14 +371,22 @@ def test(
             model = model_cls.load_from_checkpoint(checkpoint_path=checkpoint, **model.hparams)
 
         if model.label_info != self.datamodule.label_info:
-            msg = (
-                "To launch a test pipeline, the label information should be same "
-                "between the training and testing datasets. "
-                "Please check whether you use the same dataset: "
-                f"model.label_info={model.label_info}, "
-                f"datamodule.label_info={self.datamodule.label_info}"
-            )
-            raise ValueError(msg)
+            if (
+                self.task == "SEMANTIC_SEGMENTATION"
+                and "otx_background_lbl" in self.datamodule.label_info.label_names
+                and (len(self.datamodule.label_info.label_names) - len(model.label_info.label_names) == 1)
+            ):
+                # workaround for background label
+                model.label_info = copy.deepcopy(self.datamodule.label_info)
+            else:
+                msg = (
+                    "To launch a test pipeline, the label information should be same "
+                    "between the training and testing datasets. "
+                    "Please check whether you use the same dataset: "
+                    f"model.label_info={model.label_info}, "
+                    f"datamodule.label_info={self.datamodule.label_info}"
+                )
+                raise ValueError(msg)
 
         self._build_trainer(**kwargs)