diff --git a/requirements/openvino.txt b/requirements/openvino.txt
index e91ed69252a..16acdba61be 100644
--- a/requirements/openvino.txt
+++ b/requirements/openvino.txt
@@ -2,7 +2,7 @@
 # OpenVINO Requirements.                                                      #
 nncf==2.6.0
 onnx==1.13.0
-openvino-model-api==0.1.6
+openvino-model-api==0.1.8
 openvino==2023.0
 openvino-dev==2023.0
 openvino-telemetry==2023.2.*
diff --git a/src/otx/algorithms/anomaly/tasks/openvino.py b/src/otx/algorithms/anomaly/tasks/openvino.py
index a8dfa580e15..d96516a5752 100644
--- a/src/otx/algorithms/anomaly/tasks/openvino.py
+++ b/src/otx/algorithms/anomaly/tasks/openvino.py
@@ -188,13 +188,17 @@ def infer(self, dataset: DatasetEntity, inference_parameters: InferenceParameter
                 label = self.anomalous_label if image_result.pred_score >= 0.5 else self.normal_label
             elif self.task_type == TaskType.ANOMALY_SEGMENTATION:
                 annotations = create_annotation_from_segmentation_map(
-                    pred_mask, image_result.anomaly_map.squeeze(), {0: self.normal_label, 1: self.anomalous_label}
+                    pred_mask,
+                    image_result.anomaly_map.squeeze() / 255.0,
+                    {0: self.normal_label, 1: self.anomalous_label},
                 )
                 dataset_item.append_annotations(annotations)
                 label = self.normal_label if len(annotations) == 0 else self.anomalous_label
             elif self.task_type == TaskType.ANOMALY_DETECTION:
                 annotations = create_detection_annotation_from_anomaly_heatmap(
-                    pred_mask, image_result.anomaly_map.squeeze(), {0: self.normal_label, 1: self.anomalous_label}
+                    pred_mask,
+                    image_result.anomaly_map.squeeze() / 255.0,
+                    {0: self.normal_label, 1: self.anomalous_label},
                 )
                 dataset_item.append_annotations(annotations)
                 label = self.normal_label if len(annotations) == 0 else self.anomalous_label
@@ -202,13 +206,12 @@ def infer(self, dataset: DatasetEntity, inference_parameters: InferenceParameter
                 raise ValueError(f"Unknown task type: {self.task_type}")
 
             dataset_item.append_labels([ScoredLabel(label=label, probability=float(probability))])
-            anomaly_map = (image_result.anomaly_map * 255).astype(np.uint8)
             heatmap_media = ResultMediaEntity(
                 name="Anomaly Map",
                 type="anomaly_map",
                 label=label,
                 annotation_scene=dataset_item.annotation_scene,
-                numpy=anomaly_map,
+                numpy=image_result.anomaly_map,
             )
             dataset_item.append_metadata_item(heatmap_media)
             update_progress_callback(int((idx + 1) / len(dataset) * 100))
diff --git a/src/otx/api/usecases/exportable_code/demo/requirements.txt b/src/otx/api/usecases/exportable_code/demo/requirements.txt
index eb4d7eb29fd..ee7aaa3aaa5 100644
--- a/src/otx/api/usecases/exportable_code/demo/requirements.txt
+++ b/src/otx/api/usecases/exportable_code/demo/requirements.txt
@@ -1,4 +1,4 @@
 openvino==2023.0
-openvino-model-api==0.1.6
-otx==1.4.4
+openvino-model-api==0.1.8
+otx @ git+https://github.com/openvinotoolkit/training_extensions/@4abe2ea89680d4bdf54f97e1fa78abebd65c7e36#egg=otx
 numpy>=1.21.0,<=1.23.5  # np.bool was removed in 1.24.0 which was used in openvino runtime
diff --git a/src/otx/api/usecases/exportable_code/prediction_to_annotation_converter.py b/src/otx/api/usecases/exportable_code/prediction_to_annotation_converter.py
index 90ba92a0c4e..22a60455231 100644
--- a/src/otx/api/usecases/exportable_code/prediction_to_annotation_converter.py
+++ b/src/otx/api/usecases/exportable_code/prediction_to_annotation_converter.py
@@ -370,7 +370,7 @@ def convert_to_annotation(self, predictions: AnomalyResult, metadata: Dict[str,
         assert predictions.pred_mask is not None
         assert predictions.anomaly_map is not None
         annotations = create_annotation_from_segmentation_map(
-            predictions.pred_mask, predictions.anomaly_map, self.label_map
+            predictions.pred_mask, predictions.anomaly_map / 255.0, self.label_map
         )
         if len(annotations) == 0:
             # TODO: add confidence to this label