diff --git a/external/mmdetection/detection_tasks/apis/detection/inference_task.py b/external/mmdetection/detection_tasks/apis/detection/inference_task.py
index 7988eb7395f..fcdc1eb6960 100644
--- a/external/mmdetection/detection_tasks/apis/detection/inference_task.py
+++ b/external/mmdetection/detection_tasks/apis/detection/inference_task.py
@@ -100,7 +100,7 @@ def __init__(self, task_environment: TaskEnvironment):
 
         # Set default model attributes.
         self._optimization_methods = []
-        self._precision = [ModelPrecision.FP16] if self._config.get('fp16', None) else [ModelPrecision.FP32]
+        self._precision = self._precision_from_config
         self._optimization_type = ModelOptimizationType.MO
 
         # Create and initialize PyTorch model.
@@ -113,6 +113,10 @@ def __init__(self, task_environment: TaskEnvironment):
         self._should_stop = False
         logger.info('Task initialization completed')
 
+    @property
+    def _precision_from_config(self):
+        return [ModelPrecision.FP16] if self._config.get('fp16', None) else [ModelPrecision.FP32]
+
     @property
     def _hyperparams(self):
         return self._task_environment.get_hyper_parameters(OTEDetectionConfig)
@@ -402,7 +406,8 @@ def export(self,
                     model = self._model.cpu()
                 pruning_transformation = OptimizationMethod.FILTER_PRUNING in self._optimization_methods
                 export_model(model, self._config, tempdir, target='openvino',
-                             pruning_transformation=pruning_transformation, precision=self._precision[0].name)
+                             pruning_transformation=pruning_transformation,
+                             precision=self._precision_from_config[0].name)
                 bin_file = [f for f in os.listdir(tempdir) if f.endswith('.bin')][0]
                 xml_file = [f for f in os.listdir(tempdir) if f.endswith('.xml')][0]
                 with open(os.path.join(tempdir, bin_file), "rb") as f:
diff --git a/external/mmdetection/detection_tasks/apis/detection/nncf_task.py b/external/mmdetection/detection_tasks/apis/detection/nncf_task.py
index e42b4f5118f..cae71c1e9e3 100644
--- a/external/mmdetection/detection_tasks/apis/detection/nncf_task.py
+++ b/external/mmdetection/detection_tasks/apis/detection/nncf_task.py
@@ -81,17 +81,17 @@ def _set_attributes_by_hyperparams(self):
         if quantization and pruning:
             self._nncf_preset = "nncf_quantization_pruning"
             self._optimization_methods = [OptimizationMethod.QUANTIZATION, OptimizationMethod.FILTER_PRUNING]
-            self._nncf_precision = [ModelPrecision.INT8]
+            self._precision = [ModelPrecision.INT8]
             return
         if quantization and not pruning:
             self._nncf_preset = "nncf_quantization"
             self._optimization_methods = [OptimizationMethod.QUANTIZATION]
-            self._nncf_precision = [ModelPrecision.INT8]
+            self._precision = [ModelPrecision.INT8]
             return
         if not quantization and pruning:
             self._nncf_preset = "nncf_pruning"
             self._optimization_methods = [OptimizationMethod.FILTER_PRUNING]
-            self._nncf_precision = [ModelPrecision.FP32]
+            self._precision = [ModelPrecision.INT8]
             return
         raise RuntimeError('Not selected optimization algorithm')
 
@@ -249,7 +249,7 @@ def optimize(
         output_model.model_format = ModelFormat.BASE_FRAMEWORK
         output_model.optimization_type = self._optimization_type
         output_model.optimization_methods = self._optimization_methods
-        output_model.precision = self._nncf_precision
+        output_model.precision = self._precision
 
         self._is_training = False