diff --git a/docs/template_plugin/backend/evaluates_map.cpp b/docs/template_plugin/backend/evaluates_map.cpp
index dfa74b9fb82ed0..cee732e9bba204 100644
--- a/docs/template_plugin/backend/evaluates_map.cpp
+++ b/docs/template_plugin/backend/evaluates_map.cpp
@@ -2112,11 +2112,11 @@ bool evaluate(const shared_ptr<op::v6::ExperimentalDetectronDetectionOutput>& op
               const HostTensorVector& outputs,
               const HostTensorVector& inputs) {
     const auto attrs = op->get_attrs();
-    const size_t output_roi_count = attrs.max_detections_per_image;
+    size_t rois_num = attrs.max_detections_per_image;
 
-    const Shape output_boxes_shape = Shape{output_roi_count, 4};
-    const Shape output_classes_shape = Shape{output_roi_count};
-    const Shape output_scores_shape = Shape{output_roi_count};
+    const Shape output_boxes_shape = Shape{rois_num, 4};
+    const Shape output_classes_shape = Shape{rois_num};
+    const Shape output_scores_shape = Shape{rois_num};
 
     const auto output_type = op->get_input_element_type(0);
 
@@ -2124,7 +2124,6 @@ bool evaluate(const shared_ptr<op::v6::ExperimentalDetectronDetectionOutput>& op
     const auto input_deltas_data = get_floats(inputs[1], inputs[1]->get_shape());
     const auto input_scores_data = get_floats(inputs[2], inputs[2]->get_shape());
     const auto input_im_info_data = get_floats(inputs[3], inputs[3]->get_shape());
-    const auto input_roi_count = inputs[0]->get_shape()[0];
 
     std::vector<float> output_boxes(shape_size(output_boxes_shape));
     std::vector<int32_t> output_classes(shape_size(output_classes_shape));
@@ -2141,7 +2140,6 @@ bool evaluate(const shared_ptr<op::v6::ExperimentalDetectronDetectionOutput>& op
                                                                 input_deltas_data.data(),
                                                                 input_scores_data.data(),
                                                                 input_im_info_data.data(),
-                                                                input_roi_count,
                                                                 attrs,
                                                                 output_boxes.data(),
                                                                 output_scores.data(),
diff --git a/docs/template_plugin/tests/functional/op_reference/experimental_detectron_detection_output.cpp b/docs/template_plugin/tests/functional/op_reference/experimental_detectron_detection_output.cpp
index 0a64e59f25942d..0f5841fc32172e 100644
--- a/docs/template_plugin/tests/functional/op_reference/experimental_detectron_detection_output.cpp
+++ b/docs/template_plugin/tests/functional/op_reference/experimental_detectron_detection_output.cpp
@@ -2,10 +2,9 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "openvino/op/experimental_detectron_detection_output.hpp"
-
 #include <gtest/gtest.h>
 
+#include "openvino/op/experimental_detectron_detection_output.hpp"
 #include "base_reference_test.hpp"
 
 using namespace reference_tests;
@@ -19,13 +18,9 @@ struct ExperimentalDOParams {
     ExperimentalDOParams(const Attrs& attrs,
                          const size_t num_rois,
                          const element::Type& iType,
-                         const std::vector<IT>& roisValues,
-                         const std::vector<IT>& deltasValues,
-                         const std::vector<IT>& scoresValues,
-                         const std::vector<IT>& imageSizeInfoValues,
-                         const std::vector<IT>& refBoxesValues,
-                         const std::vector<int32_t>& refClassesValues,
-                         const std::vector<IT>& refScoresValues,
+                         const std::vector<IT>& roisValues, const std::vector<IT>& deltasValues,
+                         const std::vector<IT>& scoresValues, const std::vector<IT>& imageSizeInfoValues,
+                         const std::vector<IT>& refBoxesValues, const std::vector<int32_t>& refClassesValues, const std::vector<IT>& refScoresValues,
                          const std::string& testcaseName = "")
         : attrs(attrs),
           inType(iType),
@@ -38,11 +33,11 @@ struct ExperimentalDOParams {
           refClassesData(CreateTensor(ov::element::i32, refClassesValues)),
           refScoresData(CreateTensor(iType, refScoresValues)),
           testcaseName(testcaseName) {
-        roisShape = Shape{num_rois, 4};
-        deltasShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes * 4)};
-        scoresShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes)};
-        imageSizeInfoShape = Shape{1, 3};
-    }
+              roisShape = Shape{num_rois, 4};
+              deltasShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes * 4)};
+              scoresShape = Shape{num_rois, static_cast<size_t>(attrs.num_classes)};
+              imageSizeInfoShape = Shape{1, 3};
+          }
 
     Attrs attrs;
     PartialShape roisShape;
@@ -61,8 +56,7 @@ struct ExperimentalDOParams {
     std::string testcaseName;
 };
 
-class ReferenceExperimentalDOLayerTest : public testing::TestWithParam<ExperimentalDOParams>,
-                                         public CommonReferenceTest {
+class ReferenceExperimentalDOLayerTest : public testing::TestWithParam<ExperimentalDOParams>, public CommonReferenceTest {
 public:
     void SetUp() override {
         auto params = GetParam();
@@ -90,9 +84,12 @@ class ReferenceExperimentalDOLayerTest : public testing::TestWithParam<Experimen
         const auto deltas = std::make_shared<op::v0::Parameter>(params.inType, params.deltasShape);
         const auto scores = std::make_shared<op::v0::Parameter>(params.inType, params.scoresShape);
         const auto im_info = std::make_shared<op::v0::Parameter>(params.inType, params.imageSizeInfoShape);
-        const auto ExperimentalDO =
-            std::make_shared<op::v6::ExperimentalDetectronDetectionOutput>(rois, deltas, scores, im_info, params.attrs);
-        return std::make_shared<ov::Model>(ExperimentalDO->outputs(), ParameterVector{rois, deltas, scores, im_info});
+        const auto ExperimentalDO = std::make_shared<op::v6::ExperimentalDetectronDetectionOutput>(rois,
+                                                                    deltas,
+                                                                    scores,
+                                                                    im_info,
+                                                                    params.attrs);
+        return std::make_shared<ov::Model>(ExperimentalDO->outputs(), ParameterVector {rois, deltas, scores, im_info});
     }
 };
 
@@ -104,54 +101,68 @@ template <element::Type_t IN_ET>
 std::vector<ExperimentalDOParams> generateExperimentalDOFloatParams() {
     using T = typename element_type_traits<IN_ET>::value_type;
 
-    std::vector<ExperimentalDOParams> experimentalDOParams{
-        ExperimentalDOParams(
-            Attrs{
-                0.01000000074505806f,       // score_threshold
-                0.2f,                       // nms_threshold
-                2.0f,                       // max_delta_log_wh
-                2,                          // num_classes
-                500,                        // post_nms_count
-                5,                          // max_detections_per_image
-                true,                       // class_agnostic_box_regression
-                {10.0f, 10.0f, 5.0f, 5.0f}  // deltas_weights
-            },
-            16,
-            IN_ET,
-            std::vector<T>{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f,
-                           1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f},
-            std::vector<T>{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 5.0f, 1.0f, 1.0f, 1.0f, -1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                           1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f},
-
-            std::vector<T>{0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.8f, 0.9f, 0.5f,
-                           0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                           0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f},
-            std::vector<T>{16.0f, 12.0f, 1.0f},
-            std::vector<T>{4.8929863f,  0.892986298f, 12.0f, 12.1070137f, 0.0f, 0.892986298f, 10.1070137f,
-                           12.1070137f, 0.0f,         0.0f,  0.0f,        0.0f, 0.0f,         0.0f,
-                           0.0f,        0.0f,         0.0f,  0.0f,        0.0f, 0.0f},
-            std::vector<int32_t>{0, 1, 0, 0, 0},
-            std::vector<T>{0.8f, 0.9f, 0.0f, 0.0f, 0.0f}),
+    std::vector<ExperimentalDOParams> experimentalDOParams {
+        ExperimentalDOParams(Attrs{0.01000000074505806f, // score_threshold
+                              0.2f, // nms_threshold
+                              2.0f, // max_delta_log_wh
+                              2, // num_classes
+                              500, // post_nms_count
+                              5, // max_detections_per_image
+                              true, // class_agnostic_box_regression
+                              {10.0f, 10.0f, 5.0f, 5.0f} // deltas_weights
+                              },
+                              16,
+                              IN_ET,
+                              std::vector<T>{1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f,  4.0f,  1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f},
+                              std::vector<T>{5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+                                             1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f},
+                              std::vector<T>{1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                             1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f},
+                              std::vector<T>{1.0f, 1.0f, 1.0f},
+                              std::vector<T>{0.8929862f,
+                                             0.892986297607421875,
+                                             12.10701370239257812,
+                                             12.10701370239257812,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0},
+                              std::vector<int32_t>{1, 0, 0, 0, 0},
+                              std::vector<T>{1.0f, 0.0f, 0.0f, 0.0f, 0.0f}),
     };
     return experimentalDOParams;
 }
 
 std::vector<ExperimentalDOParams> generateExperimentalDOCombinedParams() {
-    const std::vector<std::vector<ExperimentalDOParams>> ExperimentalDOTypeParams{
+    const std::vector<std::vector<ExperimentalDOParams>> ExperimentalDOTypeParams {
         generateExperimentalDOFloatParams<element::Type_t::f32>(),
         generateExperimentalDOFloatParams<element::Type_t::f16>(),
         generateExperimentalDOFloatParams<element::Type_t::bf16>(),
-    };
+        };
     std::vector<ExperimentalDOParams> combinedParams;
 
     for (const auto& params : ExperimentalDOTypeParams) {
@@ -160,8 +171,6 @@ std::vector<ExperimentalDOParams> generateExperimentalDOCombinedParams() {
     return combinedParams;
 }
 
-INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput_With_Hardcoded_Refs,
-                         ReferenceExperimentalDOLayerTest,
-                         testing::ValuesIn(generateExperimentalDOCombinedParams()),
-                         ReferenceExperimentalDOLayerTest::getTestCaseName);
-}  // namespace
+INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput_With_Hardcoded_Refs, ReferenceExperimentalDOLayerTest,
+    testing::ValuesIn(generateExperimentalDOCombinedParams()), ReferenceExperimentalDOLayerTest::getTestCaseName);
+} // namespace
diff --git a/src/core/reference/include/ngraph/runtime/reference/experimental_detectron_detection_output.hpp b/src/core/reference/include/ngraph/runtime/reference/experimental_detectron_detection_output.hpp
index 0e57eace94f461..b2147fe9c461e8 100644
--- a/src/core/reference/include/ngraph/runtime/reference/experimental_detectron_detection_output.hpp
+++ b/src/core/reference/include/ngraph/runtime/reference/experimental_detectron_detection_output.hpp
@@ -34,7 +34,6 @@ void experimental_detectron_detection_output(const float* input_rois,
                                              const float* input_deltas,
                                              const float* input_scores,
                                              const float* input_im_info,
-                                             size_t roi_count,
                                              const op::v6::ExperimentalDetectronDetectionOutput::Attributes& attrs,
                                              float* output_boxes,
                                              float* output_scores,
diff --git a/src/core/reference/src/runtime/reference/experimental_detectron_detection_output.cpp b/src/core/reference/src/runtime/reference/experimental_detectron_detection_output.cpp
index 1edd600508254a..3de7f044ba84d0 100644
--- a/src/core/reference/src/runtime/reference/experimental_detectron_detection_output.cpp
+++ b/src/core/reference/src/runtime/reference/experimental_detectron_detection_output.cpp
@@ -54,7 +54,7 @@ void refine_boxes(const float* boxes,
         const float ctr_x = x0 + 0.5f * ww;
         const float ctr_y = y0 + 0.5f * hh;
 
-        for (int class_idx = 0; class_idx < classes_num; ++class_idx) {
+        for (int class_idx = 1; class_idx < classes_num; ++class_idx) {
             const int64_t deltas_base_offset = classes_num * 4 * roi_idx + 4 * class_idx;
             const float dx = deltas[deltas_base_offset + 0] / weights[0];
             const float dy = deltas[deltas_base_offset + 1] / weights[1];
@@ -76,10 +76,10 @@ void refine_boxes(const float* boxes,
             float y1_new = pred_ctr_y + 0.5f * pred_h - coordinates_offset;
 
             // adjust new corner locations to be within the image region,
-            x0_new = std::min(std::max(0.0f, x0_new), img_W);
-            y0_new = std::min(std::max(0.0f, y0_new), img_H);
-            x1_new = std::min(std::max(0.0f, x1_new), img_W);
-            y1_new = std::min(std::max(0.0f, y1_new), img_H);
+            x0_new = std::max<float>(0.0f, x0_new);
+            y0_new = std::max<float>(0.0f, y0_new);
+            x1_new = std::max<float>(0.0f, x1_new);
+            y1_new = std::max<float>(0.0f, y1_new);
 
             // recompute new width & height
             const float box_w = x1_new - x0_new + coordinates_offset;
@@ -214,7 +214,6 @@ void experimental_detectron_detection_output(const float* boxes,
                                              const float* input_deltas,
                                              const float* input_scores,
                                              const float* input_im_info,
-                                             size_t roi_count,
                                              const op::v6::ExperimentalDetectronDetectionOutput::Attributes& attrs,
                                              float* output_boxes,
                                              float* output_scores,
@@ -222,7 +221,7 @@ void experimental_detectron_detection_output(const float* boxes,
     const float img_H = input_im_info[0];
     const float img_W = input_im_info[1];
     const int64_t classes_num = attrs.num_classes;
-    const int64_t rois_num = static_cast<int64_t>(roi_count);
+    const int64_t rois_num = static_cast<int64_t>(attrs.max_detections_per_image);
     const int64_t max_detections_per_image = static_cast<int64_t>(attrs.max_detections_per_image);
     const int64_t max_detections_per_class = attrs.post_nms_count;
     const float score_threshold = attrs.score_threshold;
@@ -256,7 +255,7 @@ void experimental_detectron_detection_output(const float* boxes,
     std::vector<int64_t> detections_per_class(classes_num, 0);
     int64_t total_detections_num = 0;
 
-    for (int64_t class_idx = 0; class_idx < classes_num; ++class_idx) {
+    for (int64_t class_idx = 1; class_idx < classes_num; ++class_idx) {
         nms_cf(&refined_scores[rois_num * class_idx],
                &refined_boxes[rois_num * 4 * class_idx],
                &refined_boxes_areas[rois_num * class_idx],
diff --git a/src/frontends/tests/onnx/onnx_import_org_openvino.in.cpp b/src/frontends/tests/onnx/onnx_import_org_openvino.in.cpp
index d2ffe49c3ef987..cce55ce82800ae 100644
--- a/src/frontends/tests/onnx/onnx_import_org_openvino.in.cpp
+++ b/src/frontends/tests/onnx/onnx_import_org_openvino.in.cpp
@@ -272,34 +272,54 @@ NGRAPH_TEST(${BACKEND_NAME}, onnx_model_experimental_detectron_detection_output)
 
     auto test_case = test::TestCase(function, s_device);
     // rois
-    test_case.add_input<float>({1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f,
-                                1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
+    test_case.add_input<float>({1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                1.0f, 1.0f, 1.0f,  4.0f,  1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
     // deltas
     test_case.add_input<float>(
-        {1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f,
-         1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-         5.0f, 1.0f, 1.0f, 1.0f, -1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-         1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-         1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-         1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-         1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-         1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
+        {5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+         1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+         1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+         1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+         1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+         1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+         1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+         1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
     // scores
-    test_case.add_input<float>({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.8f, 0.9f, 0.5f,
-                                0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                                0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f});
+    test_case.add_input<float>({1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+                                1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f});
     // im_info
-    test_case.add_input<float>({16.0f, 12.0f, 1.0f});
+    test_case.add_input<float>({1.0f, 1.0f, 1.0f});
 
-    test_case.add_expected_output<float>(
-        Shape{5, 4},
-        {4.8929863f, 0.892986298f, 12.0f, 12.1070137f, 0.0f, 0.892986298f, 10.1070137f, 12.1070137f, 0.0f, 0.0f,
-         0.0f,       0.0f,         0.0f,  0.0f,        0.0f, 0.0f,         0.0f,        0.0f,        0.0f, 0.0f});
-    test_case.add_expected_output<int>(Shape{5}, {0, 1, 0, 0, 0});
-    test_case.add_expected_output<float>(Shape{5}, {0.8f, 0.9f, 0.0f, 0.0f, 0.0f});
+    test_case.add_expected_output<float>(Shape{5, 4},
+                                         {
+                                             0.8929862f,
+                                             0.892986297607421875,
+                                             12.10701370239257812,
+                                             12.10701370239257812,
+                                             0,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                             0.0f,
+                                         });
+    test_case.add_expected_output<int>(Shape{5}, {1, 0, 0, 0, 0});
+    test_case.add_expected_output<float>(Shape{5}, {1.0f, 0.0f, 0.0f, 0.0f, 0.0f});
 
     test_case.run();
 }
diff --git a/src/plugins/intel_cpu/src/nodes/experimental_detectron_detection_output.cpp b/src/plugins/intel_cpu/src/nodes/experimental_detectron_detection_output.cpp
index 1d1505b0f638cf..8bdd20a9b743b6 100644
--- a/src/plugins/intel_cpu/src/nodes/experimental_detectron_detection_output.cpp
+++ b/src/plugins/intel_cpu/src/nodes/experimental_detectron_detection_output.cpp
@@ -2,13 +2,12 @@
 // SPDX-License-Identifier: Apache-2.0
 //
 
-#include "experimental_detectron_detection_output.h"
-
-#include <ngraph/op/experimental_detectron_detection_output.hpp>
 #include <string>
 #include <vector>
 
+#include <ngraph/op/experimental_detectron_detection_output.hpp>
 #include "ie_parallel.hpp"
+#include "experimental_detectron_detection_output.h"
 
 using namespace InferenceEngine;
 
@@ -39,19 +38,13 @@ struct Indexer {
     }
 };
 
-static void refine_boxes(const float* boxes,
-                         const float* deltas,
-                         const float* weights,
-                         const float* scores,
-                         float* refined_boxes,
-                         float* refined_boxes_areas,
-                         float* refined_scores,
-                         const int rois_num,
-                         const int classes_num,
-                         const float img_H,
-                         const float img_W,
-                         const float max_delta_log_wh,
-                         float coordinates_offset) {
+static
+void refine_boxes(const float* boxes, const float* deltas, const float* weights, const float* scores,
+                  float* refined_boxes, float* refined_boxes_areas, float* refined_scores,
+                  const int rois_num, const int classes_num,
+                  const float img_H, const float img_W,
+                  const float max_delta_log_wh,
+                  float coordinates_offset) {
     Indexer box_idx({rois_num, 4});
     Indexer delta_idx({rois_num, classes_num, 4});
     Indexer score_idx({rois_num, classes_num});
@@ -76,7 +69,7 @@ static void refine_boxes(const float* boxes,
         const float ctr_x = x0 + 0.5f * ww;
         const float ctr_y = y0 + 0.5f * hh;
 
-        for (int class_idx = 0; class_idx < classes_num; ++class_idx) {
+        for (int class_idx = 1; class_idx < classes_num; ++class_idx) {
             const float dx = deltas[delta_idx({roi_idx, class_idx, 0})] / weights[0];
             const float dy = deltas[delta_idx({roi_idx, class_idx, 1})] / weights[1];
             const float d_log_w = deltas[delta_idx({roi_idx, class_idx, 2})] / weights[2];
@@ -97,10 +90,10 @@ static void refine_boxes(const float* boxes,
             float y1_new = pred_ctr_y + 0.5f * pred_h - coordinates_offset;
 
             // adjust new corner locations to be within the image region,
-            x0_new = std::min(std::max(0.0f, x0_new), img_W);
-            y0_new = std::min(std::max(0.0f, y0_new), img_H);
-            x1_new = std::min(std::max(0.0f, x1_new), img_W);
-            y1_new = std::min(std::max(0.0f, y1_new), img_H);
+            x0_new = std::max<float>(0.0f, x0_new);
+            y0_new = std::max<float>(0.0f, y0_new);
+            x1_new = std::max<float>(0.0f, x1_new);
+            y1_new = std::max<float>(0.0f, y1_new);
 
             // recompute new width & height
             const float box_w = x1_new - x0_new + coordinates_offset;
@@ -123,22 +116,21 @@ static bool SortScorePairDescend(const std::pair<float, std::pair<int, int>>& pa
     return (pair1.first > pair2.first) || ((pair1.first == pair2.first) && (pair1.second.second < pair2.second.second));
 }
 
+
 struct ConfidenceComparator {
     explicit ConfidenceComparator(const float* conf_data) : _conf_data(conf_data) {}
 
     bool operator()(int idx1, int idx2) {
-        if (_conf_data[idx1] > _conf_data[idx2])
-            return true;
-        if (_conf_data[idx1] < _conf_data[idx2])
-            return false;
+        if (_conf_data[idx1] > _conf_data[idx2]) return true;
+        if (_conf_data[idx1] < _conf_data[idx2]) return false;
         return idx1 < idx2;
     }
 
     const float* _conf_data;
 };
 
-static inline float JaccardOverlap(const float* decoded_bbox,
-                                   const float* bbox_sizes,
+static inline float JaccardOverlap(const float *decoded_bbox,
+                                   const float *bbox_sizes,
                                    const int idx1,
                                    const int idx2,
                                    const float coordinates_offset = 1) {
@@ -161,7 +153,7 @@ static inline float JaccardOverlap(const float* decoded_bbox,
     float intersect_xmax = (std::min)(xmax1, xmax2);
     float intersect_ymax = (std::min)(ymax1, ymax2);
 
-    float intersect_width = intersect_xmax - intersect_xmin + coordinates_offset;
+    float intersect_width  = intersect_xmax - intersect_xmin + coordinates_offset;
     float intersect_height = intersect_ymax - intersect_ymin + coordinates_offset;
 
     if (intersect_width <= 0 || intersect_height <= 0) {
@@ -175,6 +167,7 @@ static inline float JaccardOverlap(const float* decoded_bbox,
     return intersect_size / (bbox1_size + bbox2_size - intersect_size);
 }
 
+
 static void nms_cf(const float* conf_data,
                    const float* bboxes,
                    const float* sizes,
@@ -196,10 +189,8 @@ static void nms_cf(const float* conf_data,
 
     int num_output_scores = (pre_nms_topn == -1 ? count : (std::min)(pre_nms_topn, count));
 
-    std::partial_sort_copy(indices,
-                           indices + count,
-                           buffer,
-                           buffer + num_output_scores,
+    std::partial_sort_copy(indices, indices + count,
+                           buffer, buffer + num_output_scores,
                            ConfidenceComparator(conf_data));
 
     detections = 0;
@@ -232,13 +223,11 @@ bool ExperimentalDetectronDetectionOutput::needPrepareParams() const {
     return false;
 }
 
-bool ExperimentalDetectronDetectionOutput::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op,
-                                                                std::string& errorMessage) noexcept {
+bool ExperimentalDetectronDetectionOutput::isSupportedOperation(const std::shared_ptr<const ngraph::Node>& op, std::string& errorMessage) noexcept {
     try {
         const auto doOp = ngraph::as_type_ptr<const ngraph::op::v6::ExperimentalDetectronDetectionOutput>(op);
         if (!doOp) {
-            errorMessage =
-                "Node is not an instance of the ExperimentalDetectronDetectionOutput from the operations set v6.";
+            errorMessage = "Node is not an instance of the ExperimentalDetectronDetectionOutput from the operations set v6.";
             return false;
         }
     } catch (...) {
@@ -247,10 +236,9 @@ bool ExperimentalDetectronDetectionOutput::isSupportedOperation(const std::share
     return true;
 }
 
-ExperimentalDetectronDetectionOutput::ExperimentalDetectronDetectionOutput(const std::shared_ptr<ngraph::Node>& op,
-                                                                           const dnnl::engine& eng,
-                                                                           WeightsSharing::Ptr& cache)
-    : Node(op, eng, cache) {
+ExperimentalDetectronDetectionOutput::ExperimentalDetectronDetectionOutput
+                        (const std::shared_ptr<ngraph::Node>& op, const dnnl::engine& eng,
+                         WeightsSharing::Ptr &cache) : Node(op, eng, cache) {
     std::string errorMessage;
     if (!isSupportedOperation(op, errorMessage)) {
         IE_THROW(NotImplemented) << errorMessage;
@@ -277,10 +265,11 @@ void ExperimentalDetectronDetectionOutput::initSupportedPrimitiveDescriptors() {
     for (int i = 0; i < inputShapes.size(); ++i)
         inDataConf.emplace_back(LayoutType::ncsp, Precision::FP32);
 
-    addSupportedPrimDesc(
-        inDataConf,
-        {{LayoutType::ncsp, Precision::FP32}, {LayoutType::ncsp, Precision::I32}, {LayoutType::ncsp, Precision::FP32}},
-        impl_desc_type::ref_any);
+    addSupportedPrimDesc(inDataConf,
+                         {{LayoutType::ncsp, Precision::FP32},
+                          {LayoutType::ncsp, Precision::I32},
+                          {LayoutType::ncsp, Precision::FP32}},
+                         impl_desc_type::ref_any);
 }
 
 void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
@@ -288,14 +277,14 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
     assert(classes_num_ == static_cast<int>(getParentEdgeAt(INPUT_SCORES)->getMemory().getStaticDims()[1]));
     assert(4 * classes_num_ == static_cast<int>(getParentEdgeAt(INPUT_DELTAS)->getMemory().getStaticDims()[1]));
 
-    const auto* boxes = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_ROIS)->getMemoryPtr()->GetPtr());
-    const auto* deltas = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_DELTAS)->getMemoryPtr()->GetPtr());
-    const auto* scores = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_SCORES)->getMemoryPtr()->GetPtr());
-    const auto* im_info = reinterpret_cast<const float*>(getParentEdgeAt(INPUT_IM_INFO)->getMemoryPtr()->GetPtr());
+    const auto* boxes = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_ROIS)->getMemoryPtr()->GetPtr());
+    const auto* deltas = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_DELTAS)->getMemoryPtr()->GetPtr());
+    const auto* scores = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_SCORES)->getMemoryPtr()->GetPtr());
+    const auto* im_info = reinterpret_cast<const float *>(getParentEdgeAt(INPUT_IM_INFO)->getMemoryPtr()->GetPtr());
 
-    auto* output_boxes = reinterpret_cast<float*>(getChildEdgesAtPort(OUTPUT_BOXES)[0]->getMemoryPtr()->GetPtr());
-    auto* output_scores = reinterpret_cast<float*>(getChildEdgesAtPort(OUTPUT_SCORES)[0]->getMemoryPtr()->GetPtr());
-    auto* output_classes = reinterpret_cast<int32_t*>(getChildEdgesAtPort(OUTPUT_CLASSES)[0]->getMemoryPtr()->GetPtr());
+    auto* output_boxes = reinterpret_cast<float *>(getChildEdgesAtPort(OUTPUT_BOXES)[0]->getMemoryPtr()->GetPtr());
+    auto* output_scores = reinterpret_cast<float *>(getChildEdgesAtPort(OUTPUT_SCORES)[0]->getMemoryPtr()->GetPtr());
+    auto* output_classes = reinterpret_cast<int32_t *>(getChildEdgesAtPort(OUTPUT_CLASSES)[0]->getMemoryPtr()->GetPtr());
 
     const float img_H = im_info[0];
     const float img_W = im_info[1];
@@ -307,17 +296,10 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
     Indexer refined_box_idx({classes_num_, rois_num, 4});
     Indexer refined_score_idx({classes_num_, rois_num});
 
-    refine_boxes(boxes,
-                 deltas,
-                 &deltas_weights_[0],
-                 scores,
-                 &refined_boxes[0],
-                 &refined_boxes_areas[0],
-                 &refined_scores[0],
-                 rois_num,
-                 classes_num_,
-                 img_H,
-                 img_W,
+    refine_boxes(boxes, deltas, &deltas_weights_[0], scores,
+                 &refined_boxes[0], &refined_boxes_areas[0], &refined_scores[0],
+                 rois_num, classes_num_,
+                 img_H, img_W,
                  max_delta_log_wh_,
                  1.0f);
 
@@ -327,7 +309,7 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
     std::vector<int> detections_per_class(classes_num_, 0);
     int total_detections_num = 0;
 
-    for (int class_idx = 0; class_idx < classes_num_; ++class_idx) {
+    for (int class_idx = 1; class_idx < classes_num_; ++class_idx) {
         nms_cf(&refined_scores[refined_score_idx({class_idx, 0})],
                &refined_boxes[refined_box_idx({class_idx, 0, 0})],
                &refined_boxes_areas[refined_score_idx({class_idx, 0})],
@@ -373,7 +355,7 @@ void ExperimentalDetectronDetectionOutput::execute(dnnl::stream strm) {
     memset(output_classes, 0, max_detections_per_image_ * sizeof(output_classes[0]));
 
     int i = 0;
-    for (const auto& detection : conf_index_class_map) {
+    for (const auto & detection : conf_index_class_map) {
         float score = detection.first;
         int cls = detection.second.first;
         int idx = detection.second.second;
@@ -391,6 +373,6 @@ bool ExperimentalDetectronDetectionOutput::created() const {
     return getType() == Type::ExperimentalDetectronDetectionOutput;
 }
 
-}  // namespace node
-}  // namespace intel_cpu
-}  // namespace ov
+}   // namespace node
+}   // namespace intel_cpu
+}   // namespace ov
diff --git a/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/experimental_detectron_detection_output.cpp b/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/experimental_detectron_detection_output.cpp
index 4a1a4b8b67af91..0153b4dc61fb00 100644
--- a/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/experimental_detectron_detection_output.cpp
+++ b/src/tests/functional/plugin/gpu/shared_tests_instances/single_layer_tests/experimental_detectron_detection_output.cpp
@@ -47,7 +47,7 @@ const std::vector<std::vector<InputShape>> inputShapes = {
     static_shapes_to_test_representation({{16, 4}, {16, 8}, {16, 2}, {1, 3}}),
 };
 
-INSTANTIATE_TEST_SUITE_P(smoke_ExperimentalDetectronDetectionOutput,
+INSTANTIATE_TEST_SUITE_P(DISABLED_smoke_ExperimentalDetectronDetectionOutput,
                          ExperimentalDetectronDetectionOutputLayerTest,
                          ::testing::Combine(::testing::ValuesIn(inputShapes),
                                             ::testing::ValuesIn(score_threshold),
diff --git a/src/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_detection_output.cpp b/src/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_detection_output.cpp
index de81d356673356..41b3b8c1cced30 100644
--- a/src/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_detection_output.cpp
+++ b/src/tests/functional/shared_test_classes/src/single_layer/experimental_detectron_detection_output.cpp
@@ -3,19 +3,16 @@
 //
 
 #include "shared_test_classes/single_layer/experimental_detectron_detection_output.hpp"
-
-#include <common_test_utils/ov_tensor_utils.hpp>
-
-#include "common_test_utils/data_utils.hpp"
 #include "ngraph_functions/builders.hpp"
+#include "common_test_utils/data_utils.hpp"
+#include <common_test_utils/ov_tensor_utils.hpp>
 
 namespace ov {
 namespace test {
 namespace subgraph {
 
 namespace {
-std::ostream& operator<<(std::ostream& ss,
-                         const ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes& attributes) {
+    std::ostream& operator <<(std::ostream& ss, const ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes& attributes) {
     ss << "score_threshold=" << attributes.score_threshold << "_";
     ss << "nms_threshold=" << attributes.nms_threshold << "_";
     ss << "max_delta_log_wh=" << attributes.max_delta_log_wh << "_";
@@ -26,25 +23,26 @@ std::ostream& operator<<(std::ostream& ss,
     ss << "deltas_weights=" << CommonTestUtils::vec2str(attributes.deltas_weights);
     return ss;
 }
-}  // namespace
+} // namespace
 
 std::string ExperimentalDetectronDetectionOutputLayerTest::getTestCaseName(
-    const testing::TestParamInfo<ExperimentalDetectronDetectionOutputTestParams>& obj) {
+        const testing::TestParamInfo<ExperimentalDetectronDetectionOutputTestParams>& obj) {
     std::vector<ov::test::InputShape> inputShapes;
     ngraph::opset6::ExperimentalDetectronDetectionOutput::Attributes attributes;
     ElementType netPrecision;
     std::string targetName;
-    std::tie(inputShapes,
-             attributes.score_threshold,
-             attributes.nms_threshold,
-             attributes.max_delta_log_wh,
-             attributes.num_classes,
-             attributes.post_nms_count,
-             attributes.max_detections_per_image,
-             attributes.class_agnostic_box_regression,
-             attributes.deltas_weights,
-             netPrecision,
-             targetName) = obj.param;
+    std::tie(
+        inputShapes,
+        attributes.score_threshold,
+        attributes.nms_threshold,
+        attributes.max_delta_log_wh,
+        attributes.num_classes,
+        attributes.post_nms_count,
+        attributes.max_detections_per_image,
+        attributes.class_agnostic_box_regression,
+        attributes.deltas_weights,
+        netPrecision,
+        targetName) = obj.param;
 
     std::ostringstream result;
 
@@ -67,17 +65,18 @@ void ExperimentalDetectronDetectionOutputLayerTest::SetUp() {
 
     ElementType netPrecision;
     std::string targetName;
-    std::tie(inputShapes,
-             attributes.score_threshold,
-             attributes.nms_threshold,
-             attributes.max_delta_log_wh,
-             attributes.num_classes,
-             attributes.post_nms_count,
-             attributes.max_detections_per_image,
-             attributes.class_agnostic_box_regression,
-             attributes.deltas_weights,
-             netPrecision,
-             targetName) = this->GetParam();
+    std::tie(
+        inputShapes,
+        attributes.score_threshold,
+        attributes.nms_threshold,
+        attributes.max_delta_log_wh,
+        attributes.num_classes,
+        attributes.post_nms_count,
+        attributes.max_detections_per_image,
+        attributes.class_agnostic_box_regression,
+        attributes.deltas_weights,
+        netPrecision,
+        targetName) = this->GetParam();
 
     if (netPrecision == element::f16)
         abs_threshold = 0.01;
@@ -88,74 +87,49 @@ void ExperimentalDetectronDetectionOutputLayerTest::SetUp() {
     init_input_shapes(inputShapes);
 
     auto params = ngraph::builder::makeDynamicParams(netPrecision, {inputDynamicShapes});
-    auto paramsOuts =
-        ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
-    auto experimentalDetectron =
-        std::make_shared<ngraph::opset6::ExperimentalDetectronDetectionOutput>(params[0],  // input_rois
-                                                                               params[1],  // input_deltas
-                                                                               params[2],  // input_scores
-                                                                               params[3],  // input_im_info
-                                                                               attributes);
-    function = std::make_shared<ov::Model>(ov::OutputVector{experimentalDetectron->output(0),
-                                                            experimentalDetectron->output(1),
-                                                            experimentalDetectron->output(2)},
-                                           "ExperimentalDetectronDetectionOutput");
-}
-
-namespace {
-
-template <typename T>
-std::vector<T> getValues(const std::vector<float>& values) {
-    std::vector<T> result(values.begin(), values.end());
-    return result;
+    auto paramsOuts = ngraph::helpers::convert2OutputVector(ngraph::helpers::castOps2Nodes<ngraph::op::Parameter>(params));
+    auto experimentalDetectron = std::make_shared<ngraph::opset6::ExperimentalDetectronDetectionOutput>(
+        params[0], // input_rois
+        params[1], // input_deltas
+        params[2], // input_scores
+        params[3], // input_im_info
+        attributes);
+    function = std::make_shared<ov::Model>(
+        ov::OutputVector{experimentalDetectron->output(0), experimentalDetectron->output(1)},
+        "ExperimentalDetectronDetectionOutput");
 }
 
-template <typename T>
-std::vector<ov::Tensor> generateInputTensors() {
-    const auto netPrecision = ov::element::from<T>();
-    std::vector<ov::Tensor> inputTensors = {
+void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(const std::vector<ngraph::Shape>& targetInputStaticShapes) {
+    static const std::vector<ov::Tensor> inputTensors = {
         // 16 x 4 = 64
-        ov::test::utils::create_tensor<T>(
-            netPrecision,
-            Shape{16, 4},
-            getValues<T>({1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f,
-                          1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f})),
+        ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 4}, {
+            1.0f, 1.0f, 10.0f, 10.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  4.0f,  1.0f, 8.0f, 5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f,  1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
+        }),
         // 16 x 8
-        ov::test::utils::create_tensor<T>(
-            netPrecision,
-            Shape{16, 8},
-            getValues<T>({1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 5.0f, 1.0f, 1.0f, 1.0f, -1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
-                          1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,  1.0f})),
+        ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 8}, {
+            5.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 4.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 8.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
+        }),
         // 16 x 2 = 32
-        ov::test::utils::create_tensor<T>(
-            netPrecision,
-            Shape{16, 2},
-            getValues<T>({0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.8f, 0.9f, 0.5f,
-                          0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f,
-                          0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f, 0.5f})),
+        ov::test::utils::create_tensor<float>(ov::element::f32, Shape{16, 2}, {
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f,
+            1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f, 1.0f
+        }),
         // 1 x 3 = 3
-        ov::test::utils::create_tensor<T>(netPrecision, Shape{1, 3}, getValues<T>({16.0f, 12.0f, 1.0f}))};
-
-    return inputTensors;
-}
-}  // namespace
-
-void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(
-    const std::vector<ngraph::Shape>& targetInputStaticShapes) {
-    const auto netPrecision = std::get<9>(GetParam());
-
-    const std::vector<ov::Tensor> inputTensors =
-        (netPrecision == element::f16) ? generateInputTensors<ov::float16>() : generateInputTensors<float>();
+        ov::test::utils::create_tensor<float>(ov::element::f32, Shape{1, 3}, {1.0f, 1.0f, 1.0f})
+    };
 
     inputs.clear();
     const auto& funcInputs = function->inputs();
@@ -168,6 +142,6 @@ void ExperimentalDetectronDetectionOutputLayerTest::generate_inputs(
     }
 }
 
-}  // namespace subgraph
-}  // namespace test
-}  // namespace ov
+} // namespace subgraph
+} // namespace test
+} // namespace ov