diff --git a/tests/use_case/object_detection/InferenceTestYoloFastest.cc b/tests/use_case/object_detection/InferenceTestYoloFastest.cc
index f1c3719..b3cf37d 100644
--- a/tests/use_case/object_detection/InferenceTestYoloFastest.cc
+++ b/tests/use_case/object_detection/InferenceTestYoloFastest.cc
@@ -1,6 +1,6 @@
 /*
- * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates <open-source-office@arm.com>
- * SPDX-License-Identifier: Apache-2.0
+ * SPDX-FileCopyrightText: Copyright 2022 Arm Limited and/or its affiliates
+ * <open-source-office@arm.com> SPDX-License-Identifier: Apache-2.0
  *
  * Licensed under the Apache License, Version 2.0 (the "License");
  * you may not use this file except in compliance with the License.
@@ -14,58 +14,51 @@
  * See the License for the specific language governing permissions and
  * limitations under the License.
  */
-#include "log_macros.h"
-#include "ImageUtils.hpp"
-#include "YoloFastestModel.hpp"
-#include "TensorFlowLiteMicro.hpp"
-#include "DetectorPostProcessing.hpp"
-#include "InputFiles.hpp"
 #include "BufAttributes.hpp"
+#include "DetectorPostProcessing.hpp"
+#include "ImageUtils.hpp"
+#include "InputFiles.hpp"
+#include "TensorFlowLiteMicro.hpp"
+#include "YoloFastestModel.hpp"
+#include "log_macros.h"
 
 namespace arm {
-    namespace app {
-        static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
-        namespace object_detection {
-            extern uint8_t* GetModelPointer();
-            extern size_t GetModelLen();
-        } /* namespace object_detection */
-    } /* namespace app */
+namespace app {
+    static uint8_t tensorArena[ACTIVATION_BUF_SZ] ACTIVATION_BUF_ATTRIBUTE;
+    namespace object_detection {
+        extern uint8_t* GetModelPointer();
+        extern size_t GetModelLen();
+    } /* namespace object_detection */
+} /* namespace app */
 } /* namespace arm */
 
 #include <catch.hpp>
 
-void GetExpectedResults(std::vector<std::vector<arm::app::object_detection::DetectionResult>> &expected_results)
+void GetExpectedResults(
+    std::vector<std::vector<arm::app::object_detection::DetectionResult>>& expected_results)
 {
     /* Img1
     0)  (0.999246) -> Detection box: {x=89,y=17,w=41,h=56}
     1)  (0.995367) -> Detection box: {x=27,y=81,w=48,h=53}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,89,17,41,56),
-        arm::app::object_detection::DetectionResult(0.99,27,81,48,53)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 89, 17, 41, 56),
+                                arm::app::object_detection::DetectionResult(0.99, 27, 81, 48, 53)});
     /* Img2
     0)  (0.998107) -> Detection box: {x=87,y=35,w=53,h=64}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,87,35,53,64)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 87, 35, 53, 64)});
     /* Img3
     0)  (0.999244) -> Detection box: {x=105,y=73,w=58,h=66}
     1)  (0.985984) -> Detection box: {x=34,y=40,w=70,h=95}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,105,73,58,66),
-        arm::app::object_detection::DetectionResult(0.98,34,40,70,95)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 105, 73, 58, 66),
+                                arm::app::object_detection::DetectionResult(0.98, 34, 40, 70, 95)});
     /* Img4
     0)  (0.993294) -> Detection box: {x=22,y=43,w=39,h=53}
     1)  (0.992021) -> Detection box: {x=63,y=60,w=38,h=45}
     */
-    expected_results.push_back({
-        arm::app::object_detection::DetectionResult(0.99,22,43,39,53),
-        arm::app::object_detection::DetectionResult(0.99,63,60,38,45)
-    });
+    expected_results.push_back({arm::app::object_detection::DetectionResult(0.99, 22, 43, 39, 53),
+                                arm::app::object_detection::DetectionResult(0.99, 63, 60, 38, 45)});
 }
 
 bool RunInference(arm::app::Model& model, const uint8_t imageData[])
@@ -73,41 +66,43 @@
     TfLiteTensor* inputTensor = model.GetInputTensor(0);
     REQUIRE(inputTensor);
 
-    const size_t copySz = inputTensor->bytes < IMAGE_DATA_SIZE ?
-                            inputTensor->bytes : IMAGE_DATA_SIZE;
+    const size_t copySz =
+        inputTensor->bytes < IMAGE_DATA_SIZE ? inputTensor->bytes : IMAGE_DATA_SIZE;
 
-    arm::app::image::RgbToGrayscale(imageData,inputTensor->data.uint8,copySz);
+    arm::app::image::RgbToGrayscale(imageData, inputTensor->data.uint8, copySz);
 
-    if(model.IsDataSigned()){
+    if (model.IsDataSigned()) {
         arm::app::image::ConvertImgToInt8(inputTensor->data.data, copySz);
     }
 
     return model.RunInference();
 }
 
-template<typename T>
-void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance) {
+template <typename T>
+void TestInferenceDetectionResults(int imageIdx, arm::app::Model& model, T tolerance)
+{
 
     std::vector<arm::app::object_detection::DetectionResult> results;
-    auto image = get_img_array(imageIdx);
+    auto image = GetImgArray(imageIdx);
 
     TfLiteIntArray* inputShape = model.GetInputShape(0);
-    auto nCols = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx];
-    auto nRows = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx];
+    auto nCols                 = inputShape->data[arm::app::YoloFastestModel::ms_inputColsIdx];
+    auto nRows                 = inputShape->data[arm::app::YoloFastestModel::ms_inputRowsIdx];
 
     REQUIRE(RunInference(model, image));
 
-
     std::vector<TfLiteTensor*> output_arr{model.GetOutputTensor(0), model.GetOutputTensor(1)};
-    for (size_t i =0; i < output_arr.size(); i++) {
+    for (size_t i = 0; i < output_arr.size(); i++) {
         REQUIRE(output_arr[i]);
         REQUIRE(tflite::GetTensorData<T>(output_arr[i]));
     }
 
-    const arm::app::object_detection::PostProcessParams postProcessParams {
-            nRows, nCols, arm::app::object_detection::originalImageSize,
-            arm::app::object_detection::anchor1, arm::app::object_detection::anchor2
-    };
+    const arm::app::object_detection::PostProcessParams postProcessParams{
+        nRows,
+        nCols,
+        arm::app::object_detection::originalImageSize,
+        arm::app::object_detection::anchor1,
+        arm::app::object_detection::anchor2};
     arm::app::DetectorPostProcess postp{output_arr[0], output_arr[1], results, postProcessParams};
     postp.DoPostProcess();
 
@@ -117,18 +112,21 @@
     /* Validate got the same number of boxes */
     REQUIRE(results.size() == expected_results[imageIdx].size());
 
-
-    for (int i=0; i < (int)results.size(); i++) {
+    for (int i = 0; i < (int)results.size(); i++) {
         /* Validate confidence and box dimensions */
-        REQUIRE(std::abs(results[i].m_normalisedVal - expected_results[imageIdx][i].m_normalisedVal) < 0.1);
-        REQUIRE(static_cast<int>(results[i].m_x0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance));
-        REQUIRE(static_cast<int>(results[i].m_y0) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance));
-        REQUIRE(static_cast<int>(results[i].m_w) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance));
-        REQUIRE(static_cast<int>(results[i].m_h) == Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance));
+        REQUIRE(std::abs(results[i].m_normalisedVal -
+                         expected_results[imageIdx][i].m_normalisedVal) < 0.1);
+        REQUIRE(static_cast<int>(results[i].m_x0) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_x0)).epsilon(tolerance));
+        REQUIRE(static_cast<int>(results[i].m_y0) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_y0)).epsilon(tolerance));
+        REQUIRE(static_cast<int>(results[i].m_w) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_w)).epsilon(tolerance));
+        REQUIRE(static_cast<int>(results[i].m_h) ==
+                Approx(static_cast<int>((T)expected_results[imageIdx][i].m_h)).epsilon(tolerance));
     }
 }
 
-
 TEST_CASE("Running inference with TensorFlow Lite Micro and YoloFastest", "[YoloFastest]")
 {
     SECTION("Executing inferences sequentially")
@@ -142,12 +140,12 @@
                            arm::app::object_detection::GetModelLen()));
         REQUIRE(model.IsInited());
 
-        for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
+        for (uint32_t i = 0; i < NUMBER_OF_FILES; ++i) {
             TestInferenceDetectionResults<uint8_t>(i, model, 1);
         }
     }
 
-    for (uint32_t i = 0 ; i < NUMBER_OF_FILES; ++i) {
+    for (uint32_t i = 0; i < NUMBER_OF_FILES; ++i) {
         DYNAMIC_SECTION("Executing inference with re-init")
         {
             arm::app::YoloFastestModel model{};