diff --git a/examples/graph_yolov3.cpp b/examples/graph_yolov3.cpp
index 3c8ddbf..5c8d342 100644
--- a/examples/graph_yolov3.cpp
+++ b/examples/graph_yolov3.cpp
@@ -22,6 +22,7 @@
  * SOFTWARE.
  */
 #include "arm_compute/graph.h"
+
 #include "support/ToolchainSupport.h"
 #include "utils/CommonGraphOptions.h"
 #include "utils/GraphUtils.h"
@@ -35,8 +36,7 @@
 class GraphYOLOv3Example : public Example
 {
 public:
-    GraphYOLOv3Example()
-        : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3")
+    GraphYOLOv3Example() : cmd_parser(), common_opts(cmd_parser), common_params(), graph(0, "YOLOv3")
     {
     }
 
@@ -50,14 +50,15 @@
         common_params = consume_common_graph_parameters(common_opts);
 
         // Return when help menu is requested
-        if(common_params.help)
+        if (common_params.help)
         {
             cmd_parser.print_help(argv[0]);
             return false;
         }
 
         // Checks
-        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type), "QASYMM8 not supported for this graph");
+        ARM_COMPUTE_EXIT_ON_MSG(arm_compute::is_data_type_quantized_asymmetric(common_params.data_type),
+                                "QASYMM8 not supported for this graph");
 
         // Print parameter values
         std::cout << common_params << std::endl;
@@ -69,331 +70,322 @@
         std::unique_ptr<IPreprocessor> preprocessor = std::make_unique<TFPreproccessor>(0.f);
 
         // Create input descriptor
-        const TensorShape tensor_shape     = permute_shape(TensorShape(608U, 608U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
-        TensorDescriptor  input_descriptor = TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
+        const TensorShape tensor_shape =
+            permute_shape(TensorShape(608U, 608U, 3U, 1U), DataLayout::NCHW, common_params.data_layout);
+        TensorDescriptor input_descriptor =
+            TensorDescriptor(tensor_shape, common_params.data_type).set_layout(common_params.data_layout);
 
         // Set weights trained layout
         const DataLayout weights_layout = DataLayout::NCHW;
 
-        graph << common_params.target
-              << common_params.fast_math_hint
+        graph << common_params.target << common_params.fast_math_hint
               << InputLayer(input_descriptor, get_input_accessor(common_params, std::move(preprocessor), false));
         std::pair<SubStream, SubStream> intermediate_layers = darknet53(data_path, weights_layout);
-        graph << ConvolutionLayer(
-                  1U, 1U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_53_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(1, 1, 0, 0))
-              .set_name("conv2d_53")
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_53/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_53/LeakyRelu")
-              << ConvolutionLayer(
-                  3U, 3U, 1024U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_54_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(1, 1, 1, 1))
-              .set_name("conv2d_54")
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_54/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_54/LeakyRelu")
-              << ConvolutionLayer(
-                  1U, 1U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_55_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(1, 1, 0, 0))
-              .set_name("conv2d_55")
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_55/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_55/LeakyRelu")
-              << ConvolutionLayer(
-                  3U, 3U, 1024U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_56_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(1, 1, 1, 1))
-              .set_name("conv2d_56")
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_56/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_56/LeakyRelu")
-              << ConvolutionLayer(
-                  1U, 1U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_57_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(1, 1, 0, 0))
-              .set_name("conv2d_57")
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_57/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_57/LeakyRelu");
+        graph
+            << ConvolutionLayer(
+                   1U, 1U, 512U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_53_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_53")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_53_beta.npy"), 0.000001f)
+                   .set_name("conv2d_53/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_53/LeakyRelu")
+            << ConvolutionLayer(
+                   3U, 3U, 1024U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_54_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_54")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_54_beta.npy"), 0.000001f)
+                   .set_name("conv2d_54/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_54/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 512U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_55_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_55")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_55_beta.npy"), 0.000001f)
+                   .set_name("conv2d_55/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_55/LeakyRelu")
+            << ConvolutionLayer(
+                   3U, 3U, 1024U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_56_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_56")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_56_beta.npy"), 0.000001f)
+                   .set_name("conv2d_56/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_56/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 512U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_57_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_57")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_57_beta.npy"), 0.000001f)
+                   .set_name("conv2d_57/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_57/LeakyRelu");
         SubStream route_1(graph);
-        graph << ConvolutionLayer(
-                  3U, 3U, 1024U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_58_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(1, 1, 1, 1))
-              .set_name("conv2d_58")
-              << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_58/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_58/LeakyRelu")
-              << ConvolutionLayer(
-                  1U, 1U, 255U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_w.npy", weights_layout),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_b.npy", weights_layout),
-                  PadStrideInfo(1, 1, 0, 0))
-              .set_name("conv2d_59")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_59/Linear")
-              << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo1")
-              << OutputLayer(get_output_accessor(common_params, 5));
+        graph
+            << ConvolutionLayer(
+                   3U, 3U, 1024U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_58_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_58")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_58_beta.npy"), 0.000001f)
+                   .set_name("conv2d_58/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_58/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 255U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_w.npy", weights_layout),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_59_b.npy", weights_layout),
+                   PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_59")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f))
+                   .set_name("conv2d_59/Linear")
+            << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo1")
+            << OutputLayer(get_output_accessor(common_params, 5));
         route_1 << ConvolutionLayer(
-                    1U, 1U, 256U,
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_60_w.npy", weights_layout),
-                    std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                    PadStrideInfo(1, 1, 0, 0))
-                .set_name("conv2d_60")
+                       1U, 1U, 256U,
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_60_w.npy", weights_layout),
+                       std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                       .set_name("conv2d_60")
                 << BatchNormalizationLayer(
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_mean.npy"),
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_var.npy"),
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"),
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_beta.npy"),
-                    0.000001f)
-                .set_name("conv2d_59/BatchNorm")
-                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_60/LeakyRelu")
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_mean.npy"),
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_var.npy"),
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_gamma.npy"),
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_59_beta.npy"),
+                       0.000001f)
+                       .set_name("conv2d_59/BatchNorm")
+                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                       .set_name("conv2d_60/LeakyRelu")
                 << ResizeLayer(InterpolationPolicy::NEAREST_NEIGHBOR, 2, 2).set_name("Upsample_60");
         SubStream concat_1(route_1);
-        concat_1 << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1")
-                 << ConvolutionLayer(
-                     1U, 1U, 256U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_61")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_60/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_61/LeakyRelu")
-                 << ConvolutionLayer(
-                     3U, 3U, 512U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_62_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 1, 1))
-                 .set_name("conv2d_62")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_61/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_62/LeakyRelu")
-                 << ConvolutionLayer(
-                     1U, 1U, 256U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_63_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_63")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_62/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_63/LeakyRelu")
-                 << ConvolutionLayer(
-                     3U, 3U, 512U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_64_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 1, 1))
-                 .set_name("conv2d_64")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_63/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_64/LeakyRelu")
-                 << ConvolutionLayer(
-                     1U, 1U, 256U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_65_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_65")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_65/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_65/LeakyRelu");
+        concat_1
+            << ConcatLayer(std::move(route_1), std::move(intermediate_layers.second)).set_name("Route1")
+            << ConvolutionLayer(
+                   1U, 1U, 256U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_61_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_61")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_60_beta.npy"), 0.000001f)
+                   .set_name("conv2d_60/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_61/LeakyRelu")
+            << ConvolutionLayer(
+                   3U, 3U, 512U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_62_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_62")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_61_beta.npy"), 0.000001f)
+                   .set_name("conv2d_61/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_62/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 256U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_63_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_63")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_62_beta.npy"), 0.000001f)
+                   .set_name("conv2d_62/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_63/LeakyRelu")
+            << ConvolutionLayer(
+                   3U, 3U, 512U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_64_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_64")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_63_beta.npy"), 0.000001f)
+                   .set_name("conv2d_63/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_64/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 256U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_65_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_65")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_64_beta.npy"), 0.000001f)
+                   .set_name("conv2d_65/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_65/LeakyRelu");
         SubStream route_2(concat_1);
-        concat_1 << ConvolutionLayer(
-                     3U, 3U, 512U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_66_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 1, 1))
-                 .set_name("conv2d_66")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_65/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_66/LeakyRelu")
-                 << ConvolutionLayer(
-                     1U, 1U, 255U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_w.npy", weights_layout),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_b.npy", weights_layout),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_67")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_67/Linear")
-                 << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo2")
-                 << OutputLayer(get_output_accessor(common_params, 5));
+        concat_1
+            << ConvolutionLayer(
+                   3U, 3U, 512U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_66_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_66")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_65_beta.npy"), 0.000001f)
+                   .set_name("conv2d_65/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_66/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 255U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_w.npy", weights_layout),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_67_b.npy", weights_layout),
+                   PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_67")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f))
+                   .set_name("conv2d_67/Linear")
+            << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo2")
+            << OutputLayer(get_output_accessor(common_params, 5));
         route_2 << ConvolutionLayer(
-                    1U, 1U, 128U,
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_68_w.npy", weights_layout),
-                    std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                    PadStrideInfo(1, 1, 0, 0))
-                .set_name("conv2d_68")
+                       1U, 1U, 128U,
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_68_w.npy", weights_layout),
+                       std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                       .set_name("conv2d_68")
                 << BatchNormalizationLayer(
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_mean.npy"),
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_var.npy"),
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"),
-                    get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_beta.npy"),
-                    0.000001f)
-                .set_name("conv2d_66/BatchNorm")
-                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_68/LeakyRelu")
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_mean.npy"),
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_var.npy"),
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_gamma.npy"),
+                       get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_66_beta.npy"),
+                       0.000001f)
+                       .set_name("conv2d_66/BatchNorm")
+                << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                       .set_name("conv2d_68/LeakyRelu")
                 << ResizeLayer(InterpolationPolicy::NEAREST_NEIGHBOR, 2, 2).set_name("Upsample_68");
         SubStream concat_2(route_2);
-        concat_2 << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2")
-                 << ConvolutionLayer(
-                     1U, 1U, 128U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_69")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_67/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_69/LeakyRelu")
-                 << ConvolutionLayer(
-                     3U, 3U, 256U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_70_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 1, 1))
-                 .set_name("conv2d_70")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_68/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_70/LeakyRelu")
-                 << ConvolutionLayer(
-                     1U, 1U, 128U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_71_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_71")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_69/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_71/LeakyRelu")
-                 << ConvolutionLayer(
-                     3U, 3U, 256U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_72_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 1, 1))
-                 .set_name("conv2d_72")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_70/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_72/LeakyRelu")
-                 << ConvolutionLayer(
-                     1U, 1U, 128U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_73_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_73")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_71/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_73/LeakyRelu")
-                 << ConvolutionLayer(
-                     3U, 3U, 256U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_74_w.npy", weights_layout),
-                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                     PadStrideInfo(1, 1, 1, 1))
-                 .set_name("conv2d_74")
-                 << BatchNormalizationLayer(
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_mean.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_var.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_beta.npy"),
-                     0.000001f)
-                 .set_name("conv2d_72/BatchNorm")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_74/LeakyRelu")
-                 << ConvolutionLayer(
-                     1U, 1U, 255U,
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_w.npy", weights_layout),
-                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_b.npy", weights_layout),
-                     PadStrideInfo(1, 1, 0, 0))
-                 .set_name("conv2d_75")
-                 << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f)).set_name("conv2d_75/Linear")
-                 << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo3")
-                 << OutputLayer(get_output_accessor(common_params, 5));
+        concat_2
+            << ConcatLayer(std::move(route_2), std::move(intermediate_layers.first)).set_name("Route2")
+            << ConvolutionLayer(
+                   1U, 1U, 128U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_69_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_69")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_67_beta.npy"), 0.000001f)
+                   .set_name("conv2d_67/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_69/LeakyRelu")
+            << ConvolutionLayer(
+                   3U, 3U, 256U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_70_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_70")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_68_beta.npy"), 0.000001f)
+                   .set_name("conv2d_68/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_70/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 128U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_71_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_71")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_69_beta.npy"), 0.000001f)
+                   .set_name("conv2d_69/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_71/LeakyRelu")
+            << ConvolutionLayer(
+                   3U, 3U, 256U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_72_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_72")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_70_beta.npy"), 0.000001f)
+                   .set_name("conv2d_70/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_72/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 128U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_73_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_73")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_71_beta.npy"), 0.000001f)
+                   .set_name("conv2d_71/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_73/LeakyRelu")
+            << ConvolutionLayer(
+                   3U, 3U, 256U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_74_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_74")
+            << BatchNormalizationLayer(
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_mean.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_var.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_gamma.npy"),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_72_beta.npy"), 0.000001f)
+                   .set_name("conv2d_72/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_74/LeakyRelu")
+            << ConvolutionLayer(
+                   1U, 1U, 255U,
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_w.npy", weights_layout),
+                   get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_75_b.npy", weights_layout),
+                   PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_75")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 1.f))
+                   .set_name("conv2d_75/Linear")
+            << YOLOLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC, 0.1f)).set_name("Yolo3")
+            << OutputLayer(get_output_accessor(common_params, 5));
 
         // Finalize graph
         GraphConfig config;
@@ -422,64 +414,64 @@
     std::pair<SubStream, SubStream> darknet53(const std::string &data_path, DataLayout weights_layout)
     {
         graph << ConvolutionLayer(
-                  3U, 3U, 32U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(1, 1, 1, 1))
-              .set_name("conv2d_1/Conv2D")
+                     3U, 3U, 32U,
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_1_w.npy", weights_layout),
+                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                     .set_name("conv2d_1/Conv2D")
               << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_1/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_1/LeakyRelu")
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_mean.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_var.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_gamma.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_1_beta.npy"),
+                     0.000001f)
+                     .set_name("conv2d_1/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                     .set_name("conv2d_1/LeakyRelu")
               << ConvolutionLayer(
-                  3U, 3U, 64U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(2, 2, 1, 1))
-              .set_name("conv2d_2/Conv2D")
+                     3U, 3U, 64U,
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_2_w.npy", weights_layout),
+                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+                     .set_name("conv2d_2/Conv2D")
               << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_2/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_2/LeakyRelu");
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_mean.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_var.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_gamma.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_2_beta.npy"),
+                     0.000001f)
+                     .set_name("conv2d_2/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                     .set_name("conv2d_2/LeakyRelu");
         darknet53_block(data_path, "3", weights_layout, 32U);
         graph << ConvolutionLayer(
-                  3U, 3U, 128U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(2, 2, 1, 1))
-              .set_name("conv2d_5/Conv2D")
+                     3U, 3U, 128U,
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_5_w.npy", weights_layout),
+                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+                     .set_name("conv2d_5/Conv2D")
               << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_5/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_5/LeakyRelu");
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_mean.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_var.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_gamma.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_5_beta.npy"),
+                     0.000001f)
+                     .set_name("conv2d_5/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                     .set_name("conv2d_5/LeakyRelu");
         darknet53_block(data_path, "6", weights_layout, 64U);
         darknet53_block(data_path, "8", weights_layout, 64U);
         graph << ConvolutionLayer(
-                  3U, 3U, 256U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(2, 2, 1, 1))
-              .set_name("conv2d_10/Conv2D")
+                     3U, 3U, 256U,
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_10_w.npy", weights_layout),
+                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+                     .set_name("conv2d_10/Conv2D")
               << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_10/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_10/LeakyRelu");
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_mean.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_var.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_gamma.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_10_beta.npy"),
+                     0.000001f)
+                     .set_name("conv2d_10/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                     .set_name("conv2d_10/LeakyRelu");
         darknet53_block(data_path, "11", weights_layout, 128U);
         darknet53_block(data_path, "13", weights_layout, 128U);
         darknet53_block(data_path, "15", weights_layout, 128U);
@@ -490,19 +482,19 @@
         darknet53_block(data_path, "25", weights_layout, 128U);
         SubStream layer_36(graph);
         graph << ConvolutionLayer(
-                  3U, 3U, 512U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(2, 2, 1, 1))
-              .set_name("conv2d_27/Conv2D")
+                     3U, 3U, 512U,
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_27_w.npy", weights_layout),
+                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+                     .set_name("conv2d_27/Conv2D")
               << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_27/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_27/LeakyRelu");
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_mean.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_var.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_gamma.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_27_beta.npy"),
+                     0.000001f)
+                     .set_name("conv2d_27/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                     .set_name("conv2d_27/LeakyRelu");
         darknet53_block(data_path, "28", weights_layout, 256U);
         darknet53_block(data_path, "30", weights_layout, 256U);
         darknet53_block(data_path, "32", weights_layout, 256U);
@@ -513,19 +505,19 @@
         darknet53_block(data_path, "42", weights_layout, 256U);
         SubStream layer_61(graph);
         graph << ConvolutionLayer(
-                  3U, 3U, 1024U,
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout),
-                  std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                  PadStrideInfo(2, 2, 1, 1))
-              .set_name("conv2d_44/Conv2D")
+                     3U, 3U, 1024U,
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/conv2d_44_w.npy", weights_layout),
+                     std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(2, 2, 1, 1))
+                     .set_name("conv2d_44/Conv2D")
               << BatchNormalizationLayer(
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"),
-                  get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"),
-                  0.000001f)
-              .set_name("conv2d_44/BatchNorm")
-              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_44/LeakyRelu");
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_mean.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_var.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_gamma.npy"),
+                     get_weights_accessor(data_path, "/cnn_data/yolov3_model/batch_normalization_44_beta.npy"),
+                     0.000001f)
+                     .set_name("conv2d_44/BatchNorm")
+              << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                     .set_name("conv2d_44/LeakyRelu");
         darknet53_block(data_path, "45", weights_layout, 512U);
         darknet53_block(data_path, "47", weights_layout, 512U);
         darknet53_block(data_path, "49", weights_layout, 512U);
@@ -534,43 +526,48 @@
         return std::pair<SubStream, SubStream>(layer_36, layer_61);
     }
 
-    void darknet53_block(const std::string &data_path, std::string &&param_path, DataLayout weights_layout,
-                         unsigned int filter_size)
+    void darknet53_block(const std::string &data_path,
+                         std::string      &&param_path,
+                         DataLayout         weights_layout,
+                         unsigned int       filter_size)
     {
-        std::string total_path  = "/cnn_data/yolov3_model/";
-        std::string param_path2 = arm_compute::support::cpp11::to_string(arm_compute::support::cpp11::stoi(param_path) + 1);
-        SubStream   i_a(graph);
-        SubStream   i_b(graph);
+        std::string total_path = "/cnn_data/yolov3_model/";
+        std::string param_path2 =
+            arm_compute::support::cpp11::to_string(arm_compute::support::cpp11::stoi(param_path) + 1);
+        SubStream i_a(graph);
+        SubStream i_b(graph);
         i_a << ConvolutionLayer(
-                1U, 1U, filter_size,
-                get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout),
-                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                PadStrideInfo(1, 1, 0, 0))
-            .set_name("conv2d_" + param_path + "/Conv2D")
+                   1U, 1U, filter_size,
+                   get_weights_accessor(data_path, total_path + "conv2d_" + param_path + "_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 0, 0))
+                   .set_name("conv2d_" + param_path + "/Conv2D")
             << BatchNormalizationLayer(
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"),
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"),
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"),
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"),
-                0.000001f)
-            .set_name("conv2d_" + param_path + "/BatchNorm")
-            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path + "/LeakyRelu")
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_mean.npy"),
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_var.npy"),
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_gamma.npy"),
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path + "_beta.npy"),
+                   0.000001f)
+                   .set_name("conv2d_" + param_path + "/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_" + param_path + "/LeakyRelu")
             << ConvolutionLayer(
-                3U, 3U, filter_size * 2,
-                get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout),
-                std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr),
-                PadStrideInfo(1, 1, 1, 1))
-            .set_name("conv2d_" + param_path2 + "/Conv2D")
+                   3U, 3U, filter_size * 2,
+                   get_weights_accessor(data_path, total_path + "conv2d_" + param_path2 + "_w.npy", weights_layout),
+                   std::unique_ptr<arm_compute::graph::ITensorAccessor>(nullptr), PadStrideInfo(1, 1, 1, 1))
+                   .set_name("conv2d_" + param_path2 + "/Conv2D")
             << BatchNormalizationLayer(
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"),
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"),
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"),
-                get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"),
-                0.000001f)
-            .set_name("conv2d_" + param_path2 + "/BatchNorm")
-            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f)).set_name("conv2d_" + param_path2 + "/LeakyRelu");
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_mean.npy"),
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_var.npy"),
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_gamma.npy"),
+                   get_weights_accessor(data_path, total_path + "batch_normalization_" + param_path2 + "_beta.npy"),
+                   0.000001f)
+                   .set_name("conv2d_" + param_path2 + "/BatchNorm")
+            << ActivationLayer(ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f))
+                   .set_name("conv2d_" + param_path2 + "/LeakyRelu");
 
-        graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add).set_name("").set_name("add_" + param_path + "_" + param_path2);
+        graph << EltwiseLayer(std::move(i_a), std::move(i_b), EltwiseOperation::Add)
+                     .set_name("")
+                     .set_name("add_" + param_path + "_" + param_path2);
     }
 };