diff --git a/misc/opencv-dnn-onnx-inference/.gitignore b/misc/opencv-dnn-onnx-inference/.gitignore
new file mode 100644
index 0000000..4a0b530
--- /dev/null
+++ b/misc/opencv-dnn-onnx-inference/.gitignore
@@ -0,0 +1,74 @@
+# This file is used to ignore files which are generated
+# ----------------------------------------------------------------------------
+
+*~
+*.autosave
+*.a
+*.core
+*.moc
+*.o
+*.obj
+*.orig
+*.rej
+*.so
+*.so.*
+*_pch.h.cpp
+*_resource.rc
+*.qm
+.#*
+*.*#
+core
+!core/
+tags
+.DS_Store
+.directory
+*.debug
+Makefile*
+*.prl
+*.app
+moc_*.cpp
+ui_*.h
+qrc_*.cpp
+Thumbs.db
+*.res
+*.rc
+/.qmake.cache
+/.qmake.stash
+
+# qtcreator generated files
+*.pro.user*
+CMakeLists.txt.user*
+
+# xemacs temporary files
+*.flc
+
+# Vim temporary files
+.*.swp
+
+# Visual Studio generated files
+*.ib_pdb_index
+*.idb
+*.ilk
+*.pdb
+*.sln
+*.suo
+*.vcproj
+*vcproj.*.*.user
+*.ncb
+*.sdf
+*.opensdf
+*.vcxproj
+*vcxproj.*
+
+# MinGW generated files
+*.Debug
+*.Release
+
+# Python byte code
+*.pyc
+
+# Binaries
+# --------
+*.dll
+*.exe
+
diff --git a/misc/opencv-dnn-onnx-inference/main-opencv-example.cpp b/misc/opencv-dnn-onnx-inference/main-opencv-example.cpp
new file mode 100644
index 0000000..fc469c4
--- /dev/null
+++ b/misc/opencv-dnn-onnx-inference/main-opencv-example.cpp
@@ -0,0 +1,387 @@
+/**
+ * @file yolo_detector.cpp
+ * @brief Yolo Object Detection Sample
+ * @author OpenCV team
+ */
+
+//![includes]
+#include <opencv2/dnn.hpp>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/imgcodecs.hpp>
+#include <fstream>
+#include <sstream>
+#include "iostream"
+#include "common.hpp"
+#include <opencv2/highgui.hpp>
+//![includes]
+
+using namespace cv;
+using namespace cv::dnn;
+
+void getClasses(std::string classesFile);
+void drawPrediction(int classId, float conf, int left, int top, int right, int bottom, Mat& frame);
+void yoloPostProcessing(
+    std::vector<Mat>& outs,
+    std::vector<int>& keep_classIds,
+    std::vector<float>& keep_confidences,
+    std::vector<Rect2d>& keep_boxes,
+    float conf_threshold,
+    float iou_threshold,
+    const std::string& model_name,
+    const int nc
+    );
+
+std::vector<std::string> classes;
+
+
+std::string keys =
+    "{ help  h     |   | Print help message. }"
+    "{ device      | 0 | camera device number. }"
+    "{ model       | onnx/models/yolox_s_inf_decoder.onnx | Default model. }"
+    "{ yolo        | yolox | yolo model version. }"
+    "{ input i     | | Path to input image or video file. Skip this argument to capture frames from a camera. }"
+    "{ classes     | | Optional path to a text file with names of classes to label detected objects. }"
+    "{ nc          | 80 | Number of classes. Default is 80 (coming from COCO dataset). }"
+    "{ thr         | .5 | Confidence threshold. }"
+    "{ nms         | .4 | Non-maximum suppression threshold. }"
+    "{ mean        | 0.0 | Normalization constant. }"
+    "{ scale       | 1.0 | Preprocess input image by multiplying on a scale factor. }"
+    "{ width       | 640 | Preprocess input image by resizing to a specific width. }"
+    "{ height      | 640 | Preprocess input image by resizing to a specific height. }"
+    "{ rgb         | 1 | Indicate that model works with RGB input images instead BGR ones. }"
+    "{ padvalue    | 114.0 | padding value. }"
+    "{ paddingmode | 2 | Choose one of computation backends: "
+    "0: resize to required input size without extra processing, "
+    "1: Image will be cropped after resize, "
+    "2: Resize image to the desired size while preserving the aspect ratio of original image }"
+    "{ backend     |  0 | Choose one of computation backends: "
+    "0: automatically (by default), "
+    "1: Halide language (http://halide-lang.org/), "
+    "2: Intel's Deep Learning Inference Engine (https://software.intel.com/openvino-toolkit), "
+    "3: OpenCV implementation, "
+    "4: VKCOM, "
+    "5: CUDA }"
+    "{ target      | 0 | Choose one of target computation devices: "
+    "0: CPU target (by default), "
+    "1: OpenCL, "
+    "2: OpenCL fp16 (half-float precision), "
+    "3: VPU, "
+    "4: Vulkan, "
+    "6: CUDA, "
+    "7: CUDA fp16 (half-float preprocess) }"
+    "{ async       | 0 | Number of asynchronous forwards at the same time. "
+    "Choose 0 for synchronous mode }";
+
+void getClasses(std::string classesFile)
+{
+    std::ifstream ifs(classesFile.c_str());
+    if (!ifs.is_open())
+        CV_Error(Error::StsError, "File " + classesFile  + " not found");
+    std::string line;
+    while (std::getline(ifs, line))
+        classes.push_back(line);
+}
+
+void drawPrediction(int classId, float conf, int left, int top, int right, int bottom, Mat& frame)
+{
+    rectangle(frame, Point(left, top), Point(right, bottom), Scalar(0, 255, 0));
+
+    std::string label = format("%.2f", conf);
+    if (!classes.empty())
+    {
+        CV_Assert(classId < (int)classes.size());
+        label = classes[classId] + ": " + label;
+    }
+
+    int baseLine;
+    Size labelSize = getTextSize(label, FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+
+    top = max(top, labelSize.height);
+    rectangle(frame, Point(left, top - labelSize.height),
+              Point(left + labelSize.width, top + baseLine), Scalar::all(255), FILLED);
+    putText(frame, label, Point(left, top), FONT_HERSHEY_SIMPLEX, 0.5, Scalar());
+}
+
+void yoloPostProcessing(
+    std::vector<Mat>& outs,
+    std::vector<int>& keep_classIds,
+    std::vector<float>& keep_confidences,
+    std::vector<Rect2d>& keep_boxes,
+    float conf_threshold,
+    float iou_threshold,
+    const std::string& model_name,
+    const int nc=80)
+{
+    // Retrieve
+    std::vector<int> classIds;
+    std::vector<float> confidences;
+    std::vector<Rect2d> boxes;
+
+    if (model_name == "yolov8" || model_name == "yolov10" ||
+        model_name == "yolov9")
+    {
+        cv::transposeND(outs[0], {0, 2, 1}, outs[0]);
+    }
+
+    if (model_name == "yolonas")
+    {
+        // outs contains 2 elemets of shape [1, 8400, 80] and [1, 8400, 4]. Concat them to get [1, 8400, 84]
+        Mat concat_out;
+        // squeeze the first dimension
+        outs[0] = outs[0].reshape(1, outs[0].size[1]);
+        outs[1] = outs[1].reshape(1, outs[1].size[1]);
+        cv::hconcat(outs[1], outs[0], concat_out);
+        outs[0] = concat_out;
+        // remove the second element
+        outs.pop_back();
+        // unsqueeze the first dimension
+        outs[0] = outs[0].reshape(0, std::vector<int>{1, 8400, nc + 4});
+    }
+
+    // assert if last dim is 85 or 84
+    CV_CheckEQ(outs[0].dims, 3, "Invalid output shape. The shape should be [1, #anchors, 85 or 84]");
+    CV_CheckEQ((outs[0].size[2] == nc + 5 || outs[0].size[2] == 80 + 4), true, "Invalid output shape: ");
+
+    for (auto preds : outs)
+    {
+        preds = preds.reshape(1, preds.size[1]); // [1, 8400, 85] -> [8400, 85]
+        for (int i = 0; i < preds.rows; ++i)
+        {
+            // filter out non object
+            float obj_conf = (model_name == "yolov8" || model_name == "yolonas" ||
+                              model_name == "yolov9" || model_name == "yolov10") ? 1.0f : preds.at<float>(i, 4) ;
+            if (obj_conf < conf_threshold)
+                continue;
+
+            Mat scores = preds.row(i).colRange((model_name == "yolov8" || model_name == "yolonas" || model_name == "yolov9" || model_name == "yolov10") ? 4 : 5, preds.cols);
+            double conf;
+            Point maxLoc;
+            minMaxLoc(scores, 0, &conf, 0, &maxLoc);
+
+            conf = (model_name == "yolov8" || model_name == "yolonas" || model_name == "yolov9" || model_name == "yolov10") ? conf : conf * obj_conf;
+            if (conf < conf_threshold)
+                continue;
+
+            // get bbox coords
+            float* det = preds.ptr<float>(i);
+            double cx = det[0];
+            double cy = det[1];
+            double w = det[2];
+            double h = det[3];
+
+            // [x1, y1, x2, y2]
+            if (model_name == "yolonas" || model_name == "yolov10"){
+                boxes.push_back(Rect2d(cx, cy, w, h));
+            } else {
+                boxes.push_back(Rect2d(cx - 0.5 * w, cy - 0.5 * h,
+                                       cx + 0.5 * w, cy + 0.5 * h));
+            }
+            classIds.push_back(maxLoc.x);
+            confidences.push_back(static_cast<float>(conf));
+        }
+    }
+
+    // NMS
+    std::vector<int> keep_idx;
+    NMSBoxes(boxes, confidences, conf_threshold, iou_threshold, keep_idx);
+
+    for (auto i : keep_idx)
+    {
+        keep_classIds.push_back(classIds[i]);
+        keep_confidences.push_back(confidences[i]);
+        keep_boxes.push_back(boxes[i]);
+    }
+}
+
+/**
+ * @function main
+ * @brief Main function
+ */
+int main(int argc, char** argv)
+{
+    CommandLineParser parser(argc, argv, keys);
+    parser.about("Use this script to run object detection deep learning networks using OpenCV.");
+    if (parser.has("help"))
+    {
+        parser.printMessage();
+        return 0;
+    }
+
+    CV_Assert(parser.has("model"));
+    CV_Assert(parser.has("yolo"));
+    // if model is default, use findFile to get the full path otherwise use the given path
+    std::string weightPath = "yolov8n.onnx";
+    std::string yolo_model = "yolov8";
+    int nc = parser.get<int>("nc");
+
+    float confThreshold = parser.get<float>("thr");
+    float nmsThreshold = parser.get<float>("nms");
+    //![preprocess_params]
+    float paddingValue = parser.get<float>("padvalue");
+    bool swapRB = parser.get<bool>("rgb");
+    int inpWidth = parser.get<int>("width");
+    int inpHeight = parser.get<int>("height");
+    Scalar scale = parser.get<float>("scale");
+    Scalar mean = parser.get<Scalar>("mean");
+    ImagePaddingMode paddingMode = static_cast<ImagePaddingMode>(parser.get<int>("paddingmode"));
+    //![preprocess_params]
+
+    // check if yolo model is valid
+    if (yolo_model != "yolov5" && yolo_model != "yolov6"
+        && yolo_model != "yolov7" && yolo_model != "yolov8"
+        && yolo_model != "yolov10" && yolo_model !="yolov9"
+        && yolo_model != "yolox" && yolo_model != "yolonas")
+        CV_Error(Error::StsError, "Invalid yolo model: " + yolo_model);
+
+    // get classes
+    if (parser.has("classes"))
+    {
+        getClasses(findFile(parser.get<String>("classes")));
+    }
+
+    // load model
+    //![read_net]
+    Net net = readNet(weightPath);
+    int backend = parser.get<int>("backend");
+    net.setPreferableBackend(backend);
+    net.setPreferableTarget(parser.get<int>("target"));
+    //![read_net]
+
+    VideoCapture cap;
+    Mat img;
+    bool isImage = false;
+    bool isCamera = false;
+
+    isImage = true;
+    img = imread("bus.png");
+
+    /*
+    // Check if input is given
+    if (parser.has("input"))
+    {
+        String input = parser.get<String>("input");
+        // Check if the input is an image
+        if (input.find(".jpg") != String::npos || input.find(".png") != String::npos)
+        {
+            img = imread(findFile(input));
+            if (img.empty())
+            {
+                CV_Error(Error::StsError, "Cannot read image file: " + input);
+            }
+            isImage = true;
+        }
+        else
+        {
+            cap.open(input);
+            if (!cap.isOpened())
+            {
+                CV_Error(Error::StsError, "Cannot open video " + input);
+            }
+            isCamera = true;
+        }
+    }
+    else
+    {
+        int cameraIndex = parser.get<int>("device");
+        cap.open(cameraIndex);
+        if (!cap.isOpened())
+        {
+            CV_Error(Error::StsError, cv::format("Cannot open camera #%d", cameraIndex));
+        }
+        isCamera = true;
+    }
+    */
+
+    // image pre-processing
+    //![preprocess_call]
+    Size size(inpWidth, inpHeight);
+    Image2BlobParams imgParams(
+        scale,
+        size,
+        mean,
+        swapRB,
+        CV_32F,
+        DNN_LAYOUT_NCHW,
+        paddingMode,
+        paddingValue);
+
+    // rescale boxes back to original image
+    Image2BlobParams paramNet;
+    paramNet.scalefactor = scale;
+    paramNet.size = size;
+    paramNet.mean = mean;
+    paramNet.swapRB = swapRB;
+    paramNet.paddingmode = paddingMode;
+    //![preprocess_call]
+
+    //![forward_buffers]
+    std::vector<Mat> outs;
+    std::vector<int> keep_classIds;
+    std::vector<float> keep_confidences;
+    std::vector<Rect2d> keep_boxes;
+    std::vector<Rect> boxes;
+    //![forward_buffers]
+
+    Mat inp;
+    while (waitKey(1) < 0)
+    {
+
+        if (isCamera)
+            cap >> img;
+        if (img.empty())
+        {
+            std::cout << "Empty frame" << std::endl;
+            waitKey();
+            break;
+        }
+        //![preprocess_call_func]
+        inp = blobFromImageWithParams(img, imgParams);
+        //![preprocess_call_func]
+
+        //![forward]
+        net.setInput(inp);
+        net.forward(outs, net.getUnconnectedOutLayersNames());
+        //![forward]
+
+        //![postprocess]
+        yoloPostProcessing(
+            outs, keep_classIds, keep_confidences, keep_boxes,
+            confThreshold, nmsThreshold,
+            yolo_model,
+            nc);
+        //![postprocess]
+
+        // covert Rect2d to Rect
+        //![draw_boxes]
+        for (auto box : keep_boxes)
+        {
+            boxes.push_back(Rect(cvFloor(box.x), cvFloor(box.y), cvFloor(box.width - box.x), cvFloor(box.height - box.y)));
+        }
+
+        paramNet.blobRectsToImageRects(boxes, boxes, img.size());
+
+        for (size_t idx = 0; idx < boxes.size(); ++idx)
+        {
+            Rect box = boxes[idx];
+            drawPrediction(keep_classIds[idx], keep_confidences[idx], box.x, box.y,
+                           box.width + box.x, box.height + box.y, img);
+        }
+
+        const std::string kWinName = "Yolo Object Detector";
+        namedWindow(kWinName, WINDOW_NORMAL);
+        imshow(kWinName, img);
+        //![draw_boxes]
+
+        outs.clear();
+        keep_classIds.clear();
+        keep_confidences.clear();
+        keep_boxes.clear();
+        boxes.clear();
+
+        if (isImage)
+        {
+            waitKey();
+            break;
+        }
+    }
+}
diff --git a/misc/opencv-dnn-onnx-inference/main.cpp b/misc/opencv-dnn-onnx-inference/main.cpp
new file mode 100644
index 0000000..560df73
--- /dev/null
+++ b/misc/opencv-dnn-onnx-inference/main.cpp
@@ -0,0 +1,262 @@
+#include <QDebug>
+#include <QElapsedTimer>
+#include <opencv2/imgproc.hpp>
+#include <opencv2/opencv.hpp>
+#include <opencv2/dnn.hpp>
+
+#define CONFIDENCE_THRESHOLD 0.2
+
+using namespace std;
+using namespace cv;
+
+// function to create vector of class names
+std::vector<std::string> createClaseNames() {
+    std::vector<std::string> classNames;
+    classNames.push_back("background");
+    classNames.push_back("aeroplane");
+    classNames.push_back("bicycle");
+    classNames.push_back("bird");
+    classNames.push_back("boat");
+    classNames.push_back("bottle");
+    classNames.push_back("bus");
+    classNames.push_back("car");
+    classNames.push_back("cat");
+    classNames.push_back("chair");
+    classNames.push_back("cow");
+    classNames.push_back("diningtable");
+    classNames.push_back("dog");
+    classNames.push_back("horse");
+    classNames.push_back("motorbike");
+    classNames.push_back("person");
+    classNames.push_back("pottedplant");
+    classNames.push_back("sheep");
+    return classNames;
+};
+
+int main(int argc, char *argv[])
+{
+    if (argc < 3) {
+        qDebug() << "Give ONNX model as first argument and image as second one.";
+        return 1;
+    }
+     std::vector<String> classNames = createClaseNames();
+
+    std::string model_filename = argv[1];
+    std::string image_filename = argv[2];
+
+    // Load the ONNX model
+    cv::dnn::Net net = cv::dnn::readNetFromONNX(model_filename);
+
+    // Set backend to OpenCL
+    net.setPreferableBackend(cv::dnn::DNN_BACKEND_DEFAULT);
+    //net.setPreferableTarget(cv::dnn::DNN_TARGET_OPENCL);
+
+    // Load an image and preprocess it
+    cv::Mat image = cv::imread(image_filename);
+    cv::Mat blob = cv::dnn::blobFromImage(image, 1/255.0, cv::Size(640, 640), cv::Scalar(), true, false);
+    cv::imwrite("tmpInput.png", image);
+
+    // Set the input for the network
+    net.setInput(blob);
+    std::vector<cv::Mat> outputs;
+    net.forward(outputs);
+    cv::Mat output = outputs[0];
+
+    std::vector<cv::String> layerNames = net.getLayerNames();
+    std::vector<int> outLayerIndices = net.getUnconnectedOutLayers();
+    for (int index : outLayerIndices) {
+        std::cout << layerNames[index - 1] << std::endl;
+    }
+
+
+    cv::Mat detections = net.forward("output0");
+
+    // print some information about detections
+    std::cout << "dims: " << outputs[0].dims << std::endl;
+    std::cout << "size: " << outputs[0].size << std::endl;
+
+    int num_detections0 = outputs.size();
+    int num_classes0 = outputs[0].size[1];
+    int rows = outputs[0].size[1];
+    qDebug() << "num_detections0:" << num_detections0 << "num_classes0:" << num_classes0 << "rows:" << rows;
+
+    int num_detections = detections.size[2];  // 8400
+    int num_attributes = detections.size[1];  // 14
+    qDebug() << "num_detections:" << num_detections << "num_attributes:" << num_attributes;
+
+
+    detections = detections.reshape(1, num_detections);
+
+    for (int i = 0; i < num_detections; i++) {
+        Mat row = detections.row(i);
+        float box_confidence = row.at<float>(4);
+        float cls_confidence = row.at<float>(5);
+        if (box_confidence > 0.5 && cls_confidence > 0.5) {
+            std::cout << "box_confidence " << box_confidence << " cls_confidence: " << cls_confidence << std::endl;
+        }
+    }
+
+
+
+
+    /*
+    // Output tensor processing
+    // Assuming output tensor has shape [1, 14, 8400]
+    int num_detections = detections.size[2];  // 8400
+    int num_attributes = detections.size[1];  // 14
+    qDebug() << "num_detections:" << num_detections << "num_attributes:" << num_attributes;
+
+    // Extract and print confidence for each detection
+    const float* data = (float*)output.data;
+    for (int i = 0; i < num_detections; i++) {
+        // Confidence value is at index 4 (based on YOLO-like model output format)
+        float confidence = data[i * num_attributes + 3];
+        // Print confidence value
+        if (confidence > 0.5f) {
+            std::cout << "Detection " << i << " confidence: " << confidence << std::endl;
+        }
+    }
+    */
+
+    /*
+    // Assuming outputs is a vector of cv::Mat containing the model's output
+    for (int i = 0; i < outputs[0].size[0]; ++i) {
+        for (int j = 0; j < outputs[0].size[1]; ++j) {
+            float confidence = outputs[0].at<float>(i, j);
+            // Print or use the confidence value
+            std::cout << "Confidence: " << confidence << std::endl;
+        }
+    }
+    */
+
+    /*
+    // Output processing variables
+    std::vector<int> class_ids;
+    std::vector<float> confidences;
+    std::vector<cv::Rect> boxes;
+
+    // Analyze each output
+    for (const auto& output : outputs) {
+        // Each row is a detection: [center_x, center_y, width, height, conf, class1_score, class2_score, ...]
+        const auto* data = (float*)output.data;
+
+        for (int i = 0; i < output.rows; i++, data += output.cols) {
+            float confidence = data[4];  // Objectness confidence
+
+            if (confidence >= 0.5) {  // Apply a confidence threshold
+                cv::Mat scores = output.row(i).colRange(5, output.cols);
+                cv::Point class_id_point;
+                double max_class_score;
+
+                cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id_point);
+                int class_id = class_id_point.x;
+
+                if (max_class_score >= 0.5) {  // Apply a class score threshold
+                    // Get bounding box
+                    int center_x = (int)(data[0] * image.cols);
+                    int center_y = (int)(data[1] * image.rows);
+                    int width = (int)(data[2] * image.cols);
+                    int height = (int)(data[3] * image.rows);
+                    int left = center_x - width / 2;
+                    int top = center_y - height / 2;
+
+                    // Store the results
+                    class_ids.push_back(class_id);
+                    confidences.push_back(confidence);
+                    boxes.push_back(cv::Rect(left, top, width, height));
+                }
+            }
+        }
+    }
+    */
+
+    /*
+    for (int i = 0; i < num_detections; ++i) {
+        for (int a = 0; a < 10; a++) {
+            qDebug() << "confidence:" << confidence << "class_id" << class_id;
+        }
+        //float confidence = outputs[0].at<float>(i, 4);
+        //int class_id = outputs[0].at<float>(i, 5);
+        qDebug() << "confidence:" << confidence << "class_id" << class_id;
+    }
+    */
+
+    /*
+    std::vector<int> class_ids;
+    std::vector<float> confidences;
+    std::vector<cv::Rect> boxes;
+    // Output tensor processing
+    for (size_t i = 0; i < outputs.size(); i++) {
+        float* data = (float*)outputs[i].data;
+        for (int j = 0; j < outputs[i].rows; ++j) {
+            float confidence = data[4]; // Objectness confidence
+            qDebug() <<" Confidence: " << confidence;
+
+            if (confidence >= CONFIDENCE_THRESHOLD) {
+                // Get class with the highest score
+                cv::Mat scores = outputs[i].row(j).colRange(5, num_classes + 5);
+                cv::Point class_id_point;
+                double max_class_score;
+                cv::minMaxLoc(scores, 0, &max_class_score, 0, &class_id_point);
+
+                qDebug() << "Class ID: " << class_id_point.x << " Confidence: " << confidence;
+
+                if (max_class_score > CONFIDENCE_THRESHOLD) {
+                    int center_x = (int)(data[0] * image.cols);
+                    int center_y = (int)(data[1] * image.rows);
+                    int width = (int)(data[2] * image.cols);
+                    int height = (int)(data[3] * image.rows);
+                    int left = center_x - width / 2;
+                    int top = center_y - height / 2;
+
+                    // Store the results
+                    class_ids.push_back(class_id_point.x);
+                    confidences.push_back((float)max_class_score);
+                    boxes.push_back(cv::Rect(left, top, width, height));
+                }
+            }
+            data += num_classes + 5; // Move to the next detection
+        }
+    }
+    */
+
+    /*
+    // Process the output
+    for (int i = 0; i < num_detections; ++i) {
+        float confidence = outputs[0].at<float>(i, 4);
+        int class_id = outputs[0].at<float>(i, 5);
+        // ... extract bounding box coordinates
+        float x_center = outputs[0].at<float>(i, 0);
+        float y_center = outputs[0].at<float>(i, 1);
+        float width = outputs[0].at<float>(i, 2);
+        float height = outputs[0].at<float>(i, 3);
+
+        // Calculate bounding box corners
+        int x = (int)(x_center - width / 2.0);
+        int y = (int)(y_center - height / 2.0);
+        int width_int = (int)width;
+        int height_int = (int)height;
+
+        // Print or use the detected information
+        qDebug() << "Class ID: " << class_id << " Confidence: " << confidence << " Bounding Box: (" << x << ", " << y << ", " << width_int << ", " << height_int << ")";
+    }
+    */
+
+
+    /*
+    // Perform forward pass
+    for (int i = 0; i < 10; i++) {
+        std::vector<cv::Mat> outputs;
+        QElapsedTimer timer;
+        timer.start();
+        net.forward(outputs);
+        qDebug() << "Inference completed in" << timer.elapsed() << "ms.";
+    }
+    */
+
+    // Process the output (YOLO-specific post-processing like NMS is needed)
+    // Outputs contain class scores, bounding boxes, etc.
+
+
+    return 0;
+}
diff --git a/misc/opencv-dnn-onnx-inference/opencv-dnn-onnx-inference.pro b/misc/opencv-dnn-onnx-inference/opencv-dnn-onnx-inference.pro
new file mode 100644
index 0000000..246e760
--- /dev/null
+++ b/misc/opencv-dnn-onnx-inference/opencv-dnn-onnx-inference.pro
@@ -0,0 +1,16 @@
+QT = core
+QT -= gui
+CONFIG += c++17 cmdline concurrent console 
+# link_pkgconfig
+
+SOURCES += \
+        main-opencv-example.cpp
+        #main.cpp
+
+HEADERS += \
+        common.hpp
+
+
+INCLUDEPATH += /opt/opencv-4.10.0/include/opencv4/
+LIBS += /opt/opencv-4.10.0/lib/libopencv_world.so
+QMAKE_LFLAGS += -Wl,-rpath,/opt/opencv-4.10.0/lib
diff --git a/misc/rtsp_ai_player/src-onnx-runtime/aiengineinferenceonnxruntime.cpp b/misc/rtsp_ai_player/src-onnx-runtime/aiengineinferenceonnxruntime.cpp
index 8f7d343..ab74d32 100644
--- a/misc/rtsp_ai_player/src-onnx-runtime/aiengineinferenceonnxruntime.cpp
+++ b/misc/rtsp_ai_player/src-onnx-runtime/aiengineinferenceonnxruntime.cpp
@@ -90,6 +90,9 @@ void AiEngineInferencevOnnxRuntime::performInferenceSlot(cv::Mat frame)
                 qDebug() << "performInferenceSlot() invalid classId =" << detection.classId;
                 continue;
             }
+            else {
+                cv::imwrite("scaledImage.png", scaledImage);
+            }
 
             // Add detected objects to the results
             AiEngineObject object;
diff --git a/misc/rtsp_ai_player/src-opencv-onnx/inference.cpp b/misc/rtsp_ai_player/src-opencv-onnx/inference.cpp
index 0ef6aca..e95f8a1 100644
--- a/misc/rtsp_ai_player/src-opencv-onnx/inference.cpp
+++ b/misc/rtsp_ai_player/src-opencv-onnx/inference.cpp
@@ -28,11 +28,13 @@ std::vector<Detection> Inference::runInference(const cv::Mat &input)
     int rows = outputs[0].size[1];
     int dimensions = outputs[0].size[2];
 
-    bool yolov8 = false;
+    bool yolov8 = true;
     // yolov5 has an output of shape (batchSize, 25200, 85) (Num classes + box[x,y,w,h] + confidence[c])
     // yolov8 has an output of shape (batchSize, 84,  8400) (Num classes + box[x,y,w,h])
+    /*
     if (dimensions > rows) // Check if the shape[2] is more than shape[1] (yolov8)
     {
+        std::cout << "yolov8 = " << yolov8 << std::endl;
         yolov8 = true;
         rows = outputs[0].size[2];
         dimensions = outputs[0].size[1];
@@ -40,6 +42,8 @@ std::vector<Detection> Inference::runInference(const cv::Mat &input)
         outputs[0] = outputs[0].reshape(1, dimensions);
         cv::transpose(outputs[0], outputs[0]);
     }
+    */
+
     float *data = (float *)outputs[0].data;
 
     float x_factor = modelInput.cols / modelShape.width;