diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index a7739be27..bf3017dbe 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -69,6 +69,7 @@ if(NCNN_PIXEL)
             ncnn_add_example(yolov4)
             ncnn_add_example(rvm)
             ncnn_add_example(p2pnet)
+            ncnn_add_example(yolov8)
         endif()
     else()
         message(WARNING "OpenCV not found and NCNN_SIMPLEOCV disabled, examples won't be built")
diff --git a/examples/yolov8.cpp b/examples/yolov8.cpp
new file mode 100644
index 000000000..5b3926582
--- /dev/null
+++ b/examples/yolov8.cpp
@@ -0,0 +1,410 @@
+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2024 THL A29 Limited, a Tencent company. All rights reserved.
+//
+// Copyright (C) 2024 whyb(https://github.com/whyb). All rights reserved.
+//
+// Licensed under the BSD 3-Clause License (the "License"); you may not use this file except
+// in compliance with the License. You may obtain a copy of the License at
+//
+// https://opensource.org/licenses/BSD-3-Clause
+//
+// Unless required by applicable law or agreed to in writing, software distributed
+// under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR
+// CONDITIONS OF ANY KIND, either express or implied. See the License for the
+// specific language governing permissions and limitations under the License.
+
+// ReadMe
+// Convert yolov8 model to ncnn model workflow:
+//
+// step 1:
+// If you don't want to train the model yourself. You should go to the ultralytics website download the pretrained model file.
+// original pretrained model from https://docs.ultralytics.com/models/yolov8/#supported-tasks-and-modes
+//
+// step 2:
+// run this command.
+// conda create --name yolov8 python=3.11
+// conda activate yolov8
+// pip install ultralytics onnx numpy protobuf
+//
+// step 3:
+// save source code file(export_model_to_ncnn.py):
+// from ultralytics import YOLO
+// detection_models = [
+//     ["./Detection-pt/yolov8n.pt", "./Detection-pt/"],
+//     ["./Detection-pt/yolov8s.pt", "./Detection-pt/"],
+//     ["./Detection-pt/yolov8m.pt", "./Detection-pt/"],
+//     ["./Detection-pt/yolov8l.pt", "./Detection-pt/"],
+//     ["./Detection-pt/yolov8x.pt", "./Detection-pt/"]
+// ]
+// for model_dict in detection_models:
+//     model = YOLO(model_dict[0])  # load an official pretrained weight model
+//     model.export(format="ncnn", dynamic=True, save_dir=model_dict[1], simplify=True)
+//
+// step 4:
+// run command: python export_model_to_ncnn.py
+
+#include <memory>
+#include <vector>
+#include <algorithm>
+#include "layer.h"
+#include "net.h"
+
+#include <opencv2/opencv.hpp>
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#include <float.h>
+#include <stdio.h>
+
+#define MAX_STRIDE 32
+
+struct Object
+{
+    cv::Rect_<float> rect;
+    int label;
+    float prob;
+};
+
+static inline float intersection_area(const Object& a, const Object& b)
+{
+    cv::Rect_<float> inter = a.rect & b.rect;
+    return inter.area();
+}
+
+static void qsort_descent_inplace(std::vector<Object>& objects, int left, int right)
+{
+    int i = left;
+    int j = right;
+    float p = objects[(left + right) / 2].prob;
+
+    while (i <= j)
+    {
+        while (objects[i].prob > p)
+            i++;
+
+        while (objects[j].prob < p)
+            j--;
+
+        if (i <= j)
+        {
+            // swap
+            std::swap(objects[i], objects[j]);
+
+            i++;
+            j--;
+        }
+    }
+
+    #pragma omp parallel sections
+    {
+        #pragma omp section
+        {
+            if (left < j) qsort_descent_inplace(objects, left, j);
+        }
+        #pragma omp section
+        {
+            if (i < right) qsort_descent_inplace(objects, i, right);
+        }
+    }
+}
+
+static void qsort_descent_inplace(std::vector<Object>& objects)
+{
+    if (objects.empty())
+        return;
+
+    qsort_descent_inplace(objects, 0, objects.size() - 1);
+}
+
+static void nms_sorted_bboxes(const std::vector<Object>& faceobjects, std::vector<int>& picked, float nms_threshold, bool agnostic = false)
+{
+    picked.clear();
+
+    const int n = faceobjects.size();
+
+    std::vector<float> areas(n);
+    for (int i = 0; i < n; i++)
+    {
+        areas[i] = faceobjects[i].rect.area();
+    }
+
+    for (int i = 0; i < n; i++)
+    {
+        const Object& a = faceobjects[i];
+
+        int keep = 1;
+        for (int j = 0; j < (int)picked.size(); j++)
+        {
+            const Object& b = faceobjects[picked[j]];
+
+            if (!agnostic && a.label != b.label)
+                continue;
+
+            // intersection over union
+            float inter_area = intersection_area(a, b);
+            float union_area = areas[i] + areas[picked[j]] - inter_area;
+            // float IoU = inter_area / union_area
+            if (inter_area / union_area > nms_threshold)
+                keep = 0;
+        }
+
+        if (keep)
+            picked.push_back(i);
+    }
+}
+
+static inline float sigmoid(float x)
+{
+    return static_cast<float>(1.f / (1.f + exp(-x)));
+}
+
+static inline float clampf(float d, float min, float max)
+{
+    const float t = d < min ? min : d;
+    return t > max ? max : t;
+}
+
+static void parse_yolov8_detections(
+    float* inputs, float confidence_threshold,
+    int num_channels, int num_anchors, int num_labels,
+    int infer_img_width, int infer_img_height,
+    std::vector<Object>& objects)
+{
+    std::vector<Object> detections;
+    cv::Mat output = cv::Mat((int)num_channels, (int)num_anchors, CV_32F, inputs).t();
+
+    for (int i = 0; i < num_anchors; i++)
+    {
+        auto row_ptr = output.row(i).ptr<float>();
+        auto bboxes_ptr = row_ptr;
+        auto scores_ptr = row_ptr + 4;
+        auto max_s_ptr = std::max_element(scores_ptr, scores_ptr + num_labels);
+        float score = *max_s_ptr;
+        if (score > confidence_threshold)
+        {
+            float x = *bboxes_ptr++;
+            float y = *bboxes_ptr++;
+            float w = *bboxes_ptr++;
+            float h = *bboxes_ptr;
+
+            float x0 = clampf((x - 0.5f * w), 0.f, (float)infer_img_width);
+            float y0 = clampf((y - 0.5f * h), 0.f, (float)infer_img_height);
+            float x1 = clampf((x + 0.5f * w), 0.f, (float)infer_img_width);
+            float y1 = clampf((y + 0.5f * h), 0.f, (float)infer_img_height);
+
+            cv::Rect_<float> bbox;
+            bbox.x = x0;
+            bbox.y = y0;
+            bbox.width = x1 - x0;
+            bbox.height = y1 - y0;
+            Object object;
+            object.label = max_s_ptr - scores_ptr;
+            object.prob = score;
+            object.rect = bbox;
+            detections.emplace_back(object);
+        }
+    }
+    objects = detections;
+}
+
+static int detect_yolov8(const cv::Mat& bgr, std::vector<Object>& objects)
+{
+    ncnn::Net yolov8;
+
+    yolov8.opt.use_vulkan_compute = true; // if you want detect in hardware, then enable it
+
+    yolov8.load_param("yolov8n.param");
+    yolov8.load_model("yolov8n.bin");
+
+    const int target_size = 640;
+    const float prob_threshold = 0.25f;
+    const float nms_threshold = 0.45f;
+
+    int img_w = bgr.cols;
+    int img_h = bgr.rows;
+
+    // letterbox pad to multiple of MAX_STRIDE
+    int w = img_w;
+    int h = img_h;
+    float scale = 1.f;
+    if (w > h)
+    {
+        scale = (float)target_size / w;
+        w = target_size;
+        h = h * scale;
+    }
+    else
+    {
+        scale = (float)target_size / h;
+        h = target_size;
+        w = w * scale;
+    }
+
+    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR2RGB, img_w, img_h, w, h);
+
+    int wpad = (target_size + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - w;
+    int hpad = (target_size + MAX_STRIDE - 1) / MAX_STRIDE * MAX_STRIDE - h;
+    ncnn::Mat in_pad;
+    ncnn::copy_make_border(in, in_pad, hpad / 2, hpad - hpad / 2, wpad / 2, wpad - wpad / 2, ncnn::BORDER_CONSTANT, 114.f);
+
+    const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
+    in_pad.substract_mean_normalize(0, norm_vals);
+
+    ncnn::Extractor ex = yolov8.create_extractor();
+
+    ex.input("in0", in_pad);
+
+    std::vector<Object> proposals;
+
+    // stride 32
+    {
+        ncnn::Mat out;
+        ex.extract("out0", out);
+
+        std::vector<Object> objects32;
+        const int num_labels = 80; // COCO has detect 80 object labels.
+        parse_yolov8_detections(
+            (float*)out.data, prob_threshold,
+            out.h, out.w, num_labels,
+            in_pad.w, in_pad.h,
+            objects32);
+        proposals.insert(proposals.end(), objects32.begin(), objects32.end());
+    }
+
+    // sort all proposals by score from highest to lowest
+    qsort_descent_inplace(proposals);
+
+    // apply nms with nms_threshold
+    std::vector<int> picked;
+    nms_sorted_bboxes(proposals, picked, nms_threshold);
+
+    int count = picked.size();
+
+    objects.resize(count);
+    for (int i = 0; i < count; i++)
+    {
+        objects[i] = proposals[picked[i]];
+
+        // adjust offset to original unpadded
+        float x0 = (objects[i].rect.x - (wpad / 2)) / scale;
+        float y0 = (objects[i].rect.y - (hpad / 2)) / scale;
+        float x1 = (objects[i].rect.x + objects[i].rect.width - (wpad / 2)) / scale;
+        float y1 = (objects[i].rect.y + objects[i].rect.height - (hpad / 2)) / scale;
+
+        // clip
+        x0 = std::max(std::min(x0, (float)(img_w - 1)), 0.f);
+        y0 = std::max(std::min(y0, (float)(img_h - 1)), 0.f);
+        x1 = std::max(std::min(x1, (float)(img_w - 1)), 0.f);
+        y1 = std::max(std::min(y1, (float)(img_h - 1)), 0.f);
+
+        objects[i].rect.x = x0;
+        objects[i].rect.y = y0;
+        objects[i].rect.width = x1 - x0;
+        objects[i].rect.height = y1 - y0;
+    }
+
+    return 0;
+}
+
+static void draw_objects(const cv::Mat& bgr, const std::vector<Object>& objects)
+{
+    static const char* class_names[] = {
+        "person", "bicycle", "car", "motorcycle", "airplane", "bus", "train", "truck", "boat", "traffic light",
+        "fire hydrant", "stop sign", "parking meter", "bench", "bird", "cat", "dog", "horse", "sheep", "cow",
+        "elephant", "bear", "zebra", "giraffe", "backpack", "umbrella", "handbag", "tie", "suitcase", "frisbee",
+        "skis", "snowboard", "sports ball", "kite", "baseball bat", "baseball glove", "skateboard", "surfboard",
+        "tennis racket", "bottle", "wine glass", "cup", "fork", "knife", "spoon", "bowl", "banana", "apple",
+        "sandwich", "orange", "broccoli", "carrot", "hot dog", "pizza", "donut", "cake", "chair", "couch",
+        "potted plant", "bed", "dining table", "toilet", "tv", "laptop", "mouse", "remote", "keyboard", "cell phone",
+        "microwave", "oven", "toaster", "sink", "refrigerator", "book", "clock", "vase", "scissors", "teddy bear",
+        "hair drier", "toothbrush"
+    };
+
+    static const unsigned char colors[19][3] = {
+        {54, 67, 244},
+        {99, 30, 233},
+        {176, 39, 156},
+        {183, 58, 103},
+        {181, 81, 63},
+        {243, 150, 33},
+        {244, 169, 3},
+        {212, 188, 0},
+        {136, 150, 0},
+        {80, 175, 76},
+        {74, 195, 139},
+        {57, 220, 205},
+        {59, 235, 255},
+        {7, 193, 255},
+        {0, 152, 255},
+        {34, 87, 255},
+        {72, 85, 121},
+        {158, 158, 158},
+        {139, 125, 96}
+    };
+
+    int color_index = 0;
+
+    cv::Mat image = bgr.clone();
+
+    for (size_t i = 0; i < objects.size(); i++)
+    {
+        const Object& obj = objects[i];
+
+        const unsigned char* color = colors[color_index % 19];
+        color_index++;
+
+        cv::Scalar cc(color[0], color[1], color[2]);
+
+        fprintf(stderr, "%d = %.5f at %.2f %.2f %.2f x %.2f\n", obj.label, obj.prob,
+                obj.rect.x, obj.rect.y, obj.rect.width, obj.rect.height);
+
+        cv::rectangle(image, obj.rect, cc, 2);
+
+        char text[256];
+        sprintf(text, "%s %.1f%%", class_names[obj.label], obj.prob * 100);
+
+        int baseLine = 0;
+        cv::Size label_size = cv::getTextSize(text, cv::FONT_HERSHEY_SIMPLEX, 0.5, 1, &baseLine);
+
+        int x = obj.rect.x;
+        int y = obj.rect.y - label_size.height - baseLine;
+        if (y < 0)
+            y = 0;
+        if (x + label_size.width > image.cols)
+            x = image.cols - label_size.width;
+
+        cv::rectangle(image, cv::Rect(cv::Point(x, y), cv::Size(label_size.width, label_size.height + baseLine)),
+                      cc, -1);
+
+        cv::putText(image, text, cv::Point(x, y + label_size.height),
+                    cv::FONT_HERSHEY_SIMPLEX, 0.5, cv::Scalar(255, 255, 255));
+    }
+
+    cv::imshow("image", image);
+    cv::waitKey(0);
+}
+
+int main(int argc, char** argv)
+{
+    if (argc != 2)
+    {
+        fprintf(stderr, "Usage: %s [imagepath]\n", argv[0]);
+        return -1;
+    }
+
+    const char* imagepath = argv[1];
+
+    cv::Mat m = cv::imread(imagepath, 1);
+    if (m.empty())
+    {
+        fprintf(stderr, "cv::imread %s failed\n", imagepath);
+        return -1;
+    }
+
+    std::vector<Object> objects;
+    detect_yolov8(m, objects);
+
+    draw_objects(m, objects);
+
+    return 0;
+}