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diff --git a/examples/shufflenetv2.cpp b/examples/shufflenetv2.cpp
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+// Tencent is pleased to support the open source community by making ncnn available.
+//
+// Copyright (C) 2018 THL A29 Limited, a Tencent company. 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.
+
+#include "net.h"
+
+#include <algorithm>
+#if defined(USE_NCNN_SIMPLEOCV)
+#include "simpleocv.h"
+#else
+#include <opencv2/core/core.hpp>
+#include <opencv2/highgui/highgui.hpp>
+#endif
+#include <stdio.h>
+#include <vector>
+
+static int detect_shufflenetv2(const cv::Mat& bgr, std::vector<float>& cls_scores)
+{
+    ncnn::Net shufflenetv2;
+
+    shufflenetv2.opt.use_vulkan_compute = true;
+
+    // https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe
+    // models can be downloaded from https://github.com/miaow1988/ShuffleNet_V2_pytorch_caffe/releases
+    if (shufflenetv2.load_param("shufflenet_v2_x0.5.param"))
+        exit(-1);
+    if (shufflenetv2.load_model("shufflenet_v2_x0.5.bin"))
+        exit(-1);
+
+    ncnn::Mat in = ncnn::Mat::from_pixels_resize(bgr.data, ncnn::Mat::PIXEL_BGR, bgr.cols, bgr.rows, 224, 224);
+
+    const float norm_vals[3] = {1 / 255.f, 1 / 255.f, 1 / 255.f};
+    in.substract_mean_normalize(0, norm_vals);
+
+    ncnn::Extractor ex = shufflenetv2.create_extractor();
+
+    ex.input("data", in);
+
+    ncnn::Mat out;
+    ex.extract("fc", out);
+
+    // manually call softmax on the fc output
+    // convert result into probability
+    // skip if your model already has softmax operation
+    {
+        ncnn::Layer* softmax = ncnn::create_layer("Softmax");
+
+        ncnn::ParamDict pd;
+        softmax->load_param(pd);
+
+        softmax->forward_inplace(out, shufflenetv2.opt);
+
+        delete softmax;
+    }
+
+    out = out.reshape(out.w * out.h * out.c);
+
+    cls_scores.resize(out.w);
+    for (int j = 0; j < out.w; j++)
+    {
+        cls_scores[j] = out[j];
+    }
+
+    return 0;
+}
+
+static int print_topk(const std::vector<float>& cls_scores, int topk)
+{
+    // partial sort topk with index
+    int size = cls_scores.size();
+    std::vector<std::pair<float, int> > vec;
+    vec.resize(size);
+    for (int i = 0; i < size; i++)
+    {
+        vec[i] = std::make_pair(cls_scores[i], i);
+    }
+
+    std::partial_sort(vec.begin(), vec.begin() + topk, vec.end(),
+                      std::greater<std::pair<float, int> >());
+
+    // print topk and score
+    for (int i = 0; i < topk; i++)
+    {
+        float score = vec[i].first;
+        int index = vec[i].second;
+        fprintf(stderr, "%d = %f\n", index, score);
+    }
+
+    return 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<float> cls_scores;
+    detect_shufflenetv2(m, cls_scores);
+
+    print_topk(cls_scores, 3);
+
+    return 0;
+}