diff --git a/README.md b/README.md
index af802a2e..b5cdc030 100644
--- a/README.md
+++ b/README.md
@@ -15,6 +15,12 @@
 5. 最后你将获得一个属于自己的推理框架，对面试和知识进阶大有裨益。
 
 ## Demo效果
+> 🥰 KuiperInfer当前已支持Unet网络的推理，采用[carvana的预训练权重](https://github.com/milesial/Pytorch-UNet)
+
+![](https://imgur.com/FDXALEa.jpg)
+![](https://imgur.com/hbbZeoT.jpg)
+
+
 > Demo直接使用yolov5-s的预训练权重(coco数据集)，使用KuiperInfer推理
 
 ![](./imgs/demo_car.jpg)
@@ -61,6 +67,7 @@
 - [zpye](https://github.com/zpye)
 - [cmcamdy](https://github.com/cmcamdy)
 - [superCB](https://github.com/SuperCB)
+- [sanbuphy](https://github.com/sanbuphy)
 
 ### 如何参与项目贡献？
 1. 提交代码增加新功能或修改bug；
diff --git a/demos/CMakeLists.txt b/demos/CMakeLists.txt
index 28f41d6d..2735fc74 100644
--- a/demos/CMakeLists.txt
+++ b/demos/CMakeLists.txt
@@ -25,7 +25,6 @@ if (MSVC)
 endif()
 
 
-
 add_executable(resnet_test resnet_test.cpp image_util.hpp image_util.cpp)
 target_include_directories(resnet_test PUBLIC ../include)
 target_link_directories(resnet_test PUBLIC ${PROJECT_SOURCE_DIR}/lib)
@@ -38,3 +37,16 @@ if (MSVC)
             "$<TARGET_FILE_DIR:kuiper>/kuiper.dll"
             $<TARGET_FILE_DIR:resnet_test>)
 endif()
+
+add_executable(unet_test unet_test.cpp image_util.hpp image_util.cpp)
+target_include_directories(unet_test PUBLIC ../include)
+target_link_directories(unet_test PUBLIC ${PROJECT_SOURCE_DIR}/lib)
+target_link_libraries(unet_test ${OpenCV_LIBS} kuiper)
+
+
+if (MSVC)
+    add_custom_command(TARGET unet_test POST_BUILD
+            COMMAND ${CMAKE_COMMAND} -E copy_if_different
+            "$<TARGET_FILE_DIR:kuiper>/kuiper.dll"
+            $<TARGET_FILE_DIR:unet_test>)
+endif()
diff --git a/demos/unet_test.cpp b/demos/unet_test.cpp
new file mode 100644
index 00000000..4b56a0bd
--- /dev/null
+++ b/demos/unet_test.cpp
@@ -0,0 +1,100 @@
+#include <iostream>
+#include <algorithm>
+#include <cassert>
+#include <opencv2/opencv.hpp>
+#include "../source/layer/details/softmax.hpp"
+#include "data/tensor.hpp"
+#include "runtime/runtime_ir.hpp"
+#include "tick.hpp"
+
+kuiper_infer::sftensor PreProcessImage(const cv::Mat& image) {
+  using namespace kuiper_infer;
+  assert(!image.empty());
+  cv::Mat resize_image;
+  cv::resize(image, resize_image, cv::Size(512, 512));
+
+  cv::Mat rgb_image;
+  cv::cvtColor(resize_image, rgb_image, cv::COLOR_BGR2RGB);
+
+  rgb_image.convertTo(rgb_image, CV_32FC3);
+  std::vector<cv::Mat> split_images;
+  cv::split(rgb_image, split_images);
+  uint32_t input_w = 512;
+  uint32_t input_h = 512;
+  uint32_t input_c = 3;
+  sftensor input = std::make_shared<ftensor>(input_c, input_h, input_w);
+
+  uint32_t index = 0;
+  for (const auto& split_image : split_images) {
+    assert(split_image.total() == input_w * input_h);
+    const cv::Mat& split_image_t = split_image.t();
+    memcpy(input->slice(index).memptr(), split_image_t.data,
+           sizeof(float) * split_image.total());
+    index += 1;
+  }
+
+  assert(input->channels() == 3);
+  input->data() = input->data() / 255.f;
+  return input;
+}
+
+
+int main(int argc, char* argv[]) {
+  if (argc != 4) {
+    printf("usage: ./unet_test [image path] [pnnx_param path] [pnnx_bin path]\n");
+    exit(-1);
+  }
+  using namespace kuiper_infer;
+
+  const std::string& path = argv[1];
+  const uint32_t batch_size = 1;
+  std::vector<sftensor> inputs;
+  for (uint32_t i = 0; i < batch_size; ++i) {
+    cv::Mat image = cv::imread(path);
+    // 图像预处理
+    sftensor input = PreProcessImage(image);
+    inputs.push_back(input);
+  }
+
+  const std::string& param_path = argv[2];
+  const std::string& weight_path = argv[3];
+  RuntimeGraph graph(param_path, weight_path);
+  graph.Build();
+  graph.set_inputs("pnnx_input_0", inputs);
+  std::cout << "start inference!" << std::endl;
+  TICK(forward)
+  graph.Forward(false);
+  std::vector<std::shared_ptr<Tensor<float>>> outputs =
+      graph.get_outputs("pnnx_output_0");
+  TOCK(forward)
+  assert(outputs.size() == batch_size);
+
+  for (int i = 0; i < outputs.size(); ++i) {
+    const sftensor& output_tensor = outputs.at(i);
+    arma::fmat&  out_channel_0 = output_tensor->slice(0);
+    arma::fmat&  out_channel_1 = output_tensor->slice(1);
+    arma::fmat out_channel(512, 512);
+    assert(out_channel_0.size() == out_channel_1.size());
+    assert(out_channel_0.size() == out_channel.size());
+
+    for (int i =0; i<out_channel_0.size();i++){
+        if(out_channel_0.at(i)<out_channel_1.at(i)){
+            out_channel.at(i) = 255;
+        }
+        else{
+            out_channel.at(i) = 0;
+        }
+    }
+        
+    arma::fmat out_channel_t = out_channel.t();
+    auto output_array_ptr = out_channel_t.memptr();
+    assert(output_array_ptr!=nullptr);
+    
+    int dataType = CV_32F;
+    cv::Mat output(512, 512, dataType, output_array_ptr);
+
+    cv::imwrite(cv::String("unet_output.jpg"),output);
+  }
+    
+  return 0;
+}
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