Add model YOLOR Support (#31)

* first commit for yolov7 * pybind for yolov7 * CPP README.md * CPP README.md * modified yolov7.cc * README.md * python file modify * delete license in fastdeploy/ * repush the conflict part * README.md modified * README.md modified * file path modified * file path modified * file path modified * file path modified * file path modified * README modified * README modified * move some helpers to private * add examples for yolov7 * api.md modified * api.md modified * api.md modified * YOLOv7 * yolov7 release link * yolov7 release link * yolov7 release link * copyright * change some helpers to private * change variables to const and fix documents. * gitignore * Transfer some funtions to private member of class * Transfer some funtions to private member of class * Merge from develop (#9) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * first commit for yolor * for merge * Develop (#11) (#12) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * Develop (#13) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * documents * Develop (#14) * Fix compile problem in different python version (#26) * fix some usage problem in linux * Fix compile problem Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> * Add PaddleDetetion/PPYOLOE model support (#22) * add ppdet/ppyoloe * Add demo code and documents * add convert processor to vision (#27) * update .gitignore * Added checking for cmake include dir * fixed missing trt_backend option bug when init from trt * remove un-need data layout and add pre-check for dtype * changed RGB2BRG to BGR2RGB in ppcls model * add model_zoo yolov6 c++/python demo * fixed CMakeLists.txt typos * update yolov6 cpp/README.md * add yolox c++/pybind and model_zoo demo * move some helpers to private * fixed CMakeLists.txt typos * add normalize with alpha and beta * add version notes for yolov5/yolov6/yolox * add copyright to yolov5.cc * revert normalize * fixed some bugs in yolox * fixed examples/CMakeLists.txt to avoid conflicts * add convert processor to vision * format examples/CMakeLists summary * Fix bug while the inference result is empty with YOLOv5 (#29) * Add multi-label function for yolov5 * Update README.md Update doc * Update fastdeploy_runtime.cc fix variable option.trt_max_shape wrong name * Update runtime_option.md Update resnet model dynamic shape setting name from images to x * Fix bug when inference result boxes are empty * Delete detection.py Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <jiangjiajun@baidu.com> Co-authored-by: root <root@bjyz-sys-gpu-kongming3.bjyz.baidu.com> Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com> Co-authored-by: huangjianhui <852142024@qq.com> Co-authored-by: Jason <928090362@qq.com>
2025-10-16 21:51:31 +08:00 · 2022-07-21 10:23:39 +08:00
parent 39aca6787a
commit 8b0a0c6a10
21 changed files with 820 additions and 32 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -11,4 +11,4 @@ fastdeploy.egg-info
 .setuptools-cmake-build
 fastdeploy/version.py
 fastdeploy/LICENSE*
-fastdeploy/ThirdPartyNotices*
+fastdeploy/ThirdPartyNotices*
--- a/examples/vision/wongkinyiu_yolor.cc
+++ b/examples/vision/wongkinyiu_yolor.cc
@@ -0,0 +1,52 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// 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 "fastdeploy/vision.h"
+
+int main() {
+  namespace vis = fastdeploy::vision;
+
+  std::string model_file = "../resources/models/yolor.onnx";
+  std::string img_path = "../resources/images/horses.jpg";
+  std::string vis_path = "../resources/outputs/wongkinyiu_yolor_vis_result.jpg";
+
+  auto model = vis::wongkinyiu::YOLOR(model_file);
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed! Model: " << model_file << std::endl;
+    return -1;
+  } else {
+    std::cout << "Init Done! Model:" << model_file << std::endl;
+  }
+  model.EnableDebug();
+
+  cv::Mat im = cv::imread(img_path);
+  cv::Mat vis_im = im.clone();
+
+  vis::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Prediction Failed." << std::endl;
+    return -1;
+  } else {
+    std::cout << "Prediction Done!" << std::endl;
+  }
+
+  // 输出预测框结果
+  std::cout << res.Str() << std::endl;
+
+  // 可视化预测结果
+  vis::Visualize::VisDetection(&vis_im, res);
+  cv::imwrite(vis_path, vis_im);
+  std::cout << "Detect Done! Saved: " << vis_path << std::endl;
+  return 0;
+}
--- a/fastdeploy/utils/utils.h
+++ b/fastdeploy/utils/utils.h
@@ -26,10 +26,10 @@
 #define FASTDEPLOY_DECL __declspec(dllexport)
 #else
 #define FASTDEPLOY_DECL __declspec(dllimport)
-#endif // FASTDEPLOY_LIB
+#endif  // FASTDEPLOY_LIB
 #else
 #define FASTDEPLOY_DECL __attribute__((visibility("default")))
-#endif // _WIN32
+#endif  // _WIN32

 namespace fastdeploy {

@@ -42,7 +42,8 @@ class FASTDEPLOY_DECL FDLogger {
  }
  explicit FDLogger(bool verbose, const std::string& prefix = "[FastDeploy]");

-  template <typename T> FDLogger& operator<<(const T& val) {
+  template <typename T>
+  FDLogger& operator<<(const T& val) {
    if (!verbose_) {
      return *this;
    }
@@ -72,10 +73,14 @@ class FASTDEPLOY_DECL FDLogger {
  FDLogger(true, "[ERROR]")                                                    \
      << __REL_FILE__ << "(" << __LINE__ << ")::" << __FUNCTION__ << "\t"

-#define FDASSERT(condition, message)                                           \
-  if (!(condition)) {                                                          \
-    FDERROR << message << std::endl;                                           \
-    std::abort();                                                              \
+#define FDERROR                                                \
+  FDLogger(true, "[ERROR]") << __REL_FILE__ << "(" << __LINE__ \
+                            << ")::" << __FUNCTION__ << "\t"
+
+#define FDASSERT(condition, message) \
+  if (!(condition)) {                \
+    FDERROR << message << std::endl; \
+    std::abort();                    \
  }

-} // namespace fastdeploy
+}  // namespace fastdeploy
--- a/fastdeploy/vision.h
+++ b/fastdeploy/vision.h
@@ -15,12 +15,13 @@

 #include "fastdeploy/core/config.h"
 #ifdef ENABLE_VISION
+#include "fastdeploy/vision/megvii/yolox.h"
+#include "fastdeploy/vision/meituan/yolov6.h"
 #include "fastdeploy/vision/ppcls/model.h"
 #include "fastdeploy/vision/ppdet/ppyoloe.h"
 #include "fastdeploy/vision/ultralytics/yolov5.h"
+#include "fastdeploy/vision/wongkinyiu/yolor.h"
 #include "fastdeploy/vision/wongkinyiu/yolov7.h"
-#include "fastdeploy/vision/meituan/yolov6.h"
-#include "fastdeploy/vision/megvii/yolox.h"
 #endif

 #include "fastdeploy/vision/visualize/visualize.h"
--- a/fastdeploy/vision/ppcls/model.h
+++ b/fastdeploy/vision/ppcls/model.h
@@ -46,6 +46,6 @@ class FASTDEPLOY_DECL Model : public FastDeployModel {
  std::vector<std::shared_ptr<Processor>> processors_;
  std::string config_file_;
 };
-} // namespace ppcls
-} // namespace vision
-} // namespace fastdeploy
+}  // namespace ppcls
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/ppcls/ppcls_pybind.cc
+++ b/fastdeploy/vision/ppcls/ppcls_pybind.cc
@@ -27,4 +27,4 @@ void BindPPCls(pybind11::module& m) {
             return res;
           });
 }
-} // namespace fastdeploy
+}  // namespace fastdeploy
--- a/fastdeploy/vision/visualize/detection.cc
+++ b/fastdeploy/vision/visualize/detection.cc
@@ -56,6 +56,6 @@ void Visualize::VisDetection(cv::Mat* im, const DetectionResult& result,
  }
 }

-} // namespace vision
-} // namespace fastdeploy
+}  // namespace vision
+}  // namespace fastdeploy
 #endif
--- a/fastdeploy/vision/wongkinyiu/init.py
+++ b/fastdeploy/vision/wongkinyiu/init.py
@@ -114,3 +114,101 @@ class YOLOv7(FastDeployModel):
        assert isinstance(
            value, float), "The value to set `max_wh` must be type of float."
        self._model.max_wh = value
+
+
+class YOLOR(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=Frontend.ONNX):
+        # 调用基函数进行backend_option的初始化
+        # 初始化后的option保存在self._runtime_option
+        super(YOLOR, self).__init__(runtime_option)
+
+        self._model = C.vision.wongkinyiu.YOLOR(
+            model_file, params_file, self._runtime_option, model_format)
+        # 通过self.initialized判断整个模型的初始化是否成功
+        assert self.initialized, "YOLOR initialize failed."
+
+    def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
+        return self._model.predict(input_image, conf_threshold,
+                                   nms_iou_threshold)
+
+    # 一些跟YOLOR模型有关的属性封装
+    # 多数是预处理相关，可通过修改如model.size = [1280, 1280]改变预处理时resize的大小（前提是模型支持）
+    @property
+    def size(self):
+        return self._model.size
+
+    @property
+    def padding_value(self):
+        return self._model.padding_value
+
+    @property
+    def is_no_pad(self):
+        return self._model.is_no_pad
+
+    @property
+    def is_mini_pad(self):
+        return self._model.is_mini_pad
+
+    @property
+    def is_scale_up(self):
+        return self._model.is_scale_up
+
+    @property
+    def stride(self):
+        return self._model.stride
+
+    @property
+    def max_wh(self):
+        return self._model.max_wh
+
+    @size.setter
+    def size(self, wh):
+        assert isinstance(wh, [list, tuple]),\
+            "The value to set `size` must be type of tuple or list."
+        assert len(wh) == 2,\
+            "The value to set `size` must contatins 2 elements means [width, height], but now it contains {} elements.".format(
+            len(wh))
+        self._model.size = wh
+
+    @padding_value.setter
+    def padding_value(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `padding_value` must be type of list."
+        self._model.padding_value = value
+
+    @is_no_pad.setter
+    def is_no_pad(self, value):
+        assert isinstance(
+            value, bool), "The value to set `is_no_pad` must be type of bool."
+        self._model.is_no_pad = value
+
+    @is_mini_pad.setter
+    def is_mini_pad(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_mini_pad` must be type of bool."
+        self._model.is_mini_pad = value
+
+    @is_scale_up.setter
+    def is_scale_up(self, value):
+        assert isinstance(
+            value,
+            bool), "The value to set `is_scale_up` must be type of bool."
+        self._model.is_scale_up = value
+
+    @stride.setter
+    def stride(self, value):
+        assert isinstance(
+            value, int), "The value to set `stride` must be type of int."
+        self._model.stride = value
+
+    @max_wh.setter
+    def max_wh(self, value):
+        assert isinstance(
+            value, float), "The value to set `max_wh` must be type of float."
+        self._model.max_wh = value
--- a/fastdeploy/vision/wongkinyiu/wongkinyiu_pybind.cc
+++ b/fastdeploy/vision/wongkinyiu/wongkinyiu_pybind.cc
@@ -17,7 +17,7 @@
 namespace fastdeploy {
 void BindWongkinyiu(pybind11::module& m) {
  auto wongkinyiu_module =
-      m.def_submodule("wongkinyiu", "https://github.com/WongKinYiu/yolov7");
+      m.def_submodule("wongkinyiu", "https://github.com/WongKinYiu");
  pybind11::class_<vision::wongkinyiu::YOLOv7, FastDeployModel>(
      wongkinyiu_module, "YOLOv7")
      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
@@ -37,5 +37,24 @@ void BindWongkinyiu(pybind11::module& m) {
      .def_readwrite("is_scale_up", &vision::wongkinyiu::YOLOv7::is_scale_up)
      .def_readwrite("stride", &vision::wongkinyiu::YOLOv7::stride)
      .def_readwrite("max_wh", &vision::wongkinyiu::YOLOv7::max_wh);
+
+  pybind11::class_<vision::wongkinyiu::YOLOR, FastDeployModel>(
+      wongkinyiu_module, "YOLOR")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
+      .def("predict",
+           [](vision::wongkinyiu::YOLOR& self, pybind11::array& data,
+              float conf_threshold, float nms_iou_threshold) {
+             auto mat = PyArrayToCvMat(data);
+             vision::DetectionResult res;
+             self.Predict(&mat, &res, conf_threshold, nms_iou_threshold);
+             return res;
+           })
+      .def_readwrite("size", &vision::wongkinyiu::YOLOR::size)
+      .def_readwrite("padding_value", &vision::wongkinyiu::YOLOR::padding_value)
+      .def_readwrite("is_mini_pad", &vision::wongkinyiu::YOLOR::is_mini_pad)
+      .def_readwrite("is_no_pad", &vision::wongkinyiu::YOLOR::is_no_pad)
+      .def_readwrite("is_scale_up", &vision::wongkinyiu::YOLOR::is_scale_up)
+      .def_readwrite("stride", &vision::wongkinyiu::YOLOR::stride)
+      .def_readwrite("max_wh", &vision::wongkinyiu::YOLOR::max_wh);
 }
 }  // namespace fastdeploy
--- a/fastdeploy/vision/wongkinyiu/yolor.cc
+++ b/fastdeploy/vision/wongkinyiu/yolor.cc
@@ -0,0 +1,243 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// 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 "fastdeploy/vision/wongkinyiu/yolor.h"
+#include "fastdeploy/utils/perf.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace wongkinyiu {
+
+void YOLOR::LetterBox(Mat* mat, const std::vector<int>& size,
+                      const std::vector<float>& color, bool _auto,
+                      bool scale_fill, bool scale_up, int stride) {
+  float scale =
+      std::min(size[1] * 1.0 / mat->Height(), size[0] * 1.0 / mat->Width());
+  if (!scale_up) {
+    scale = std::min(scale, 1.0f);
+  }
+
+  int resize_h = int(round(mat->Height() * scale));
+  int resize_w = int(round(mat->Width() * scale));
+
+  int pad_w = size[0] - resize_w;
+  int pad_h = size[1] - resize_h;
+  if (_auto) {
+    pad_h = pad_h % stride;
+    pad_w = pad_w % stride;
+  } else if (scale_fill) {
+    pad_h = 0;
+    pad_w = 0;
+    resize_h = size[1];
+    resize_w = size[0];
+  }
+  Resize::Run(mat, resize_w, resize_h);
+  if (pad_h > 0 || pad_w > 0) {
+    float half_h = pad_h * 1.0 / 2;
+    int top = int(round(half_h - 0.1));
+    int bottom = int(round(half_h + 0.1));
+    float half_w = pad_w * 1.0 / 2;
+    int left = int(round(half_w - 0.1));
+    int right = int(round(half_w + 0.1));
+    Pad::Run(mat, top, bottom, left, right, color);
+  }
+}
+
+YOLOR::YOLOR(const std::string& model_file, const std::string& params_file,
+             const RuntimeOption& custom_option, const Frontend& model_format) {
+  if (model_format == Frontend::ONNX) {
+    valid_cpu_backends = {Backend::ORT};  // 指定可用的CPU后端
+    valid_gpu_backends = {Backend::ORT, Backend::TRT};  // 指定可用的GPU后端
+  } else {
+    valid_cpu_backends = {Backend::PDINFER, Backend::ORT};
+    valid_gpu_backends = {Backend::PDINFER, Backend::ORT, Backend::TRT};
+  }
+  runtime_option = custom_option;
+  runtime_option.model_format = model_format;
+  runtime_option.model_file = model_file;
+  runtime_option.params_file = params_file;
+  initialized = Initialize();
+}
+
+bool YOLOR::Initialize() {
+  // parameters for preprocess
+  size = {640, 640};
+  padding_value = {114.0, 114.0, 114.0};
+  is_mini_pad = false;
+  is_no_pad = false;
+  is_scale_up = false;
+  stride = 32;
+  max_wh = 7680.0;
+
+  if (!InitRuntime()) {
+    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
+    return false;
+  }
+  return true;
+}
+
+bool YOLOR::Preprocess(Mat* mat, FDTensor* output,
+                       std::map<std::string, std::array<float, 2>>* im_info) {
+  // process after image load
+  double ratio = (size[0] * 1.0) / std::max(static_cast<float>(mat->Height()),
+                                            static_cast<float>(mat->Width()));
+  if (ratio != 1.0) {
+    int interp = cv::INTER_AREA;
+    if (ratio > 1.0) {
+      interp = cv::INTER_LINEAR;
+    }
+    int resize_h = int(mat->Height() * ratio);
+    int resize_w = int(mat->Width() * ratio);
+    Resize::Run(mat, resize_w, resize_h, -1, -1, interp);
+  }
+  // yolor's preprocess steps
+  // 1. letterbox
+  // 2. BGR->RGB
+  // 3. HWC->CHW
+  YOLOR::LetterBox(mat, size, padding_value, is_mini_pad, is_no_pad,
+                   is_scale_up, stride);
+  BGR2RGB::Run(mat);
+  Normalize::Run(mat, std::vector<float>(mat->Channels(), 0.0),
+                 std::vector<float>(mat->Channels(), 1.0));
+
+  // Record output shape of preprocessed image
+  (*im_info)["output_shape"] = {static_cast<float>(mat->Height()),
+                                static_cast<float>(mat->Width())};
+
+  HWC2CHW::Run(mat);
+  Cast::Run(mat, "float");
+  mat->ShareWithTensor(output);
+  output->shape.insert(output->shape.begin(), 1);  // reshape to n, h, w, c
+  return true;
+}
+
+bool YOLOR::Postprocess(
+    FDTensor& infer_result, DetectionResult* result,
+    const std::map<std::string, std::array<float, 2>>& im_info,
+    float conf_threshold, float nms_iou_threshold) {
+  FDASSERT(infer_result.shape[0] == 1, "Only support batch =1 now.");
+  result->Clear();
+  result->Reserve(infer_result.shape[1]);
+  if (infer_result.dtype != FDDataType::FP32) {
+    FDERROR << "Only support post process with float32 data." << std::endl;
+    return false;
+  }
+  float* data = static_cast<float*>(infer_result.Data());
+  for (size_t i = 0; i < infer_result.shape[1]; ++i) {
+    int s = i * infer_result.shape[2];
+    float confidence = data[s + 4];
+    float* max_class_score =
+        std::max_element(data + s + 5, data + s + infer_result.shape[2]);
+    confidence *= (*max_class_score);
+    // filter boxes by conf_threshold
+    if (confidence <= conf_threshold) {
+      continue;
+    }
+    int32_t label_id = std::distance(data + s + 5, max_class_score);
+    // convert from [x, y, w, h] to [x1, y1, x2, y2]
+    result->boxes.emplace_back(std::array<float, 4>{
+        data[s] - data[s + 2] / 2.0f + label_id * max_wh,
+        data[s + 1] - data[s + 3] / 2.0f + label_id * max_wh,
+        data[s + 0] + data[s + 2] / 2.0f + label_id * max_wh,
+        data[s + 1] + data[s + 3] / 2.0f + label_id * max_wh});
+    result->label_ids.push_back(label_id);
+    result->scores.push_back(confidence);
+  }
+  utils::NMS(result, nms_iou_threshold);
+
+  // scale the boxes to the origin image shape
+  auto iter_out = im_info.find("output_shape");
+  auto iter_ipt = im_info.find("input_shape");
+  FDASSERT(iter_out != im_info.end() && iter_ipt != im_info.end(),
+           "Cannot find input_shape or output_shape from im_info.");
+  float out_h = iter_out->second[0];
+  float out_w = iter_out->second[1];
+  float ipt_h = iter_ipt->second[0];
+  float ipt_w = iter_ipt->second[1];
+  float scale = std::min(out_h / ipt_h, out_w / ipt_w);
+  for (size_t i = 0; i < result->boxes.size(); ++i) {
+    float pad_h = (out_h - ipt_h * scale) / 2;
+    float pad_w = (out_w - ipt_w * scale) / 2;
+    int32_t label_id = (result->label_ids)[i];
+    // clip box
+    result->boxes[i][0] = result->boxes[i][0] - max_wh * label_id;
+    result->boxes[i][1] = result->boxes[i][1] - max_wh * label_id;
+    result->boxes[i][2] = result->boxes[i][2] - max_wh * label_id;
+    result->boxes[i][3] = result->boxes[i][3] - max_wh * label_id;
+    result->boxes[i][0] = std::max((result->boxes[i][0] - pad_w) / scale, 0.0f);
+    result->boxes[i][1] = std::max((result->boxes[i][1] - pad_h) / scale, 0.0f);
+    result->boxes[i][2] = std::max((result->boxes[i][2] - pad_w) / scale, 0.0f);
+    result->boxes[i][3] = std::max((result->boxes[i][3] - pad_h) / scale, 0.0f);
+    result->boxes[i][0] = std::min(result->boxes[i][0], ipt_w);
+    result->boxes[i][1] = std::min(result->boxes[i][1], ipt_h);
+    result->boxes[i][2] = std::min(result->boxes[i][2], ipt_w);
+    result->boxes[i][3] = std::min(result->boxes[i][3], ipt_h);
+  }
+  return true;
+}
+
+bool YOLOR::Predict(cv::Mat* im, DetectionResult* result, float conf_threshold,
+                    float nms_iou_threshold) {
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_START(0)
+#endif
+
+  Mat mat(*im);
+  std::vector<FDTensor> input_tensors(1);
+
+  std::map<std::string, std::array<float, 2>> im_info;
+
+  // Record the shape of image and the shape of preprocessed image
+  im_info["input_shape"] = {static_cast<float>(mat.Height()),
+                            static_cast<float>(mat.Width())};
+  im_info["output_shape"] = {static_cast<float>(mat.Height()),
+                             static_cast<float>(mat.Width())};
+
+  if (!Preprocess(&mat, &input_tensors[0], &im_info)) {
+    FDERROR << "Failed to preprocess input image." << std::endl;
+    return false;
+  }
+
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(0, "Preprocess")
+  TIMERECORD_START(1)
+#endif
+
+  input_tensors[0].name = InputInfoOfRuntime(0).name;
+  std::vector<FDTensor> output_tensors;
+  if (!Infer(input_tensors, &output_tensors)) {
+    FDERROR << "Failed to inference." << std::endl;
+    return false;
+  }
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(1, "Inference")
+  TIMERECORD_START(2)
+#endif
+
+  if (!Postprocess(output_tensors[0], result, im_info, conf_threshold,
+                   nms_iou_threshold)) {
+    FDERROR << "Failed to post process." << std::endl;
+    return false;
+  }
+
+#ifdef FASTDEPLOY_DEBUG
+  TIMERECORD_END(2, "Postprocess")
+#endif
+  return true;
+}
+
+}  // namespace wongkinyiu
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/wongkinyiu/yolor.h
+++ b/fastdeploy/vision/wongkinyiu/yolor.h
@@ -0,0 +1,95 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// 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.
+
+#pragma once
+#include "fastdeploy/fastdeploy_model.h"
+#include "fastdeploy/vision/common/processors/transform.h"
+#include "fastdeploy/vision/common/result.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace wongkinyiu {
+
+class FASTDEPLOY_DECL YOLOR : public FastDeployModel {
+ public:
+  // 当model_format为ONNX时，无需指定params_file
+  // 当model_format为Paddle时，则需同时指定model_file & params_file
+  YOLOR(const std::string& model_file, const std::string& params_file = "",
+        const RuntimeOption& custom_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX);
+
+  // 定义模型的名称
+  virtual std::string ModelName() const { return "WongKinYiu/yolor"; }
+
+  // 模型预测接口，即用户调用的接口
+  // im 为用户的输入数据，目前对于CV均定义为cv::Mat
+  // result 为模型预测的输出结构体
+  // conf_threshold 为后处理的参数
+  // nms_iou_threshold 为后处理的参数
+  virtual bool Predict(cv::Mat* im, DetectionResult* result,
+                       float conf_threshold = 0.25,
+                       float nms_iou_threshold = 0.5);
+
+  // 以下为模型在预测时的一些参数，基本是前后处理所需
+  // 用户在创建模型后，可根据模型的要求，以及自己的需求
+  // 对参数进行修改
+  // tuple of (width, height)
+  std::vector<int> size;
+  // padding value, size should be same with Channels
+  std::vector<float> padding_value;
+  // only pad to the minimum rectange which height and width is times of stride
+  bool is_mini_pad;
+  // while is_mini_pad = false and is_no_pad = true, will resize the image to
+  // the set size
+  bool is_no_pad;
+  // if is_scale_up is false, the input image only can be zoom out, the maximum
+  // resize scale cannot exceed 1.0
+  bool is_scale_up;
+  // padding stride, for is_mini_pad
+  int stride;
+  // for offseting the boxes by classes when using NMS
+  float max_wh;
+
+ private:
+  // 初始化函数，包括初始化后端，以及其它模型推理需要涉及的操作
+  bool Initialize();
+
+  // 输入图像预处理操作
+  // Mat为FastDeploy定义的数据结构
+  // FDTensor为预处理后的Tensor数据，传给后端进行推理
+  // im_info为预处理过程保存的数据，在后处理中需要用到
+  bool Preprocess(Mat* mat, FDTensor* outputs,
+                  std::map<std::string, std::array<float, 2>>* im_info);
+
+  // 后端推理结果后处理，输出给用户
+  // infer_result 为后端推理后的输出Tensor
+  // result 为模型预测的结果
+  // im_info 为预处理记录的信息，后处理用于还原box
+  // conf_threshold 后处理时过滤box的置信度阈值
+  // nms_iou_threshold 后处理时NMS设定的iou阈值
+  bool Postprocess(FDTensor& infer_result, DetectionResult* result,
+                   const std::map<std::string, std::array<float, 2>>& im_info,
+                   float conf_threshold, float nms_iou_threshold);
+
+  // 对图片进行LetterBox处理
+  // mat 为读取到的原图
+  // size 为输入模型的图像尺寸
+  void LetterBox(Mat* mat, const std::vector<int>& size,
+                 const std::vector<float>& color, bool _auto,
+                 bool scale_fill = false, bool scale_up = true,
+                 int stride = 32);
+};
+}  // namespace wongkinyiu
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/wongkinyiu/yolov7.cc
+++ b/fastdeploy/vision/wongkinyiu/yolov7.cc
@@ -20,9 +20,9 @@ namespace fastdeploy {
 namespace vision {
 namespace wongkinyiu {

-void LetterBox(Mat* mat, const std::vector<int>& size,
-               const std::vector<float>& color, bool _auto,
-               bool scale_fill = false, bool scale_up = true, int stride = 32) {
+void YOLOv7::LetterBox(Mat* mat, const std::vector<int>& size,
+                       const std::vector<float>& color, bool _auto,
+                       bool scale_fill, bool scale_up, int stride) {
  float scale =
      std::min(size[1] * 1.0 / mat->Height(), size[0] * 1.0 / mat->Width());
  if (!scale_up) {
@@ -107,8 +107,8 @@ bool YOLOv7::Preprocess(Mat* mat, FDTensor* output,
  // 1. letterbox
  // 2. BGR->RGB
  // 3. HWC->CHW
-  LetterBox(mat, size, padding_value, is_mini_pad, is_no_pad, is_scale_up,
-            stride);
+  YOLOv7::LetterBox(mat, size, padding_value, is_mini_pad, is_no_pad,
+                    is_scale_up, stride);
  BGR2RGB::Run(mat);
  Normalize::Run(mat, std::vector<float>(mat->Channels(), 0.0),
                 std::vector<float>(mat->Channels(), 1.0));
--- a/fastdeploy/vision/wongkinyiu/yolov7.h
+++ b/fastdeploy/vision/wongkinyiu/yolov7.h
@@ -70,7 +70,7 @@ class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
  // FDTensor为预处理后的Tensor数据，传给后端进行推理
  // im_info为预处理过程保存的数据，在后处理中需要用到
  bool Preprocess(Mat* mat, FDTensor* outputs,
-                          std::map<std::string, std::array<float, 2>>* im_info);
+                  std::map<std::string, std::array<float, 2>>* im_info);

  // 后端推理结果后处理，输出给用户
  // infer_result 为后端推理后的输出Tensor
@@ -78,10 +78,17 @@ class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
  // im_info 为预处理记录的信息，后处理用于还原box
  // conf_threshold 后处理时过滤box的置信度阈值
  // nms_iou_threshold 后处理时NMS设定的iou阈值
-  bool Postprocess(
-      FDTensor& infer_result, DetectionResult* result,
-      const std::map<std::string, std::array<float, 2>>& im_info,
-      float conf_threshold, float nms_iou_threshold);
+  bool Postprocess(FDTensor& infer_result, DetectionResult* result,
+                   const std::map<std::string, std::array<float, 2>>& im_info,
+                   float conf_threshold, float nms_iou_threshold);
+
+  // 对图片进行LetterBox处理
+  // mat 为读取到的原图
+  // size 为输入模型的图像尺寸
+  void LetterBox(Mat* mat, const std::vector<int>& size,
+                 const std::vector<float>& color, bool _auto,
+                 bool scale_fill = false, bool scale_up = true,
+                 int stride = 32);
 };
 }  // namespace wongkinyiu
 }  // namespace vision
--- a/model_zoo/vision/yolor/README.md
+++ b/model_zoo/vision/yolor/README.md
@@ -0,0 +1,66 @@
+# 编译YOLOR示例
+
+当前支持模型版本为：[YOLOR weights](https://github.com/WongKinYiu/yolor/releases/tag/weights)
+(tips: 如果使用 `git clone` 的方式下载仓库代码，请将分支切换(checkout)到 `paper` 分支).
+
+本文档说明如何进行[YOLOR](https://github.com/WongKinYiu/yolor)的快速部署推理。本目录结构如下
+
+```
+.
+├── cpp
+│   ├── CMakeLists.txt
+│   ├── README.md
+│   └── yolor.cc
+├── README.md
+└── yolor.py
+```
+
+## 获取ONNX文件
+
+- 手动获取
+
+  访问[YOLOR](https://github.com/WongKinYiu/yolor)官方github库，按照指引下载安装，下载`yolor.pt` 模型，利用 `models/export.py` 得到`onnx`格式文件。如果您导出的`onnx`模型出现精度不达标或者是数据维度的问题，可以参考[yolor#32](https://github.com/WongKinYiu/yolor/issues/32)的解决办法
+
+  ```
+  #下载yolor模型文件
+  wget https://github.com/WongKinYiu/yolor/releases/download/weights/yolor-d6-paper-570.pt
+
+  # 导出onnx格式文件
+  python models/export.py  --weights PATH/TO/yolor-xx-xx-xx.pt --img-size 640
+
+  # 移动onnx文件到demo目录
+  cp PATH/TO/yolor.onnx PATH/TO/model_zoo/vision/yolor/
+  ```
+
+## 安装FastDeploy
+
+使用如下命令安装FastDeploy，注意到此处安装的是`vision-cpu`，也可根据需求安装`vision-gpu`
+
+```
+# 安装fastdeploy-python工具
+pip install fastdeploy-python
+
+# 安装vision-cpu模块
+fastdeploy install vision-cpu
+```
+## Python部署
+
+执行如下代码即会自动下载测试图片
+```
+python yolor.py
+```
+
+执行完成后会将可视化结果保存在本地`vis_result.jpg`，同时输出检测结果如下
+```
+DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
+0.000000,185.201431, 315.673126, 410.071594, 0.959289, 17
+433.802826,211.603455, 595.489319, 346.425537, 0.952615, 17
+230.446854,195.618805, 418.365479, 362.712128, 0.884253, 17
+336.545624,208.555618, 457.704315, 323.543152, 0.788450, 17
+0.896423,183.936996, 154.788727, 304.916412, 0.672804, 17
+```
+
+## 其它文档
+
+- [C++部署](./cpp/README.md)
+- [YOLOR API文档](./api.md)
--- a/model_zoo/vision/yolor/api.md
+++ b/model_zoo/vision/yolor/api.md
@@ -0,0 +1,71 @@
+# YOLOR API说明
+
+## Python API
+
+### YOLOR类
+```
+fastdeploy.vision.wongkinyiu.YOLOR(model_file, params_file=None, runtime_option=None, model_format=fd.Frontend.ONNX)
+```
+YOLOR模型加载和初始化，当model_format为`fd.Frontend.ONNX`时，只需提供model_file，如`yolor.onnx`；当model_format为`fd.Frontend.PADDLE`时，则需同时提供model_file和params_file。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式
+
+#### predict函数
+> ```
+> YOLOR.predict(image_data, conf_threshold=0.25, nms_iou_threshold=0.5)
+> ```
+> 模型预测结口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，BGR格式
+> > * **conf_threshold**(float): 检测框置信度过滤阈值
+> > * **nms_iou_threshold**(float): NMS处理过程中iou阈值
+
+示例代码参考[yolor.py](./yolor.py)
+
+
+## C++ API
+
+### YOLOR类
+```
+fastdeploy::vision::wongkinyiu::YOLOR(
+        const string& model_file,
+        const string& params_file = "",
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX)
+```
+YOLOR模型加载和初始化，当model_format为`Frontend::ONNX`时，只需提供model_file，如`yolor.onnx`；当model_format为`Frontend::PADDLE`时，则需同时提供model_file和params_file。
+
+**参数**
+
+> * **model_file**(str): 模型文件路径
+> * **params_file**(str): 参数文件路径
+> * **runtime_option**(RuntimeOption): 后端推理配置，默认为None，即采用默认配置
+> * **model_format**(Frontend): 模型格式
+
+#### Predict函数
+> ```
+> YOLOR::Predict(cv::Mat* im, DetectionResult* result,
+>                 float conf_threshold = 0.25,
+>                 float nms_iou_threshold = 0.5)
+> ```
+> 模型预测接口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **im**: 输入图像，注意需为HWC，BGR格式
+> > * **result**: 检测结果，包括检测框，各个框的置信度
+> > * **conf_threshold**: 检测框置信度过滤阈值
+> > * **nms_iou_threshold**: NMS处理过程中iou阈值
+
+示例代码参考[cpp/yolor.cc](cpp/yolor.cc)
+
+## 其它API使用
+
+- [模型部署RuntimeOption配置](../../../docs/api/runtime_option.md)
--- a/model_zoo/vision/yolor/cpp/CMakeLists.txt
+++ b/model_zoo/vision/yolor/cpp/CMakeLists.txt
@@ -0,0 +1,17 @@
+PROJECT(yolor_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.16)
+
+# 在低版本ABI环境中，通过如下代码进行兼容性编译
+# add_definitions(-D_GLIBCXX_USE_CXX11_ABI=0)
+
+# 指定下载解压后的fastdeploy库路径
+set(FASTDEPLOY_INSTALL_DIR ${PROJECT_SOURCE_DIR}/fastdeploy-linux-x64-0.3.0/)
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(yolor_demo ${PROJECT_SOURCE_DIR}/yolor.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(yolor_demo ${FASTDEPLOY_LIBS})
--- a/model_zoo/vision/yolor/cpp/README.md
+++ b/model_zoo/vision/yolor/cpp/README.md
@@ -0,0 +1,53 @@
+# 编译YOLOR示例
+
+当前支持模型版本为：[YOLOR weights](https://github.com/WongKinYiu/yolor/releases/tag/weights)
+(tips: 如果使用 `git clone` 的方式下载仓库代码，请将分支切换(checkout)到 `paper` 分支).
+## 获取ONNX文件
+
+- 手动获取
+
+  访问[YOLOR](https://github.com/WongKinYiu/yolor)官方github库，按照指引下载安装，下载`yolor.pt` 模型，利用 `models/export.py` 得到`onnx`格式文件。如果您导出的`onnx`模型出现精度不达标或者是数据维度的问题，可以参考[yolor#32](https://github.com/WongKinYiu/yolor/issues/32)的解决办法
+
+  ```
+  #下载yolor模型文件
+  wget https://github.com/WongKinYiu/yolor/releases/download/weights/yolor-d6-paper-570.pt
+
+  # 导出onnx格式文件
+  python models/export.py  --weights PATH/TO/yolor-xx-xx-xx.pt --img-size 640
+
+  # 移动onnx文件到demo目录
+  cp PATH/TO/yolor.onnx PATH/TO/model_zoo/vision/yolor/
+  ```
+
+
+## 运行demo
+
+```
+# 下载和解压预测库
+wget https://bj.bcebos.com/paddle2onnx/fastdeploy/fastdeploy-linux-x64-0.0.3.tgz
+tar xvf fastdeploy-linux-x64-0.0.3.tgz
+
+# 编译示例代码
+mkdir build & cd build
+cmake ..
+make -j
+
+# 移动onnx文件到demo目录
+cp PATH/TO/yolor.onnx PATH/TO/model_zoo/vision/yolor/cpp/build/
+
+# 下载图片
+wget https://raw.githubusercontent.com/WongKinYiu/yolor/paper/inference/images/horses.jpg
+
+# 执行
+./yolor_demo
+```
+
+执行完后可视化的结果保存在本地`vis_result.jpg`，同时会将检测框输出在终端，如下所示
+```
+DetectionResult: [xmin, ymin, xmax, ymax, score, label_id]
+0.000000,185.201431, 315.673126, 410.071594, 0.959289, 17
+433.802826,211.603455, 595.489319, 346.425537, 0.952615, 17
+230.446854,195.618805, 418.365479, 362.712128, 0.884253, 17
+336.545624,208.555618, 457.704315, 323.543152, 0.788450, 17
+0.896423,183.936996, 154.788727, 304.916412, 0.672804, 17
+```
--- a/model_zoo/vision/yolor/cpp/yolor.cc
+++ b/model_zoo/vision/yolor/cpp/yolor.cc
@@ -0,0 +1,40 @@
+// Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+//
+// Licensed under the Apache License, Version 2.0 (the "License");
+// you may not use this file except in compliance with the License.
+// You may obtain a copy of the License at
+//
+//     http://www.apache.org/licenses/LICENSE-2.0
+//
+// 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 "fastdeploy/vision.h"
+
+int main() {
+  namespace vis = fastdeploy::vision;
+  auto model = vis::wongkinyiu::YOLOR("yolor.onnx");
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed." << std::endl;
+    return -1;
+  }
+  cv::Mat im = cv::imread("horses.jpg");
+  cv::Mat vis_im = im.clone();
+
+  vis::DetectionResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Prediction Failed." << std::endl;
+    return -1;
+  }
+
+  // 输出预测框结果
+  std::cout << res.Str() << std::endl;
+
+  // 可视化预测结果
+  vis::Visualize::VisDetection(&vis_im, res);
+  cv::imwrite("vis_result.jpg", vis_im);
+  return 0;
+}
--- a/model_zoo/vision/yolor/yolor.py
+++ b/model_zoo/vision/yolor/yolor.py
@@ -0,0 +1,21 @@
+import fastdeploy as fd
+import cv2
+
+# 下载模型和测试图片
+test_jpg_url = "https://raw.githubusercontent.com/WongKinYiu/yolor/paper/inference/images/horses.jpg"
+fd.download(test_jpg_url, ".", show_progress=True)
+
+# 加载模型
+model = fd.vision.wongkinyiu.YOLOR("yolor.onnx")
+
+# 预测图片
+im = cv2.imread("horses.jpg")
+result = model.predict(im, conf_threshold=0.25, nms_iou_threshold=0.5)
+
+# 可视化结果
+fd.vision.visualize.vis_detection(im, result)
+cv2.imwrite("vis_result.jpg", im)
+
+# 输出预测结果
+print(result)
+print(model.runtime_option)
--- a/model_zoo/vision/yolov7/README.md
+++ b/model_zoo/vision/yolov7/README.md
@@ -27,10 +27,10 @@
  wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt

  # 导出onnx格式文件
-  python models/export.py --grid --dynamic --weights PATH/TO/yolo7.pt
+  python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt

  # 移动onnx文件到demo目录
-  cp PATH/TO/yolo7.onnx PATH/TO/model_zoo/vision/yolov7/
+  cp PATH/TO/yolov7.onnx PATH/TO/model_zoo/vision/yolov7/
  ```

 ## 安装FastDeploy
--- a/model_zoo/vision/yolov7/cpp/README.md
+++ b/model_zoo/vision/yolov7/cpp/README.md
@@ -13,7 +13,7 @@
  wget https://github.com/WongKinYiu/yolov7/releases/download/v0.1/yolov7.pt

  # 导出onnx格式文件
-  python models/export.py --grid --dynamic --weights PATH/TO/yolo7.pt
+  python models/export.py --grid --dynamic --weights PATH/TO/yolov7.pt

  ```

@@ -31,7 +31,7 @@ cmake ..
 make -j

 # 移动onnx文件到demo目录
-cp PATH/TO/yolo7.onnx PATH/TO/model_zoo/vision/yolov7/cpp/build/
+cp PATH/TO/yolov7.onnx PATH/TO/model_zoo/vision/yolov7/cpp/build/

 # 下载图片
 wget https://raw.githubusercontent.com/WongKinYiu/yolov7/main/inference/images/horses.jpg