add yolov6 c++ and yolov6 pybind (#16)

* 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
2025-10-06 09:07:10 +08:00 · 2022-07-14 16:12:28 +08:00
parent 5f83b3c532
commit de7c06a309
27 changed files with 955 additions and 6 deletions
--- a/.gitignore
+++ b/.gitignore
@@ -9,4 +9,6 @@ build-debug.sh
 *dist
 fastdeploy.egg-info
 .setuptools-cmake-build
-fastdeploy/version.py
+fastdeploy/version.py
+fastdeploy/LICENSE*
+fastdeploy/ThirdPartyNotices*
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -38,6 +38,9 @@ option(ENABLE_VISION_VISUALIZE "if to enable visualize vision model result toolb
 option(ENABLE_OPENCV_CUDA "if to enable opencv with cuda, this will allow process image with GPU." OFF)
 option(ENABLE_DEBUG "if to enable print debug information, this may reduce performance." OFF)

+# Whether to build fastdeply with vision/text/... examples, only for testings.
+option(WTIH_VISION_EXAMPLES "Whether to build fastdeply with vision examples" ON)
+
 if(ENABLE_DEBUG)
  add_definitions(-DFASTDEPLOY_DEBUG)
 endif()
@@ -50,6 +53,13 @@ option(BUILD_FASTDEPLOY_PYTHON "if build python lib for fastdeploy." OFF)
 include_directories(${PROJECT_SOURCE_DIR})
 include_directories(${CMAKE_CURRENT_BINARY_DIR})

+if (WTIH_VISION_EXAMPLES AND EXISTS ${PROJECT_SOURCE_DIR}/examples)
+  # ENABLE_VISION and ENABLE_VISION_VISUALIZE must be ON if enable vision examples.
+  message(STATUS "Found WTIH_VISION_EXAMPLES ON, so, force ENABLE_VISION and ENABLE_VISION_VISUALIZE ON")
+  set(ENABLE_VISION ON CACHE BOOL "force to enable vision models usage" FORCE)
+  set(ENABLE_VISION_VISUALIZE ON CACHE BOOL "force to enable visualize vision model result toolbox" FORCE)
+endif()
+
 add_definitions(-DFASTDEPLOY_LIB)
 file(GLOB_RECURSE ALL_DEPLOY_SRCS ${PROJECT_SOURCE_DIR}/fastdeploy/*.cc)
 file(GLOB_RECURSE DEPLOY_ORT_SRCS ${PROJECT_SOURCE_DIR}/fastdeploy/backends/ort/*.cc)
@@ -170,6 +180,13 @@ endif()
 set_target_properties(fastdeploy PROPERTIES VERSION ${FASTDEPLOY_VERSION})
 target_link_libraries(fastdeploy ${DEPEND_LIBS})

+# add examples after prepare include paths for third-parties
+if (WTIH_VISION_EXAMPLES AND EXISTS ${PROJECT_SOURCE_DIR}/examples)
+  add_definitions(-DWTIH_VISION_EXAMPLES)
+  set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/examples/bin)
+  add_subdirectory(examples)
+endif()
+
 include(external/summary.cmake)
 fastdeploy_summary()

--- a/examples/.gitignore
+++ b/examples/.gitignore
@@ -0,0 +1,8 @@
+*.jpg
+*.png
+*.jpeg
+*.onnx
+*.engine
+*.pd*
+*.nb
+bin
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -0,0 +1,22 @@
+function(add_fastdeploy_executable field url model)   
+  # temp target name/file var in function scope
+  set(TEMP_TARGET_FILE ${PROJECT_SOURCE_DIR}/examples/${field}/${url}_${model}.cc)
+  set(TEMP_TARGET_NAME ${field}_${url}_${model})
+  if (EXISTS ${TEMP_TARGET_FILE} AND TARGET fastdeploy)
+    add_executable(${TEMP_TARGET_NAME} ${TEMP_TARGET_FILE})
+    target_link_libraries(${TEMP_TARGET_NAME} PUBLIC fastdeploy)
+    message(STATUS "Found source file: [${field}/${url}_${model}.cc], ADD!!! fastdeploy executable: [${TEMP_TARGET_NAME}] !")
+  else ()
+    message(WARNING "Can not found source file: [${field}/${url}_${model}.cc], SKIP!!! fastdeploy executable: [${TEMP_TARGET_NAME}] !")          
+  endif()
+  unset(TEMP_TARGET_FILE)
+  unset(TEMP_TARGET_NAME)
+endfunction()
+
+# vision examples
+if (WTIH_VISION_EXAMPLES)
+  add_fastdeploy_executable(vision ultralytics yolov5)
+  add_fastdeploy_executable(vision meituan yolov6)
+endif()
+
+# other examples ...
--- a/examples/resources/.gitignore
+++ b/examples/resources/.gitignore
@@ -0,0 +1,11 @@
+images/*.jpg
+images/*.jpeg
+images/*.png
+models/*.onnx
+models/*.pd*
+models/*.engine
+models/*.trt
+models/*.nb
+outputs/*.jpg
+outputs/*.jpeg
+outputs/*.png
--- a/examples/resources/images/.gitignore
+++ b/examples/resources/images/.gitignore
@@ -0,0 +1,3 @@
+*.jpg
+*.jpeg
+*.png
--- a/examples/resources/models/.gitignore
+++ b/examples/resources/models/.gitignore
@@ -0,0 +1,5 @@
+*.onnx
+*.engine
+*.pd*
+*.nb
+*.trt
--- a/examples/resources/outputs/.gitignore
+++ b/examples/resources/outputs/.gitignore
@@ -0,0 +1,3 @@
+*.jpg
+*.png
+*.jpeg
--- a/examples/vision/meituan_yolov6.cc
+++ b/examples/vision/meituan_yolov6.cc
@@ -0,0 +1,53 @@
+// 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/yolov6s.onnx";
+  std::string img_path = "../resources/images/bus.jpg";
+  std::string vis_path  = "../resources/outputs/meituan_yolov6_vis_result.jpg";
+
+  auto model = vis::meituan::YOLOv6(model_file);
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed." << std::endl;
+    return -1;
+  } else {
+    std::cout << "Init Done! Dynamic Mode: " 
+              << model.IsDynamicShape() << 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/examples/vision/ultralytics_yolov5.cc
+++ b/examples/vision/ultralytics_yolov5.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/yolov5s.onnx";
+  std::string img_path = "../resources/images/bus.jpg";
+  std::string vis_path  = "../resources/outputs/ultralytics_yolov5_vis_result.jpg";
+
+  auto model = vis::ultralytics::YOLOv5(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/download.py
+++ b/fastdeploy/download.py
@@ -18,6 +18,7 @@ import shutil
 import requests
 import time
 import zipfile
+import tarfile
 import hashlib
 import tqdm
 import logging
--- a/fastdeploy/fastdeploy_runtime.py
+++ b/fastdeploy/fastdeploy_runtime.py
@@ -51,5 +51,5 @@ class FastDeployModel:
    @property
    def initialized(self):
        if self._model is None:
-            return false
+            return False
        return self._model.initialized()
--- a/fastdeploy/vision.h
+++ b/fastdeploy/vision.h
@@ -17,6 +17,7 @@
 #ifdef ENABLE_VISION
 #include "fastdeploy/vision/ppcls/model.h"
 #include "fastdeploy/vision/ultralytics/yolov5.h"
+#include "fastdeploy/vision/meituan/yolov6.h"
 #endif

 #include "fastdeploy/vision/visualize/visualize.h"
--- a/fastdeploy/vision/init.py
+++ b/fastdeploy/vision/init.py
@@ -16,4 +16,5 @@ from __future__ import absolute_import
 from . import evaluation
 from . import ppcls
 from . import ultralytics
+from . import meituan
 from . import visualize
--- a/fastdeploy/vision/meituan/init.py
+++ b/fastdeploy/vision/meituan/init.py
@@ -0,0 +1,120 @@
+# 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.
+
+from __future__ import absolute_import
+import logging
+from ... import FastDeployModel, Frontend
+from ... import fastdeploy_main as C
+
+
+class YOLOv6(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(YOLOv6, self).__init__(runtime_option)
+
+        self._model = C.vision.meituan.YOLOv6(
+            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, "YOLOv6 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)
+
+    # BOOL: 查看输入的模型是否为动态维度的 
+    def is_dynamic_shape(self):
+        return self._model.is_dynamic_shape()                               
+
+    # 一些跟YOLOv6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/meituan/meituan_pybind.cc
+++ b/fastdeploy/vision/meituan/meituan_pybind.cc
@@ -0,0 +1,45 @@
+// 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/pybind/main.h"
+
+namespace fastdeploy {
+void BindMeituan(pybind11::module& m) {
+  auto meituan_module =
+      m.def_submodule("meituan", "https://github.com/meituan/YOLOv6");
+  pybind11::class_<vision::meituan::YOLOv6, FastDeployModel>(
+      meituan_module, "YOLOv6")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
+      .def("predict",
+           [](vision::meituan::YOLOv6& 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("is_dynamic_shape",
+           [](vision::meituan::YOLOv6& self) {
+             return self.IsDynamicShape();
+           })     
+      .def_readwrite("size", &vision::meituan::YOLOv6::size)
+      .def_readwrite("padding_value",
+                     &vision::meituan::YOLOv6::padding_value)
+      .def_readwrite("is_mini_pad", &vision::meituan::YOLOv6::is_mini_pad)
+      .def_readwrite("is_no_pad", &vision::meituan::YOLOv6::is_no_pad)
+      .def_readwrite("is_scale_up", &vision::meituan::YOLOv6::is_scale_up)
+      .def_readwrite("stride", &vision::meituan::YOLOv6::stride)
+      .def_readwrite("max_wh", &vision::meituan::YOLOv6::max_wh);
+}
+}  // namespace fastdeploy
--- a/fastdeploy/vision/meituan/yolov6.cc
+++ b/fastdeploy/vision/meituan/yolov6.cc
@@ -0,0 +1,262 @@
+// 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/meituan/yolov6.h"
+#include "fastdeploy/utils/perf.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+
+namespace vision {
+
+namespace meituan {
+
+void LetterBox(Mat* mat, std::vector<int> size, std::vector<float> color,
+               bool _auto, bool scale_fill = false, bool scale_up = true,
+               int stride = 32) {
+  float scale = std::min(size[1] * 1.0f / static_cast<float>(mat->Height()), 
+                         size[0] * 1.0f / static_cast<float>(mat->Width()));       
+  if (!scale_up) {
+    scale = std::min(scale, 1.0f);
+  }
+
+  int resize_h = int(round(static_cast<float>(mat->Height()) * scale));
+  int resize_w = int(round(static_cast<float>(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];
+  }
+  if (resize_h != mat->Height() || resize_w != mat->Width()) {
+    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);
+  }
+}
+
+YOLOv6::YOLOv6(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 YOLOv6::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 = 4096.0f;
+  
+  if (!InitRuntime()) {
+    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
+    return false;
+  }
+  // Check if the input shape is dynamic after Runtime already initialized, 
+  // Note that, YOLOv6 has 1 input only. We need to force is_mini_pad 
+  // 'false' to keep static shape after padding (LetterBox) 
+  // when the is_dynamic_shape is 'false'.
+  is_dynamic_shape_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (const auto &d: shape) {
+    if (d <= 0) {
+      is_dynamic_shape_ = true;
+    }
+  }
+  if (!is_dynamic_shape_) {  
+    is_mini_pad = false;
+  }
+  return true;
+}
+
+bool YOLOv6::Preprocess(Mat* mat, FDTensor* output,
+                        std::map<std::string, std::array<float, 2>>* im_info) {
+  // process after image load
+  float ratio = std::min(size[1] * 1.0f / static_cast<float>(mat->Height()), 
+                         size[0] * 1.0f / 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(round(static_cast<float>(mat->Height()) * ratio));
+    int resize_w = int(round(static_cast<float>(mat->Width())  * ratio));
+    Resize::Run(mat, resize_w, resize_h, -1, -1, interp);
+  }
+  // yolov6's preprocess steps
+  // 1. letterbox
+  // 2. BGR->RGB
+  // 3. HWC->CHW
+  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 YOLOv6::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 - 1.0f);
+    result->boxes[i][1] = std::min(result->boxes[i][1], ipt_h - 1.0f);
+    result->boxes[i][2] = std::min(result->boxes[i][2], ipt_w - 1.0f);
+    result->boxes[i][3] = std::min(result->boxes[i][3], ipt_h - 1.0f);
+  }
+  return true;
+}
+
+bool YOLOv6::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 meituan
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/meituan/yolov6.h
+++ b/fastdeploy/vision/meituan/yolov6.h
@@ -0,0 +1,100 @@
+// 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 meituan {
+
+class FASTDEPLOY_DECL YOLOv6 : 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
+  YOLOv6(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 "meituan/YOLOv6"; }
+
+  // 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
+  virtual 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
+  virtual 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
+  virtual 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);
+
+  // 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);
+
+  // 用户可以通过该接口 查看输入的模型是否为动态维度
+  virtual bool IsDynamicShape() const { return is_dynamic_shape_; }                     
+
+  // 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, default 4096 in meituan/YOLOv6
+  float max_wh;
+
+ protected:
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape or not.)
+  // meituan/YOLOv6 official 'export_onnx.py' script will export static ONNX by default.
+  // while is_dynamic_shape if 'false', is_mini_pad will force 'false'. This value will
+  // auto check by fastdeploy after the internal Runtime already initialized. 
+  bool is_dynamic_shape_;
+};
+}  // namespace meituan
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/vision_pybind.cc
+++ b/fastdeploy/vision/vision_pybind.cc
@@ -18,6 +18,7 @@ namespace fastdeploy {

 void BindPpClsModel(pybind11::module& m);
 void BindUltralytics(pybind11::module& m);
+void BindMeituan(pybind11::module& m);
 #ifdef ENABLE_VISION_VISUALIZE
 void BindVisualize(pybind11::module& m);
 #endif
@@ -40,6 +41,9 @@ void BindVision(pybind11::module& m) {

  BindPpClsModel(m);
  BindUltralytics(m);
+  BindMeituan(m);
+#ifdef ENABLE_VISION_VISUALIZE
  BindVisualize(m);
+#endif  
 }
 } // namespace fastdeploy
--- a/model_zoo/.gitignore
+++ b/model_zoo/.gitignore
@@ -0,0 +1,12 @@
+*.png
+*.jpg
+*.jpeg
+*.onnx
+*.zip
+*.tar
+*.pd*
+*.engine
+*.trt
+*.nb
+*.tgz
+*.gz
--- a/model_zoo/vision/yolov5/api.md
+++ b/model_zoo/vision/yolov5/api.md
@@ -23,7 +23,7 @@ YOLOv5模型加载和初始化，当model_format为`fd.Frontend.ONNX`时，只
 >
 > **参数**
 >
-> > * **image_data**(np.ndarray): 输入数据，注意需为HWC，RGB格式
+> > * **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

@@ -49,9 +49,9 @@ YOLOv5模型加载和初始化，当model_format为`Frontend::ONNX`时，只需
 > * **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
+#### Predict函数
 > ```
-> YOLOv5::predict(cv::Mat* im, DetectionResult* result,
+> YOLOv5::Predict(cv::Mat* im, DetectionResult* result,
 >                 float conf_threshold = 0.25,
 >                 float nms_iou_threshold = 0.5)
 > ```
@@ -59,7 +59,7 @@ YOLOv5模型加载和初始化，当model_format为`Frontend::ONNX`时，只需
 >
 > **参数**
 >
-> > * **im**: 输入图像，注意需为HWC，RGB格式
+> > * **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
--- a/model_zoo/vision/yolov6/README.md
+++ b/model_zoo/vision/yolov6/README.md
@@ -0,0 +1,45 @@
+# YOLOv6部署示例
+
+本文档说明如何进行[YOLOv6](https://github.com/meituan/YOLOv6)的快速部署推理。本目录结构如下
+```
+.
+├── cpp                 # C++ 代码目录
+│   ├── CMakeLists.txt  # C++ 代码编译CMakeLists文件
+│   ├── README.md       # C++ 代码编译部署文档
+│   └── yolov6.cc       # C++ 示例代码
+├── README.md           # YOLOv6 部署文档
+└── yolov6.py           # Python示例代码
+```
+
+## 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YOLOv6模型和测试图片
+```
+python yolov6.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]
+11.772949,229.269287, 792.933838, 748.294189, 0.954794, 5
+667.140381,396.185455, 807.701721, 881.810120, 0.900997, 0
+223.271011,405.105743, 345.740723, 859.328552, 0.898938, 0
+50.135777,405.863129, 245.485519, 904.153809, 0.888936, 0
+0.000000,549.002869, 77.864723, 869.455017, 0.614145, 0
+```
+
+## 其它文档
+
+- [C++部署](./cpp/README.md)
+- [YOLOv6 API文档](./api.md)
--- a/model_zoo/vision/yolov6/api.md
+++ b/model_zoo/vision/yolov6/api.md
@@ -0,0 +1,71 @@
+# YOLOv6 API说明
+
+## Python API
+
+### YOLOv6类
+```
+fastdeploy.vision.meituan.YOLOv6(model_file, params_file=None, runtime_option=None, model_format=fd.Frontend.ONNX)
+```
+YOLOv6模型加载和初始化，当model_format为`fd.Frontend.ONNX`时，只需提供model_file，如`yolov6s.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
+> ```
+> YOLOv6.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[yolov6.py](./yolov6.py)
+
+
+## C++ API
+
+### YOLOv6类
+```
+fastdeploy::vision::meituan::YOLOv6(
+        const string& model_file,
+        const string& params_file = "",
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX)
+```
+YOLOv6模型加载和初始化，当model_format为`Frontend::ONNX`时，只需提供model_file，如`yolov6s.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
+> ```
+> YOLOv6::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/yolov6.cc](cpp/yolov6.cc)
+
+## 其它API使用
+
+- [模型部署RuntimeOption配置](../../../docs/api/runtime_option.md)
--- a/model_zoo/vision/yolov6/cpp/CMakeLists.txt
+++ b/model_zoo/vision/yolov6/cpp/CMakeLists.txt
@@ -0,0 +1,17 @@
+PROJECT(yolov6_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 ${PROJECT_SOURCE_DIR}/fastdeploy-linux-x64-0.0.3/)
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(yolov6_demo ${PROJECT_SOURCE_DIR}/yolov6.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(yolov6_demo ${FASTDEPLOY_LIBS})
--- a/model_zoo/vision/yolov6/cpp/README.md
+++ b/model_zoo/vision/yolov6/cpp/README.md
@@ -0,0 +1,30 @@
+# 编译YOLOv6示例
+
+
+```
+# 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
+
+# 下载模型和图片
+wget https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.onnx
+wget https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg
+
+# 执行
+./yolov6_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]
+11.772949,229.269287, 792.933838, 748.294189, 0.954794, 5
+667.140381,396.185455, 807.701721, 881.810120, 0.900997, 0
+223.271011,405.105743, 345.740723, 859.328552, 0.898938, 0
+50.135777,405.863129, 245.485519, 904.153809, 0.888936, 0
+0.000000,549.002869, 77.864723, 869.455017, 0.614145, 0
+```
--- a/model_zoo/vision/yolov6/cpp/yolov6.cc
+++ b/model_zoo/vision/yolov6/cpp/yolov6.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::meituan::YOLOv6("yolov6s.onnx");
+  if (!model.Initialized()) {
+    std::cerr << "Init Failed." << std::endl;
+    return -1;
+  }
+  cv::Mat im = cv::imread("bus.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/yolov6/yolov6.py
+++ b/model_zoo/vision/yolov6/yolov6.py
@@ -0,0 +1,24 @@
+import fastdeploy as fd
+import cv2 
+
+# 下载模型和测试图片
+model_url = "https://github.com/meituan/YOLOv6/releases/download/0.1.0/yolov6s.onnx"
+test_jpg_url = "https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg"
+fd.download(model_url, ".", show_progress=True)
+fd.download(test_jpg_url, ".", show_progress=True)
+
+# 加载模型
+model = fd.vision.meituan.YOLOv6("yolov6s.onnx")
+print(model.is_dynamic_shape())
+
+# 预测图片
+im = cv2.imread("bus.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)