diff --git a/CMakeLists.txt b/CMakeLists.txt
index 6bb2638b2c1..71aeb8165ce 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -39,7 +39,7 @@ option(ENABLE_OPENCV_CUDA "if to enable opencv with cuda, this will allow proces
 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)
+option(WITH_VISION_EXAMPLES "Whether to build fastdeply with vision examples" ON)
 
 if(ENABLE_DEBUG)
   add_definitions(-DFASTDEPLOY_DEBUG)
@@ -53,7 +53,7 @@ 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)
+if (WITH_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)
@@ -181,8 +181,8 @@ 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)
+if (WITH_VISION_EXAMPLES AND EXISTS ${PROJECT_SOURCE_DIR}/examples)
+  add_definitions(-DWITH_VISION_EXAMPLES)
   set(EXECUTABLE_OUTPUT_PATH ${PROJECT_SOURCE_DIR}/examples/bin)
   add_subdirectory(examples)
 endif()
diff --git a/examples/CMakeLists.txt b/examples/CMakeLists.txt
index 4228a3e01f7..7dd3e0e25e3 100644
--- a/examples/CMakeLists.txt
+++ b/examples/CMakeLists.txt
@@ -14,9 +14,10 @@ function(add_fastdeploy_executable field url model)
 endfunction()
 
 # vision examples
-if (WTIH_VISION_EXAMPLES)
+if (WITH_VISION_EXAMPLES)
   add_fastdeploy_executable(vision ultralytics yolov5)
   add_fastdeploy_executable(vision meituan yolov6)
+  add_fastdeploy_executable(vision megvii yolox)
 endif()
 
 # other examples ...
\ No newline at end of file
diff --git a/examples/vision/megvii_yolox.cc b/examples/vision/megvii_yolox.cc
new file mode 100644
index 00000000000..340694b54f6
--- /dev/null
+++ b/examples/vision/megvii_yolox.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/yolox_s.onnx";
+  std::string img_path = "../resources/images/bus.jpg";
+  std::string vis_path  = "../resources/outputs/megvii_yolox_vis_result.jpg";
+
+  auto model = vis::megvii::YOLOX(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;
+}
diff --git a/examples/vision/meituan_yolov6.cc b/examples/vision/meituan_yolov6.cc
index b92abcd4296..7bdd78e5dca 100644
--- a/examples/vision/meituan_yolov6.cc
+++ b/examples/vision/meituan_yolov6.cc
@@ -23,11 +23,10 @@ int main() {
 
   auto model = vis::meituan::YOLOv6(model_file);
   if (!model.Initialized()) {
-    std::cerr << "Init Failed." << std::endl;
+    std::cerr << "Init Failed! Model: " << model_file << std::endl;
     return -1;
   } else {
-    std::cout << "Init Done! Dynamic Mode: " 
-              << model.IsDynamicShape() << std::endl; 
+    std::cout << "Init Done! Model:" << model_file << std::endl; 
   }
   model.EnableDebug();
   
diff --git a/fastdeploy/vision.h b/fastdeploy/vision.h
index 1bcf9a26f97..d0e83ed030c 100644
--- a/fastdeploy/vision.h
+++ b/fastdeploy/vision.h
@@ -18,6 +18,7 @@
 #include "fastdeploy/vision/ppcls/model.h"
 #include "fastdeploy/vision/ultralytics/yolov5.h"
 #include "fastdeploy/vision/meituan/yolov6.h"
+#include "fastdeploy/vision/megvii/yolox.h"
 #endif
 
 #include "fastdeploy/vision/visualize/visualize.h"
diff --git a/fastdeploy/vision/__init__.py b/fastdeploy/vision/__init__.py
index 81a1424727f..f2de6190b08 100644
--- a/fastdeploy/vision/__init__.py
+++ b/fastdeploy/vision/__init__.py
@@ -17,4 +17,5 @@
 from . import ppcls
 from . import ultralytics
 from . import meituan
+from . import megvii
 from . import visualize
diff --git a/fastdeploy/vision/common/processors/normalize.h b/fastdeploy/vision/common/processors/normalize.h
index eeb839d0245..b8a66e945a4 100644
--- a/fastdeploy/vision/common/processors/normalize.h
+++ b/fastdeploy/vision/common/processors/normalize.h
@@ -45,7 +45,6 @@ class Normalize : public Processor {
                   const std::vector<float>& min = std::vector<float>(),
                   const std::vector<float>& max = std::vector<float>(),
                   ProcLib lib = ProcLib::OPENCV_CPU);
-
  private:
   std::vector<float> alpha_;
   std::vector<float> beta_;
diff --git a/fastdeploy/vision/megvii/__init__.py b/fastdeploy/vision/megvii/__init__.py
new file mode 100644
index 00000000000..67096e4fc8f
--- /dev/null
+++ b/fastdeploy/vision/megvii/__init__.py
@@ -0,0 +1,96 @@
+# 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 YOLOX(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(YOLOX, self).__init__(runtime_option)
+
+        self._model = C.vision.megvii.YOLOX(
+            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, "YOLOX 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YOLOX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_decode_exported(self):
+        return self._model.is_decode_exported
+
+    @property
+    def downsample_strides(self):
+        return self._model.downsample_strides        
+    
+    @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_decode_exported.setter
+    def is_decode_exported(self, value):
+        assert isinstance(
+            value, 
+            bool), "The value to set `is_decode_exported` must be type of bool."
+        self._model.max_wh = value
+
+    @downsample_strides.setter
+    def downsample_strides(self, value):
+        assert isinstance(
+            value,
+            list), "The value to set `downsample_strides` must be type of list."
+        self._model.downsample_strides = 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
diff --git a/fastdeploy/vision/megvii/megvii_pybind.cc b/fastdeploy/vision/megvii/megvii_pybind.cc
new file mode 100644
index 00000000000..7e7fbc79aa5
--- /dev/null
+++ b/fastdeploy/vision/megvii/megvii_pybind.cc
@@ -0,0 +1,41 @@
+// 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 BindMegvii(pybind11::module& m) {
+  auto megvii_module =
+      m.def_submodule("megvii", "https://github.com/megvii/YOLOX");
+  pybind11::class_<vision::megvii::YOLOX, FastDeployModel>(
+      megvii_module, "YOLOX")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, Frontend>())
+      .def("predict",
+           [](vision::megvii::YOLOX& 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::megvii::YOLOX::size)
+      .def_readwrite("padding_value",
+                     &vision::megvii::YOLOX::padding_value)
+      .def_readwrite("is_decode_exported", 
+                     &vision::megvii::YOLOX::is_decode_exported)
+      .def_readwrite("downsample_strides",
+                     &vision::megvii::YOLOX::downsample_strides)
+      .def_readwrite("max_wh", &vision::megvii::YOLOX::max_wh);
+}
+}  // namespace fastdeploy
diff --git a/fastdeploy/vision/megvii/yolox.cc b/fastdeploy/vision/megvii/yolox.cc
new file mode 100644
index 00000000000..f2cb5d1c346
--- /dev/null
+++ b/fastdeploy/vision/megvii/yolox.cc
@@ -0,0 +1,339 @@
+// 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/megvii/yolox.h"
+#include "fastdeploy/utils/perf.h"
+#include "fastdeploy/vision/utils/utils.h"
+
+namespace fastdeploy {
+
+namespace vision {
+
+namespace megvii {
+
+struct YOLOXAnchor {
+  int grid0;
+  int grid1;
+  int stride;
+};
+
+void GenerateYOLOXAnchors(const std::vector<int>& size,
+                          const std::vector<int>& downsample_strides,
+                          std::vector<YOLOXAnchor>* anchors) {
+  // size: tuple of input (width, height)
+  // downsample_strides: downsample strides in YOLOX, e.g (8,16,32)
+  const int width = size[0];
+  const int height = size[1];
+  for (const auto& ds : downsample_strides) {
+    int num_grid_w = width / ds;
+    int num_grid_h = height / ds;
+    for (int g1 = 0; g1 < num_grid_h; ++g1) {
+      for (int g0 = 0; g0 < num_grid_w; ++g0) {
+        (*anchors).emplace_back(YOLOXAnchor{g0, g1, ds});
+      }
+    }
+  }
+}
+
+void LetterBoxWithRightBottomPad(Mat* mat, std::vector<int> size,
+                                 std::vector<float> color) {
+  // specific pre process for YOLOX, not the same as YOLOv5
+  // reference: YOLOX/yolox/data/data_augment.py#L142
+  float r = std::min(size[1] * 1.0f / static_cast<float>(mat->Height()),
+                     size[0] * 1.0f / static_cast<float>(mat->Width()));
+
+  int resize_h = int(round(static_cast<float>(mat->Height()) * r));
+  int resize_w = int(round(static_cast<float>(mat->Width()) * r));
+
+  if (resize_h != mat->Height() || resize_w != mat->Width()) {
+    Resize::Run(mat, resize_w, resize_h);
+  }
+
+  int pad_w = size[0] - resize_w;
+  int pad_h = size[1] - resize_h;
+  // right-bottom padding for YOLOX
+  if (pad_h > 0 || pad_w > 0) {
+    int top = 0;
+    int left = 0;
+    int right = pad_w;
+    int bottom = pad_h;
+    Pad::Run(mat, top, bottom, left, right, color);
+  }
+}
+
+YOLOX::YOLOX(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 YOLOX::Initialize() {
+  // parameters for preprocess
+  size = {640, 640};
+  padding_value = {114.0, 114.0, 114.0};
+  downsample_strides = {8, 16, 32};
+  max_wh = 4096.0f;
+  is_decode_exported = false;
+
+  if (!InitRuntime()) {
+    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
+    return false;
+  }
+  // Check if the input shape is dynamic after Runtime already initialized.
+  is_dynamic_input_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
+    }
+  }
+  return true;
+}
+
+bool YOLOX::Preprocess(Mat* mat, FDTensor* output,
+                       std::map<std::string, std::array<float, 2>>* im_info) {
+  // YOLOX ( >= v0.1.1) preprocess steps
+  // 1. preproc
+  // 2. HWC->CHW
+  // 3. NO!!! BRG2GRB and Normalize needed in YOLOX
+  LetterBoxWithRightBottomPad(mat, size, padding_value);
+  // 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 YOLOX::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 r = std::min(out_h / ipt_h, out_w / ipt_w);
+  for (size_t i = 0; i < result->boxes.size(); ++i) {
+    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] / r, 0.0f);
+    result->boxes[i][1] = std::max(result->boxes[i][1] / r, 0.0f);
+    result->boxes[i][2] = std::max(result->boxes[i][2] / r, 0.0f);
+    result->boxes[i][3] = std::max(result->boxes[i][3] / r, 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 YOLOX::PostprocessWithDecode(
+    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;
+  }
+  // generate anchors with dowmsample strides
+  std::vector<YOLOXAnchor> anchors;
+  GenerateYOLOXAnchors(size, downsample_strides, &anchors);
+
+  // infer_result shape might look like (1,n,85=5+80)
+  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);
+    // fetch i-th anchor
+    float grid0 = static_cast<float>(anchors.at(i).grid0);
+    float grid1 = static_cast<float>(anchors.at(i).grid1);
+    float downsample_stride = static_cast<float>(anchors.at(i).stride);
+    // convert from offsets to [x, y, w, h]
+    float dx = data[s];
+    float dy = data[s + 1];
+    float dw = data[s + 2];
+    float dh = data[s + 3];
+
+    float x = (dx + grid0) * downsample_stride;
+    float y = (dy + grid1) * downsample_stride;
+    float w = std::exp(dw) * downsample_stride;
+    float h = std::exp(dh) * downsample_stride;
+
+    // convert from [x, y, w, h] to [x1, y1, x2, y2]
+    result->boxes.emplace_back(std::array<float, 4>{
+        x - w / 2.0f + label_id * max_wh, y - h / 2.0f + label_id * max_wh,
+        x + w / 2.0f + label_id * max_wh, y + h / 2.0f + label_id * max_wh});
+    // label_id * max_wh for multi classes NMS
+    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 r = std::min(out_h / ipt_h, out_w / ipt_w);
+  for (size_t i = 0; i < result->boxes.size(); ++i) {
+    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] / r, 0.0f);
+    result->boxes[i][1] = std::max(result->boxes[i][1] / r, 0.0f);
+    result->boxes[i][2] = std::max(result->boxes[i][2] / r, 0.0f);
+    result->boxes[i][3] = std::max(result->boxes[i][3] / r, 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 YOLOX::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 (is_decode_exported) {
+    if (!Postprocess(output_tensors[0], result, im_info, conf_threshold,
+                     nms_iou_threshold)) {
+      FDERROR << "Failed to post process." << std::endl;
+      return false;
+    }
+  } else {
+    if (!PostprocessWithDecode(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 megvii
+}  // namespace vision
+}  // namespace fastdeploy
\ No newline at end of file
diff --git a/fastdeploy/vision/megvii/yolox.h b/fastdeploy/vision/megvii/yolox.h
new file mode 100644
index 00000000000..7ff8edcf007
--- /dev/null
+++ b/fastdeploy/vision/megvii/yolox.h
@@ -0,0 +1,105 @@
+// 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 megvii {
+
+class FASTDEPLOY_DECL YOLOX : 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
+  YOLOX(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
+  std::string ModelName() const { return "megvii/YOLOX"; }
+
+  // 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;
+  // whether the model_file was exported with decode module. The official
+  // YOLOX/tools/export_onnx.py script will export ONNX file without 
+  // decode module. Please set it 'true' manually if the model file 
+  // was exported with decode module.
+  bool is_decode_exported;
+  // downsample strides for YOLOX to generate anchors, will take 
+  // (8,16,32) as default values, might have stride=64.
+  std::vector<int> downsample_strides;
+  // for offseting the boxes by classes when using NMS, default 4096.
+  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);
+
+  // YOLOX的官方脚本默认导出不带decode模块的模型文件 需要在后处理进行decode
+  bool PostprocessWithDecode(
+      FDTensor& infer_result, DetectionResult* result,
+      const std::map<std::string, std::array<float, 2>>& im_info,
+      float conf_threshold, float nms_iou_threshold);    
+
+  // 查看输入是否为动态维度的 不建议直接使用 不同模型的逻辑可能不一致
+  bool IsDynamicInput() const { return is_dynamic_input_; }                     
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape or not.)
+  // megvii/YOLOX 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_input_;    
+};
+
+}  // namespace megvii
+}  // namespace vision
+}  // namespace fastdeploy
diff --git a/fastdeploy/vision/meituan/__init__.py b/fastdeploy/vision/meituan/__init__.py
index 5ff45f0fcef..7b6635dd368 100644
--- a/fastdeploy/vision/meituan/__init__.py
+++ b/fastdeploy/vision/meituan/__init__.py
@@ -37,10 +37,6 @@ 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
diff --git a/fastdeploy/vision/meituan/meituan_pybind.cc b/fastdeploy/vision/meituan/meituan_pybind.cc
index 2667bf8bedf..d1e81fa582c 100644
--- a/fastdeploy/vision/meituan/meituan_pybind.cc
+++ b/fastdeploy/vision/meituan/meituan_pybind.cc
@@ -29,10 +29,6 @@ void BindMeituan(pybind11::module& m) {
              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)
diff --git a/fastdeploy/vision/meituan/yolov6.cc b/fastdeploy/vision/meituan/yolov6.cc
index 213f30b87f7..8f37bf89c6f 100644
--- a/fastdeploy/vision/meituan/yolov6.cc
+++ b/fastdeploy/vision/meituan/yolov6.cc
@@ -91,17 +91,18 @@ bool YOLOv6::Initialize() {
     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;
+  // Note that, We need to force is_mini_pad 'false' to keep static 
+  // shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
+  is_dynamic_input_ = false;
   auto shape = InputInfoOfRuntime(0).shape;
-  for (const auto &d: shape) {
-    if (d <= 0) {
-      is_dynamic_shape_ = true;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
     }
   }
-  if (!is_dynamic_shape_) {  
+  if (!is_dynamic_input_) {  
     is_mini_pad = false;
   }
   return true;
diff --git a/fastdeploy/vision/meituan/yolov6.h b/fastdeploy/vision/meituan/yolov6.h
index 81215b2342f..b2d6a062df0 100644
--- a/fastdeploy/vision/meituan/yolov6.h
+++ b/fastdeploy/vision/meituan/yolov6.h
@@ -33,28 +33,7 @@ class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
          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);
+  std::string ModelName() const { return "meituan/YOLOv6"; }
 
   // 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
@@ -65,9 +44,6 @@ class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
                        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
@@ -88,13 +64,38 @@ class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
   // for offseting the boxes by classes when using NMS, default 4096 in meituan/YOLOv6
   float max_wh;
 
- protected:
+ 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);
+
+  // 查看输入是否为动态维度的 不建议直接使用 不同模型的逻辑可能不一致
+  bool IsDynamicInput() const { return is_dynamic_input_; }         
+
   // 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
+  // while is_dynamic_input 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_;
+  bool is_dynamic_input_;
 };
+
 }  // namespace meituan
 }  // namespace vision
 }  // namespace fastdeploy
\ No newline at end of file
diff --git a/fastdeploy/vision/ppcls/model.h b/fastdeploy/vision/ppcls/model.h
index f649ca1977f..36841d74c68 100644
--- a/fastdeploy/vision/ppcls/model.h
+++ b/fastdeploy/vision/ppcls/model.h
@@ -16,6 +16,10 @@ class FASTDEPLOY_DECL Model : public FastDeployModel {
 
   std::string ModelName() const { return "ppclas-classify"; }
 
+  // TODO(jiangjiajun) Batch is on the way
+  virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
+
+ private:
   bool Initialize();
 
   bool BuildPreprocessPipelineFromConfig();
@@ -25,10 +29,6 @@ class FASTDEPLOY_DECL Model : public FastDeployModel {
   bool Postprocess(const FDTensor& infer_result, ClassifyResult* result,
                    int topk = 1);
 
-  // TODO(jiangjiajun) Batch is on the way
-  virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
-
- private:
   std::vector<std::shared_ptr<Processor>> processors_;
   std::string config_file_;
 };
diff --git a/fastdeploy/vision/ultralytics/yolov5.cc b/fastdeploy/vision/ultralytics/yolov5.cc
index 372f6c060a4..193cfe97948 100644
--- a/fastdeploy/vision/ultralytics/yolov5.cc
+++ b/fastdeploy/vision/ultralytics/yolov5.cc
@@ -1,3 +1,17 @@
+// 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/ultralytics/yolov5.h"
 #include "fastdeploy/utils/perf.h"
 #include "fastdeploy/vision/utils/utils.h"
@@ -73,6 +87,21 @@ bool YOLOv5::Initialize() {
     FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
     return false;
   }
+  // Check if the input shape is dynamic after Runtime already initialized, 
+  // Note that, We need to force is_mini_pad 'false' to keep static 
+  // shape after padding (LetterBox) when the is_dynamic_shape is 'false'.
+  is_dynamic_input_ = false;
+  auto shape = InputInfoOfRuntime(0).shape;
+  for (int i = 0; i < shape.size(); ++i) {
+    // if height or width is dynamic
+    if (i >= 2 && shape[i] <= 0) {
+      is_dynamic_input_ = true;
+      break;
+    }
+  }
+  if (!is_dynamic_input_) {  
+    is_mini_pad = false;
+  }
   return true;
 }
 
diff --git a/fastdeploy/vision/ultralytics/yolov5.h b/fastdeploy/vision/ultralytics/yolov5.h
index 9a8197e53bf..573b0294f2b 100644
--- a/fastdeploy/vision/ultralytics/yolov5.h
+++ b/fastdeploy/vision/ultralytics/yolov5.h
@@ -30,29 +30,7 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
          const Frontend& model_format = Frontend::ONNX);
 
   // 定义模型的名称
-  virtual std::string ModelName() const { return "ultralytics/yolov5"; }
-
-  // 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
-  // multi_label 后处理时box选取是否采用多标签方式
-  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, bool multi_label);
+  std::string ModelName() const { return "ultralytics/yolov5"; }
 
   // 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
@@ -61,7 +39,7 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
   // 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);
+                       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
@@ -84,7 +62,40 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
   float max_wh;
   // for different strategies to get boxes when postprocessing
   bool multi_label;
+
+ 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
+  // multi_label 后处理时box选取是否采用多标签方式
+  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 multi_label);
+
+  // 查看输入是否为动态维度的 不建议直接使用 不同模型的逻辑可能不一致
+  bool IsDynamicInput() const { return is_dynamic_input_; }        
+
+  // whether to inference with dynamic shape (e.g ONNX export with dynamic shape or not.)
+  // YOLOv5 official 'export_onnx.py' script will export dynamic 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_input_;
 };
+
 }  // namespace ultralytics
 }  // namespace vision
 }  // namespace fastdeploy
diff --git a/fastdeploy/vision/vision_pybind.cc b/fastdeploy/vision/vision_pybind.cc
index 604d808e074..23d0e91e0e2 100644
--- a/fastdeploy/vision/vision_pybind.cc
+++ b/fastdeploy/vision/vision_pybind.cc
@@ -19,6 +19,7 @@ namespace fastdeploy {
 void BindPpClsModel(pybind11::module& m);
 void BindUltralytics(pybind11::module& m);
 void BindMeituan(pybind11::module& m);
+void BindMegvii(pybind11::module& m);
 #ifdef ENABLE_VISION_VISUALIZE
 void BindVisualize(pybind11::module& m);
 #endif
@@ -42,6 +43,7 @@ void BindVision(pybind11::module& m) {
   BindPpClsModel(m);
   BindUltralytics(m);
   BindMeituan(m);
+  BindMegvii(m);
 #ifdef ENABLE_VISION_VISUALIZE
   BindVisualize(m);
 #endif  
diff --git a/model_zoo/vision/yolov5/README.md b/model_zoo/vision/yolov5/README.md
index efb5510759c..03b19d44cc3 100644
--- a/model_zoo/vision/yolov5/README.md
+++ b/model_zoo/vision/yolov5/README.md
@@ -1,5 +1,7 @@
 # YOLOv5部署示例
 
+当前支持模型版本为：[YOLOv5 v6.0](https://github.com/ultralytics/yolov5/releases/download/v6.0)
+
 本文档说明如何进行[YOLOv5](https://github.com/ultralytics/yolov5)的快速部署推理。本目录结构如下
 ```
 .
diff --git a/model_zoo/vision/yolov5/cpp/README.md b/model_zoo/vision/yolov5/cpp/README.md
index dd740ff58aa..a1f1bde49c9 100644
--- a/model_zoo/vision/yolov5/cpp/README.md
+++ b/model_zoo/vision/yolov5/cpp/README.md
@@ -1,5 +1,6 @@
 # 编译YOLOv5示例
 
+当前支持模型版本为：[YOLOv5 v6.0](https://github.com/ultralytics/yolov5/releases/download/v6.0)
 
 ```
 # 下载和解压预测库
diff --git a/model_zoo/vision/yolov6/README.md b/model_zoo/vision/yolov6/README.md
index 5fa3578bfce..accc6bdbb74 100644
--- a/model_zoo/vision/yolov6/README.md
+++ b/model_zoo/vision/yolov6/README.md
@@ -1,5 +1,7 @@
 # YOLOv6部署示例
 
+当前支持模型版本为：[YOLOv6 v0.1.0](https://github.com/meituan/YOLOv6/releases/download/0.1.0)
+
 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
 ```
 .
diff --git a/model_zoo/vision/yolov6/cpp/README.md b/model_zoo/vision/yolov6/cpp/README.md
index c7b4d4d7ab3..0e2c03dbfa7 100644
--- a/model_zoo/vision/yolov6/cpp/README.md
+++ b/model_zoo/vision/yolov6/cpp/README.md
@@ -1,5 +1,6 @@
 # 编译YOLOv6示例
 
+当前支持模型版本为：[YOLOv6 v0.1.0](https://github.com/meituan/YOLOv6/releases/download/0.1.0)
 
 ```
 # 下载和解压预测库
diff --git a/model_zoo/vision/yolov6/yolov6.py b/model_zoo/vision/yolov6/yolov6.py
index 5172679c970..fa8aca07409 100644
--- a/model_zoo/vision/yolov6/yolov6.py
+++ b/model_zoo/vision/yolov6/yolov6.py
@@ -9,7 +9,6 @@
 
 # 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")
diff --git a/model_zoo/vision/yolox/README.md b/model_zoo/vision/yolox/README.md
new file mode 100644
index 00000000000..d64a2f0ffae
--- /dev/null
+++ b/model_zoo/vision/yolox/README.md
@@ -0,0 +1,47 @@
+# YOLOX部署示例
+
+当前支持模型版本为：[YOLOX v0.1.1](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0)
+
+本文档说明如何进行[YOLOX](https://github.com/Megvii-BaseDetection/YOLOX)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
+│   └── yolox.cc        # C++ 示例代码
+├── README.md           # YOLOX 部署文档
+└── yolox.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YOLOX模型和测试图片
+```
+python yolox.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]
+17.151855,225.294434, 805.329712, 735.578613, 0.940478, 5
+671.162109,387.403961, 809.000000, 879.525513, 0.909566, 0
+54.373432,400.188110, 204.652756, 893.662537, 0.894507, 0
+221.339310,406.614960, 347.045593, 857.299927, 0.887144, 0
+0.083759,554.987305, 61.894527, 881.098816, 0.450202, 0
+```
+
+## 其它文档
+
+- [C++部署](./cpp/README.md)
+- [YOLOX API文档](./api.md)
diff --git a/model_zoo/vision/yolox/api.md b/model_zoo/vision/yolox/api.md
new file mode 100644
index 00000000000..c7a6f254b1e
--- /dev/null
+++ b/model_zoo/vision/yolox/api.md
@@ -0,0 +1,71 @@
+# YOLOX API说明
+
+## Python API
+
+### YOLOX类
+```
+fastdeploy.vision.megvii.YOLOX(model_file, params_file=None, runtime_option=None, model_format=fd.Frontend.ONNX)
+```
+YOLOX模型加载和初始化，当model_format为`fd.Frontend.ONNX`时，只需提供model_file，如`yolox_s.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
+> ```
+> YOLOX.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[yolox.py](./yolox.py)
+
+
+## C++ API
+
+### YOLOX类
+```
+fastdeploy::vision::megvii::YOLOX(
+        const string& model_file,
+        const string& params_file = "",
+        const RuntimeOption& runtime_option = RuntimeOption(),
+        const Frontend& model_format = Frontend::ONNX)
+```
+YOLOX模型加载和初始化，当model_format为`Frontend::ONNX`时，只需提供model_file，如`yolox_s.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
+> ```
+> YOLOX::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/yolox.cc](cpp/yolox.cc)
+
+## 其它API使用
+
+- [模型部署RuntimeOption配置](../../../docs/api/runtime_option.md)
diff --git a/model_zoo/vision/yolox/cpp/CMakeLists.txt b/model_zoo/vision/yolox/cpp/CMakeLists.txt
new file mode 100644
index 00000000000..fe9668f6a0a
--- /dev/null
+++ b/model_zoo/vision/yolox/cpp/CMakeLists.txt
@@ -0,0 +1,17 @@
+PROJECT(yolox_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(yolox_demo ${PROJECT_SOURCE_DIR}/yolox.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(yolox_demo ${FASTDEPLOY_LIBS})
diff --git a/model_zoo/vision/yolox/cpp/README.md b/model_zoo/vision/yolox/cpp/README.md
new file mode 100644
index 00000000000..cc48878f60b
--- /dev/null
+++ b/model_zoo/vision/yolox/cpp/README.md
@@ -0,0 +1,31 @@
+# 编译YOLOX示例
+
+当前支持模型版本为：[YOLOX v0.1.1](https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0)
+
+```
+# 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/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.onnx
+wget https://raw.githubusercontent.com/ultralytics/yolov5/master/data/images/bus.jpg
+
+# 执行
+./yolox_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]
+17.151855,225.294434, 805.329712, 735.578613, 0.940478, 5
+671.162109,387.403961, 809.000000, 879.525513, 0.909566, 0
+54.373432,400.188110, 204.652756, 893.662537, 0.894507, 0
+221.339310,406.614960, 347.045593, 857.299927, 0.887144, 0
+0.083759,554.987305, 61.894527, 881.098816, 0.450202, 0
+```
diff --git a/model_zoo/vision/yolox/cpp/yolox.cc b/model_zoo/vision/yolox/cpp/yolox.cc
new file mode 100644
index 00000000000..934a50bea8e
--- /dev/null
+++ b/model_zoo/vision/yolox/cpp/yolox.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::megvii::YOLOX("yolox_s.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;
+}
diff --git a/model_zoo/vision/yolox/yolox.py b/model_zoo/vision/yolox/yolox.py
new file mode 100644
index 00000000000..8fd1a8a021a
--- /dev/null
+++ b/model_zoo/vision/yolox/yolox.py
@@ -0,0 +1,23 @@
+import fastdeploy as fd
+import cv2 
+
+# 下载模型和测试图片
+model_url = "https://github.com/Megvii-BaseDetection/YOLOX/releases/download/0.1.1rc0/yolox_s.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.megvii.YOLOX("yolox_s.onnx")
+
+# 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)