[Tutorial] Add ResNet Support and FastDeploy Tutorial for External Models (#347)

* 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) * 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> * Yolor (#16) * 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> * add is_dynamic for YOLO series (#22) * modify ppmatting backend and docs * modify ppmatting docs * fix the PPMatting size problem * fix LimitShort's log * retrigger ci * modify PPMatting docs * modify the way for dealing with LimitShort * first commit for ResNet and AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * modify AddModel.md * add explaination to code * modify add_new_model.md * move add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * add C++ Comment * add link to add_new_model.md * modify for doxygen * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * modify add_new_model.md * add resnet explaination and modify docs 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>
2025-10-05 16:48:03 +08:00 · 2022-10-21 09:29:47 +08:00
parent b565c15bf7
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--- a/.new_docs/cn/faq/add_new_model.md
+++ b/.new_docs/cn/faq/add_new_model.md
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+# FastDeploy外部模型集成指引
+
+在FastDeploy里面新增一个模型，包括增加C++/Python的部署支持。 本文以torchvision v0.12.0中的ResNet50模型为例，介绍使用FastDeploy做外部[模型集成](#modelsupport)，具体包括如下3步。
+
+| 步骤 | 说明                                | 创建或修改的文件                            |
+|:------:|:-------------------------------------:|:---------------------------------------------:|
+| [1](#step2)    |  在fastdeploy/vision相应任务模块增加模型实现       | resnet.h、resnet.cc、vision.h                     |
+| [2](#step4)     | 通过pybind完成Python接口绑定 | resnet_pybind.cc、classification_pybind.cc |
+| [3](#step5)     | 实现Python相应调用接口    | resnet.py、\_\_init\_\_.py                        |
+
+在完成上述3步之后，一个外部模型就集成好了。
+<br />
+如果您想为FastDeploy贡献代码，还需要为新增模型添加测试代码、说明文档和代码注释，可在[测试](#test)中查看。
+## 模型集成     <span id="modelsupport"></span>
+
+### 模型准备  <span id="step1"></span>
+
+
+在集成外部模型之前，先要将训练好的模型（.pt，.pdparams 等）转换成FastDeploy支持部署的模型格式（.onnx，.pdmodel）。多数开源仓库会提供模型转换脚本，可以直接利用脚本做模型的转换。由于torchvision没有提供转换脚本，因此手动编写转换脚本，本文中将 `torchvison.models.resnet50` 转换为 `resnet50.onnx`， 参考代码如下：
+
+```python
+import torch
+import torchvision.models as models
+model = models.resnet50(pretrained=True)
+batch_size = 1  #批处理大小
+input_shape = (3, 224, 224)   #输入数据,改成自己的输入shape
+model.eval()
+x = torch.randn(batch_size, *input_shape)	# 生成张量
+export_onnx_file = "resnet50.onnx"			# 目的ONNX文件名
+torch.onnx.export(model,
+                    x,
+                    export_onnx_file,
+                    opset_version=12,
+                    input_names=["input"],	# 输入名
+                    output_names=["output"],	# 输出名
+                    dynamic_axes={"input":{0:"batch_size"},  # 批处理变量
+                                    "output":{0:"batch_size"}})
+```
+执行上述脚本将会得到 `resnet50.onnx` 文件。
+
+### C++部分  <span id="step2"></span>
+* 创建`resnet.h`文件
+  * 创建位置
+    * FastDeploy/fastdeploy/vision/classification/contrib/resnet.h (FastDeploy/C++代码存放位置/视觉模型/任务名称/外部模型/模型名.h)
+  * 创建内容
+    * 首先在resnet.h中创建 ResNet类并继承FastDeployModel父类，之后声明`Predict`、`Initialize`、`Preprocess`、`Postprocess`和`构造函数`，以及必要的变量，具体的代码细节请参考[resnet.h](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-69128489e918f305c208476ba793d8167e77de2aa7cadf5dcbac30da448bd28e)。
+
+```C++
+class FASTDEPLOY_DECL ResNet : public FastDeployModel {
+ public:
+  ResNet(...);
+  virtual bool Predict(...);
+ private:
+  bool Initialize();
+  bool Preprocess(...);
+  bool Postprocess(...);
+};
+```
+
+* 创建`resnet.cc`文件
+  * 创建位置
+    * FastDeploy/fastdeploy/vision/classification/contrib/resnet.cc (FastDeploy/C++代码存放位置/视觉模型/任务名称/外部模型/模型名.cc)
+  * 创建内容
+    * 在`resnet.cc`中实现`resnet.h`中声明函数的具体逻辑，其中`PreProcess` 和 `PostProcess`需要参考源官方库的前后处理逻辑复现，ResNet每个函数具体逻辑如下，具体的代码请参考[resnet.cc](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-d229d702de28345253a53f2a5839fd2c638f3d32fffa6a7d04d23db9da13a871)。
+
+```C++
+ResNet::ResNet(...) {
+  // 构造函数逻辑
+  // 1. 指定 Backend 2. 设置RuntimeOption 3. 调用Initialize()函数
+}
+bool ResNet::Initialize() {
+  // 初始化逻辑
+  // 1. 全局变量赋值 2. 调用InitRuntime()函数
+  return true;
+}
+bool ResNet::Preprocess(Mat* mat, FDTensor* output) {
+// 前处理逻辑
+// 1. Resize 2. BGR2RGB 3. Normalize 4. HWC2CHW 5. 处理结果存入 FDTensor类中  
+  return true;
+}
+bool ResNet::Postprocess(FDTensor& infer_result, ClassifyResult* result, int topk) {
+  //后处理逻辑
+  // 1. Softmax 2. Choose topk labels 3. 结果存入 ClassifyResult类
+  return true;
+}
+bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
+  Preprocess(...)
+  Infer(...)
+  Postprocess(...)
+  return true;
+}
+```
+<span id="step3"></span>
+* 在`vision.h`文件中加入新增模型文件
+  * 修改位置
+    * FastDeploy/fastdeploy/vision.h
+  * 修改内容
+
+```C++
+#ifdef ENABLE_VISION
+#include "fastdeploy/vision/classification/contrib/resnet.h"
+#endif
+```
+
+
+### Pybind部分  <span id="step4"></span>
+
+* 创建Pybind文件  
+  * 创建位置
+    * FastDeploy/fastdeploy/vision/classification/contrib/resnet_pybind.cc (FastDeploy/C++代码存放位置/视觉模型/任务名称/外部模型/模型名_pybind.cc)
+  * 创建内容
+    * 利用Pybind将C++中的函数变量绑定到Python中，具体代码请参考[resnet_pybind.cc](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-270af0d65720310e2cfbd5373c391b2110d65c0f4efa547f7b7eeffcb958bdec)。
+```C++
+void BindResNet(pybind11::module& m) {
+  pybind11::class_<vision::classification::ResNet, FastDeployModel>(
+      m, "ResNet")
+      .def(pybind11::init<std::string, std::string, RuntimeOption, ModelFormat>())
+      .def("predict", ...)
+      .def_readwrite("size", &vision::classification::ResNet::size)
+      .def_readwrite("mean_vals", &vision::classification::ResNet::mean_vals)
+      .def_readwrite("std_vals", &vision::classification::ResNet::std_vals);
+}
+```
+
+* 调用Pybind函数
+  * 修改位置
+    * FastDeploy/fastdeploy/vision/classification/classification_pybind.cc (FastDeploy/C++代码存放位置/视觉模型/任务名称/任务名称}_pybind.cc)
+  * 修改内容
+```C++
+void BindResNet(pybind11::module& m);
+void BindClassification(pybind11::module& m) {
+  auto classification_module =
+      m.def_submodule("classification", "Image classification models.");
+  BindResNet(classification_module);
+}
+```
+
+
+### Python部分  <span id="step5"></span>
+
+
+* 创建`resnet.py`文件
+  * 创建位置
+    * FastDeploy/python/fastdeploy/vision/classification/contrib/resnet.py (FastDeploy/Python代码存放位置/fastdeploy/视觉模型/任务名称/外部模型/模型名.py)
+  * 创建内容
+    * 创建ResNet类继承自FastDeployModel，实现 `\_\_init\_\_`、Pybind绑定的函数（如`predict()`）、以及`对Pybind绑定的全局变量进行赋值和获取的函数`，具体代码请参考[resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157)。
+
+```python
+
+class ResNet(FastDeployModel):
+    def __init__(self, ...):
+        self._model = C.vision.classification.ResNet(...)
+    def predict(self, input_image, topk=1):
+        return self._model.predict(input_image, topk)
+    @property
+    def size(self):
+        return self._model.size
+    @size.setter
+    def size(self, wh):
+        ...
+```
+<span id="step6"></span>
+* 导入ResNet类
+  * 修改位置
+    * FastDeploy/python/fastdeploy/vision/classification/\_\_init\_\_.py (FastDeploy/Python代码存放位置/fastdeploy/视觉模型/任务名称/\_\_init\_\_.py)
+  * 修改内容
+
+```Python
+from .contrib.resnet import ResNet
+```
+
+## 测试  <span id="test"></span>
+### 编译
+  * C++
+    * 位置：FastDeploy/
+
+```
+mkdir build & cd build
+cmake .. -DENABLE_ORT_BACKEND=ON -DENABLE_VISION=ON -DCMAKE_INSTALL_PREFIX=${PWD/fastdeploy-0.0.3
+-DENABLE_PADDLE_BACKEND=ON -DENABLE_TRT_BACKEND=ON -DWITH_GPU=ON -DTRT_DIRECTORY=/PATH/TO/TensorRT/
+make -j8
+make install
+```
+
+ 编译会得到 build/fastdeploy-0.0.3/。
+
+  * Python
+    * 位置：FastDeploy/python/
+
+```
+export TRT_DIRECTORY=/PATH/TO/TensorRT/    # 如果用TensorRT 需要填写TensorRT所在位置，并开启 ENABLE_TRT_BACKEND
+export ENABLE_TRT_BACKEND=ON
+export WITH_GPU=ON
+export ENABLE_PADDLE_BACKEND=ON
+export ENABLE_OPENVINO_BACKEND=ON
+export ENABLE_VISION=ON
+export ENABLE_ORT_BACKEND=ON
+python setup.py build
+python setup.py bdist_wheel
+cd dist
+pip install fastdeploy_gpu_python-版本号-cpxx-cpxxm-系统架构.whl
+```
+
+### 编写测试代码
+  * 创建位置: FastDeploy/examples/vision/classification/resnet/ (FastDeploy/示例目录/视觉模型/任务名称/模型名/)
+  * 创建目录结构
+
+```
+.
+├── cpp
+│   ├── CMakeLists.txt
+│   ├── infer.cc    // C++ 版本测试代码
+│   └── README.md   // C++版本使用文档
+├── python
+│   ├── infer.py    // Python 版本测试代码
+│   └── README.md   // Python版本使用文档
+└── README.md   // ResNet 模型集成说明文档
+```
+
+* C++
+  * 编写CmakeLists文件、C++ 代码以及 README.md 内容请参考[cpp/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-afcbe607b796509581f89e38b84190717f1eeda2df0419a2ac9034197ead5f96)。
+  * 编译 infer.cc
+    * 位置：FastDeploy/examples/vision/classification/resnet/cpp/
+
+```
+mkdir build & cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=/PATH/TO/FastDeploy/build/fastdeploy-0.0.3/
+make
+```
+
+* Python
+  * Python 代码以及 README.md 内容请参考[python/](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-5a0d6be8c603a8b81454ac14c17fb93555288d9adf92bbe40454449309700135)。
+
+### 为代码添加注释
+为了方便用户理解代码，我们需要为新增代码添加注释，添加注释方法可参考如下示例。
+- C++ 代码
+您需要在resnet.h文件中为函数和变量增加注释，有如下三种注释方式，具体可参考[resnet.h](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-69128489e918f305c208476ba793d8167e77de2aa7cadf5dcbac30da448bd28e)。
+
+```C++
+/** \brief Predict for the input "im", the result will be saved in "result".
+*
+* \param[in] im Input image for inference.
+* \param[in] result Saving the inference result.
+* \param[in] topk The length of return values, e.g., if topk==2, the result will include the 2 most possible class label for input image.
+*/
+virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
+
+/// Tuple of (width, height)
+std::vector<int> size;
+/*! @brief Initialize for ResNet model, assign values to the global variables and call InitRuntime()
+*/
+bool Initialize();
+```
+- Python 代码
+你需要为resnet.py文件中的函数和变量增加适当的注释，示例如下，具体可参考[resnet.py](https://github.com/PaddlePaddle/FastDeploy/pull/347/files#diff-a4dc5ec2d450e91f1c03819bf314c238b37ac678df56d7dea3aab7feac10a157)。
+
+```python  
+  def predict(self, input_image, topk=1):
+    """Classify an input image
+
+    :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+    :param topk: (int)The topk result by the classify confidence score, default 1
+    :return: ClassifyResult
+    """
+    return self._model.predict(input_image, topk)
+```
+
+对于集成模型过程中的其他文件，您也可以对实现的细节添加适当的注释说明。
--- a/docs/api_docs/python/image_classification.md
+++ b/docs/api_docs/python/image_classification.md
@@ -15,3 +15,11 @@
    :members:
    :inherited-members:
 ```
+
+## fastdeploy.vision.classification.ResNet
+
+```{eval-rst}
+.. autoclass:: fastdeploy.vision.classification.ResNet
+    :members:
+    :inherited-members:
+```
--- a/examples/vision/classification/resnet/README.md
+++ b/examples/vision/classification/resnet/README.md
@@ -0,0 +1,53 @@
+# ResNet准备部署模型
+
+- ResNet部署实现来自[Torchvision](https://github.com/pytorch/vision/tree/v0.12.0)的代码，和[基于ImageNet2012的预训练模型](https://github.com/pytorch/vision/tree/v0.12.0)。
+
+  - （1）[官方库](https://github.com/pytorch/vision/tree/v0.12.0)提供的*.pt通过[导出ONNX模型](#导出ONNX模型)操作后，可进行部署；
+  - （2）自己数据训练的ResNet模型，按照[导出ONNX模型](#%E5%AF%BC%E5%87%BAONNX%E6%A8%A1%E5%9E%8B)操作后，参考[详细部署文档](#详细部署文档)完成部署。
+
+
+## 导出ONNX模型
+
+
+  导入[Torchvision](https://github.com/pytorch/vision/tree/v0.12.0)，加载预训练模型，并进行模型转换，具体转换步骤如下。
+
+  ```python
+    import torch
+    import torchvision.models as models
+
+    model = models.resnet50(pretrained=True)
+    batch_size = 1  #批处理大小
+    input_shape = (3, 224, 224)   #输入数据,改成自己的输入shape
+    # #set the model to inference mode
+    model.eval()
+    x = torch.randn(batch_size, *input_shape)	# 生成张量
+    export_onnx_file = "ResNet50.onnx"			# 目的ONNX文件名
+    torch.onnx.export(model,
+                        x,
+                        export_onnx_file,
+                        opset_version=12,
+                        input_names=["input"],	# 输入名
+                        output_names=["output"],	# 输出名
+                        dynamic_axes={"input":{0:"batch_size"},  # 批处理变量
+                                        "output":{0:"batch_size"}})
+  ```
+
+## 下载预训练ONNX模型
+
+为了方便开发者的测试，下面提供了ResNet导出的各系列模型，开发者可直接下载使用。（下表中模型的精度来源于源官方库）
+| 模型                                                               | 大小    | 精度    |
+|:---------------------------------------------------------------- |:----- |:----- |
+| [ResNet-18](https://bj.bcebos.com/paddlehub/fastdeploy/resnet18.onnx) | 45MB  | |
+| [ResNet-34](https://bj.bcebos.com/paddlehub/fastdeploy/resnet34.onnx) | 84MB | |
+| [ResNet-50](https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx) | 98MB | |
+| [ResNet-101](https://bj.bcebos.com/paddlehub/fastdeploy/resnet101.onnx) | 170MB | |
+
+
+## 详细部署文档
+
+- [Python部署](python)
+- [C++部署](cpp)
+
+## 版本说明
+
+- 本版本文档和代码基于[Torchvision v0.12.0](https://github.com/pytorch/vision/tree/v0.12.0) 编写
--- a/examples/vision/classification/resnet/cpp/CMakeLists.txt
+++ b/examples/vision/classification/resnet/cpp/CMakeLists.txt
@@ -0,0 +1,14 @@
+PROJECT(infer_demo C CXX)
+CMAKE_MINIMUM_REQUIRED (VERSION 3.10)
+
+# 指定下载解压后的fastdeploy库路径
+option(FASTDEPLOY_INSTALL_DIR "Path of downloaded fastdeploy sdk.")
+
+include(${FASTDEPLOY_INSTALL_DIR}/FastDeploy.cmake)
+
+# 添加FastDeploy依赖头文件
+include_directories(${FASTDEPLOY_INCS})
+
+add_executable(infer_demo ${PROJECT_SOURCE_DIR}/infer.cc)
+# 添加FastDeploy库依赖
+target_link_libraries(infer_demo ${FASTDEPLOY_LIBS})
--- a/examples/vision/classification/resnet/cpp/README.md
+++ b/examples/vision/classification/resnet/cpp/README.md
@@ -0,0 +1,77 @@
+# ResNet C++部署示例
+
+本目录下提供`infer.cc`快速完成ResNet系列模型在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)  
+- 2. 根据开发环境，下载预编译部署库和samples代码，参考[FastDeploy预编译库](../../../../../docs/quick_start)
+
+以Linux上 ResNet50 推理为例，在本目录执行如下命令即可完成编译测试
+
+```bash
+#下载SDK，编译模型examples代码（SDK中包含了examples代码）
+wget https://bj.bcebos.com/fastdeploy/release/cpp/fastdeploy-linux-x64-gpu-0.2.1.tgz
+tar xvf fastdeploy-linux-x64-gpu-0.2.1.tgz
+cd fastdeploy-linux-x64-gpu-0.2.1/examples/vision/classification/resnet/cpp
+mkdir build
+cd build
+cmake .. -DFASTDEPLOY_INSTALL_DIR=${PWD}/../../../../../../../fastdeploy-linux-x64-gpu-0.2.1
+make -j
+
+# 下载ResNet模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+
+# CPU推理
+./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 0
+# GPU推理
+./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 1
+# GPU上TensorRT推理
+./infer_demo resnet50.onnx ILSVRC2012_val_00000010.jpeg 2
+```
+
+以上命令只适用于Linux或MacOS, Windows下SDK的使用方式请参考:  
+- [如何在Windows中使用FastDeploy C++ SDK](../../../../../docs/compile/how_to_use_sdk_on_windows.md)
+
+## ResNet C++接口
+
+### ResNet类
+
+```c++
+
+fastdeploy::vision::classification::ResNet(
+        const std::string& model_file,
+        const std::string& params_file = "",
+        const RuntimeOption& custom_option = RuntimeOption(),
+        const ModelFormat& model_format = ModelFormat::ONNX)
+```
+
+
+**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**(ModelFormat): 模型格式，默认为ONNX格式
+
+#### Predict函数
+
+> ```c++
+> ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk = 1)
+> ```
+>
+> 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**: 分类结果，包括label_id，以及相应的置信度, ClassifyResult说明参考[视觉模型预测结果](../../../../../docs/api/vision_results/)
+> > * **topk**(int):返回预测概率最高的topk个分类结果，默认为1
+
+
+- [模型介绍](../../)
+- [Python部署](../python)
+- [视觉模型预测结果](../../../../../docs/api/vision_results/)
+- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
--- a/examples/vision/classification/resnet/cpp/infer.cc
+++ b/examples/vision/classification/resnet/cpp/infer.cc
@@ -0,0 +1,94 @@
+// 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"
+
+void CpuInfer(const std::string& model_file, const std::string& image_file) {
+  auto model = fastdeploy::vision::classification::ResNet(model_file);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  std::cout << res.Str() << std::endl;
+
+}
+
+void GpuInfer(const std::string& model_file, const std::string& image_file) {
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  auto model = fastdeploy::vision::classification::ResNet(model_file, "", option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  std::cout << res.Str() << std::endl;
+}
+
+void TrtInfer(const std::string& model_file, const std::string& image_file) {
+  auto option = fastdeploy::RuntimeOption();
+  option.UseGpu();
+  option.UseTrtBackend();
+  option.SetTrtInputShape("images", {1, 3, 224, 224});
+  auto model = fastdeploy::vision::classification::ResNet(model_file, "", option);
+  if (!model.Initialized()) {
+    std::cerr << "Failed to initialize." << std::endl;
+    return;
+  }
+
+  auto im = cv::imread(image_file);
+  
+  fastdeploy::vision::ClassifyResult res;
+  if (!model.Predict(&im, &res)) {
+    std::cerr << "Failed to predict." << std::endl;
+    return;
+  }
+  std::cout << res.Str() << std::endl;
+}
+
+int main(int argc, char* argv[]) {
+  if (argc < 4) {
+    std::cout << "Usage: infer_demo path/to/model path/to/image run_option, "
+                 "e.g ./infer_model ./resnet50.onnx ./test.jpeg 0"
+              << std::endl;
+    std::cout << "The data type of run_option is int, 0: run with cpu; 1: run "
+                 "with gpu; 2: run with gpu and use tensorrt backend."
+              << std::endl;
+    return -1;
+  }
+
+  if (std::atoi(argv[3]) == 0) {
+    CpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 1) {
+    GpuInfer(argv[1], argv[2]);
+  } else if (std::atoi(argv[3]) == 2) {
+    TrtInfer(argv[1], argv[2]);
+  }
+  return 0;
+}
--- a/examples/vision/classification/resnet/python/README.md
+++ b/examples/vision/classification/resnet/python/README.md
@@ -0,0 +1,72 @@
+# ResNet模型 Python部署示例
+
+在部署前，需确认以下两个步骤
+
+- 1. 软硬件环境满足要求，参考[FastDeploy环境要求](../../../../../docs/environment.md)  
+- 2. FastDeploy Python whl包安装，参考[FastDeploy Python安装](../../../../../docs/quick_start)
+
+本目录下提供`infer.py`快速完成ResNet50_vd在CPU/GPU，以及GPU上通过TensorRT加速部署的示例。执行如下脚本即可完成
+
+```bash
+#下载部署示例代码
+git clone https://github.com/PaddlePaddle/FastDeploy.git
+cd  FastDeploy/examples/vision/classification/resnet/python
+
+# 下载ResNet50_vd模型文件和测试图片
+wget https://bj.bcebos.com/paddlehub/fastdeploy/resnet50.onnx
+wget https://gitee.com/paddlepaddle/PaddleClas/raw/release/2.4/deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
+
+# CPU推理
+python infer.py --model resnet50.onnx --image ILSVRC2012_val_00000010.jpeg --device cpu --topk 1
+# GPU推理
+python infer.py --model resnet50.onnx --image ILSVRC2012_val_00000010.jpeg --device gpu --topk 1
+# GPU上使用TensorRT推理 （注意：TensorRT推理第一次运行，有序列化模型的操作，有一定耗时，需要耐心等待）
+python infer.py --model resnet50.onnx --image ILSVRC2012_val_00000010.jpeg --device gpu --use_trt True --topk 1
+```
+
+运行完成后返回结果如下所示
+```bash
+ClassifyResult(
+label_ids: 332,
+scores: 0.825349,
+)
+```
+
+## ResNet Python接口
+
+```python
+fd.vision.classification.ResNet(model_file, params_file, runtime_option=None, model_format=ModelFormat.ONNX)
+```
+
+
+**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**(ModelFormat): 模型格式，默认为ONNX格式
+
+### predict函数
+
+> ```python
+> ResNet.predict(input_image, topk=1)
+> ```
+>
+> 模型预测结口，输入图像直接输出检测结果。
+>
+> **参数**
+>
+> > * **input_image**(np.ndarray): 输入数据，注意需为HWC，BGR格式
+> > * **topk**(int):返回预测概率最高的topk个分类结果，默认为1
+
+> **返回**
+>
+> > 返回`fastdeploy.vision.ClassifyResult`结构体，结构体说明参考文档[视觉模型预测结果](../../../../../docs/api/vision_results/)
+
+
+## 其它文档
+
+- [ResNet 模型介绍](..)
+- [ResNet C++部署](../cpp)
+- [模型预测结果说明](../../../../../docs/api/vision_results/)
+- [如何切换模型推理后端引擎](../../../../../docs/runtime/how_to_change_backend.md)
--- a/examples/vision/classification/resnet/python/infer.py
+++ b/examples/vision/classification/resnet/python/infer.py
@@ -0,0 +1,50 @@
+import fastdeploy as fd
+import cv2
+import os
+
+
+def parse_arguments():
+    import argparse
+    import ast
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--model", required=True, help="Path of PaddleClas model.")
+    parser.add_argument(
+        "--image", type=str, required=True, help="Path of test image file.")
+    parser.add_argument(
+        "--topk", type=int, default=1, help="Return topk results.")
+    parser.add_argument(
+        "--device",
+        type=str,
+        default='cpu',
+        help="Type of inference device, support 'cpu' or 'gpu'.")
+    parser.add_argument(
+        "--use_trt",
+        type=ast.literal_eval,
+        default=False,
+        help="Wether to use tensorrt.")
+    return parser.parse_args()
+
+
+def build_option(args):
+    option = fd.RuntimeOption()
+
+    if args.device.lower() == "gpu":
+        option.use_gpu()
+
+    if args.use_trt:
+        option.use_trt_backend()
+    return option
+
+
+args = parse_arguments()
+
+# 配置runtime，加载模型
+runtime_option = build_option(args)
+
+model = fd.vision.classification.ResNet(
+    args.model, runtime_option=runtime_option)
+# 预测图片分类结果
+im = cv2.imread(args.image)
+result = model.predict(im.copy(), args.topk)
+print(result)
--- a/fastdeploy/vision.h
+++ b/fastdeploy/vision.h
@@ -17,6 +17,7 @@
 #ifdef ENABLE_VISION
 #include "fastdeploy/vision/classification/contrib/yolov5cls.h"
 #include "fastdeploy/vision/classification/ppcls/model.h"
+#include "fastdeploy/vision/classification/contrib/resnet.h"
 #include "fastdeploy/vision/detection/contrib/nanodet_plus.h"
 #include "fastdeploy/vision/detection/contrib/scaledyolov4.h"
 #include "fastdeploy/vision/detection/contrib/yolor.h"
--- a/fastdeploy/vision/classification/classification_pybind.cc
+++ b/fastdeploy/vision/classification/classification_pybind.cc
@@ -18,11 +18,12 @@ namespace fastdeploy {

 void BindYOLOv5Cls(pybind11::module& m);
 void BindPaddleClas(pybind11::module& m);
-
+void BindResNet(pybind11::module& m);
 void BindClassification(pybind11::module& m) {
  auto classification_module =
      m.def_submodule("classification", "Image classification models.");
  BindYOLOv5Cls(classification_module);
  BindPaddleClas(classification_module);
+  BindResNet(classification_module);
 }
 }  // namespace fastdeploy
--- a/fastdeploy/vision/classification/contrib/resnet.cc
+++ b/fastdeploy/vision/classification/contrib/resnet.cc
@@ -0,0 +1,134 @@
+// 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/classification/contrib/resnet.h"
+#include "fastdeploy/vision/utils/utils.h"
+#include "fastdeploy/utils/perf.h"
+
+namespace fastdeploy {
+namespace vision {
+namespace classification {
+
+ResNet::ResNet(const std::string& model_file,
+               const std::string& params_file,
+               const RuntimeOption& custom_option,
+               const ModelFormat& model_format) {
+  // In constructor, the 3 steps below are necessary.
+  // 1. set the Backend 2. set RuntimeOption 3. call Initialize()
+
+  if (model_format == ModelFormat::ONNX) {
+    valid_cpu_backends = {Backend::ORT, Backend::OPENVINO}; 
+    valid_gpu_backends = {Backend::ORT, Backend::TRT};  
+  } else {
+    valid_cpu_backends = {Backend::PDINFER};
+    valid_gpu_backends = {Backend::PDINFER};
+  }
+  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 ResNet::Initialize() {
+
+  // In this function, the 3 steps below are necessary.
+  // 1. assign values to the global variables 2. call InitRuntime()
+
+  size = {224, 224};
+  mean_vals = {0.485f, 0.456f, 0.406f};
+  std_vals = {0.229f, 0.224f, 0.225f};
+
+  if (!InitRuntime()) {
+    FDERROR << "Failed to initialize fastdeploy backend." << std::endl;
+    return false;
+  }
+  return true;
+}
+
+
+bool ResNet::Preprocess(Mat* mat, FDTensor* output) {
+
+// In this function, the preprocess need be implemented according to the original Repos,
+// The result of preprocess has to be saved in FDTensor variable, because the input of Infer() need to be std::vector<FDTensor>.
+// 1. Resize 2. BGR2RGB 3. Normalize 4. HWC2CHW 5. Put the result into FDTensor variable.
+        
+  if (mat->Height()!=size[0] || mat->Width()!=size[1]){
+    int interp = cv::INTER_LINEAR;
+    Resize::Run(mat, size[1], size[0], -1, -1, interp);
+  }
+
+  BGR2RGB::Run(mat);
+  Normalize::Run(mat, mean_vals, std_vals);
+
+  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 ResNet::Postprocess(FDTensor& infer_result,
+                                  ClassifyResult* result, int topk) {
+
+  // In this function, the postprocess need be implemented according to the original Repos,
+  // Finally the reslut of postprocess should be saved in ClassifyResult variable.
+  // 1. Softmax 2. Choose topk labels 3. Put the result into ClassifyResult variable.
+
+  int num_classes = infer_result.shape[1];
+  Softmax(infer_result, &infer_result);
+  const float* infer_result_buffer = reinterpret_cast<float*>(infer_result.Data());
+  topk = std::min(num_classes, topk);
+  result->label_ids =
+      utils::TopKIndices(infer_result_buffer, num_classes, topk);
+  result->scores.resize(topk);
+  for (int i = 0; i < topk; ++i) {
+    result->scores[i] = *(infer_result_buffer + result->label_ids[i]);
+  }
+  return true;
+}
+
+bool ResNet::Predict(cv::Mat* im, ClassifyResult* result, int topk) {
+
+  // In this function, the Preprocess(), Infer(), and Postprocess() are called sequentially.
+
+  Mat mat(*im);
+  std::vector<FDTensor> processed_data(1);
+  if (!Preprocess(&mat, &(processed_data[0]))) {
+    FDERROR << "Failed to preprocess input data while using model:"
+            << ModelName() << "." << std::endl;
+    return false;
+  }
+  processed_data[0].name = InputInfoOfRuntime(0).name;
+
+  std::vector<FDTensor> output_tensors;
+  if (!Infer(processed_data, &output_tensors)) {
+    FDERROR << "Failed to inference while using model:" << ModelName() << "."
+            << std::endl;
+    return false;
+  }
+
+  if (!Postprocess(output_tensors[0], result, topk)) {
+    FDERROR << "Failed to postprocess while using model:" << ModelName() << "."
+            << std::endl;
+    return false;
+  }
+
+  return true;
+}
+
+
+}  // namespace classification
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/classification/contrib/resnet.h
+++ b/fastdeploy/vision/classification/contrib/resnet.h
@@ -0,0 +1,74 @@
+// 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"
+
+// The namespace shoulde be
+// fastdeploy::vision::classification (fastdeploy::vision::${task})
+namespace fastdeploy {
+namespace vision {
+/** \brief All object classification model APIs are defined inside this namespace
+ *
+ */
+namespace classification {
+/*! @brief ResNet series model
+ */
+class FASTDEPLOY_DECL ResNet : public FastDeployModel {
+ public:
+  /** \brief  Set path of model file and the configuration of runtime.
+   *
+   * \param[in] model_file Path of model file, e.g ./resnet50.onnx
+   * \param[in] params_file Path of parameter file, e.g ppyoloe/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] custom_option RuntimeOption for inference, the default will use cpu, and choose the backend defined in "valid_cpu_backends"
+   * \param[in] model_format Model format of the loaded model, default is ONNX format
+   */
+  ResNet(const std::string& model_file,
+               const std::string& params_file = "",
+               const RuntimeOption& custom_option = RuntimeOption(),
+               const ModelFormat& model_format = ModelFormat::ONNX);
+
+  virtual std::string ModelName() const { return "ResNet"; }
+   /** \brief Predict for the input "im", the result will be saved in "result".
+   *
+   * \param[in] im Input image for inference.
+   * \param[in] result Saving the inference result.
+   * \param[in] topk The length of return values, e.g., if topk==2, the result will include the 2 most possible class label for input image.
+   */
+  virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
+
+  /// Tuple of (width, height)
+  std::vector<int> size;
+  /// Mean parameters for normalize
+  std::vector<float> mean_vals;
+  /// Std parameters for normalize
+  std::vector<float> std_vals;
+
+
+ private:
+  /*! @brief Initialize for ResNet model, assign values to the global variables and call InitRuntime()
+  */
+  bool Initialize();
+  /// PreProcessing for the input "mat", the result will be saved in "outputs".
+  bool Preprocess(Mat* mat, FDTensor* outputs);
+  /*! @brief PostProcessing for the input "infer_result", the result will be saved in "result".
+  */
+  bool Postprocess(FDTensor& infer_result, ClassifyResult* result,
+                   int topk = 1);
+};
+}  // namespace classification
+}  // namespace vision
+}  // namespace fastdeploy
--- a/fastdeploy/vision/classification/contrib/resnet_pybind.cc
+++ b/fastdeploy/vision/classification/contrib/resnet_pybind.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/pybind/main.h"
+// namespace should be  `fastdeploy`
+namespace fastdeploy {
+// the name of Pybind function should be Bind${model_name}
+void BindResNet(pybind11::module& m) {
+  // the constructor and the predict funtion are necessary
+  // the constructor is used to initialize the python model class.
+  // the necessary public functions and variables like `size`, `mean_vals` should also be binded.
+  pybind11::class_<vision::classification::ResNet, FastDeployModel>(
+      m, "ResNet")
+      .def(pybind11::init<std::string, std::string, RuntimeOption,
+                          ModelFormat>())
+      .def("predict",
+           [](vision::classification::ResNet& self, pybind11::array& data,
+             int topk = 1) {
+             auto mat = PyArrayToCvMat(data);
+             vision::ClassifyResult res;
+             self.Predict(&mat, &res, topk);
+             return res;
+           })
+      .def_readwrite("size", &vision::classification::ResNet::size)
+      .def_readwrite("mean_vals", &vision::classification::ResNet::mean_vals)
+      .def_readwrite("std_vals", &vision::classification::ResNet::std_vals);
+}
+}  // namespace fastdeploy
+
--- a/python/fastdeploy/vision/classification/init.py
+++ b/python/fastdeploy/vision/classification/init.py
@@ -15,7 +15,7 @@ from __future__ import absolute_import

 from .contrib.yolov5cls import YOLOv5Cls
 from .ppcls import PaddleClasModel
-
+from .contrib.resnet import ResNet
 PPLCNet = PaddleClasModel
 PPLCNetv2 = PaddleClasModel
 EfficientNet = PaddleClasModel
--- a/python/fastdeploy/vision/classification/contrib/resnet.py
+++ b/python/fastdeploy/vision/classification/contrib/resnet.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, ModelFormat
+from .... import c_lib_wrap as C
+
+
+class ResNet(FastDeployModel):
+    def __init__(self,
+                 model_file,
+                 params_file="",
+                 runtime_option=None,
+                 model_format=ModelFormat.ONNX):
+        """Load a image classification model exported by ResNet.
+
+        :param model_file: (str)Path of model file, e.g resnet/resnet50.onnx
+        :param params_file: (str)Path of parameters file, if the model_fomat is ModelFormat.ONNX, this param will be ignored, can be set as empty string
+        :param runtime_option: (fastdeploy.RuntimeOption)RuntimeOption for inference this model, if it's None, will use the default backend on CPU
+        :param model_format: (fastdeploy.ModelForamt)Model format of the loaded model, default is ONNX
+        """
+
+        # call super() to initialize the backend_option
+        # the result of initialization will be saved in self._runtime_option
+        super(ResNet, self).__init__(runtime_option)
+
+        self._model = C.vision.classification.ResNet(
+            model_file, params_file, self._runtime_option, model_format)
+        # self.initialized shows the initialization of the model is successful or not
+
+        assert self.initialized, "ResNet initialize failed."
+
+    # Predict and return the inference result of "input_image".
+    def predict(self, input_image, topk=1):
+        """Classify an input image
+
+        :param input_image: (numpy.ndarray)The input image data, 3-D array with layout HWC, BGR format
+        :param topk: (int)The topk result by the classify confidence score, default 1
+        :return: ClassifyResult
+        """
+        return self._model.predict(input_image, topk)
+
+    # Implement the setter and getter method for variables
+    @property
+    def size(self):
+        """
+        Returns the preprocess image size
+        """
+        return self._model.size
+
+    @property
+    def mean_vals(self):
+        """
+        Returns the mean value of normlization
+        """
+        return self._model.mean_vals
+
+    @property
+    def std_vals(self):
+        """
+        Returns the std value of normlization
+        """
+        return self._model.std_vals
+
+    @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
+
+    @mean_vals.setter
+    def mean_vals(self, value):
+        assert isinstance(
+            value, list), "The value to set `mean_vals` must be type of list."
+        self._model.mean_vals = value
+
+    @std_vals.setter
+    def std_vals(self, value):
+        assert isinstance(
+            value, list), "The value to set `std_vals` must be type of list."
+        self._model.std_vals = value