[Doc] add doxygen docs for c sharp api (#1495)

add doxygen docs for c sharp api

Co-authored-by: DefTruth <31974251+DefTruth@users.noreply.github.com>

csharp/fastdeploy/enum_varaibles.cs

@@ -24,20 +24,20 @@ public enum ModelFormat {
 }
 
 public enum rknpu2_CpuName {
-  RK356X = 0, /* run on RK356X. */
+  RK356X = 0, ///< run on RK356X. 
-  RK3588 = 1, /* default,run on RK3588. */
+  RK3588 = 1, ///< default,run on RK3588. 
   UNDEFINED,
 }
 
 public enum rknpu2_CoreMask {
-  RKNN_NPU_CORE_AUTO = 0,  //< default, run on NPU core randomly.
+  RKNN_NPU_CORE_AUTO = 0,  ///< default, run on NPU core randomly.
-  RKNN_NPU_CORE_0 = 1,     //< run on NPU core 0.
+  RKNN_NPU_CORE_0 = 1,     ///< run on NPU core 0.
-  RKNN_NPU_CORE_1 = 2,     //< run on NPU core 1.
+  RKNN_NPU_CORE_1 = 2,     ///< run on NPU core 1.
-  RKNN_NPU_CORE_2 = 4,     //< run on NPU core 2.
+  RKNN_NPU_CORE_2 = 4,     ///< run on NPU core 2.
   RKNN_NPU_CORE_0_1 =
-      RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1,  //< run on NPU core 1 and core 2.
+      RKNN_NPU_CORE_0 | RKNN_NPU_CORE_1,  ///< run on NPU core 1 and core 2.
   RKNN_NPU_CORE_0_1_2 =
-      RKNN_NPU_CORE_0_1 | RKNN_NPU_CORE_2,  //< run on NPU core 1 and core 2.
+      RKNN_NPU_CORE_0_1 | RKNN_NPU_CORE_2,  ///< run on NPU core 1 and core 2.
   RKNN_NPU_CORE_UNDEFINED,
 }
 

csharp/fastdeploy/runtime_option.cs

@@ -18,6 +18,8 @@ using System.Runtime.InteropServices;
 
 namespace fastdeploy {
 
+/*! @brief Option object used when create a new Runtime object
+ */
 public class RuntimeOption {
 
   public RuntimeOption() {
@@ -28,26 +30,41 @@ public class RuntimeOption {
     FD_C_DestroyRuntimeOptionWrapper(fd_runtime_option_wrapper);
   }
 
+  /** \brief Set path of model file and parameter file
+   *
+   * \param[in] model_path Path of model file, e.g ResNet50/model.pdmodel for Paddle format model / ResNet50/model.onnx for ONNX format model
+   * \param[in] params_path Path of parameter file, this only used when the model format is Paddle, e.g Resnet50/model.pdiparams
+   * \param[in] format Format of the loaded model
+   */
   public void SetModelPath(string model_path, string params_path = "",
                            ModelFormat format = ModelFormat.PADDLE) {
     FD_C_RuntimeOptionWrapperSetModelPath(fd_runtime_option_wrapper, model_path,
                                           params_path, format);
   }
 
+  /** \brief Specify the memory buffer of model and parameter. Used when model and params are loaded directly from memory
+   *
+   * \param[in] model_buffer The string of model memory buffer
+   * \param[in] params_buffer The string of parameters memory buffer
+   * \param[in] format Format of the loaded model
+   */
   public void SetModelBuffer(string model_buffer, string params_buffer = "",
                              ModelFormat format = ModelFormat.PADDLE) {
     FD_C_RuntimeOptionWrapperSetModelBuffer(
         fd_runtime_option_wrapper, model_buffer, params_buffer, format);
   }
 
+  /// Use cpu to inference, the runtime will inference on CPU by default
   public void UseCpu() {
     FD_C_RuntimeOptionWrapperUseCpu(fd_runtime_option_wrapper);
   }
 
+  /// Use Nvidia GPU to inference
   public void UseGpu(int gpu_id = 0) {
     FD_C_RuntimeOptionWrapperUseGpu(fd_runtime_option_wrapper, gpu_id);
   }
 
+  /// Use RKNPU2 e.g RK3588/RK356X to inference
   public void
   UseRKNPU2(rknpu2_CpuName rknpu2_name = rknpu2_CpuName.RK3588,
             rknpu2_CoreMask rknpu2_core = rknpu2_CoreMask.RKNN_NPU_CORE_0) {
@@ -55,14 +72,38 @@ public class RuntimeOption {
                                        rknpu2_core);
   }
 
+  /// Use TimVX e.g RV1126/A311D to inference
   public void UseTimVX() {
     FD_C_RuntimeOptionWrapperUseTimVX(fd_runtime_option_wrapper);
   }
 
+  /// Use Huawei Ascend to inference
   public void UseAscend() {
     FD_C_RuntimeOptionWrapperUseAscend(fd_runtime_option_wrapper);
   }
 
+  /// \brief Turn on KunlunXin XPU.
+  ///
+  /// \param kunlunxin_id the KunlunXin XPU card to use (default is 0).
+  /// \param l3_workspace_size The size of the video memory allocated by the l3
+  ///         cache, the maximum is 16M.
+  /// \param locked Whether the allocated L3 cache can be locked. If false,
+  ///       it means that the L3 cache is not locked, and the allocated L3
+  ///       cache can be shared by multiple models, and multiple models
+  ///       sharing the L3 cache will be executed sequentially on the card.
+  /// \param autotune Whether to autotune the conv operator in the model. If
+  ///       true, when the conv operator of a certain dimension is executed
+  ///       for the first time, it will automatically search for a better
+  ///       algorithm to improve the performance of subsequent conv operators
+  ///       of the same dimension.
+  /// \param autotune_file Specify the path of the autotune file. If
+  ///       autotune_file is specified, the algorithm specified in the
+  ///       file will be used and autotune will not be performed again.
+  /// \param precision Calculation accuracy of multi_encoder
+  /// \param adaptive_seqlen Is the input of multi_encoder variable length
+  /// \param enable_multi_stream Whether to enable the multi stream of
+  ///        KunlunXin XPU.
+  ///
   public void
   UseKunlunXin(int kunlunxin_id = 0, int l3_workspace_size = 0xfffc00,
                bool locked = false, bool autotune = true,
@@ -74,6 +115,7 @@ public class RuntimeOption {
         enable_multi_stream);
   }
 
+  /// Use Sophgo to inference
   public void UseSophgo() {
     FD_C_RuntimeOptionWrapperUseSophgo(fd_runtime_option_wrapper);
   }
@@ -83,6 +125,9 @@ public class RuntimeOption {
                                                external_stream);
   }
 
+  /*
+   * @brief Set number of cpu threads while inference on CPU, by default it will decided by the different backends
+   */
   public void SetCpuThreadNum(int thread_num) {
     FD_C_RuntimeOptionWrapperSetCpuThreadNum(fd_runtime_option_wrapper,
                                              thread_num);
@@ -97,38 +142,47 @@ public class RuntimeOption {
     FD_C_RuntimeOptionWrapperUsePaddleBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set Paddle Inference as inference backend, support CPU/GPU
   public void UsePaddleInferBackend() {
     FD_C_RuntimeOptionWrapperUsePaddleInferBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set ONNX Runtime as inference backend, support CPU/GPU
   public void UseOrtBackend() {
     FD_C_RuntimeOptionWrapperUseOrtBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set SOPHGO Runtime as inference backend, support SOPHGO
   public void UseSophgoBackend() {
     FD_C_RuntimeOptionWrapperUseSophgoBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set TensorRT as inference backend, only support GPU
   public void UseTrtBackend() {
     FD_C_RuntimeOptionWrapperUseTrtBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set Poros backend as inference backend, support CPU/GPU
   public void UsePorosBackend() {
     FD_C_RuntimeOptionWrapperUsePorosBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set OpenVINO as inference backend, only support CPU
   public void UseOpenVINOBackend() {
     FD_C_RuntimeOptionWrapperUseOpenVINOBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set Paddle Lite as inference backend, only support arm cpu
   public void UseLiteBackend() {
     FD_C_RuntimeOptionWrapperUseLiteBackend(fd_runtime_option_wrapper);
   }
 
+  /// Set Paddle Lite as inference backend, only support arm cpu
   public void UsePaddleLiteBackend() {
     FD_C_RuntimeOptionWrapperUsePaddleLiteBackend(fd_runtime_option_wrapper);
   }
 
+  
   public void SetPaddleMKLDNN(bool pd_mkldnn = true) {
     FD_C_RuntimeOptionWrapperSetPaddleMKLDNN(fd_runtime_option_wrapper,
                                              pd_mkldnn);

csharp/fastdeploy/vision/classification/ppcls/model.cs

@@ -23,8 +23,18 @@ namespace fastdeploy {
 namespace vision {
 namespace classification {
 
+/*! @brief PaddleClas serials model object used when to load a PaddleClas model exported by PaddleClas repository
+ */
 public class PaddleClasModel {
 
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g resnet/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g resnet/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] config_file Path of configuration file for deployment, e.g resnet/infer_cfg.yml
+   * \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 Paddle format
+   */
   public PaddleClasModel(string model_file, string params_file,
                          string config_file, RuntimeOption custom_option = null,
                          ModelFormat model_format = ModelFormat.PADDLE) {
@@ -40,11 +50,17 @@ public class PaddleClasModel {
     FD_C_DestroyPaddleClasModelWrapper(fd_paddleclas_model_wrapper);
   }
 
-
+  /// Get model's name
   public string ModelName() {
     return "PaddleClas/Model";
   }
 
+  /** \brief DEPRECATED Predict the classification result for an input image, remove at 1.0 version
+   *
+   * \param[in] im The input image data, comes from cv::imread()
+   *  
+   * \return ClassifyResult
+   */
   public ClassifyResult Predict(Mat img) {
     FD_ClassifyResult fd_classify_result = new FD_ClassifyResult();
     if(! FD_C_PaddleClasModelWrapperPredict(
@@ -59,6 +75,12 @@ public class PaddleClasModel {
     return classify_result;
   }
 
+  /** \brief Predict the classification results for a batch of input images
+   *
+   * \param[in] imgs, The input image list, each element comes from cv::imread()
+   * 
+   * \return List<ClassifyResult> 
+   */
   public List<ClassifyResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -86,6 +108,7 @@ public class PaddleClasModel {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PaddleClasModelWrapperInitialized(fd_paddleclas_model_wrapper);
   }

csharp/fastdeploy/vision/detection/contrib/model.cs

@@ -24,6 +24,9 @@ namespace vision {
 namespace detection {
 
 // YOLOv5
+
+/*! @brief YOLOv5 model
+ */
 public class YOLOv5 {
 
   public YOLOv5( string model_file, string params_file,
@@ -39,6 +42,12 @@ public class YOLOv5 {
 
   ~YOLOv5() { FD_C_DestroyYOLOv5Wrapper(fd_yolov5_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_YOLOv5WrapperPredict(fd_yolov5_wrapper, img.CvPtr,
@@ -53,6 +62,12 @@ public class YOLOv5 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -80,6 +95,8 @@ public class YOLOv5 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_YOLOv5WrapperInitialized(fd_yolov5_wrapper);
   }
@@ -123,6 +140,9 @@ public class YOLOv5 {
 
 // YOLOv7
 
+
+/*! @brief YOLOv7 model
+ */
 public class YOLOv7 {
 
   public YOLOv7( string model_file, string params_file,
@@ -138,6 +158,12 @@ public class YOLOv7 {
 
   ~YOLOv7() { FD_C_DestroyYOLOv7Wrapper(fd_yolov7_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_YOLOv7WrapperPredict(fd_yolov7_wrapper, img.CvPtr,
@@ -152,6 +178,12 @@ public class YOLOv7 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -179,6 +211,8 @@ public class YOLOv7 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_YOLOv7WrapperInitialized(fd_yolov7_wrapper);
   }
@@ -221,6 +255,9 @@ public class YOLOv7 {
 
 // YOLOv8
 
+
+/*! @brief YOLOv8 model
+ */
 public class YOLOv8 {
 
   public YOLOv8( string model_file, string params_file,
@@ -236,6 +273,12 @@ public class YOLOv8 {
 
   ~YOLOv8() { FD_C_DestroyYOLOv8Wrapper(fd_yolov8_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_YOLOv8WrapperPredict(fd_yolov8_wrapper, img.CvPtr,
@@ -250,6 +293,12 @@ public class YOLOv8 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -277,6 +326,8 @@ public class YOLOv8 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_YOLOv8WrapperInitialized(fd_yolov8_wrapper);
   }
@@ -321,6 +372,9 @@ public class YOLOv8 {
 
 // YOLOv6
 
+
+/*! @brief YOLOv6 model
+ */
 public class YOLOv6 {
 
   public YOLOv6( string model_file, string params_file,
@@ -352,6 +406,8 @@ public class YOLOv6 {
     return detection_result;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_YOLOv6WrapperInitialized(fd_yolov6_wrapper);
   }
@@ -390,6 +446,9 @@ public class YOLOv6 {
 
 // YOLOR
 
+
+/*! @brief YOLOR model
+ */
 public class YOLOR {
 
   public YOLOR( string model_file, string params_file,
@@ -421,6 +480,8 @@ public class YOLOR {
     return detection_result;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_YOLORWrapperInitialized(fd_yolor_wrapper);
   }
@@ -460,6 +521,9 @@ public class YOLOR {
 
 // YOLOX
 
+
+/*! @brief YOLOX model
+ */
 public class YOLOX {
 
   public YOLOX( string model_file, string params_file,
@@ -491,6 +555,8 @@ public class YOLOX {
     return detection_result;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_YOLOXWrapperInitialized(fd_yolox_wrapper);
   }

csharp/fastdeploy/vision/detection/ppdet/model.cs

@@ -23,9 +23,17 @@ namespace fastdeploy {
 namespace vision {
 namespace detection {
 
-// PPYOLOE
+/*! @brief PPYOLOE model
+ */
 public class PPYOLOE {
-
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ppyoloe/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g picodet/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] config_file Path of configuration file for deployment, e.g picodet/infer_cfg.yml
+   * \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 Paddle format
+   */
   public PPYOLOE(string model_file, string params_file, string config_file,
                  RuntimeOption custom_option = null,
                  ModelFormat model_format = ModelFormat.PADDLE) {
@@ -39,6 +47,11 @@ public class PPYOLOE {
 
   ~PPYOLOE() { FD_C_DestroyPPYOLOEWrapper(fd_ppyoloe_wrapper); }
 
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PPYOLOEWrapperPredict(fd_ppyoloe_wrapper, img.CvPtr,
@@ -53,6 +66,11 @@ public class PPYOLOE {
     return detection_result;
   }
 
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -80,6 +98,7 @@ public class PPYOLOE {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PPYOLOEWrapperInitialized(fd_ppyoloe_wrapper);
   }
@@ -129,6 +148,9 @@ public class PPYOLOE {
 }
 
 // PicoDet
+
+/*! @brief PicoDet model
+ */
 public class PicoDet {
 
   public PicoDet(string model_file, string params_file, string config_file,
@@ -144,6 +166,12 @@ public class PicoDet {
 
   ~PicoDet() { FD_C_DestroyPicoDetWrapper(fd_picodet_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PicoDetWrapperPredict(fd_picodet_wrapper, img.CvPtr,
@@ -158,6 +186,12 @@ public class PicoDet {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -185,6 +219,8 @@ public class PicoDet {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PicoDetWrapperInitialized(fd_picodet_wrapper);
   }
@@ -236,6 +272,9 @@ public class PicoDet {
 
 // PPYOLO
 
+
+/*! @brief PPYOLO model
+ */
 public class PPYOLO {
 
   public PPYOLO(string model_file, string params_file, string config_file,
@@ -251,6 +290,12 @@ public class PPYOLO {
 
   ~PPYOLO() { FD_C_DestroyPPYOLOWrapper(fd_ppyolo_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PPYOLOWrapperPredict(fd_ppyolo_wrapper, img.CvPtr,
@@ -265,6 +310,12 @@ public class PPYOLO {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -292,6 +343,8 @@ public class PPYOLO {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PPYOLOWrapperInitialized(fd_ppyolo_wrapper);
   }
@@ -342,6 +395,9 @@ public class PPYOLO {
 
 // YOLOv3
 
+
+/*! @brief YOLOv3 model
+ */
 public class YOLOv3 {
 
   public YOLOv3(string model_file, string params_file, string config_file,
@@ -357,6 +413,12 @@ public class YOLOv3 {
 
   ~YOLOv3() { FD_C_DestroyYOLOv3Wrapper(fd_yolov3_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_YOLOv3WrapperPredict(fd_yolov3_wrapper, img.CvPtr,
@@ -371,6 +433,12 @@ public class YOLOv3 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -398,6 +466,8 @@ public class YOLOv3 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_YOLOv3WrapperInitialized(fd_yolov3_wrapper);
   }
@@ -448,6 +518,9 @@ public class YOLOv3 {
 
 // PaddleYOLOX
 
+
+/*! @brief PaddleYOLOX model
+ */
 public class PaddleYOLOX {
 
   public PaddleYOLOX(string model_file, string params_file, string config_file,
@@ -463,6 +536,12 @@ public class PaddleYOLOX {
 
   ~PaddleYOLOX() { FD_C_DestroyPaddleYOLOXWrapper(fd_paddleyolox_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PaddleYOLOXWrapperPredict(fd_paddleyolox_wrapper, img.CvPtr,
@@ -477,6 +556,12 @@ public class PaddleYOLOX {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -504,6 +589,8 @@ public class PaddleYOLOX {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PaddleYOLOXWrapperInitialized(fd_paddleyolox_wrapper);
   }
@@ -554,6 +641,9 @@ public class PaddleYOLOX {
 
 // FasterRCNN
 
+
+/*! @brief FasterRCNN model
+ */
 public class FasterRCNN {
 
   public FasterRCNN(string model_file, string params_file, string config_file,
@@ -569,6 +659,12 @@ public class FasterRCNN {
 
   ~FasterRCNN() { FD_C_DestroyFasterRCNNWrapper(fd_fasterrcnn_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_FasterRCNNWrapperPredict(fd_fasterrcnn_wrapper, img.CvPtr,
@@ -583,6 +679,12 @@ public class FasterRCNN {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -610,6 +712,8 @@ public class FasterRCNN {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_FasterRCNNWrapperInitialized(fd_fasterrcnn_wrapper);
   }
@@ -660,6 +764,9 @@ public class FasterRCNN {
 
 // MaskRCNN
 
+
+/*! @brief MaskRCNN model
+ */
 public class MaskRCNN {
 
   public MaskRCNN(string model_file, string params_file, string config_file,
@@ -675,6 +782,12 @@ public class MaskRCNN {
 
   ~MaskRCNN() { FD_C_DestroyMaskRCNNWrapper(fd_maskrcnn_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_MaskRCNNWrapperPredict(fd_maskrcnn_wrapper, img.CvPtr,
@@ -689,6 +802,12 @@ public class MaskRCNN {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -716,6 +835,8 @@ public class MaskRCNN {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_MaskRCNNWrapperInitialized(fd_maskrcnn_wrapper);
   }
@@ -766,6 +887,9 @@ public class MaskRCNN {
 
 // SSD
 
+
+/*! @brief SSD model
+ */
 public class SSD {
 
   public SSD(string model_file, string params_file, string config_file,
@@ -781,6 +905,12 @@ public class SSD {
 
   ~SSD() { FD_C_DestroySSDWrapper(fd_ssd_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_SSDWrapperPredict(fd_ssd_wrapper, img.CvPtr,
@@ -795,6 +925,12 @@ public class SSD {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -822,6 +958,8 @@ public class SSD {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_SSDWrapperInitialized(fd_ssd_wrapper);
   }
@@ -872,6 +1010,9 @@ public class SSD {
 
 // PaddleYOLOv5
 
+
+/*! @brief PaddleYOLOv5 model
+ */
 public class PaddleYOLOv5 {
 
   public PaddleYOLOv5(string model_file, string params_file, string config_file,
@@ -887,6 +1028,12 @@ public class PaddleYOLOv5 {
 
   ~PaddleYOLOv5() { FD_C_DestroyPaddleYOLOv5Wrapper(fd_paddleyolov5_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PaddleYOLOv5WrapperPredict(fd_paddleyolov5_wrapper, img.CvPtr,
@@ -901,6 +1048,12 @@ public class PaddleYOLOv5 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -928,6 +1081,8 @@ public class PaddleYOLOv5 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PaddleYOLOv5WrapperInitialized(fd_paddleyolov5_wrapper);
   }
@@ -978,6 +1133,9 @@ public class PaddleYOLOv5 {
 
 // PaddleYOLOv6
 
+
+/*! @brief PaddleYOLOv6 model
+ */
 public class PaddleYOLOv6 {
 
   public PaddleYOLOv6(string model_file, string params_file, string config_file,
@@ -993,6 +1151,12 @@ public class PaddleYOLOv6 {
 
   ~PaddleYOLOv6() { FD_C_DestroyPaddleYOLOv6Wrapper(fd_paddleyolov6_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PaddleYOLOv6WrapperPredict(fd_paddleyolov6_wrapper, img.CvPtr,
@@ -1007,6 +1171,12 @@ public class PaddleYOLOv6 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1034,6 +1204,8 @@ public class PaddleYOLOv6 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PaddleYOLOv6WrapperInitialized(fd_paddleyolov6_wrapper);
   }
@@ -1084,6 +1256,9 @@ public class PaddleYOLOv6 {
 
 // PaddleYOLOv7
 
+
+/*! @brief PaddleYOLOv7 model
+ */
 public class PaddleYOLOv7 {
 
   public PaddleYOLOv7(string model_file, string params_file, string config_file,
@@ -1099,6 +1274,12 @@ public class PaddleYOLOv7 {
 
   ~PaddleYOLOv7() { FD_C_DestroyPaddleYOLOv7Wrapper(fd_paddleyolov7_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PaddleYOLOv7WrapperPredict(fd_paddleyolov7_wrapper, img.CvPtr,
@@ -1113,6 +1294,12 @@ public class PaddleYOLOv7 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1140,6 +1327,8 @@ public class PaddleYOLOv7 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PaddleYOLOv7WrapperInitialized(fd_paddleyolov7_wrapper);
   }
@@ -1190,6 +1379,9 @@ public class PaddleYOLOv7 {
 
 // PaddleYOLOv8
 
+
+/*! @brief PaddleYOLOv8 model
+ */
 public class PaddleYOLOv8 {
 
   public PaddleYOLOv8(string model_file, string params_file, string config_file,
@@ -1205,6 +1397,12 @@ public class PaddleYOLOv8 {
 
   ~PaddleYOLOv8() { FD_C_DestroyPaddleYOLOv8Wrapper(fd_paddleyolov8_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PaddleYOLOv8WrapperPredict(fd_paddleyolov8_wrapper, img.CvPtr,
@@ -1219,6 +1417,12 @@ public class PaddleYOLOv8 {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1246,6 +1450,8 @@ public class PaddleYOLOv8 {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PaddleYOLOv8WrapperInitialized(fd_paddleyolov8_wrapper);
   }
@@ -1296,6 +1502,9 @@ public class PaddleYOLOv8 {
 
 // RTMDet
 
+
+/*! @brief RTMDet model
+ */
 public class RTMDet {
 
   public RTMDet(string model_file, string params_file, string config_file,
@@ -1311,6 +1520,12 @@ public class RTMDet {
 
   ~RTMDet() { FD_C_DestroyRTMDetWrapper(fd_rtmdet_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_RTMDetWrapperPredict(fd_rtmdet_wrapper, img.CvPtr,
@@ -1325,6 +1540,12 @@ public class RTMDet {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1352,6 +1573,8 @@ public class RTMDet {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_RTMDetWrapperInitialized(fd_rtmdet_wrapper);
   }
@@ -1402,6 +1625,9 @@ public class RTMDet {
 
 // CascadeRCNN
 
+
+/*! @brief CascadeRCNN model
+ */
 public class CascadeRCNN {
 
   public CascadeRCNN(string model_file, string params_file, string config_file,
@@ -1417,6 +1643,12 @@ public class CascadeRCNN {
 
   ~CascadeRCNN() { FD_C_DestroyCascadeRCNNWrapper(fd_cascadercnn_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_CascadeRCNNWrapperPredict(fd_cascadercnn_wrapper, img.CvPtr,
@@ -1431,6 +1663,12 @@ public class CascadeRCNN {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1458,6 +1696,8 @@ public class CascadeRCNN {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_CascadeRCNNWrapperInitialized(fd_cascadercnn_wrapper);
   }
@@ -1508,6 +1748,9 @@ public class CascadeRCNN {
 
 // PSSDet
 
+
+/*! @brief PSSDet model
+ */
 public class PSSDet {
 
   public PSSDet(string model_file, string params_file, string config_file,
@@ -1523,6 +1766,12 @@ public class PSSDet {
 
   ~PSSDet() { FD_C_DestroyPSSDetWrapper(fd_pssdet_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_PSSDetWrapperPredict(fd_pssdet_wrapper, img.CvPtr,
@@ -1537,6 +1786,12 @@ public class PSSDet {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1564,6 +1819,8 @@ public class PSSDet {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PSSDetWrapperInitialized(fd_pssdet_wrapper);
   }
@@ -1614,6 +1871,9 @@ public class PSSDet {
 
 // RetinaNet
 
+
+/*! @brief RetinaNet model
+ */
 public class RetinaNet {
 
   public RetinaNet(string model_file, string params_file, string config_file,
@@ -1629,6 +1889,12 @@ public class RetinaNet {
 
   ~RetinaNet() { FD_C_DestroyRetinaNetWrapper(fd_retinanet_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_RetinaNetWrapperPredict(fd_retinanet_wrapper, img.CvPtr,
@@ -1643,6 +1909,12 @@ public class RetinaNet {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1670,6 +1942,8 @@ public class RetinaNet {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_RetinaNetWrapperInitialized(fd_retinanet_wrapper);
   }
@@ -1720,6 +1994,9 @@ public class RetinaNet {
 
 // FCOS
 
+
+/*! @brief FCOS model
+ */
 public class FCOS {
 
   public FCOS(string model_file, string params_file, string config_file,
@@ -1735,6 +2012,12 @@ public class FCOS {
 
   ~FCOS() { FD_C_DestroyFCOSWrapper(fd_fcos_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_FCOSWrapperPredict(fd_fcos_wrapper, img.CvPtr,
@@ -1749,6 +2032,12 @@ public class FCOS {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1776,6 +2065,8 @@ public class FCOS {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_FCOSWrapperInitialized(fd_fcos_wrapper);
   }
@@ -1826,6 +2117,9 @@ public class FCOS {
 
 // TTFNet
 
+
+/*! @brief TTFNet model
+ */
 public class TTFNet {
 
   public TTFNet(string model_file, string params_file, string config_file,
@@ -1841,6 +2135,12 @@ public class TTFNet {
 
   ~TTFNet() { FD_C_DestroyTTFNetWrapper(fd_ttfnet_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_TTFNetWrapperPredict(fd_ttfnet_wrapper, img.CvPtr,
@@ -1855,6 +2155,12 @@ public class TTFNet {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1882,6 +2188,8 @@ public class TTFNet {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_TTFNetWrapperInitialized(fd_ttfnet_wrapper);
   }
@@ -1932,6 +2240,9 @@ public class TTFNet {
 
 // TOOD
 
+
+/*! @brief TOOD model
+ */
 public class TOOD {
 
   public TOOD(string model_file, string params_file, string config_file,
@@ -1947,6 +2258,12 @@ public class TOOD {
 
   ~TOOD() { FD_C_DestroyTOODWrapper(fd_tood_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_TOODWrapperPredict(fd_tood_wrapper, img.CvPtr,
@@ -1961,6 +2278,12 @@ public class TOOD {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -1988,6 +2311,8 @@ public class TOOD {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_TOODWrapperInitialized(fd_tood_wrapper);
   }
@@ -2038,6 +2363,9 @@ public class TOOD {
 
 // GFL
 
+
+/*! @brief GFL model
+ */
 public class GFL {
 
   public GFL(string model_file, string params_file, string config_file,
@@ -2053,6 +2381,12 @@ public class GFL {
 
   ~GFL() { FD_C_DestroyGFLWrapper(fd_gfl_wrapper); }
 
+
+  /** \brief Predict the detection result for an input image
+    * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * 
+    * \return DetectionResult
+    */
   public DetectionResult Predict(Mat img) {
     FD_DetectionResult fd_detection_result = new FD_DetectionResult();
     if(! FD_C_GFLWrapperPredict(fd_gfl_wrapper, img.CvPtr,
@@ -2067,6 +2401,12 @@ public class GFL {
     return detection_result;
   }
 
+
+  /** \brief Predict the detection result for an input image list
+   * \param[in] im The input image list, all the elements come from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return List<DetectionResult>
+   */
   public List<DetectionResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -2094,6 +2434,8 @@ public class GFL {
     return results_out;
   }
 
+
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_GFLWrapperInitialized(fd_gfl_wrapper);
   }

csharp/fastdeploy/vision/ocr/model.cs

@@ -27,8 +27,18 @@ namespace ocr {
 
 // Recognizer
 
+/*! @brief Recognizer object is used to load the recognition model provided by PaddleOCR.
+ */
 public class Recognizer {
 
+  /** \brief Set path of model file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_rec_infer/model.pdmodel.
+   * \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_rec_infer/model.pdiparams, if the model format is ONNX, this parameter will be ignored.
+   * \param[in] label_path Path of label file used by OCR recognition model. e.g ./ppocr_keys_v1.txt
+   * \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 Paddle format.
+   */
   public Recognizer(string model_file, string params_file,
                     string label_path,
                     RuntimeOption custom_option = null,
@@ -45,11 +55,17 @@ public class Recognizer {
     FD_C_DestroyRecognizerWrapper(fd_recognizer_model_wrapper);
   }
 
-
+  /// Get model's name
   public string ModelName() {
     return "ppocr/ocr_rec";
   }
 
+  /** \brief Predict the input image and get OCR recognition model result.
+   *
+   * \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return The output of OCR recognition model result
+   */
   public OCRRecognizerResult Predict(Mat img) {
     OCRRecognizerResult ocr_recognizer_result = new OCRRecognizerResult();
     FD_Cstr text = new FD_Cstr();
@@ -64,6 +80,12 @@ public class Recognizer {
     return ocr_recognizer_result;
   }
 
+  /** \brief BatchPredict the input image and get OCR recognition model result.
+   *
+   * \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return The output of OCR recognition model result.
+   */
   public List<OCRRecognizerResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -152,6 +174,7 @@ public class Recognizer {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_RecognizerWrapperInitialized(fd_recognizer_model_wrapper);
   }
@@ -219,8 +242,17 @@ public class Recognizer {
 
 // Classifier
 
+/*! @brief Classifier object is used to load the classification model provided by PaddleOCR.
+ */
 public class Classifier {
 
+  /** \brief Set path of model file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/model.pdmodel.
+   * \param[in] params_file Path of parameter file, e.g ./ch_ppocr_mobile_v2.0_cls_infer/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 Paddle format.
+   */
   public Classifier(string model_file, string params_file,
                     RuntimeOption custom_option = null,
                     ModelFormat model_format = ModelFormat.PADDLE) {
@@ -236,11 +268,17 @@ public class Classifier {
     FD_C_DestroyClassifierWrapper(fd_classifier_model_wrapper);
   }
 
-
+  /// Get model's name
   public string ModelName() {
     return "ppocr/ocr_cls";
   }
 
+  /** \brief Predict the input image and get OCR classification model cls_result.
+   *
+   * \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return OCRClassifierResult
+   */
   public OCRClassifierResult Predict(Mat img) {
     OCRClassifierResult ocr_classify_result = new OCRClassifierResult();
     if(! FD_C_ClassifierWrapperPredict(
@@ -252,6 +290,12 @@ public class Classifier {
     return ocr_classify_result;
   }
 
+  /** \brief BatchPredict the input image and get OCR classification model result.
+   *
+   * \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return List<OCRClassifierResult>
+   */
   public List<OCRClassifierResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -334,6 +378,7 @@ public class Classifier {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_ClassifierWrapperInitialized(fd_classifier_model_wrapper);
   }
@@ -395,8 +440,17 @@ public class Classifier {
 
 // DBDetector
 
+/*! @brief DBDetector object is used to load the detection model provided by PaddleOCR.
+ */
 public class DBDetector {
 
+  /** \brief Set path of model file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g ./ch_PP-OCRv3_det_infer/model.pdmodel.
+   * \param[in] params_file Path of parameter file, e.g ./ch_PP-OCRv3_det_infer/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 Paddle format.
+   */
   public DBDetector(string model_file, string params_file,
                     RuntimeOption custom_option = null,
                     ModelFormat model_format = ModelFormat.PADDLE) {
@@ -412,11 +466,17 @@ public class DBDetector {
     FD_C_DestroyDBDetectorWrapper(fd_dbdetector_model_wrapper);
   }
 
-
+  /// Get model's name
   public string ModelName() {
     return "ppocr/ocr_det";
   }
 
+  /** \brief Predict the input image and get OCR detection model result.
+   *
+   * \param[in] img The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return OCRDBDetectorResult
+   */
   public OCRDBDetectorResult Predict(Mat img) {
     OCRDBDetectorResult ocr_detector_result = new OCRDBDetectorResult();
     FD_TwoDimArrayInt32 fd_box_result = new FD_TwoDimArrayInt32();
@@ -441,6 +501,12 @@ public class DBDetector {
     return ocr_detector_result;
   }
 
+  /** \brief BatchPredict the input image and get OCR detection model result.
+   *
+   * \param[in] images The list input of image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return List<OCRDBDetectorResult>
+   */
   public List<OCRDBDetectorResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -484,6 +550,7 @@ public class DBDetector {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_DBDetectorWrapperInitialized(fd_dbdetector_model_wrapper);
   }
@@ -541,8 +608,16 @@ namespace pipeline {
 
 // PPOCRv2
 
+/*! @brief PPOCRv2 is used to load PP-OCRv2 series models provided by PaddleOCR.
+ */
 public class PPOCRv2 {
 
+  /** \brief Set up the detection model path, classification model path and recognition model path respectively.
+   *
+   * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv2_det_infer
+   * \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
+   * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv2_rec_infer
+   */
   public PPOCRv2(DBDetector ppocrv2, Classifier classifier,
                  Recognizer recognizer) {
     fd_ppocrv2_wrapper = FD_C_CreatePPOCRv2Wrapper(
@@ -560,6 +635,12 @@ public class PPOCRv2 {
     return "PPOCRv2";
   }
 
+  /** \brief Predict the input image and get OCR result.
+   *
+   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return OCRResult
+   */
   public OCRResult Predict(Mat img) {
     FD_OCRResult fd_ocr_result = new FD_OCRResult();
     if(! FD_C_PPOCRv2WrapperPredict(
@@ -573,6 +654,12 @@ public class PPOCRv2 {
     return ocr_detector_result;
   }
 
+  /** \brief BatchPredict the input image and get OCR result.
+   *
+   * \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return List<OCRResult>
+   */
   public List<OCRResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -601,6 +688,7 @@ public class PPOCRv2 {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PPOCRv2WrapperInitialized(fd_ppocrv2_wrapper);
   }
@@ -643,8 +731,16 @@ public class PPOCRv2 {
 
 // PPOCRv3
 
+/*! @brief PPOCRv3 is used to load PP-OCRv3 series models provided by PaddleOCR.
+ */
 public class PPOCRv3 {
 
+  /** \brief Set up the detection model path, classification model path and recognition model path respectively.
+   *
+   * \param[in] det_model Path of detection model, e.g ./ch_PP-OCRv3_det_infer
+   * \param[in] cls_model Path of classification model, e.g ./ch_ppocr_mobile_v2.0_cls_infer
+   * \param[in] rec_model Path of recognition model, e.g ./ch_PP-OCRv3_rec_infer
+   */
   public PPOCRv3(DBDetector ppocrv3, Classifier classifier,
                  Recognizer recognizer) {
     fd_ppocrv3_wrapper = FD_C_CreatePPOCRv3Wrapper(
@@ -662,6 +758,12 @@ public class PPOCRv3 {
     return "PPOCRv3";
   }
 
+  /** \brief Predict the input image and get OCR result.
+   *
+   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return OCRResult
+   */
   public OCRResult Predict(Mat img) {
     FD_OCRResult fd_ocr_result = new FD_OCRResult();
     if(! FD_C_PPOCRv3WrapperPredict(
@@ -675,6 +777,12 @@ public class PPOCRv3 {
     return ocr_detector_result;
   }
 
+  /** \brief BatchPredict the input image and get OCR result.
+   *
+   * \param[in] images The list of input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format.
+   * 
+   * \return List<OCRResult>
+   */
   public List<OCRResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -703,6 +811,7 @@ public class PPOCRv3 {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PPOCRv3WrapperInitialized(fd_ppocrv3_wrapper);
   }

csharp/fastdeploy/vision/result.cs

@@ -37,9 +37,11 @@ public enum ResultType {
   HEADPOSE
 }
 
+/*! Mask structure, used in DetectionResult for instance segmentation models
+ */
 public class Mask {
-  public List<byte> data;
+  public List<byte> data; /// Mask data buffer
-  public List<long> shape;
+  public List<long> shape; /// Shape of mask
   public ResultType type;
   public Mask() {
     this.data = new List<byte>();
@@ -47,6 +49,7 @@ public class Mask {
     this.type = ResultType.MASK;
   }
 
+  /// convert the result to string to print
   public override string ToString() {
     string information = "Mask(" ;
     int ndim = this.shape.Count;
@@ -63,16 +66,19 @@ public class Mask {
 
 }
 
+/*! @brief Classify result structure for all the image classify models
+ */
 public class ClassifyResult {
-  public List<int> label_ids;
+  public List<int> label_ids; /// Classify result for an image
-  public List<float> scores;
+  public List<float> scores; /// The confidence for each classify result
-  public ResultType type;
+  public ResultType type;   
   public ClassifyResult() {
     this.label_ids = new List<int>();
     this.scores = new List<float>();
     this.type = ResultType.CLASSIFY;
   }
 
+  /// convert the result to string to print
   public string ToString() {  
     string information;
     information = "ClassifyResult(\nlabel_ids: ";
@@ -89,12 +95,14 @@ public class ClassifyResult {
   }
 }
 
+/*! @brief Detection result structure for all the object detection models and instance segmentation models
+ */
 public class DetectionResult {
-  public List<float[]> boxes;
+  public List<float[]> boxes;  /// Member variable which indicates the coordinates of all detected target boxes in a single image, each box is represented by 4 float values in order of xmin, ymin, xmax, ymax, i.e. the coordinates of the top left and bottom right corner.
-  public List<float> scores;
+  public List<float> scores;   /// Member variable which indicates the confidence level of all targets detected in a single image
-  public List<int> label_ids;
+  public List<int> label_ids;  /// Member variable which indicates all target categories detected in a single image
-  public List<Mask> masks;
+  public List<Mask> masks;  ///  Member variable which indicates all detected instance masks of a single image
-  public bool contain_masks;
+  public bool contain_masks; /// Member variable which indicates whether the detected result contains instance masks
   public ResultType type;
   public DetectionResult() {
     this.boxes = new List<float[]>();
@@ -105,7 +113,7 @@ public class DetectionResult {
     this.type = ResultType.DETECTION;
   }
 
-  
+  /// convert the result to string to print
   public string ToString() {
     string information;
     if (!contain_masks) {
@@ -130,12 +138,14 @@ public class DetectionResult {
 
 }
 
+/*! @brief OCR result structure for all the OCR models.
+ */
 public class OCRResult {
-  public List<int[]> boxes;
+  public List<int[]> boxes;   /// Member variable which indicates the coordinates of all detected target boxes in a single image. Each box is represented by 8 int values to indicate the 4 coordinates of the box, in the order of lower left, lower right, upper right, upper left.
-  public List<string> text;
+  public List<string> text;  /// Member variable which indicates the content of the recognized text in multiple text boxes
-  public List<float> rec_scores;
+  public List<float> rec_scores; ///  Member variable which indicates the confidence level of the recognized text.
-  public List<float> cls_scores;
+  public List<float> cls_scores;  ///  Member variable which indicates the confidence level of the classification result of the text box
-  public List<int> cls_labels;
+  public List<int> cls_labels;  /// Member variable which indicates the directional category of the textbox
   public ResultType type;
 
   public OCRResult() {
@@ -146,6 +156,8 @@ public class OCRResult {
     this.cls_labels = new List<int>();
     this.type = ResultType.OCR;
   }
+
+  /// convert the result to string to print
   public string ToString() {
   string no_result = "";
   if (boxes.Count > 0) {
@@ -225,11 +237,13 @@ public class OCRRecognizerResult{
   public float rec_score;
 }
 
+/*! @brief Segmentation result structure for all the segmentation models
+ */
 public class SegmentationResult{
-  public List<byte> label_map;
+  public List<byte> label_map;  /// `label_map` stores the pixel-level category labels for input image. 
-  public List<float> score_map;
+  public List<float> score_map;  /// `score_map` stores the probability of the predicted label for each pixel of input image.
-  public List<long> shape;
+  public List<long> shape;  /// The output shape, means [H, W]
-  public bool contain_score_map;
+  public bool contain_score_map;  /// SegmentationResult whether containing score_map
   public ResultType type;
   public SegmentationResult() {
     this.label_map = new List<byte>();
@@ -239,6 +253,7 @@ public class SegmentationResult{
     this.type = ResultType.SEGMENTATION;
   }
 
+  /// convert the result to string to print
   public string ToString() {
     string information;
     information = "SegmentationResult Image masks 10 rows x 10 cols: \n";

csharp/fastdeploy/vision/segmentation/model.cs

@@ -23,8 +23,18 @@ namespace fastdeploy {
 namespace vision {
 namespace segmentation {
 
+/*! @brief PaddleSeg serials model object used when to load a PaddleSeg model exported by PaddleSeg repository
+ */
 public class PaddleSegModel {
 
+  /** \brief Set path of model file and configuration file, and the configuration of runtime
+   *
+   * \param[in] model_file Path of model file, e.g unet/model.pdmodel
+   * \param[in] params_file Path of parameter file, e.g unet/model.pdiparams, if the model format is ONNX, this parameter will be ignored
+   * \param[in] config_file Path of configuration file for deployment, e.g unet/deploy.yml
+   * \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 Paddle format
+   */
   public PaddleSegModel(string model_file, string params_file,
                          string config_file, RuntimeOption custom_option = null,
                          ModelFormat model_format = ModelFormat.PADDLE) {
@@ -40,11 +50,17 @@ public class PaddleSegModel {
     FD_C_DestroyPaddleSegModelWrapper(fd_paddleseg_model_wrapper);
   }
 
-
+  /// Get model's name
   public string ModelName() {
     return "PaddleSeg";
   }
 
+  /** \brief DEPRECATED Predict the segmentation result for an input image
+   *
+   * \param[in] im The input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+   * 
+   * \return SegmentationResult
+   */
   public SegmentationResult Predict(Mat img) {
     FD_SegmentationResult fd_segmentation_result = new FD_SegmentationResult();
     if(! FD_C_PaddleSegModelWrapperPredict(
@@ -59,6 +75,12 @@ public class PaddleSegModel {
     return segmentation_result;
   }
 
+  /** \brief Predict the segmentation results for a batch of input images
+   *
+   * \param[in] imgs, The input image list, each element comes from cv::imread()
+   * 
+   * \return List<SegmentationResult>
+   */
   public List<SegmentationResult> BatchPredict(List<Mat> imgs){
     FD_OneDimMat imgs_in = new FD_OneDimMat();
     imgs_in.size = (nuint)imgs.Count;
@@ -86,6 +108,7 @@ public class PaddleSegModel {
     return results_out;
   }
 
+  /// Check whether model is initialized successfully
   public bool Initialized() {
     return FD_C_PaddleSegModelWrapperInitialized(fd_paddleseg_model_wrapper);
   }

csharp/fastdeploy/vision/visualize.cs

@@ -24,6 +24,15 @@ namespace vision {
 
 public class Visualize {
 
+  /** \brief Show the visualized results for detection models
+    *
+    * \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * \param[in] result the result produced by model
+    * \param[in] score_threshold threshold for result scores, the bounding box will not be shown if the score is less than score_threshold
+    * \param[in] line_size line size for bounding boxes
+    * \param[in] font_size font size for text
+    * \return Mat type stores the visualized results
+    */
   public static Mat VisDetection(Mat im, DetectionResult detection_result,
                                  float score_threshold = 0.0f,
                                  int line_size = 1, float font_size = 0.5f) {
@@ -35,7 +44,16 @@ public class Visualize {
     return new Mat(result_ptr);
   }
 
-
+  /** \brief Show the visualized results with custom labels for detection models
+    *
+    * \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * \param[in] result the result produced by model
+    * \param[in] labels the visualized result will show the bounding box contain class label
+    * \param[in] score_threshold threshold for result scores, the bounding box will not be shown if the score is less than score_threshold
+    * \param[in] line_size line size for bounding boxes
+    * \param[in] font_size font size for text
+    * \return Mat type stores the visualized results
+    */
   public static Mat VisDetection(Mat im, DetectionResult detection_result,
                                  string[] labels, 
                                  float score_threshold = 0.0f,
@@ -50,6 +68,12 @@ public class Visualize {
     return new Mat(result_ptr);
   }
 
+  /** \brief Show the visualized results for Ocr models
+    *
+    * \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * \param[in] result the result produced by model
+    * \return Mat type stores the visualized results
+    */
   public static Mat VisOcr(Mat im, OCRResult ocr_result){
     FD_OCRResult fd_ocr_result =
         ConvertResult.ConvertOCRResultToCResult(ocr_result);
@@ -58,6 +82,13 @@ public class Visualize {
     return new Mat(result_ptr);
   }
   
+  /** \brief Show the visualized results for segmentation models
+    *
+    * \param[in] im the input image data, comes from cv::imread(), is a 3-D array with layout HWC, BGR format
+    * \param[in] result the result produced by model
+    * \param[in] weight transparent weight of visualized result image
+    * \return Mat type stores the visualized results
+    */
   public static Mat VisSegmentation(Mat im,
                                     SegmentationResult segmentation_result,
                                     float weight = 0.5f){

docs/api_docs/csharp/main_page.md

@@ -0,0 +1,7 @@
+# FastDeploy C# API Summary
+
+- Github: [https://github.com/PaddlePaddle/FastDeploy](https://github.com/PaddlePaddle/FastDeploy)
+- [Installation](https://github.com/PaddlePaddle/FastDeploy/blob/develop/docs/en/build_and_install)
+- [Usage Documents](https://github.com/PaddlePaddle/FastDeploy/blob/develop/csharp)
+- [Release Notes](https://github.com/PaddlePaddle/FastDeploy/releases)
+- [Examples](https://github.com/PaddlePaddle/FastDeploy/tree/develop/examples)