[Doc] Add default values for public variables for external models

fastdeploy/vision/classification/contrib/resnet.h

@@ -50,12 +50,16 @@ class FASTDEPLOY_DECL ResNet : public FastDeployModel {
    */
   virtual bool Predict(cv::Mat* im, ClassifyResult* result, int topk = 1);
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {224, 224}
   */
   std::vector<int> size;
-  /// Mean parameters for normalize, size should be the the same as channels
+  /*! @brief
+  Mean parameters for normalize, size should be the the same as channels, default mean_vals = {0.485f, 0.456f, 0.406f}
+  */
   std::vector<float> mean_vals;
-  /// Std parameters for normalize, size should be the the same as channels
+  /*! @brief
+  Std parameters for normalize, size should be the the same as channels, default std_vals = {0.229f, 0.224f, 0.225f}
+  */
   std::vector<float> std_vals;
 
 

fastdeploy/vision/detection/contrib/nanodet_plus.h

@@ -54,7 +54,7 @@ class FASTDEPLOY_DECL NanoDetPlus : public FastDeployModel {
                        float nms_iou_threshold = 0.5f);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of input size (width, height), e.g (320, 320)
+  Argument for image preprocessing step, tuple of input size (width, height), default (320, 320)
   */
   std::vector<int> size;
   // padding value, size should be the same as channels

fastdeploy/vision/detection/contrib/scaledyolov4.h

@@ -51,7 +51,7 @@ class FASTDEPLOY_DECL ScaledYOLOv4 : public FastDeployModel {
                        float nms_iou_threshold = 0.5);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels

fastdeploy/vision/detection/contrib/yolor.h

@@ -39,7 +39,7 @@ class FASTDEPLOY_DECL YOLOR : public FastDeployModel {
   virtual std::string ModelName() const { return "YOLOR"; }
   /** \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
+   * \param[in] im The input image data, comes from cv::imread()
    * \param[in] result The output detection result will be writen to this structure
    * \param[in] conf_threshold confidence threashold for postprocessing, default is 0.25
    * \param[in] nms_iou_threshold iou threashold for NMS, default is 0.5
@@ -50,7 +50,7 @@ class FASTDEPLOY_DECL YOLOR : public FastDeployModel {
                        float nms_iou_threshold = 0.5);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels

fastdeploy/vision/detection/contrib/yolov5.h

@@ -78,7 +78,7 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
       float max_wh = 7680.0);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size_;
   // padding value, size should be the same as channels
@@ -96,7 +96,9 @@ class FASTDEPLOY_DECL YOLOv5 : public FastDeployModel {
   int stride_;
   // for offseting the boxes by classes when using NMS
   float max_wh_;
-  /// for different strategies to get boxes when postprocessing
+  /*! @brief
+  Argument for image preprocessing step, for different strategies to get boxes when postprocessing, default true
+  */
   bool multi_label_;
 
  private:

fastdeploy/vision/detection/contrib/yolov5lite.h

@@ -54,7 +54,7 @@ class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
   void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels
@@ -84,7 +84,7 @@ class FASTDEPLOY_DECL YOLOv5Lite : public FastDeployModel {
     decode module. Please set it 'true' manually if the model file
     was exported with decode module.
     false : ONNX files without decode module.
-    true : ONNX file with decode module.
+    true : ONNX file with decode module. default false.
   */
   bool is_decode_exported;
 

fastdeploy/vision/detection/contrib/yolov6.h

@@ -57,7 +57,7 @@ class FASTDEPLOY_DECL YOLOv6 : public FastDeployModel {
   void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640};
   */
   std::vector<int> size;
   // padding value, size should be the same as channels

fastdeploy/vision/detection/contrib/yolov7.h

@@ -54,7 +54,7 @@ class FASTDEPLOY_DECL YOLOv7 : public FastDeployModel {
   void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels

fastdeploy/vision/detection/contrib/yolov7end2end_ort.h

@@ -48,7 +48,7 @@ class FASTDEPLOY_DECL YOLOv7End2EndORT : public FastDeployModel {
                        float conf_threshold = 0.25);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels

fastdeploy/vision/detection/contrib/yolov7end2end_trt.h

@@ -53,7 +53,7 @@ class FASTDEPLOY_DECL YOLOv7End2EndTRT : public FastDeployModel {
   void UseCudaPreprocessing(int max_img_size = 3840 * 2160);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels

fastdeploy/vision/detection/contrib/yolox.h

@@ -52,7 +52,7 @@ class FASTDEPLOY_DECL YOLOX : public FastDeployModel {
                        float nms_iou_threshold = 0.5);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels
@@ -61,7 +61,7 @@ class FASTDEPLOY_DECL YOLOX : public FastDeployModel {
     whether the model_file was exported with decode module. The official
     YOLOX/tools/export_onnx.py script will export ONNX file without
     decode module. Please set it 'true' manually if the model file
-    was exported with decode module.
+    was exported with decode module. default false.
   */
   bool is_decode_exported;
   // downsample strides for YOLOX to generate anchors,

fastdeploy/vision/facedet/contrib/retinaface.h

@@ -65,7 +65,7 @@ class FASTDEPLOY_DECL RetinaFace : public FastDeployModel {
   */
   std::vector<int> downsample_strides;
   /*! @brief
-  Argument for image postprocessing step, min sizes, width and height for each anchor
+  Argument for image postprocessing step, min sizes, width and height for each anchor, default min_sizes = {{16, 32}, {64, 128}, {256, 512}}
   */
   std::vector<std::vector<int>> min_sizes;
   /*! @brief

fastdeploy/vision/facedet/contrib/scrfd.h

@@ -77,14 +77,16 @@ class FASTDEPLOY_DECL SCRFD : public FastDeployModel {
   */
   int landmarks_per_face;
   /*! @brief
-  Argument for image postprocessing step, the outputs of onnx file with key points features or not
+  Argument for image postprocessing step, the outputs of onnx file with key points features or not, default true
   */
   bool use_kps;
   /*! @brief
-  Argument for image postprocessing step, the upperbond number of boxes processed by nms
+  Argument for image postprocessing step, the upperbond number of boxes processed by nms, default 30000
   */
   int max_nms;
-  /// Argument for image postprocessing step, anchor number of each stride
+  /*! @brief
+  Argument for image postprocessing step, anchor number of each stride, default 2
+  */
   unsigned int num_anchors;
 
  private:

fastdeploy/vision/facedet/contrib/yolov5face.h

@@ -51,7 +51,7 @@ class FASTDEPLOY_DECL YOLOv5Face : public FastDeployModel {
                        float nms_iou_threshold = 0.5);
 
   /*! @brief
-  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize
+  Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default size = {640, 640}
   */
   std::vector<int> size;
   // padding value, size should be the same as channels
@@ -72,7 +72,7 @@ class FASTDEPLOY_DECL YOLOv5Face : public FastDeployModel {
   /*! @brief
     Argument for image postprocessing step, setup the number of landmarks for per face (if have), default 5 in
     official yolov5face note that, the outupt tensor's shape must be:
-    (1,n,4+1+2*landmarks_per_face+1=box+obj+landmarks+cls)
+    (1,n,4+1+2*landmarks_per_face+1=box+obj+landmarks+cls), default 5
   */
   int landmarks_per_face;
 

fastdeploy/vision/faceid/contrib/insightface_rec.h

@@ -44,9 +44,13 @@ class FASTDEPLOY_DECL InsightFaceRecognitionModel : public FastDeployModel {
   Argument for image preprocessing step, tuple of (width, height), decide the target size after resize, default (112, 112)
   */
   std::vector<int> size;
-  ///  Argument for image preprocessing step, alpha values for normalization
+  /*! @brief
+  Argument for image preprocessing step, alpha values for normalization, default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f};
+  */
   std::vector<float> alpha;
-  ///  Argument for image preprocessing step, beta values for normalization
+  /*! @brief
+  Argument for image preprocessing step, beta values for normalization, default beta = {-1.f, -1.f, -1.f}
+  */
   std::vector<float> beta;
   /*! @brief
   Argument for image preprocessing step, whether to swap the B and R channel, such as BGR->RGB, default true.

fastdeploy/vision/matting/contrib/modnet.h

@@ -44,11 +44,11 @@ class FASTDEPLOY_DECL MODNet : public FastDeployModel {
   */
   std::vector<int> size;
   /*! @brief
-  Argument for image preprocessing step, parameters for normalization, size should be the the same as channels
+  Argument for image preprocessing step, parameters for normalization, size should be the the same as channels, default alpha = {1.f / 127.5f, 1.f / 127.5f, 1.f / 127.5f}
   */
   std::vector<float> alpha;
   /*! @brief
-  Argument for image preprocessing step, parameters for normalization, size should be the the same as channels
+  Argument for image preprocessing step, parameters for normalization, size should be the the same as channels, default beta = {-1.f, -1.f, -1.f}
   */
   std::vector<float> beta;
   /*! @brief

python/fastdeploy/vision/classification/contrib/resnet.py

@@ -56,21 +56,21 @@ def predict(self, input_image, topk=1):
     @property
     def size(self):
         """
-        Returns the preprocess image size
+        Returns the preprocess image size, default size = [224, 224];
         """
         return self._model.size
 
     @property
     def mean_vals(self):
         """
-        Returns the mean value of normlization
+        Returns the mean value of normlization, default mean_vals = [0.485f, 0.456f, 0.406f];
         """
         return self._model.mean_vals
 
     @property
     def std_vals(self):
         """
-        Returns the std value of normlization
+        Returns the std value of normlization, default std_vals = [0.229f, 0.224f, 0.225f];
         """
         return self._model.std_vals
 

python/fastdeploy/vision/classification/contrib/yolov5cls.py

@@ -52,7 +52,7 @@ def predict(self, input_image, topk=1):
     @property
     def size(self):
         """
-        Returns the preprocess image size
+        Returns the preprocess image size, default is (224, 224)
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/nanodet_plus.py

@@ -56,7 +56,7 @@ def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height),  default (320, 320)
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/scaled_yolov4.py

@@ -56,7 +56,8 @@ def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
+
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/yolor.py

@@ -56,7 +56,7 @@ def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/yolov5.py

@@ -81,7 +81,7 @@ def postprocess(infer_result,
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 
@@ -117,6 +117,9 @@ def max_wh(self):
 
     @property
     def multi_label(self):
+        """
+        Argument for image preprocessing step, for different strategies to get boxes when postprocessing, default True
+        """
         return self._model.multi_label
 
     @size.setter

python/fastdeploy/vision/detection/contrib/yolov5lite.py

@@ -56,7 +56,7 @@ def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 
@@ -96,7 +96,8 @@ def is_decode_exported(self):
         whether the model_file was exported with decode module.
         The official YOLOv5Lite/export.py script will export ONNX file without decode module.
         Please set it 'true' manually if the model file was exported with decode module.
-        false : ONNX files without decode module. true : ONNX file with decode module.
+        False : ONNX files without decode module. True : ONNX file with decode module.
+        default False
         """
         return self._model.is_decode_exported
 

python/fastdeploy/vision/detection/contrib/yolov6.py

@@ -56,7 +56,7 @@ def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/yolov7.py

@@ -56,7 +56,7 @@ def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/yolov7end2end_ort.py

@@ -54,7 +54,7 @@ def predict(self, input_image, conf_threshold=0.25):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/yolov7end2end_trt.py

@@ -54,7 +54,7 @@ def predict(self, input_image, conf_threshold=0.25):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 

python/fastdeploy/vision/detection/contrib/yolox.py

@@ -56,7 +56,7 @@ def predict(self, input_image, conf_threshold=0.25, nms_iou_threshold=0.5):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default size = [640, 640]
         """
         return self._model.size
 
@@ -71,6 +71,7 @@ def is_decode_exported(self):
         whether the model_file was exported with decode module.
         The official YOLOX/tools/export_onnx.py script will export ONNX file without decode module.
         Please set it 'true' manually if the model file was exported with decode module.
+        Defalut False.
         """
         return self._model.is_decode_exported
 

python/fastdeploy/vision/facedet/contrib/retinaface.py

@@ -56,7 +56,7 @@ def predict(self, input_image, conf_threshold=0.7, nms_iou_threshold=0.3):
     @property
     def size(self):
         """
-        Argument for image preprocessing step, the preprocess image size, tuple of (width, height)
+        Argument for image preprocessing step, the preprocess image size, tuple of (width, height), default (640, 640)
         """
         return self._model.size
 
@@ -77,7 +77,7 @@ def downsample_strides(self):
     @property
     def min_sizes(self):
         """
-        Argument for image postprocessing step, min sizes, width and height for each anchor
+        Argument for image postprocessing step, min sizes, width and height for each anchor, default min_sizes = [[16, 32], [64, 128], [256, 512]]
         """
         return self._model.min_sizes