Custom YOLO Model in the DeepStream YOLO App
=============================================

How to Use the Custom YOLO Model
----------------------------------

The ``objectDetector_Yolo`` sample application provides a working example of the open source YOLO models: ``YOLOv2``, ``YOLOv3``, ``tiny YOLOv2``, ``tiny YOLOv3``, and ``YOLOV3-SPP``. You can find more information about the models here: https://pjreddie.com/darknet/yolo/. The sample also illustrates NVIDIA® TensorRT™ INT8 calibration (``yolov3-calibration.table.trt7.0``).

Set up the sample
~~~~~~~~~~~~~~~~~~
Compile the open source model and run the DeepStream app as explained in the ``objectDetector_Yolo`` README. This is done to confirm that you can run the open source YOLO model with the sample app.

**To use the custom YOLOv3 and tiny YOLOv3 models:**

1. Open ``nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp``.
2. Change the value of the ``NUM_CLASSES_YOLO`` constant to reflect the number of classes in your model. For example, if your model uses 80 classes: ::

     static const int NUM_CLASSES_YOLO = 80;

  The default values in the file are from:

  * https://pjreddie.com/media/files/papers/YOLOv3.pdf
  * https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3.cfg
  * https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-tiny.cfg
  * https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov3-spp.cfg

3. Replace the model parameters with your new model parameters in ``NvDsInferParseCustomYoloV3()`` (if you are using the ``YOLOv3``) or ``NvDsInferParseCustomYoloV3Tiny()`` (if you are using ``tiny YOLOv3``). Taking ``YOLOv3`` as an example: ::

         extern "C" bool NvDsInferParseCustomYoloV3(
          std::vector<NvDsInferLayerInfo> const& outputLayersInfo,
          NvDsInferNetworkInfo const& networkInfo,
          NvDsInferParseDetectionParams const& detectionParams,
          std::vector<NvDsInferParseObjectInfo>& objectList)
         {
           . . .
            ## 9 clusters from COCO dataset
            const std::vector<float> kANCHORS =
            {10.0, 13.0, 16.0, 30.0, 33.0, 23.0, 30.0, 61.0, 62.0,
            45.0, 59.0, 119.0, 116.0, 90.0, 156.0, 198.0, 373.0, 326.0};

           ## Specifies which of the 9 anchors above to use
           static const std::vector<std::vector<int>> kMASKS = {
            {6, 7, 8},
            {3, 4, 5},
            {0, 1, 2}};
         }

4. Update the corresponding ``NMS IOU Threshold`` and ``confidence threshold`` in the ``nvinfer`` plugin config file. Set the ``cluster-mode=2`` to select ``NMS algorithm``. ::

     [class-attrs-all]
     nms-iou-threshold=0.3
     pre-cluster-threshold=0.7

**To use custom models of YOLOv2 and YOLOv2-tiny**

1. Open ``nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo.cpp``.
2. Change the value of the ``NUM_CLASSES_YOLO`` constant to reflect the number of classes in your model. For example, if your model uses 80 classes: ::

      static const int NUM_CLASSES_YOLO = 80;

  The default values in the file are from:
 
    * https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2.cfg
    * https://raw.githubusercontent.com/pjreddie/darknet/master/cfg/yolov2-tiny.cfg

3. Change the model parameters for ``NvDsInferParseCustomYoloV2()`` (if you are using ``YOLOv2``) or ``NvDsInferParseCustomYoloV2Tiny()`` (if you are using ``tiny YOLOv2``). Taking ``YOLOv2`` as an example: ::

      # specify NMS and confidence threshold
      static const float kNMS_THRESH = 0.3f;
      static const float kPROB_THRESH = 0.6f;

      # specify anchors and in NvDsInferParseYoloV2, kANCHORS = {[anchors] in yolov2.cfg} * stride
      static const std::vector<float> kANCHORS = {
    	 18.3273602, 21.6763191, 59.9827194, 66.0009613,
    	 106.829758, 175.178879, 252.250244, 112.888962,
    	 312.656647, 293.384949 };
      # Predicted boxes in NvDsInferParseYoloV2
        const uint kNUM_BBOXES = 5;

4. Update the corresponding ``NMS IOU Threshold`` and ``confidence threshold`` in the ``nvinfer`` plugin config file. Set ``cluster-mode=2`` to select NMS algorithm. ::

     [class-attrs-all]
     nms-iou-threshold=0.3
     pre-cluster-threshold=0.6


.. note::
   The built-in example ships with the TensorRT INT8 calibration file ``yolov3-calibration.table.trt7.0``. The example runs at INT8 precision for optimal performance. To compare the performance to the built-in example, generate a new INT8 calibration file for your model.
   You can run the sample with another precision type, but it will be slower. If you run with FP16 or FP32 precision, change the network-mode parameter in the configuration file (``config_infer_primary_yolo*.txt``). ::

      ## 0=FP32, 1=INT8, 2=FP16 mode
      network-mode=1 <== Change to 0 or 2