Minor fixes

config_infer_primary.txt

@@ -21,5 +21,5 @@ custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
 engine-create-func-name=NvDsInferYoloCudaEngineGet
 
 [class-attrs-all]
-nms-iou-threshold=0.6
+nms-iou-threshold=0.45
 pre-cluster-threshold=0.25

config_infer_primary_yoloV5.txt

@@ -21,5 +21,5 @@ custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
 engine-create-func-name=NvDsInferYoloCudaEngineGet
 
 [class-attrs-all]
-nms-iou-threshold=0.6
+nms-iou-threshold=0.45
 pre-cluster-threshold=0.25

config_infer_primary_yolor.txt

@@ -21,5 +21,5 @@ custom-lib-path=nvdsinfer_custom_impl_Yolo/libnvdsinfer_custom_impl_Yolo.so
 engine-create-func-name=NvDsInferYoloCudaEngineGet
 
 [class-attrs-all]
-nms-iou-threshold=0.6
+nms-iou-threshold=0.5
 pre-cluster-threshold=0.25

nvdsinfer_custom_impl_Yolo/nvdsinfer_yolo_engine.cpp

@@ -50,7 +50,6 @@ static bool getYoloNetworkInfo (NetworkInfo &networkInfo, const NvDsInferContext
     networkInfo.deviceType = (initParams->useDLA ? "kDLA" : "kGPU");
     networkInfo.numDetectedClasses = initParams->numDetectedClasses;
     networkInfo.clusterMode = initParams->clusterMode;
-    networkInfo.iouThreshold = initParams->perClassDetectionParams->nmsIOUThreshold;
 
     if(initParams->networkMode == 0) {
         networkInfo.networkMode = "FP32";

nvdsinfer_custom_impl_Yolo/yolo.cpp

@@ -40,15 +40,13 @@ Yolo::Yolo(const NetworkInfo& networkInfo)
       m_DeviceType(networkInfo.deviceType),
       m_NumDetectedClasses(networkInfo.numDetectedClasses),
       m_ClusterMode(networkInfo.clusterMode),
-      m_IouThreshold(networkInfo.iouThreshold),
       m_NetworkMode(networkInfo.networkMode),
       m_InputH(0),
       m_InputW(0),
       m_InputC(0),
       m_InputSize(0),
       m_NumClasses(0),
-      m_LetterBox(0),
+      m_LetterBox(0)
-      m_BetaNMS(networkInfo.iouThreshold)
 {}
 
 Yolo::~Yolo()
@@ -77,9 +75,6 @@ nvinfer1::ICudaEngine *Yolo::createEngine (nvinfer1::IBuilder* builder, nvinfer1
     if (m_LetterBox == 1) {
         std::cout << "\nNOTE: letter_box is set in cfg file, make sure to set maintain-aspect-ratio=1 in config_infer file to get better accuracy" << std::endl;
     }
-    if (m_BetaNMS != m_IouThreshold) {
-        std::cout << "\nNOTE: beta_nms is set in cfg file, make sure to set nms-iou-threshold=" << m_BetaNMS << " in config_infer file to get better accuracy" << std::endl;
-    }
     if (m_ClusterMode != 2) {
         std::cout << "\nNOTE: Wrong cluster-mode is set, make sure to set cluster-mode=2 in config_infer file" << std::endl;
     }
@@ -174,9 +169,12 @@ NvDsInferStatus Yolo::buildYoloNetwork(
         
         else if (m_ConfigBlocks.at(i).at("type") == "convolutional") {
             float eps = 1.0e-5;
-            /*if (m_NetworkType.find("yolov5") != std::string::npos) {
+            if (m_NetworkType.find("yolov5") != std::string::npos) {
                 eps = 1.0e-3;
-            }*/
+            }
+            else if (m_NetworkType.find("yolor") != std::string::npos) {
+                eps = 1.0e-4;
+            }
             std::string inputVol = dimsToString(previous->getDimensions());
             nvinfer1::ILayer* out = convolutionalLayer(i, m_ConfigBlocks.at(i), weights, m_TrtWeights, weightPtr, weightsType, channels, eps, previous, &network);
             previous = out->getOutput(0);
@@ -343,9 +341,6 @@ NvDsInferStatus Yolo::buildYoloNetwork(
             if (m_ConfigBlocks.at(i).find("scale_x_y") != m_ConfigBlocks.at(i).end()) {
                 scaleXY = std::stof(m_ConfigBlocks.at(i).at("scale_x_y"));
             }
-            if (m_ConfigBlocks.at(i).find("beta_nms") != m_ConfigBlocks.at(i).end()) {
-                m_BetaNMS = std::stof(m_ConfigBlocks.at(i).at("beta_nms"));
-            }
 
             std::string layerName = "yolo_" + std::to_string(i);
             nvinfer1::Dims prevTensorDims = previous->getDimensions();

nvdsinfer_custom_impl_Yolo/yolo.h

@@ -48,7 +48,6 @@ struct NetworkInfo
     std::string deviceType;
     uint numDetectedClasses;
     int clusterMode;
-    float iouThreshold;
     std::string networkMode;
 };
 
@@ -83,7 +82,6 @@ protected:
     const std::string m_DeviceType;
     const uint m_NumDetectedClasses;
     const int m_ClusterMode;
-    const float m_IouThreshold;
     const std::string m_NetworkMode;
 
     uint m_InputH;
@@ -92,7 +90,6 @@ protected:
     uint64_t m_InputSize;
     uint m_NumClasses;
     uint m_LetterBox;
-    float m_BetaNMS;
 
     std::vector<TensorInfo> m_OutputTensors;
     std::vector<std::map<std::string, std::string>> m_ConfigBlocks;

readme.md

@@ -85,7 +85,7 @@ NVIDIA DeepStream SDK 6.0.1 configuration for YOLO models
 ### Benchmarks
 
 ```
-nms = 0.45 (changed to beta_nms when used in Darknet cfg file) / 0.6 (YOLOv5 and YOLOR models)
+nms-iou-threshold = 0.6
 pre-cluster-threshold = 0.001 (mAP eval) / 0.25 (FPS measurement)
 batch-size = 1
 valid = val2017 (COCO) - 1000 random images for INT8 calibration
@@ -175,6 +175,7 @@ sudo reboot
 sudo apt-get install gcc make git libtool autoconf autogen pkg-config cmake
 sudo apt-get install python3 python3-dev python3-pip
 sudo apt install libssl1.0.0 libgstreamer1.0-0 gstreamer1.0-tools gstreamer1.0-plugins-good gstreamer1.0-plugins-bad gstreamer1.0-plugins-ugly gstreamer1.0-libav libgstrtspserver-1.0-0 libjansson4
+sudo apt-get install libglvnd-dev
 sudo apt-get install linux-headers-$(uname -r)
 ```
 
@@ -351,8 +352,8 @@ num-detected-classes=80
 ...
 [class-attrs-all]
 # IOU threshold
-nms-iou-threshold=0.6
+nms-iou-threshold=0.45
-# Socre threshold
+# Score threshold
 pre-cluster-threshold=0.25
 ```
 
@@ -438,8 +439,8 @@ num-detected-classes=80
 ...
 [class-attrs-all]
 # IOU threshold
-nms-iou-threshold=0.6
+nms-iou-threshold=0.45
-# Socre threshold
+# Score threshold
 pre-cluster-threshold=0.25
 ```
 
@@ -557,8 +558,8 @@ num-detected-classes=80
 ...
 [class-attrs-all]
 # IOU threshold
-nms-iou-threshold=0.6
+nms-iou-threshold=0.5
-# Socre threshold
+# Score threshold
 pre-cluster-threshold=0.25
 ```