diff --git a/nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo_cuda.cu b/nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo_cuda.cu
index 0b57043..d586a09 100644
--- a/nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo_cuda.cu
+++ b/nvdsinfer_custom_impl_Yolo/nvdsparsebbox_Yolo_cuda.cu
@@ -32,7 +32,7 @@
 #include "yoloPlugins.h"
 
 __global__ void decodeTensor_YOLO_ONNX(NvDsInferParseObjectInfo *binfo, const float* detections, const int numClasses,
-    const int outputSize, float netW, float netH)
+    const int outputSize, float netW, float netH, const float* preclusterThreshold, int* numDetections)
 {
     uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
 
@@ -52,6 +52,11 @@ __global__ void decodeTensor_YOLO_ONNX(NvDsInferParseObjectInfo *binfo, const fl
 
     const float objectness = detections[x_id * (5 + numClasses) + 4];
 
+    if (objectness * maxProb < preclusterThreshold[maxIndex])
+      return;
+
+    int count = (int)atomicAdd(numDetections, 1);
+
     const float bxc = detections[x_id * (5 + numClasses) + 0];
     const float byc = detections[x_id * (5 + numClasses) + 1];
     const float bw = detections[x_id * (5 + numClasses) + 2];
@@ -66,16 +71,16 @@ __global__ void decodeTensor_YOLO_ONNX(NvDsInferParseObjectInfo *binfo, const fl
     x1 = fminf(float(netW), fmaxf(float(0.0), x1));
     y1 = fminf(float(netH), fmaxf(float(0.0), y1));
 
-    binfo[x_id].left = x0;
-    binfo[x_id].top = y0;
-    binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
-    binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
-    binfo[x_id].detectionConfidence = objectness * maxProb;
-    binfo[x_id].classId = maxIndex;
+    binfo[count].left = x0;
+    binfo[count].top = y0;
+    binfo[count].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
+    binfo[count].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
+    binfo[count].detectionConfidence = objectness * maxProb;
+    binfo[count].classId = maxIndex;
 }
 
-__global__ void decodeTensor_YOLOV8_ONNX(NvDsInferParseObjectInfo *binfo, const float* detections, const int numClasses,
-    const int outputSize, float netW, float netH)
+__global__ void decodeTensor_YOLOV8_ONNX(NvDsInferParseObjectInfo* binfo, const float* detections, const int numClasses,
+    const int outputSize, float netW, float netH, const float* preclusterThreshold, int* numDetections)
 {
     uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
 
@@ -93,6 +98,11 @@ __global__ void decodeTensor_YOLOV8_ONNX(NvDsInferParseObjectInfo *binfo, const
       }
     }
 
+    if (maxProb < preclusterThreshold[maxIndex])
+      return;
+
+    int count = (int)atomicAdd(numDetections, 1);
+
     const float bxc = detections[x_id + outputSize * 0];
     const float byc = detections[x_id + outputSize * 1];
     const float bw = detections[x_id + outputSize * 2];
@@ -107,16 +117,17 @@ __global__ void decodeTensor_YOLOV8_ONNX(NvDsInferParseObjectInfo *binfo, const
     x1 = fminf(float(netW), fmaxf(float(0.0), x1));
     y1 = fminf(float(netH), fmaxf(float(0.0), y1));
 
-    binfo[x_id].left = x0;
-    binfo[x_id].top = y0;
-    binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
-    binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
-    binfo[x_id].detectionConfidence = maxProb;
-    binfo[x_id].classId = maxIndex;
+    binfo[count].left = x0;
+    binfo[count].top = y0;
+    binfo[count].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
+    binfo[count].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
+    binfo[count].detectionConfidence = maxProb;
+    binfo[count].classId = maxIndex;
 }
 
 __global__ void decodeTensor_YOLOX_ONNX(NvDsInferParseObjectInfo *binfo, const float* detections, const int numClasses,
-    const int outputSize, float netW, float netH, const int *grid0, const int *grid1, const int *strides)
+    const int outputSize, float netW, float netH, const int *grid0, const int *grid1, const int *strides,
+    const float* preclusterThreshold, int* numDetections)
 {
     uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
 
@@ -136,6 +147,11 @@ __global__ void decodeTensor_YOLOX_ONNX(NvDsInferParseObjectInfo *binfo, const f
 
     const float objectness = detections[x_id * (5 + numClasses) + 4];
 
+    if (objectness * maxProb < preclusterThreshold[maxIndex])
+      return;
+
+    int count = (int)atomicAdd(numDetections, 1);
+
     const float bxc = (detections[x_id * (5 + numClasses) + 0] + grid0[x_id]) * strides[x_id];
     const float byc = (detections[x_id * (5 + numClasses) + 1] + grid1[x_id]) * strides[x_id];
     const float bw = __expf(detections[x_id * (5 + numClasses) + 2]) * strides[x_id];
@@ -150,16 +166,16 @@ __global__ void decodeTensor_YOLOX_ONNX(NvDsInferParseObjectInfo *binfo, const f
     x1 = fminf(float(netW), fmaxf(float(0.0), x1));
     y1 = fminf(float(netH), fmaxf(float(0.0), y1));
 
-    binfo[x_id].left = x0;
-    binfo[x_id].top = y0;
-    binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
-    binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
-    binfo[x_id].detectionConfidence = objectness * maxProb;
-    binfo[x_id].classId = maxIndex;
+    binfo[count].left = x0;
+    binfo[count].top = y0;
+    binfo[count].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
+    binfo[count].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
+    binfo[count].detectionConfidence = objectness * maxProb;
+    binfo[count].classId = maxIndex;
 }
 
 __global__ void decodeTensor_YOLO_NAS_ONNX(NvDsInferParseObjectInfo *binfo, const float* scores, const float* boxes,
-    const int numClasses, const int outputSize, float netW, float netH)
+    const int numClasses, const int outputSize, float netW, float netH, const float* preclusterThreshold, int* numDetections)
 {
     uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
 
@@ -177,6 +193,11 @@ __global__ void decodeTensor_YOLO_NAS_ONNX(NvDsInferParseObjectInfo *binfo, cons
       }
     }
 
+    if (maxProb < preclusterThreshold[maxIndex])
+      return;
+
+    int count = (int)atomicAdd(numDetections, 1);
+
     float x0 = boxes[x_id * 4 + 0];
     float y0 = boxes[x_id * 4 + 1];
     float x1 = boxes[x_id * 4 + 2];
@@ -187,16 +208,16 @@ __global__ void decodeTensor_YOLO_NAS_ONNX(NvDsInferParseObjectInfo *binfo, cons
     x1 = fminf(float(netW), fmaxf(float(0.0), x1));
     y1 = fminf(float(netH), fmaxf(float(0.0), y1));
 
-    binfo[x_id].left = x0;
-    binfo[x_id].top = y0;
-    binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
-    binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
-    binfo[x_id].detectionConfidence = maxProb;
-    binfo[x_id].classId = maxIndex;
+    binfo[count].left = x0;
+    binfo[count].top = y0;
+    binfo[count].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
+    binfo[count].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
+    binfo[count].detectionConfidence = maxProb;
+    binfo[count].classId = maxIndex;
 }
 
 __global__ void decodeTensor_PPYOLOE_ONNX(NvDsInferParseObjectInfo *binfo, const float* scores, const float* boxes,
-    const int numClasses, const int outputSize, float netW, float netH)
+    const int numClasses, const int outputSize, float netW, float netH, const float* preclusterThreshold, int* numDetections)
 {
     uint x_id = blockIdx.x * blockDim.x + threadIdx.x;
 
@@ -214,6 +235,11 @@ __global__ void decodeTensor_PPYOLOE_ONNX(NvDsInferParseObjectInfo *binfo, const
       }
     }
 
+    if (maxProb < preclusterThreshold[maxIndex])
+      return;
+
+    int count = (int)atomicAdd(numDetections, 1);
+
     float x0 = boxes[x_id * 4 + 0];
     float y0 = boxes[x_id * 4 + 1];
     float x1 = boxes[x_id * 4 + 2];
@@ -224,12 +250,12 @@ __global__ void decodeTensor_PPYOLOE_ONNX(NvDsInferParseObjectInfo *binfo, const
     x1 = fminf(float(netW), fmaxf(float(0.0), x1));
     y1 = fminf(float(netH), fmaxf(float(0.0), y1));
 
-    binfo[x_id].left = x0;
-    binfo[x_id].top = y0;
-    binfo[x_id].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
-    binfo[x_id].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
-    binfo[x_id].detectionConfidence = maxProb;
-    binfo[x_id].classId = maxIndex;
+    binfo[count].left = x0;
+    binfo[count].top = y0;
+    binfo[count].width = fminf(float(netW), fmaxf(float(0.0), x1 - x0));
+    binfo[count].height = fminf(float(netH), fmaxf(float(0.0), y1 - y0));
+    binfo[count].detectionConfidence = maxProb;
+    binfo[count].classId = maxIndex;
 }
 
 static bool
@@ -254,15 +280,22 @@ NvDsInferParseCustom_YOLO_ONNX(std::vector<NvDsInferLayerInfo> const& outputLaye
 
   thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
 
+  std::vector<int> numDetections = { 0 };
+  thrust::device_vector<int> d_numDetections(numDetections);
+
+  thrust::device_vector<float> preclusterThreshold(detectionParams.perClassPreclusterThreshold);
+
   int threads_per_block = 1024;
   int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
 
   decodeTensor_YOLO_ONNX<<<threads_per_block, number_of_blocks>>>(
       thrust::raw_pointer_cast(objects.data()), (const float*) (layer.buffer), numClasses, outputSize,
-      static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
+      static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height),
+      thrust::raw_pointer_cast(preclusterThreshold.data()), thrust::raw_pointer_cast(d_numDetections.data()));
 
-  objectList.resize(outputSize);
-  thrust::copy(objects.begin(), objects.end(), objectList.begin());
+  thrust::copy(d_numDetections.begin(), d_numDetections.end(), numDetections.begin());
+  objectList.resize(numDetections[0]);
+  thrust::copy(objects.begin(), objects.begin() + numDetections[0], objectList.begin());
 
   return true;
 }
@@ -289,15 +322,22 @@ NvDsInferParseCustom_YOLOV8_ONNX(std::vector<NvDsInferLayerInfo> const& outputLa
 
   thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
 
+  std::vector<int> numDetections = { 0 };
+  thrust::device_vector<int> d_numDetections(numDetections);
+
+  thrust::device_vector<float> preclusterThreshold(detectionParams.perClassPreclusterThreshold);
+
   int threads_per_block = 1024;
   int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
 
   decodeTensor_YOLOV8_ONNX<<<threads_per_block, number_of_blocks>>>(
       thrust::raw_pointer_cast(objects.data()), (const float*) (layer.buffer), numClasses, outputSize,
-      static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
+      static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height),
+      thrust::raw_pointer_cast(preclusterThreshold.data()), thrust::raw_pointer_cast(d_numDetections.data()));
 
-  objectList.resize(outputSize);
-  thrust::copy(objects.begin(), objects.end(), objectList.begin());
+  thrust::copy(d_numDetections.begin(), d_numDetections.end(), numDetections.begin());
+  objectList.resize(numDetections[0]);
+  thrust::copy(objects.begin(), objects.begin() + numDetections[0], objectList.begin());
 
   return true;
 }
@@ -324,11 +364,16 @@ NvDsInferParseCustom_YOLOX_ONNX(std::vector<NvDsInferLayerInfo> const& outputLay
 
   thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
 
+  std::vector<int> numDetections = { 0 };
+  thrust::device_vector<int> d_numDetections(numDetections);
+
+  thrust::device_vector<float> preclusterThreshold(detectionParams.perClassPreclusterThreshold);
+
   std::vector<int> strides = {8, 16, 32};
 
   std::vector<int> grid0;
   std::vector<int> grid1;
-  std::vector<int> grid_strides;
+  std::vector<int> gridStrides;
 
   for (uint s = 0; s < strides.size(); ++s) {
     int num_grid_y = networkInfo.height / strides[s];
@@ -337,14 +382,14 @@ NvDsInferParseCustom_YOLOX_ONNX(std::vector<NvDsInferLayerInfo> const& outputLay
       for (int g0 = 0; g0 < num_grid_x; ++g0) {
         grid0.push_back(g0);
         grid1.push_back(g1);
-        grid_strides.push_back(strides[s]);
+        gridStrides.push_back(strides[s]);
       }
     }
   }
 
   thrust::device_vector<int> d_grid0(grid0);
   thrust::device_vector<int> d_grid1(grid1);
-  thrust::device_vector<int> d_grid_strides(grid_strides);
+  thrust::device_vector<int> d_gridStrides(gridStrides);
 
   int threads_per_block = 1024;
   int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
@@ -353,10 +398,12 @@ NvDsInferParseCustom_YOLOX_ONNX(std::vector<NvDsInferLayerInfo> const& outputLay
       thrust::raw_pointer_cast(objects.data()), (const float*) (layer.buffer), numClasses, outputSize,
       static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height),
       thrust::raw_pointer_cast(d_grid0.data()), thrust::raw_pointer_cast(d_grid1.data()),
-      thrust::raw_pointer_cast(d_grid_strides.data()));
+      thrust::raw_pointer_cast(d_gridStrides.data()), thrust::raw_pointer_cast(preclusterThreshold.data()),
+      thrust::raw_pointer_cast(d_numDetections.data()));
 
-  objectList.resize(outputSize);
-  thrust::copy(objects.begin(), objects.end(), objectList.begin());
+  thrust::copy(d_numDetections.begin(), d_numDetections.end(), numDetections.begin());
+  objectList.resize(numDetections[0]);
+  thrust::copy(objects.begin(), objects.begin() + numDetections[0], objectList.begin());
 
   return true;
 }
@@ -384,15 +431,22 @@ NvDsInferParseCustom_YOLO_NAS_ONNX(std::vector<NvDsInferLayerInfo> const& output
 
   thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
 
+  std::vector<int> numDetections = { 0 };
+  thrust::device_vector<int> d_numDetections(numDetections);
+
+  thrust::device_vector<float> preclusterThreshold(detectionParams.perClassPreclusterThreshold);
+
   int threads_per_block = 1024;
   int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
 
   decodeTensor_YOLO_NAS_ONNX<<<threads_per_block, number_of_blocks>>>(
       thrust::raw_pointer_cast(objects.data()), (const float*) (scores.buffer), (const float*) (boxes.buffer), numClasses,
-      outputSize, static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
+      outputSize, static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height),
+      thrust::raw_pointer_cast(preclusterThreshold.data()), thrust::raw_pointer_cast(d_numDetections.data()));
 
-  objectList.resize(outputSize);
-  thrust::copy(objects.begin(), objects.end(), objectList.begin());
+  thrust::copy(d_numDetections.begin(), d_numDetections.end(), numDetections.begin());
+  objectList.resize(numDetections[0]);
+  thrust::copy(objects.begin(), objects.begin() + numDetections[0], objectList.begin());
 
   return true;
 }
@@ -420,15 +474,22 @@ NvDsInferParseCustom_PPYOLOE_ONNX(std::vector<NvDsInferLayerInfo> const& outputL
 
   thrust::device_vector<NvDsInferParseObjectInfo> objects(outputSize);
 
+  std::vector<int> numDetections = { 0 };
+  thrust::device_vector<int> d_numDetections(numDetections);
+
+  thrust::device_vector<float> preclusterThreshold(detectionParams.perClassPreclusterThreshold);
+
   int threads_per_block = 1024;
   int number_of_blocks = ((outputSize - 1) / threads_per_block) + 1;
 
   decodeTensor_PPYOLOE_ONNX<<<threads_per_block, number_of_blocks>>>(
       thrust::raw_pointer_cast(objects.data()), (const float*) (scores.buffer), (const float*) (boxes.buffer), numClasses,
-      outputSize, static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height));
+      outputSize, static_cast<float>(networkInfo.width), static_cast<float>(networkInfo.height),
+      thrust::raw_pointer_cast(preclusterThreshold.data()), thrust::raw_pointer_cast(d_numDetections.data()));
 
-  objectList.resize(outputSize);
-  thrust::copy(objects.begin(), objects.end(), objectList.begin());
+  thrust::copy(d_numDetections.begin(), d_numDetections.end(), numDetections.begin());
+  objectList.resize(numDetections[0]);
+  thrust::copy(objects.begin(), objects.begin() + numDetections[0], objectList.begin());
 
   return true;
 }