diff --git a/docs/YOLONAS.md b/docs/YOLONAS.md
index 13ca78e..81f68cf 100644
--- a/docs/YOLONAS.md
+++ b/docs/YOLONAS.md
@@ -190,7 +190,7 @@ parse-bbox-func-name=NvDsInferParseYoloE
 ...
 ```
 
-**NOTE**: If you are using a custom model, you should edit the `config_infer_primary_yolonas_custom.txt` file.
+**NOTE**: If you are using a **custom** model, you should edit the `config_infer_primary_yolonas_custom.txt` file.
 
 **NOTE**: The **YOLO-NAS** resizes the input with left/top padding. To get better accuracy, use
 
@@ -201,6 +201,22 @@ symmetric-padding=0
 ...
 ```
 
+**NOTE**: The **pre-trained YOLO-NAS** uses zero mean normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
+
+```
+...
+net-scale-factor=0.0039215697906911373
+...
+```
+
+**NOTE**: The **custom YOLO-NAS** uses no normalization on the image preprocess. It is important to change the `net-scale-factor` according to the trained values.
+
+```
+...
+net-scale-factor=1
+...
+```
+
 **NOTE**: By default, the dynamic batch-size is set. To use implicit batch-size, uncomment the line
 
 ```
diff --git a/nvdsinfer_custom_impl_Yolo/yoloForward.cu b/nvdsinfer_custom_impl_Yolo/yoloForward.cu
index be6ebcb..b353f86 100644
--- a/nvdsinfer_custom_impl_Yolo/yoloForward.cu
+++ b/nvdsinfer_custom_impl_Yolo/yoloForward.cu
@@ -7,7 +7,7 @@
 
 inline __device__ float sigmoidGPU(const float& x) { return 1.0f / (1.0f + __expf(-x)); }
 
-__global__ void gpuYoloLayer(const float* input, float* boxes, float* scores, int* classes, const uint netWidth,
+__global__ void gpuYoloLayer(const float* input, float* boxes, float* scores, float* classes, const uint netWidth,
     const uint netHeight, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes,
     const uint64_t lastInputSize, const float scaleXY, const float* anchors, const int* mask)
 {
@@ -54,7 +54,7 @@ __global__ void gpuYoloLayer(const float* input, float* boxes, float* scores, in
   boxes[count * 4 + 2] = w;
   boxes[count * 4 + 3] = h;
   scores[count] = maxProb * objectness;
-  classes[count] = maxIndex;
+  classes[count] = (float) maxIndex;
 }
 
 cudaError_t cudaYoloLayer(const void* input, void* boxes, void* scores, void* classes, const uint& batchSize,
@@ -76,7 +76,7 @@ cudaError_t cudaYoloLayer(const void* input, void* boxes, void* scores, void* cl
         reinterpret_cast<const float*> (input) + (batch * inputSize),
         reinterpret_cast<float*> (boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*> (scores) + (batch * 1 * outputSize),
-        reinterpret_cast<int*> (classes) + (batch * 1 * outputSize),
+        reinterpret_cast<float*> (classes) + (batch * 1 * outputSize),
         netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, lastInputSize, scaleXY,
         reinterpret_cast<const float*> (anchors), reinterpret_cast<const int*> (mask));
   }
diff --git a/nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu b/nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu
index 03c01f0..e8418b4 100644
--- a/nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu
+++ b/nvdsinfer_custom_impl_Yolo/yoloForward_nc.cu
@@ -5,7 +5,7 @@
 
 #include <stdint.h>
 
-__global__ void gpuYoloLayer_nc(const float* input, float* boxes, float* scores, int* classes, const uint netWidth,
+__global__ void gpuYoloLayer_nc(const float* input, float* boxes, float* scores, float* classes, const uint netWidth,
     const uint netHeight, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses, const uint numBBoxes,
     const uint64_t lastInputSize, const float scaleXY, const float* anchors, const int* mask)
 {
@@ -52,7 +52,7 @@ __global__ void gpuYoloLayer_nc(const float* input, float* boxes, float* scores,
   boxes[count * 4 + 2] = w;
   boxes[count * 4 + 3] = h;
   scores[count] = maxProb * objectness;
-  classes[count] = maxIndex;
+  classes[count] = (float) maxIndex;
 }
 
 cudaError_t cudaYoloLayer_nc(const void* input, void* boxes, void* scores, void* classes, const uint& batchSize,
@@ -74,7 +74,7 @@ cudaError_t cudaYoloLayer_nc(const void* input, void* boxes, void* scores, void*
         reinterpret_cast<const float*> (input) + (batch * inputSize),
         reinterpret_cast<float*> (boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*> (scores) + (batch * 1 * outputSize),
-        reinterpret_cast<int*> (classes) + (batch * 1 * outputSize),
+        reinterpret_cast<float*> (classes) + (batch * 1 * outputSize),
         netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, lastInputSize, scaleXY,
         reinterpret_cast<const float*> (anchors), reinterpret_cast<const int*> (mask));
   }
diff --git a/nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu b/nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu
index c5ebc4a..66fff49 100644
--- a/nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu
+++ b/nvdsinfer_custom_impl_Yolo/yoloForward_v2.cu
@@ -27,7 +27,7 @@ __device__ void softmaxGPU(const float* input, const int bbindex, const int numG
   }
 }
 
-__global__ void gpuRegionLayer(const float* input, float* softmax, float* boxes, float* scores, int* classes,
+__global__ void gpuRegionLayer(const float* input, float* softmax, float* boxes, float* scores, float* classes,
     const uint netWidth, const uint netHeight, const uint gridSizeX, const uint gridSizeY, const uint numOutputClasses,
     const uint numBBoxes, const uint64_t lastInputSize, const float* anchors)
 {
@@ -73,7 +73,7 @@ __global__ void gpuRegionLayer(const float* input, float* softmax, float* boxes,
   boxes[count * 4 + 2] = w;
   boxes[count * 4 + 3] = h;
   scores[count] = maxProb * objectness;
-  classes[count] = maxIndex;
+  classes[count] = (float) maxIndex;
 }
 
 cudaError_t cudaRegionLayer(const void* input, void* softmax, void* boxes, void* scores, void* classes,
@@ -96,7 +96,7 @@ cudaError_t cudaRegionLayer(const void* input, void* softmax, void* boxes, void*
         reinterpret_cast<float*> (softmax) + (batch * inputSize),
         reinterpret_cast<float*> (boxes) + (batch * 4 * outputSize),
         reinterpret_cast<float*> (scores) + (batch * 1 * outputSize),
-        reinterpret_cast<int*> (classes) + (batch * 1 * outputSize),
+        reinterpret_cast<float*> (classes) + (batch * 1 * outputSize),
         netWidth, netHeight, gridSizeX, gridSizeY, numOutputClasses, numBBoxes, lastInputSize,
         reinterpret_cast<const float*> (anchors));
   }
diff --git a/nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp b/nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
index 5c921b9..967cb4e 100644
--- a/nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
+++ b/nvdsinfer_custom_impl_Yolo/yoloPlugins.cpp
@@ -181,17 +181,13 @@ YoloLayer::getOutputDimensions(INT index, const nvinfer1::DimsExprs* inputs, INT
 bool
 YoloLayer::supportsFormatCombination(INT pos, const nvinfer1::PluginTensorDesc* inOut, INT nbInputs, INT nbOutputs) noexcept
 {
-  return inOut[pos].format == nvinfer1::TensorFormat::kLINEAR && (inOut[pos].type == nvinfer1::DataType::kFLOAT ||
-      inOut[pos].type == nvinfer1::DataType::kINT32);
+  return inOut[pos].format == nvinfer1::TensorFormat::kLINEAR && inOut[pos].type == nvinfer1::DataType::kFLOAT;
 }
 
 nvinfer1::DataType
 YoloLayer::getOutputDataType(INT index, const nvinfer1::DataType* inputTypes, INT nbInputs) const noexcept
 {
   assert(index < 3);
-  if (index == 2) {
-    return nvinfer1::DataType::kINT32;
-  }
   return nvinfer1::DataType::kFLOAT;
 }