diff --git a/CMakeLists.txt b/CMakeLists.txt
index b366416..5e5d60b 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -18,7 +18,7 @@ if(DEBUG)
 endif()
 
 if(TKDNN_PATH)
-    message("SET TKDNN_PATH:"${TKDNN_PATH})
+    message("SET TKDNN_PATH:${TKDNN_PATH}")
     add_definitions(-DTKDNN_PATH="${TKDNN_PATH}")
 else()
     add_definitions(-DTKDNN_PATH="${CMAKE_CURRENT_SOURCE_DIR}")
@@ -51,11 +51,11 @@ find_package(Eigen3 REQUIRED)
 message("Eigen DIR: " ${EIGEN3_INCLUDE_DIR})
 include_directories(${EIGEN3_INCLUDE_DIR})
 
-find_package(OpenCV REQUIRED)
+find_package(OpenCV 4.5 REQUIRED)
 set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -DOPENCV")
-# if(OpenCV_CUDA_VERSION)
-#     add_compile_definitions(OPENCV_CUDACONTRIB)
-# endif()
+if(OpenCV_CUDA_VERSION)
+    add_compile_definitions(OPENCV_CUDACONTRIB)
+endif()
 
 # gives problems in cross-compiling, probably malformed cmake config
 find_package(yaml-cpp REQUIRED)
@@ -71,7 +71,7 @@ include_directories(${CMAKE_CURRENT_SOURCE_DIR}/include ${CUDA_INCLUDE_DIRS} ${O
 add_library(tkDNN SHARED ${tkdnn_SRC})
 target_link_libraries(tkDNN ${tkdnn_LIBS})
 
-#static 
+#static
 #add_library(tkDNN_static STATIC ${tkdnn_SRC})
 #target_link_libraries(tkDNN_static ${tkdnn_LIBS})
 
@@ -163,7 +163,7 @@ target_link_libraries(seg_demo tkDNN)
 # Install
 #-------------------------------------------------------------------------------
 #if (CMAKE_INSTALL_PREFIX_INITIALIZED_TO_DEFAULT)
-#    set (CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install" 
+#    set (CMAKE_INSTALL_PREFIX "${CMAKE_BINARY_DIR}/install"
 #         CACHE PATH "default install path" FORCE)
 #endif()
 message("install dir:" ${CMAKE_INSTALL_PREFIX})
diff --git a/include/tkDNN/DetectionNN.h b/include/tkDNN/DetectionNN.h
index a8c81f7..6711ebf 100644
--- a/include/tkDNN/DetectionNN.h
+++ b/include/tkDNN/DetectionNN.h
@@ -3,10 +3,10 @@
 
 #include <iostream>
 #include <signal.h>
-#include <stdlib.h>    
+#include <stdlib.h>
 #ifdef __linux__
 #include <unistd.h>
-#endif 
+#endif
 
 #include <mutex>
 #include "utils.h"
@@ -17,7 +17,7 @@
 
 #include "tkdnn.h"
 
-//#define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
+#define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
 
 #ifdef OPENCV_CUDACONTRIB
 #include <opencv2/cudawarping.hpp>
@@ -37,7 +37,7 @@ class DetectionNN {
 
         cv::Scalar colors[256];
 
-        int nBatches = 1;
+        int nBatches = 2;
 
 #ifdef OPENCV_CUDACONTRIB
         cv::cuda::GpuMat bgr[3];
@@ -57,11 +57,11 @@ class DetectionNN {
         virtual void preprocess(cv::Mat &frame, const int bi=0) = 0;
 
         /**
-         * This method postprocess the output of the NN to obtain the correct 
-         * boundig boxes. 
-         * 
+         * This method postprocess the output of the NN to obtain the correct
+         * boundig boxes.
+         *
          * @param bi batch index
-         * @param mAP set to true only if all the probabilities for a bounding 
+         * @param mAP set to true only if all the probabilities for a bounding
          *            box are needed, as in some cases for the mAP calculation
          */
         virtual void postprocess(const int bi=0,const bool mAP=false) = 0;
@@ -79,25 +79,25 @@ class DetectionNN {
         ~DetectionNN(){};
 
         /**
-         * Method used to initialize the class, allocate memory and compute 
+         * Method used to initialize the class, allocate memory and compute
          * needed data.
-         * 
+         *
          * @param tensor_path path to the rt file of the NN.
          * @param n_classes number of classes for the given dataset.
          * @param n_batches maximum number of batches to use in inference
          * @return true if everything is correct, false otherwise.
          */
         virtual bool init(const std::string& tensor_path, const int n_classes=80, const int n_batches=1, const float conf_thresh=0.3) = 0;
-        
+
         /**
          * This method performs the whole detection of the NN.
-         * 
+         *
          * @param frames frames to run detection on.
          * @param cur_batches number of batches to use in inference
-         * @param save_times if set to true, preprocess, inference and postprocess times 
+         * @param save_times if set to true, preprocess, inference and postprocess times
          *        are saved on a csv file, otherwise not.
          * @param times pointer to the output stream where to write times
-         * @param mAP set to true only if all the probabilities for a bounding 
+         * @param mAP set to true only if all the probabilities for a bounding
          *            box are needed, as in some cases for the mAP calculation
          */
         void update(std::vector<cv::Mat>& frames, const int cur_batches=1, bool save_times=false, std::ofstream *times=nullptr, const bool mAP=false){
@@ -107,14 +107,14 @@ class DetectionNN {
                 FatalError("A batch size greater than nBatches cannot be used");
 
             originalSize.clear();
-            if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30); 
+            if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30);
             {
                 TKDNN_TSTART
                 for(int bi=0; bi<cur_batches;++bi){
                     if(!frames[bi].data)
                         FatalError("No image data feed to detection");
                     originalSize.push_back(frames[bi].size());
-                    preprocess(frames[bi], bi);    
+                    preprocess(frames[bi], bi);
                 }
                 TKDNN_TSTOP
                 if(save_times) *times<<t_ns<<";";
@@ -141,11 +141,11 @@ class DetectionNN {
                 TKDNN_TSTOP
                 if(save_times) *times<<t_ns<<"\n";
             }
-        }      
+        }
 
         /**
          * Method to draw bounding boxes and labels on a frame.
-         * 
+         *
          * @param frames original frame to draw bounding box on.
          */
         void draw(std::vector<cv::Mat>& frames) {
@@ -155,11 +155,11 @@ class DetectionNN {
             std::string det_class;
             int baseline = 0;
             float font_scale = 0.5;
-            int thickness = 2;   
+            int thickness = 2;
 
             for(int bi=0; bi<frames.size(); ++bi){
                 // draw dets
-                for(int i=0; i<batchDetected[bi].size(); i++) { 
+                for(int i=0; i<batchDetected[bi].size(); i++) {
                     b           = batchDetected[bi][i];
                     x0   		= b.x;
                     x1   		= b.x + b.w;
@@ -168,11 +168,11 @@ class DetectionNN {
                     det_class 	= classesNames[b.cl];
 
                     // draw rectangle
-                    cv::rectangle(frames[bi], cv::Point(x0, y0), cv::Point(x1, y1), colors[b.cl], 2); 
+                    cv::rectangle(frames[bi], cv::Point(x0, y0), cv::Point(x1, y1), colors[b.cl], 2);
 
                     // draw label
                     cv::Size text_size = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
-                    cv::rectangle(frames[bi], cv::Point(x0, y0), cv::Point((x0 + text_size.width - 2), (y0 - text_size.height - 2)), colors[b.cl], -1);                      
+                    cv::rectangle(frames[bi], cv::Point(x0, y0), cv::Point((x0 + text_size.width - 2), (y0 - text_size.height - 2)), colors[b.cl], -1);
                     cv::putText(frames[bi], det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
                 }
             }
diff --git a/include/tkDNN/DetectionNN3D.h b/include/tkDNN/DetectionNN3D.h
index af6eaf9..6d47687 100644
--- a/include/tkDNN/DetectionNN3D.h
+++ b/include/tkDNN/DetectionNN3D.h
@@ -4,7 +4,7 @@
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h>
-#ifdef __linux__    
+#ifdef __linux__
 #include <unistd.h>
 #endif
 
@@ -17,7 +17,7 @@
 
 #include "tkdnn.h"
 
-// #define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
+#define OPENCV_CUDACONTRIB //if OPENCV has been compiled with CUDA and contrib.
 
 #ifdef OPENCV_CUDACONTRIB
 #include <opencv2/cudawarping.hpp>
@@ -57,11 +57,11 @@ class DetectionNN3D {
         virtual void preprocess(cv::Mat &frame, const int bi=0) = 0;
 
         /**
-         * This method postprocess the output of the NN to obtain the correct 
-         * boundig boxes. 
-         * 
+         * This method postprocess the output of the NN to obtain the correct
+         * boundig boxes.
+         *
          * @param bi batch index
-         * @param mAP set to true only if all the probabilities for a bounding 
+         * @param mAP set to true only if all the probabilities for a bounding
          *            box are needed, as in some cases for the mAP calculation
          */
         virtual void postprocess(const int bi=0,const bool mAP=false) = 0;
@@ -69,7 +69,7 @@ class DetectionNN3D {
     public:
         int classes = 0;
         float confThreshold = 0.3; /*threshold on the confidence of the boxes*/
-        
+
         std::vector<tk::dnn::box3D> detected3D; /*bounding boxes in output*/
         std::vector<std::vector<tk::dnn::box3D>> batchDetected; /*bounding boxes in output*/
         std::vector<double> pre_stats, stats, post_stats, visual_stats; /*keeps track of inference times (ms)*/
@@ -79,29 +79,29 @@ class DetectionNN3D {
         ~DetectionNN3D(){};
 
         /**
-         * Method used to initialize the class, allocate memory and compute 
+         * Method used to initialize the class, allocate memory and compute
          * needed data.
-         * 
+         *
          * @param tensor_path path to the rt file of the NN.
          * @param n_classes number of classes for the given dataset.
          * @param n_batches maximum number of batches to use in inference.
          * @return true if everything is correct, false otherwise.
          */
-        virtual bool init(const std::string& tensor_path, const int n_classes=3, const int n_batches=1, 
+        virtual bool init(const std::string& tensor_path, const int n_classes=3, const int n_batches=1,
                             const float conf_thresh=0.3, const std::vector<cv::Mat>& k_calibs=std::vector<cv::Mat>()) = 0;
 
         /**
          * This method performs the whole detection of the NN.
-         * 
+         *
          * @param frames frames to run detection on.
          * @param cur_batches number of batches to use in inference.
-         * @param save_times if set to true, preprocess, inference and postprocess times 
+         * @param save_times if set to true, preprocess, inference and postprocess times
          *        are saved on a csv file, otherwise not.
          * @param times pointer to the output stream where to write times.
-         * @param mAP set to true only if all the probabilities for a bounding 
+         * @param mAP set to true only if all the probabilities for a bounding
          *            box are needed, as in some cases for the mAP calculation.
          */
-        void update(std::vector<cv::Mat>& frames, const int cur_batches=1, bool save_times=false, 
+        void update(std::vector<cv::Mat>& frames, const int cur_batches=1, bool save_times=false,
                     std::ofstream *times=nullptr, const bool mAP=false){
             if(save_times && times==nullptr)
                 FatalError("save_times set to true, but no valid ofstream given");
@@ -109,17 +109,17 @@ class DetectionNN3D {
                 FatalError("A batch size greater than nBatches cannot be used");
 
             originalSize.clear();
-            if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30); 
+            if(TKDNN_VERBOSE) printCenteredTitle(" TENSORRT detection ", '=', 30);
             {
                 TKDNN_TSTART
                 for(int bi=0; bi<cur_batches;++bi){
                     if(!frames[bi].data)
                         FatalError("No image data feed to detection");
                     originalSize.push_back(frames[bi].size());
-                    preprocess(frames[bi], bi);   
+                    preprocess(frames[bi], bi);
                 }
                 TKDNN_TSTOP
-                pre_stats.push_back(t_ns); 
+                pre_stats.push_back(t_ns);
                 if(save_times) *times<<t_ns<<";";
             }
 
@@ -149,11 +149,11 @@ class DetectionNN3D {
 
         /**
          * Method to draw bounding boxes and labels on a frame.
-         * 
+         *
          * @param frames original frame to draw bounding box on.
-         */    
+         */
         virtual void draw(std::vector<cv::Mat>& frames){};
-          
+
 };
 
 }}