diff --git a/demo/config.yaml b/demo/config.yaml
index e86bf2b..e6f91a7 100644
--- a/demo/config.yaml
+++ b/demo/config.yaml
@@ -3,5 +3,5 @@ map_points  : 101   #number of recall points (0 for all, 101 for COCO, 11 Pascal
 map_levels  : 10    #number of IoU step for the AP
 map_step    : 0.05  #step of IoU
 IoU_thresh  : 0.5   #starting IoU threshold
-conf_thresh : 0.3   #threshold on the condifence of the bbox
+conf_thresh : 0.0   #threshold on the condifence of the bbox
 verbose     : false #print on screen information
diff --git a/demo/demo/map.cpp b/demo/demo/map.cpp
index fcfd5a5..31ea821 100644
--- a/demo/demo/map.cpp
+++ b/demo/demo/map.cpp
@@ -38,6 +38,14 @@ int main(int argc, char *argv[])
     bool write_res_on_file = true;
     int n_images = 5000;
 
+    bool verbose;
+    int classes, map_points, map_levels;
+    float map_step, IoU_thresh, conf_thresh;
+
+    //read mAP parameters
+    readParams( config_filename, classes,  map_points, map_levels, map_step, 
+                IoU_thresh, conf_thresh, verbose);
+
     if(argc > 1)
         net = argv[1]; 
     if(argc > 2)
@@ -62,26 +70,32 @@ int main(int argc, char *argv[])
         times<<net<<";";
     }
 
-
     tk::dnn::Yolo3Detection yolo;
     tk::dnn::CenternetDetection cnet;
-    tk::dnn::MobilenetDetection mbnet;    
+    tk::dnn::MobilenetDetection mbnet;
+
+    tk::dnn::DetectionNN *detNN;  
+
+    int n_classes = classes;   
     
     switch(ntype)
     {
         case 'y':
-            yolo.init(net);
+            detNN = &yolo;
             break;
         case 'c':
-            cnet.init(net);
+            detNN = &cnet;
             break;
         case 'm':
-            mbnet.init(net, 81);
+            detNN = &mbnet;
+            n_classes++;
             break;
         default:
         FatalError("Network type not allowed (3rd parameter)\n");
     }
 
+    detNN->init(net, n_classes);
+
     std::ifstream all_labels(labels_path);
     std::string l_filename;
     std::vector<Frame> images;
@@ -115,36 +129,18 @@ int main(int argc, char *argv[])
         dnn_input = frame.clone();
 
         //inference 
+        
+	    TIMER_START
+        
         detected_bbox.clear();
-	TIMER_START
-        switch(ntype)
-        {
-            case 'y':
-                yolo.update(dnn_input);
-                detected_bbox = yolo.detected;
-                break;
-            case 'c':
-                cnet.update(dnn_input);
-                detected_bbox = cnet.detected;
-                break;
-            case 'm':
-                mbnet.update(dnn_input);
-                detected_bbox = mbnet.detected;
-                for(auto& d:detected_bbox)
-                {
-                    d.x = d.x;
-                    d.y = d.y;
-                    d.w = d.w - d.x; //in mobilenet b.w represents x1
-                    d.h = d.h - d.y; //in mobilenet b.h repsresnts y1
-                    d.cl = d.cl -1; //remove background class
-                }
-                break;
-            default:
-                FatalError("Network type not allowed!\n");
-        }
-	TIMER_STOP
-	if(write_res_on_file)
-	    times<<t_ns<<";";
+        detNN->update(dnn_input);
+        frame = detNN->draw(frame);
+        detected_bbox = detNN->detected;
+        
+        TIMER_STOP
+
+        if(write_res_on_file)
+	        times<<t_ns<<";";
 
         std::ofstream myfile;
         if(write_dets)
@@ -200,13 +196,7 @@ int main(int argc, char *argv[])
 
     std::cout<<"Done."<<std::endl;
 
-    bool verbose;
-    int classes, map_points, map_levels;
-    float map_step, IoU_thresh, conf_thresh;
-
-    //read mAP parameters
-    readParams( config_filename, classes,  map_points, map_levels, map_step, 
-                IoU_thresh, conf_thresh, verbose);
+    
     
     //compute mAP
     double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net);
diff --git a/include/tkDNN/CenternetDetection.h b/include/tkDNN/CenternetDetection.h
index 4e987e3..3d01e26 100644
--- a/include/tkDNN/CenternetDetection.h
+++ b/include/tkDNN/CenternetDetection.h
@@ -19,8 +19,6 @@ namespace tk { namespace dnn {
 class CenternetDetection : public DetectionNN
 {
 private:
-    std::vector<std::string> classesNames;
-
     tk::dnn::dataDim_t dim;
     tk::dnn::dataDim_t dim2;
     tk::dnn::dataDim_t dim_hm; 
@@ -79,7 +77,6 @@ public:
     void preprocess(cv::Mat &frame);
     void update(cv::Mat &frame);
     void postprocess(dnnType **rt_out, const int n_out);
-    cv::Mat draw(cv::Mat &frame);
 };
 
 
diff --git a/include/tkDNN/DetectionNN.h b/include/tkDNN/DetectionNN.h
index ddc2d43..50659e6 100644
--- a/include/tkDNN/DetectionNN.h
+++ b/include/tkDNN/DetectionNN.h
@@ -49,6 +49,7 @@ class DetectionNN {
 
         std::vector<tk::dnn::box> detected; /*bounding boxes in output*/
         std::vector<double> stats; /*keeps track of inference times (ms)*/
+        std::vector<std::string> classesNames;
 
         DetectionNN() {};
         ~DetectionNN(){};
@@ -70,9 +71,9 @@ class DetectionNN {
         virtual void preprocess(cv::Mat &frame) = 0;
 
         /**
-         * This method performs the inference of the NN.
+         * This method performs the whole detection of the NN.
          * 
-         * @param frame to run inference on.
+         * @param frame to run detection on.
          */
         virtual void update(cv::Mat &frame) = 0;
 
@@ -91,7 +92,36 @@ class DetectionNN {
          * @param orginal frame to draw bounding box on.
          * @return frame with boundig boxes.
          */
-        virtual cv::Mat draw(cv::Mat &frame) = 0;
+        cv::Mat draw(cv::Mat &frame) 
+        {
+            tk::dnn::box b;
+            int x0, w, x1, y0, h, y1;
+            int objClass;
+            std::string det_class;
+
+            int baseline = 0;
+            float font_scale = 0.5;
+            int thickness = 2;   
+            // draw dets
+            for(int i=0; i<detected.size(); i++) {
+                b           = detected[i];
+                x0   		= b.x;
+                x1   		= b.x + b.w;
+                y0   		= b.y;
+                y1   		= b.y + b.h;
+                det_class 	= classesNames[b.cl];
+
+                // draw rectangle
+                cv::rectangle(frame, 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(frame, cv::Point(x0, y0), cv::Point((x0 + text_size.width - 2), (y0 - text_size.height - 2)), colors[b.cl], -1);                      
+                cv::putText(frame, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
+            }
+            return frame;
+        }
+
 };
 
 }}
diff --git a/include/tkDNN/Layer.h b/include/tkDNN/Layer.h
index 625e945..910435b 100644
--- a/include/tkDNN/Layer.h
+++ b/include/tkDNN/Layer.h
@@ -475,7 +475,7 @@ public:
 
     dnnType *predictions;
 
-    static const int MAX_DETECTIONS = 1024;
+    static const int MAX_DETECTIONS = 2048;
     static Yolo::detection *allocateDetections(int nboxes, int classes);
     static void             mergeDetections(Yolo::detection *dets, int ndets, int classes);
 };
diff --git a/include/tkDNN/MobilenetDetection.h b/include/tkDNN/MobilenetDetection.h
index 44fd120..87a9ea5 100644
--- a/include/tkDNN/MobilenetDetection.h
+++ b/include/tkDNN/MobilenetDetection.h
@@ -53,7 +53,7 @@ private:
     int nPriors = 0;
     float *locations_h, *confidences_h;
 
-    std::vector<std::string> classesNames;
+    
 
     void generate_ssd_priors(const SSDSpec *specs, const int n_specs, bool clamp = true);
     void convert_locatios_to_boxes_and_center();
@@ -69,7 +69,6 @@ public:
     void preprocess(cv::Mat &frame);
     void update(cv::Mat &frame);
     void postprocess(dnnType **rt_out, const int n_out);
-    cv::Mat draw(cv::Mat &frame);
 };
 
 
diff --git a/include/tkDNN/Yolo3Detection.h b/include/tkDNN/Yolo3Detection.h
index 148a53f..547b0e0 100644
--- a/include/tkDNN/Yolo3Detection.h
+++ b/include/tkDNN/Yolo3Detection.h
@@ -26,7 +26,6 @@ public:
     void preprocess(cv::Mat &frame);
     void update(cv::Mat &frame);
     void postprocess(dnnType **rt_out, const int n_out);
-    cv::Mat draw(cv::Mat &frame);
 };
 
 
diff --git a/src/CenternetDetection.cpp b/src/CenternetDetection.cpp
index 29cdec6..acf438e 100644
--- a/src/CenternetDetection.cpp
+++ b/src/CenternetDetection.cpp
@@ -131,7 +131,7 @@ void CenternetDetection::preprocess(cv::Mat &frame)
     // auto step_t = std::chrono::steady_clock::now();
     // auto end_t = std::chrono::steady_clock::now();
     cv::Size sz = originalSize;
-    std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
+    // std::cout<<"image: "<<sz.width<<", "<<sz.height<<std::endl;
     cv::Size sz_old;
     float scale = 1.0;
     float new_height = sz.height * scale;
@@ -186,7 +186,7 @@ void CenternetDetection::preprocess(cv::Mat &frame)
     checkCuda( cudaDeviceSynchronize() );
     
     sz = imageF1_d.size();
-    std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
+    // std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
     // end_t = std::chrono::steady_clock::now();
     // std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
     // step_t = end_t;
@@ -226,7 +226,7 @@ void CenternetDetection::preprocess(cv::Mat &frame)
     cv::Mat imageF;
     resize(frame, imageF, cv::Size(new_width, new_height));
     sz = imageF.size();
-    std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
+    // std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
     // end_t = std::chrono::steady_clock::now();
     // std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
     // step_t = end_t;
@@ -238,7 +238,7 @@ void CenternetDetection::preprocess(cv::Mat &frame)
     // step_t = end_t;
 
     sz = imageF.size();
-    std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
+    // std::cout<<"size: "<<sz.height<<" "<<sz.width<<" - "<<std::endl;
     imageF.convertTo(imageF, CV_32FC3, 1/255.0); 
     // end_t = std::chrono::steady_clock::now();
     // std::cout << " TIME convertto: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
@@ -425,37 +425,6 @@ void CenternetDetection::postprocess(dnnType **rt_out, const int n_out)
     // step_t = end_t;
 }
 
-cv::Mat CenternetDetection::draw(cv::Mat &frame)
-{
-    tk::dnn::box b;
-    int x0, w, x1, y0, h, y1;
-    int objClass;
-    std::string det_class;
-    int baseline = 0;
-    float font_scale = 0.5;
-    int thickness = 2;   
-
-    int num_detected = detected.size();
-    for (int i = 0; i < num_detected; i++){
-        b = detected[i];
-        x0 = b.x;
-        w = b.w;
-        x1 = b.x + w;
-        y0 = b.y;
-        h = b.h;
-        y1 = b.y + h;
-        objClass = b.cl;
-        det_class = classesNames[objClass];
-        cv::rectangle(frame, cv::Point(x0, y0), cv::Point(x1, y1), colors[objClass], 2);
-        // draw label
-        cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
-        cv::rectangle(frame, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), colors[b.cl], -1);
-        cv::putText(frame, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
-        
-    }
-    return frame;
-}
-
 
 }}
 
diff --git a/src/MobilenetDetection.cpp b/src/MobilenetDetection.cpp
index adee317..8d4f363 100644
--- a/src/MobilenetDetection.cpp
+++ b/src/MobilenetDetection.cpp
@@ -131,8 +131,7 @@ float MobilenetDetection::iou(const tk::dnn::box &a, const tk::dnn::box &b)
 
 bool MobilenetDetection::init(const std::string& tensor_path, const int n_classes)
 {
-
-    std::cout<<"MobilenetDetection Init"<<std::endl;
+    std::cout<<(tensor_path).c_str()<<"\n";
     netRT = new tk::dnn::NetworkRT(NULL, (tensor_path).c_str());
     imageSize = netRT->input_dim.h;
     classes = n_classes;
@@ -179,7 +178,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
 
     if(classes == 21){
         const char *classes_names_[] = {
-        "BACKGROUND", "aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
+        "aeroplane", "bicycle", "bird", "boat", "bottle", "bus",
         "car", "cat", "chair", "cow", "diningtable", "dog", "horse", "motorbike",
         "person", "pottedplant", "sheep", "sofa", "train", "tvmonitor"};
         classesNames = std::vector<std::string>(classes_names_, std::end(classes_names_));
@@ -187,7 +186,7 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
     }
     else if (classes == 81){
         const char *classes_names_[] = {
-        "BACKGROUND", "person" , "bicycle" , "car" , "motorbike" , "aeroplane" , "bus" ,
+        "person" , "bicycle" , "car" , "motorbike" , "aeroplane" , "bus" ,
         "train" , "truck" , "boat" , "traffic light" , "fire hydrant" , "stop sign" , 
         "parking meter" , "bench" , "bird" , "cat" , "dog" , "horse" , "sheep" , "cow" , 
         "elephant" , "bear" , "zebra" , "giraffe" , "backpack" , "umbrella" , "handbag" , 
@@ -210,7 +209,6 @@ bool MobilenetDetection::init(const std::string& tensor_path, const int n_classe
 
 void MobilenetDetection::preprocess(cv::Mat &frame)
 {
-    std::cout<<"preprocess"<<std::endl;
 #ifdef OPENCV_CUDA
         //move original image on GPU
         cv::cuda::GpuMat orig_img, frame_nomean;
@@ -248,8 +246,10 @@ void MobilenetDetection::preprocess(cv::Mat &frame)
 void MobilenetDetection::update(cv::Mat &frame)
 {
     TIMER_START
-    detected.clear();
-
+    if(!frame.data) {
+        std::cout<<"MOBILENET: NO IMAGE DATA\n";
+        return;
+    } 
     originalSize = frame.size();
 
     //preprocess
@@ -271,6 +271,7 @@ void MobilenetDetection::update(cv::Mat &frame)
     rt_out[0] = (dnnType *)netRT->buffersRT[3];
     rt_out[1] = (dnnType *)netRT->buffersRT[4];
 
+    detected.clear();
     //postprocess
     postprocess(rt_out, 2);
 
@@ -313,12 +314,12 @@ void MobilenetDetection::postprocess(dnnType **rt_out, const int n_out)
             remaining.clear();
 
             tk::dnn::box b;
-            b.cl = boxes[0].cl;
+            b.cl = boxes[0].cl -1 ;             //remove background class
             b.prob = boxes[0].prob;
-            b.x = boxes[0].x * width;
+            b.x = boxes[0].x * width; 
             b.y = boxes[0].y * height;
-            b.w = boxes[0].w * width;
-            b.h = boxes[0].h * height;
+            b.w = boxes[0].w * width - b.x;     //convert from x1 to width
+            b.h = boxes[0].h * height - b.y;    //convert from y1 to height
             detected.push_back(b);
             for (size_t j = 1; j < boxes.size(); j++){
                 if (iou(boxes[0], boxes[j]) <= IoUThreshold){
@@ -331,25 +332,5 @@ void MobilenetDetection::postprocess(dnnType **rt_out, const int n_out)
 }
 
 
-cv::Mat MobilenetDetection::draw(cv::Mat &frame)
-{
-    int baseline = 0;
-    float font_scale = 0.5;
-    int thickness = 2;
-    
-    tk::dnn::box b;
-    for (size_t i = 0; i < detected.size(); i++){
-        b = detected[i];
-        std::string det_class = classesNames[b.cl];
-        cv::rectangle(frame, cv::Point(b.x, b.y), cv::Point(b.w, b.h), colors[b.cl], 2);
-        // draw label
-        cv::Size text_size = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, font_scale, thickness, &baseline);
-        cv::rectangle(frame, cv::Point(b.x, b.y), cv::Point((b.x + text_size.width - 2), (b.y - text_size.height - 2)), colors[b.cl], -1);
-        cv::putText(frame, det_class, cv::Point(b.x, (b.y - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
-    }
-    return frame;
-}
-
-
 } // namespace dnn
 } // namespace tk
\ No newline at end of file
diff --git a/src/Yolo3Detection.cpp b/src/Yolo3Detection.cpp
index f94dd94..987924d 100644
--- a/src/Yolo3Detection.cpp
+++ b/src/Yolo3Detection.cpp
@@ -43,6 +43,8 @@ bool Yolo3Detection::init(const std::string& tensor_path, const int n_classes) {
         float b = getColor(0, offset, classes);
         colors[c] = cv::Scalar(int(255.0*b), int(255.0*g), int(255.0*r));
     }
+
+    classesNames = getYoloLayer()->classesNames;
     return true;
 } 
 
@@ -60,7 +62,6 @@ void Yolo3Detection::preprocess(cv::Mat &frame)
 
     //write channels
     for(int i=0; i<netRT->input_dim.c; i++) {
-        std::cout<<"copio il channel"<<i<<std::endl;
         int idx = i*imagePreproc.rows*imagePreproc.cols;
         int ch = netRT->input_dim.c-1 -i;
         checkCuda( cudaMemcpy((void*)&input_d[idx], (void*)bgr[ch].data, imagePreproc.rows*imagePreproc.cols*sizeof(dnnType), cudaMemcpyDeviceToDevice));
@@ -122,8 +123,6 @@ void Yolo3Detection::postprocess(dnnType **rt_out, const int n_out)
     float x_ratio =  float(originalSize.width) / float(netRT->input_dim.w);
     float y_ratio =  float(originalSize.height) / float(netRT->input_dim.h);
 
-    std::cout<<"RATIO:"<<x_ratio<<" "<<y_ratio<<std::endl;
-
     // compute dets
     nDets = 0;
     for(int i=0; i<n_out; i++) {
@@ -166,37 +165,8 @@ void Yolo3Detection::postprocess(dnnType **rt_out, const int n_out)
             detected.push_back(res);
         }
     }
-    std::cout<<"N detections: "<<detected.size()<<std::endl;
 }
 
-cv::Mat Yolo3Detection::draw(cv::Mat &frame) 
-{
-    tk::dnn::box b;
-    int x0, w, x1, y0, h, y1;
-    int objClass;
-    std::string det_class;
-    int baseline = 0;
-    float font_scale = 0.5;
-    int thickness = 2;   
-    // draw dets
-    for(int i=0; i<detected.size(); i++) {
-        b           = detected[i];
-        x0   		= b.x;
-        x1   		= b.x + b.w;
-        y0   		= b.y;
-        y1   		= b.y + b.h;
-        det_class 	= getYoloLayer()->classesNames[b.cl];
-
-        // draw rectangle
-        cv::rectangle(frame, 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(frame, cv::Point(x0, y0), cv::Point((x0 + text_size.width - 2), (y0 - text_size.height - 2)), colors[b.cl], -1);                      
-        cv::putText(frame, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, font_scale, cv::Scalar(255, 255, 255), thickness);
-    }
-    return frame;
-}
 
 tk::dnn::Yolo* Yolo3Detection::getYoloLayer(int n) 
 {