diff --git a/CMakeLists.txt b/CMakeLists.txt
index 0a765a4..139bd9c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -115,6 +115,9 @@ target_link_libraries(test_dla34_cnet tkDNN)
 add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
 target_link_libraries(test_rtinference tkDNN)
 
+add_executable(map_demo demo/demo/map.cpp)
+target_link_libraries(map_demo tkDNN)
+
 add_executable(demo demo/demo/demo.cpp)
 target_link_libraries(demo tkDNN)
 
diff --git a/demo/demo/map.cpp b/demo/demo/map.cpp
index 88f879f..ac2b96a 100644
--- a/demo/demo/map.cpp
+++ b/demo/demo/map.cpp
@@ -34,7 +34,15 @@ int main(int argc, char *argv[])
     char * labels_path = "../demo/COCO_val2017/all_labels.txt";
     bool show = false;
     bool write_dets = false;
+    bool write_res_on_file = true;
     int n_images = 5000;
+
+    std::ofstream times;
+    if(write_res_on_file)
+    {
+	times.open ("times.csv", std::ios_base::app);
+	times<<net<<";";
+    }
     
     if(argc > 1)
         net = argv[1]; 
@@ -91,6 +99,7 @@ int main(int argc, char *argv[])
 
         //inference 
         detected_bbox.clear();
+	TIMER_START
         switch(ntype)
         {
             case 'y':
@@ -104,6 +113,9 @@ int main(int argc, char *argv[])
             default:
                 FatalError("Network type not allowed!\n");
         }
+	TIMER_STOP
+	if(write_res_on_file)
+	    times<<t_ns<<";";
 
         std::ofstream myfile;
         if(write_dets)
@@ -168,11 +180,17 @@ int main(int argc, char *argv[])
                 IoU_thresh, conf_thresh, verbose);
     
     //compute mAP
-    double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose);
+    double AP = computeMapNIoULevels(images,classes,IoU_thresh,conf_thresh, map_points, map_step, map_levels, verbose, write_res_on_file, net);
     std::cout<<"mAP "<<IoU_thresh<<":"<<IoU_thresh+map_step*(map_levels-1)<<" = "<<AP<<std::endl;
 
     //compute average precision, recall and f1score
-    computeTPFPFN(images,classes,IoU_thresh,conf_thresh);
+    computeTPFPFN(images,classes,IoU_thresh,conf_thresh, verbose, write_res_on_file, net);
+
+    if(write_res_on_file)
+    {
+	times<<"\n";
+        times.close();
+    }
 
 
     return 0;
diff --git a/include/evaluation.h b/include/evaluation.h
index 68ccec9..eb92bc6 100644
--- a/include/evaluation.h
+++ b/include/evaluation.h
@@ -52,8 +52,8 @@ void readParams(char* config_filename, int& classes, int& map_points,
                 float& conf_thresh, bool& verbose);
 
 double computeMap(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh=0.3, const int map_points=101, const bool verbose=false);
-double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh=0.5, const float conf_thresh=0.3, const int map_points=101, const float map_step=0.05, const int map_levels=10, const bool verbose=false);
+double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh=0.5, const float conf_thresh=0.3, const int map_points=101, const float map_step=0.05, const int map_levels=10, const bool verbose=false, const bool write_on_file = false, std::string net = "");
 
-void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh=0.5, const float conf_thresh=0.3, bool verbose=false);
+void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh=0.5, const float conf_thresh=0.3, bool verbose=false, const bool write_on_file=false, std::string net="");
 
-#endif /*EVALUATION_H*/
\ No newline at end of file
+#endif /*EVALUATION_H*/
diff --git a/include/tkDNN/Yolo3Detection.h b/include/tkDNN/Yolo3Detection.h
index 3700fa6..51994e1 100644
--- a/include/tkDNN/Yolo3Detection.h
+++ b/include/tkDNN/Yolo3Detection.h
@@ -37,7 +37,7 @@ class Yolo3Detection {
         int classes = 0;
         int num = 0;
         int n_masks = 0;
-        float thresh = 0.3;
+        float thresh = 0.05;
         cv::Scalar colors[256];
 
         // this is filled with results
@@ -70,4 +70,4 @@ class Yolo3Detection {
 
 }}
 
-#endif /* YOLODETECTION_H*/
\ No newline at end of file
+#endif /* YOLODETECTION_H*/
diff --git a/src/evaluation.cpp b/src/evaluation.cpp
index c65ce50..80b2e61 100644
--- a/src/evaluation.cpp
+++ b/src/evaluation.cpp
@@ -1,4 +1,5 @@
 #include "evaluation.h"
+#include <fstream>
 
 
 void BoundingBox::clear()
@@ -283,27 +284,51 @@ double computeMap(std::vector<Frame> &images,const int classes,const float IoU_t
     return mean_average_precision;
 }
 
-double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh, const float conf_thresh, const int map_points, const float map_step, const int map_levels, const bool verbose)
+double computeMapNIoULevels(std::vector<Frame> &images,const int classes,const float i_IoU_thresh, const float conf_thresh, const int map_points, const float map_step, const int map_levels, const bool verbose, const bool write_on_file, std::string net)
 {
-    double AP = 0;
+    std::ofstream out_file;
+    if(write_on_file)
+    {
+	out_file.open("map.csv", std::ios_base::app);
+        out_file<<net<<";";
+    }
+
+    double AP = 0, cur_AP = 0;
     float IoU_thresh = i_IoU_thresh;
     for(int i=0; i<map_levels; ++i)
     {
         for(auto& img:images)
             for(auto & d:img.det)
                 d.clear();
-        AP += computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
+        cur_AP = computeMap(images,classes,IoU_thresh,conf_thresh,map_points, verbose);
+	if(write_on_file)
+	    out_file<<cur_AP<<";";
+	AP += cur_AP;
         IoU_thresh +=map_step;
     }
     AP/=map_levels;
+
+    if(write_on_file)
+    {
+	out_file<<AP<<"\n";
+	out_file.close();
+    }
     return AP;
 }
 
-void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose)
+void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_thresh, const float conf_thresh, bool verbose, const bool write_on_file, std::string net)
 {
+
+    std::ofstream out_file;
+    if(write_on_file)
+    {
+	out_file.open("pr.csv", std::ios_base::app);
+        out_file<<net<<";";
+    }
+
     std::vector<int> truth_classes_count(classes,0);
     std::vector<int> dets_classes_count(classes,0);
-    std::vector<PR> pr( classes);
+    std::vector<PR> pr(classes);
     
     for(auto &img:images)
     {
@@ -355,6 +380,8 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
 
     double avg_precision = 0, avg_recall = 0, f1_score = 0;
 
+
+    int TP = 0, FP = 0, FN = 0;
     for(size_t i=0; i<classes; i++)
     {
         pr[i].precision = (pr[i].tp + pr[i].fp) > 0 ? (double)pr[i].tp / (double)(pr[i].tp +pr[i].fp) : 0;
@@ -364,13 +391,23 @@ void computeTPFPFN(std::vector<Frame> &images,const int classes,const float IoU_
             // std::cout<<i<<"\t"<<pr[i].tp<<"\t"<<pr[i].fp<<"\t"<<pr[i].fn<<"\t"<<pr[i].precision<<"\t"<<pr[i].recall<<std::endl;
         avg_precision += pr[i].precision;
         avg_recall += pr[i].recall;
+
+	TP += pr[i].tp;
+	FP += pr[i].fp;
+	FN += pr[i].fn;
     }
     avg_precision /= classes;
     avg_recall /= classes;
 
     f1_score = avg_precision + avg_recall > 0 ? 2 * ( avg_precision * avg_recall ) / ( avg_precision + avg_recall ) : 0;
 
+    if(write_on_file)
+    {
+	out_file<<TP<<";"<<FP<<";"<<FN<<";"<<avg_precision<<";"<<avg_recall<<";"<<f1_score<<"\n";
+	out_file.close();
+    }
+
     std::cout<<"avg precision: "<<avg_precision<<"\tavg recall: "<<avg_recall<<"\tavg f1 score:"<<f1_score<<std::endl;
 
     
-}
\ No newline at end of file
+}