From fe206ea24c12d7b07ecd6cd6f7047c975a3277e2 Mon Sep 17 00:00:00 2001
From: Micaela Verucchi <micaelaverucchi@gmail.com>
Date: Thu, 27 Feb 2020 16:48:10 +0100
Subject: [PATCH 1/7] Fix dependencies problems

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
---
 demo/demo/demo_mobilenet.cpp            |  5 --
 include/sorting.h                       |  5 ++
 include/tkDNN/MobilenetDetection.h      |  3 +-
 include/tkDNN/Yolo3Detection.h          |  5 ++
 include/tkDNN/pluginsRT/SoftmaxRT.h     | 64 -------------------------
 src/CenternetDetection.cpp              | 13 +++--
 tests/mobilenetv2ssd/mobilenetv2ssd.cpp |  4 --
 7 files changed, 21 insertions(+), 78 deletions(-)
 delete mode 100644 include/tkDNN/pluginsRT/SoftmaxRT.h

diff --git a/demo/demo/demo_mobilenet.cpp b/demo/demo/demo_mobilenet.cpp
index 1c4099a..f46df39 100644
--- a/demo/demo/demo_mobilenet.cpp
+++ b/demo/demo/demo_mobilenet.cpp
@@ -3,12 +3,7 @@
 #include <stdlib.h> /* srand, rand */
 #include <unistd.h>
 #include <mutex>
-#include "utils.h"
 
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/videoio.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
 
 #include "MobilenetDetection.h"
 
diff --git a/include/sorting.h b/include/sorting.h
index 7e90f11..5153f3c 100644
--- a/include/sorting.h
+++ b/include/sorting.h
@@ -1,3 +1,6 @@
+#ifndef SORTING_H
+#define SORTING_H
+
 #include <thrust/sort.h>
 #include <thrust/execution_policy.h>
 #include <thrust/functional.h>
@@ -32,3 +35,5 @@ void topKxyAddOffset(int * ids_begin, const int K, const int size, int *intxs_be
                      float *xs_begin, float *ys_begin, dnnType *src_begin, float *src_out, int *ids_out);
 void bboxes(int * ids_begin, const int K, const int size, float *xs_begin, float *ys_begin, 
             dnnType *src_begin, float *bbx0, float *bbx1, float *bby0, float *bby1, float *src_out, int *ids_out);
+
+#endif /*SORTING_H*/
\ No newline at end of file
diff --git a/include/tkDNN/MobilenetDetection.h b/include/tkDNN/MobilenetDetection.h
index 6bfd1b2..840a2cc 100644
--- a/include/tkDNN/MobilenetDetection.h
+++ b/include/tkDNN/MobilenetDetection.h
@@ -2,13 +2,14 @@
 #define MOBILENETDETECTION_H
 
 #include <iostream>
-#include "tkdnn.h"
 
 #include <opencv2/core/core.hpp>
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/videoio.hpp>
 #include <opencv2/imgproc/imgproc.hpp>
 
+#include "tkdnn.h"
+
 #define N_COORDS 4
 
 
diff --git a/include/tkDNN/Yolo3Detection.h b/include/tkDNN/Yolo3Detection.h
index ac12a4f..72ea3f8 100644
--- a/include/tkDNN/Yolo3Detection.h
+++ b/include/tkDNN/Yolo3Detection.h
@@ -1,3 +1,6 @@
+#ifndef YOLODETECTION_H
+#define YOLODETECTION_H
+
 #include <iostream>
 #include <signal.h>
 #include <stdlib.h>     /* srand, rand */
@@ -66,3 +69,5 @@ class Yolo3Detection {
 };
 
 }}
+
+#endif /* YOLODETECTION_H*/
\ No newline at end of file
diff --git a/include/tkDNN/pluginsRT/SoftmaxRT.h b/include/tkDNN/pluginsRT/SoftmaxRT.h
deleted file mode 100644
index 226f6f6..0000000
--- a/include/tkDNN/pluginsRT/SoftmaxRT.h
+++ /dev/null
@@ -1,64 +0,0 @@
-#include<cassert>
-
-class SoftmaxRT : public IPlugin {
-
-public:
-	SoftmaxRT(const tk::dnn::dataDim_t* dim) {
-		assert(dim != nullptr);
-		this->dim.n = dim->n;
-		this->dim.c = dim->c;
-		this->dim.h = dim->h;
-		this->dim.w = dim->w;
-		this->dim.l = dim->l;
-	}
-
-	~SoftmaxRT(){
-
-	}
-
-	int getNbOutputs() const override {
-		return 1;
-	}
-
-	Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override {
-		return DimsNCHW{this->dim.n,this->dim.c,this->dim.h,this->dim.w };
-	}
-
-	void configure(const Dims* inputDims, int nbInputs, const Dims* outputDims, int nbOutputs, int maxBatchSize) override {		
-	}
-
-	int initialize() override {
-		return 0;
-	}
-
-	virtual void terminate() override {
-	}
-
-	virtual size_t getWorkspaceSize(int maxBatchSize) const override {
-		return 0;
-	}
-
-	virtual int enqueue(int batchSize, const void*const * inputs, void** outputs, void* workspace, cudaStream_t stream) override {
-		dnnType *srcData = (dnnType*)reinterpret_cast<const dnnType*>(inputs[0]);
-		dnnType *dstData = reinterpret_cast<dnnType*>(outputs[0]);
-		
-		
-		return 0;
-	}
-
-
-	virtual size_t getSerializationSize() override {
-		return 5*sizeof(int);
-	}
-
-	virtual void serialize(void* buffer) override {
-		char *buf = reinterpret_cast<char*>(buffer);
-		tk::dnn::writeBUF(buf, this->dim.n);
-		tk::dnn::writeBUF(buf, this->dim.c);
-		tk::dnn::writeBUF(buf, this->dim.h);
-		tk::dnn::writeBUF(buf, this->dim.w);
-		tk::dnn::writeBUF(buf, this->dim.l);
-	}
-
-	dataDim_t dim;
-};
diff --git a/src/CenternetDetection.cpp b/src/CenternetDetection.cpp
index edfe1c2..512d0e0 100644
--- a/src/CenternetDetection.cpp
+++ b/src/CenternetDetection.cpp
@@ -1,3 +1,6 @@
+#ifndef CENTERNETDETECTION_H
+#define CENTERNETDETECTION_H
+
 #include "CenternetDetection.h"
 
 namespace tk { namespace dnn {
@@ -226,7 +229,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
     sz_old = sz;
     cv::cuda::GpuMat im_Orig; 
     im_Orig = cv::cuda::GpuMat(imageORIG);
-    // cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height)); 
+    cv::cuda::resize (im_Orig, imageF1_d, cv::Size(new_width, new_height)); 
     checkCuda( cudaDeviceSynchronize() );
     
     sz = imageF1_d.size();
@@ -235,7 +238,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
     std::cout << " TIME resize: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
     step_t = end_t;
     
-    // cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
+    cv::cuda::warpAffine(imageF1_d, imageF2_d, trans, cv::Size(inp_width, inp_height), cv::INTER_LINEAR );
     checkCuda( cudaDeviceSynchronize() );
     end_t = std::chrono::steady_clock::now();
     std::cout << " TIME warpAffine: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
@@ -248,7 +251,7 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
     step_t = end_t;
     
     dim2 = dim;
-    // cv::cuda::split(imageF1_d,bgr);//split source
+    cv::cuda::split(imageF1_d,bgr);//split source
     end_t = std::chrono::steady_clock::now();
     std::cout << " TIME split: " << std::chrono::duration_cast<std::chrono:: microseconds>(end_t - step_t).count() << "  us" << std::endl;
     step_t = end_t;
@@ -413,4 +416,6 @@ void CenternetDetection::update(cv::Mat &imageORIG) {
     TIMER_STOP
     stats.push_back(t_ns);
 }
-}}
\ No newline at end of file
+}}
+
+#endif /*CENTERNETDETECTION_H*/
\ No newline at end of file
diff --git a/tests/mobilenetv2ssd/mobilenetv2ssd.cpp b/tests/mobilenetv2ssd/mobilenetv2ssd.cpp
index f2ec51b..cec97e2 100644
--- a/tests/mobilenetv2ssd/mobilenetv2ssd.cpp
+++ b/tests/mobilenetv2ssd/mobilenetv2ssd.cpp
@@ -1,10 +1,6 @@
 #include <iostream>
 #include "tkdnn.h"
 
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/videoio.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
 
 const char *output_bin1 = "../tests/mobilenetv2ssd/debug/classification_headers-5.bin";
 const char *output_bin2 = "../tests/mobilenetv2ssd/debug/regression_headers-5.bin";

From 296a6cbc87912a12c662090a39d3d6522b65b85a Mon Sep 17 00:00:00 2001
From: Davide Sapienza <sapienza.dav@gmail.com>
Date: Tue, 3 Mar 2020 17:08:25 +0100
Subject: [PATCH 2/7] Add draw method to Yolo3Detection class.

Update yolo demo

Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
---
 demo/demo/demo_yolo3.cpp       | 23 +----------------------
 include/tkDNN/Yolo3Detection.h |  2 +-
 src/Yolo3Detection.cpp         | 31 +++++++++++++++++++++++++++++++
 3 files changed, 33 insertions(+), 23 deletions(-)

diff --git a/demo/demo/demo_yolo3.cpp b/demo/demo/demo_yolo3.cpp
index 2b34386..0abcc2c 100644
--- a/demo/demo/demo_yolo3.cpp
+++ b/demo/demo/demo_yolo3.cpp
@@ -67,28 +67,7 @@ int main(int argc, char *argv[]) {
         // TODO: async infer
         yolo.update(dnn_input);
 
-        // draw dets
-        for(int i=0; i<yolo.detected.size(); i++) {
-            tk::dnn::box b = yolo.detected[i];
-            int x0   				= b.x;
-            int x1   				= b.x + b.w;
-            int y0   				= b.y;
-            int y1   				= b.y + b.h;
-            std::string det_class 	= yolo.getYoloLayer()->classesNames[b.cl];
-            float prob 				= b.prob;
-
-            // std::cout<<det_class<<" ("<<prob<<"): "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
-			// draw rectangle
-            cv::rectangle(frame, cv::Point(x0, y0), cv::Point(x1, y1), yolo.colors[b.cl], 2); 
-
-	        // draw label
-            int baseline = 0;
-            float fontScale = 0.5;
-            int thickness = 2;
-            cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
-            cv::rectangle(frame, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), yolo.colors[b.cl], -1);                      
-            cv::putText(frame, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
-        }
+        frame = yolo.draw(frame);
         
         cv::imshow("detection", frame);
         cv::waitKey(1);
diff --git a/include/tkDNN/Yolo3Detection.h b/include/tkDNN/Yolo3Detection.h
index 72ea3f8..3700fa6 100644
--- a/include/tkDNN/Yolo3Detection.h
+++ b/include/tkDNN/Yolo3Detection.h
@@ -56,7 +56,7 @@ class Yolo3Detection {
          * @return Success of the initialization
          */
         bool init(std::string tensor_path);
-
+        cv::Mat draw(cv::Mat &frame);
         void update(cv::Mat &frame);
 
         tk::dnn::Yolo* getYoloLayer(int n=0) {
diff --git a/src/Yolo3Detection.cpp b/src/Yolo3Detection.cpp
index f21efa7..08742c6 100644
--- a/src/Yolo3Detection.cpp
+++ b/src/Yolo3Detection.cpp
@@ -60,6 +60,37 @@ bool Yolo3Detection::init(std::string tensor_path) {
     return true;
 } 
 
+cv::Mat Yolo3Detection::draw(cv::Mat &imageORIG) {
+
+    tk::dnn::box b;
+    int x0, w, x1, y0, h, y1;
+    int objClass;
+    std::string det_class;
+    float prob;
+    int baseline = 0;
+    float fontScale = 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];
+        prob    	= b.prob;
+
+        // std::cout<<det_class<<" ("<<prob<<"): "<<x0<<" "<<y0<<" "<<x1<<" "<<y1<<"\n";
+        // draw rectangle
+        cv::rectangle(imageORIG, cv::Point(x0, y0), cv::Point(x1, y1), colors[b.cl], 2); 
+
+        // draw label
+        cv::Size textSize = getTextSize(det_class, cv::FONT_HERSHEY_SIMPLEX, fontScale, thickness, &baseline);
+        cv::rectangle(imageORIG, cv::Point(x0, y0), cv::Point((x0 + textSize.width - 2), (y0 - textSize.height - 2)), colors[b.cl], -1);                      
+        cv::putText(imageORIG, det_class, cv::Point(x0, (y0 - (baseline / 2))), cv::FONT_HERSHEY_SIMPLEX, fontScale, cv::Scalar(255, 255, 255), thickness);
+    }
+    return imageORIG;
+}
 
 void Yolo3Detection::update(cv::Mat &imageORIG) {
 

From 01a42ffe277cc1570384adc246b7ca5737588b49 Mon Sep 17 00:00:00 2001
From: Davide Sapienza <sapienza.dav@gmail.com>
Date: Tue, 3 Mar 2020 17:30:35 +0100
Subject: [PATCH 3/7] Move multiple demo files into one

Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
---
 CMakeLists.txt               |  14 +---
 demo/demo/demo.cpp           | 138 +++++++++++++++++++++++++++++++++++
 demo/demo/demo_centernet.cpp |  88 ----------------------
 demo/demo/demo_mobilenet.cpp |  88 ----------------------
 demo/demo/demo_yolo3.cpp     |  88 ----------------------
 5 files changed, 140 insertions(+), 276 deletions(-)
 create mode 100644 demo/demo/demo.cpp
 delete mode 100644 demo/demo/demo_centernet.cpp
 delete mode 100644 demo/demo/demo_mobilenet.cpp
 delete mode 100644 demo/demo/demo_yolo3.cpp

diff --git a/CMakeLists.txt b/CMakeLists.txt
index 421e53a..0a765a4 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -115,18 +115,8 @@ target_link_libraries(test_dla34_cnet tkDNN)
 add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
 target_link_libraries(test_rtinference tkDNN)
 
-add_executable(yolo3_demo demo/demo/demo_yolo3.cpp)
-target_link_libraries(yolo3_demo tkDNN)
-
-add_executable(centernet_demo demo/demo/demo_centernet.cpp)
-target_link_libraries(centernet_demo tkDNN)
-
-add_executable(mobilenet_demo demo/demo/demo_mobilenet.cpp)
-target_link_libraries(mobilenet_demo 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)
 
 #-------------------------------------------------------------------------------
 # Install
diff --git a/demo/demo/demo.cpp b/demo/demo/demo.cpp
new file mode 100644
index 0000000..a5a9d1a
--- /dev/null
+++ b/demo/demo/demo.cpp
@@ -0,0 +1,138 @@
+#include <iostream>
+#include <signal.h>
+#include <stdlib.h>     /* srand, rand */
+#include <unistd.h>
+#include <mutex>
+
+#include "CenternetDetection.h"
+#include "MobilenetDetection.h"
+#include "Yolo3Detection.h"
+
+bool gRun;
+bool SAVE_RESULT = false;
+
+void sig_handler(int signo) {
+    std::cout<<"request gateway stop\n";
+    gRun = false;
+}
+
+int main(int argc, char *argv[]) {
+
+    std::cout<<"detection\n";
+    signal(SIGINT, sig_handler);
+
+
+    char *net = "yolo3_berkeley.rt";
+    if(argc > 1)
+        net = argv[1]; 
+    char *input = "../demo/yolo_test.mp4";
+    if(argc > 2)
+        input = argv[2]; 
+    char ntype = 'y';
+    if(argc > 3)
+        ntype = argv[3][0]; 
+
+    tk::dnn::Yolo3Detection yolo;
+    tk::dnn::CenternetDetection cnet;
+    tk::dnn::MobilenetDetection mbnet;    
+    switch(ntype)
+    {
+        case 'y':
+            yolo.init(net);
+            break;
+        case 'c':
+            cnet.init(net);
+            break;
+        case 'm':
+            mbnet.init(net);
+            break;
+        default:
+        FatalError("Network type not allowed (3rd parameter)\n");
+    }
+
+    gRun = true;
+
+    cv::VideoCapture cap(input);
+    if(!cap.isOpened())
+        gRun = false; 
+    else
+        std::cout<<"camera started\n";
+
+    cv::VideoWriter resultVideo;
+    if(SAVE_RESULT) {
+        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
+        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
+        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
+    }
+
+    cv::Mat frame;
+    cv::Mat dnn_input;
+    cv::namedWindow("detection", cv::WINDOW_NORMAL);
+    
+    std::vector<tk::dnn::box> detected_bbox;
+
+    while(gRun) {
+        cap >> frame; 
+        if(!frame.data) {
+            break;
+        }  
+ 
+        // this will be resized to the net format
+        dnn_input = frame.clone();
+        // TODO: async infer
+        switch(ntype)
+        {
+            case 'y':
+                yolo.update(dnn_input);
+                frame = yolo.draw(frame);
+                break;
+            case 'c':
+                cnet.update(dnn_input);
+                frame = cnet.draw(dnn_input);
+                break;
+            case 'm':
+                mbnet.update(dnn_input);
+                frame = mbnet.draw();
+                break;
+            default:
+                FatalError("Network type not allowed!\n");
+        }
+                
+        cv::imshow("detection", frame);
+        cv::waitKey(1);
+        if(SAVE_RESULT)
+            resultVideo << frame;
+    }
+
+    std::cout<<"detection end\n";   
+    double mean = 0; 
+    switch(ntype)
+    {
+        case 'y':
+            std::cout<<COL_GREENB<<"\n\nTime stats:\n";
+            std::cout<<"Min: "<<*std::min_element(yolo.stats.begin(), yolo.stats.end())<<" ms\n";    
+            std::cout<<"Max: "<<*std::max_element(yolo.stats.begin(), yolo.stats.end())<<" ms\n";    
+            for(int i=0; i<yolo.stats.size(); i++) mean += yolo.stats[i]; mean /= yolo.stats.size();
+            std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;  
+            break;
+        case 'c':
+            std::cout<<COL_GREENB<<"\n\nTime stats:\n";
+            std::cout<<"Min: "<<*std::min_element(cnet.stats.begin(), cnet.stats.end())<<" ms\n";    
+            std::cout<<"Max: "<<*std::max_element(cnet.stats.begin(), cnet.stats.end())<<" ms\n";    
+            for(int i=0; i<cnet.stats.size(); i++) mean += cnet.stats[i]; mean /= cnet.stats.size();
+            std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;
+            break;
+        case 'm':
+            std::cout<<COL_GREENB<<"\n\nTime stats:\n";
+            std::cout<<"Min: "<<*std::min_element(mbnet.stats.begin(), mbnet.stats.end())<<" ms\n";    
+            std::cout<<"Max: "<<*std::max_element(mbnet.stats.begin(), mbnet.stats.end())<<" ms\n";    
+            for(int i=0; i<mbnet.stats.size(); i++) mean += mbnet.stats[i]; mean /= mbnet.stats.size();
+            std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;   
+            break;
+        default:
+            FatalError("Network type not allowed!\n");
+    }
+
+    return 0;
+}
+
diff --git a/demo/demo/demo_centernet.cpp b/demo/demo/demo_centernet.cpp
deleted file mode 100644
index 0f9c86e..0000000
--- a/demo/demo/demo_centernet.cpp
+++ /dev/null
@@ -1,88 +0,0 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-#include <unistd.h>
-#include <mutex>
-#include "utils.h"
-
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/videoio.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
-
-#include "CenternetDetection.h"
-
-bool 			gRun;
-bool SAVE_RESULT = false;
-
-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
-    gRun = false;
-}
-
-int main(int argc, char *argv[]) {
-
-    std::cout<<"detection\n";
-    signal(SIGINT, sig_handler);
-
-
-    char *net = "resnet101_cnet.rt";
-    if(argc > 1)
-        net = argv[1]; 
-    char *input = "../demo/yolo_test.mp4";
-    if(argc > 2)
-        input = argv[2]; 
-
-    tk::dnn::CenternetDetection cnet;
-    cnet.init(net);
-
-    gRun = true;
-
-    cv::VideoCapture cap(input);
-    if(!cap.isOpened())
-        gRun = false; 
-    else
-        std::cout<<"camera started\n";
-
-
-    cv::VideoWriter resultVideo;
-    if(SAVE_RESULT) {
-        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
-        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
-    }
-
-    cv::Mat frame;
-    cv::Mat dnn_input;
-    cv::namedWindow("detection", cv::WINDOW_NORMAL);
-    
-    while(gRun) {
-        cap >> frame; 
-        if(!frame.data) {
-            break;
-        }  
- 
-        // this will be resized to the net format
-        dnn_input = frame.clone();
-        // TODO: async infer
-        cnet.update(dnn_input);
-        // draw dets
-        frame = cnet.draw(dnn_input);
-                
-        cv::imshow("detection", frame);
-        cv::waitKey(1);
-        if(SAVE_RESULT)
-            resultVideo << frame;
-    }
-
-    std::cout<<"detection end\n";   
-
-
-    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
-    std::cout<<"Min: "<<*std::min_element(cnet.stats.begin(), cnet.stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(cnet.stats.begin(), cnet.stats.end())<<" ms\n";    
-    double mean = 0; for(int i=0; i<cnet.stats.size(); i++) mean += cnet.stats[i]; mean /= cnet.stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;    
-    return 0;
-}
-
diff --git a/demo/demo/demo_mobilenet.cpp b/demo/demo/demo_mobilenet.cpp
deleted file mode 100644
index f46df39..0000000
--- a/demo/demo/demo_mobilenet.cpp
+++ /dev/null
@@ -1,88 +0,0 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h> /* srand, rand */
-#include <unistd.h>
-#include <mutex>
-
-
-#include "MobilenetDetection.h"
-
-bool gRun;
-bool SAVE_RESULT = false;
-
-void sig_handler(int signo)
-{
-    std::cout << "request gateway stop\n";
-    gRun = false;
-}
-
-int main(int argc, char *argv[])
-{
-
-    std::cout << "detection\n";
-    signal(SIGINT, sig_handler);
-
-    char *net = "mobilenetv2ssd.rt";
-    if (argc > 1)
-        net = argv[1];
-    char *input = "../demo/yolo_test.mp4";
-    if (argc > 2)
-        input = argv[2];
-
-    tk::dnn::MobilenetDetection mbnet;
-    mbnet.init(net);
-
-    gRun = true;
-
-    cv::VideoCapture cap(input);
-    if (!cap.isOpened())
-        gRun = false;
-    else
-        std::cout << "camera started\n";
-
-    cv::VideoWriter resultVideo;
-    if (SAVE_RESULT)
-    {
-        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
-        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M', 'P', '4', 'V'), 30, cv::Size(w, h));
-    }
-
-    cv::Mat frame;
-    cv::Mat dnn_input;
-    cv::namedWindow("detection", cv::WINDOW_NORMAL);
-
-    while (gRun)
-    {
-        cap >> frame;
-        if (!frame.data)
-        {
-            break;
-        }
-
-        // this will be resized to the net format
-        dnn_input = frame.clone();
-        // TODO: async infer
-        mbnet.update(dnn_input);
-        // draw dets
-        frame = mbnet.draw();
-
-        cv::imshow("detection", frame);
-        cv::waitKey(1);
-        if (SAVE_RESULT)
-            resultVideo << frame;
-    }
-
-    std::cout << "detection end\n";
-
-    std::cout << COL_GREENB << "\n\nTime stats:\n";
-    std::cout << "Min: " << *std::min_element(mbnet.stats.begin(), mbnet.stats.end()) << " ms\n";
-    std::cout << "Max: " << *std::max_element(mbnet.stats.begin(), mbnet.stats.end()) << " ms\n";
-    double mean = 0;
-    for (int i = 0; i < mbnet.stats.size(); i++)
-        mean += mbnet.stats[i];
-    mean /= mbnet.stats.size();
-    std::cout << "Avg: " << mean << " ms\n"
-              << COL_END;
-    return 0;
-}
diff --git a/demo/demo/demo_yolo3.cpp b/demo/demo/demo_yolo3.cpp
deleted file mode 100644
index 0abcc2c..0000000
--- a/demo/demo/demo_yolo3.cpp
+++ /dev/null
@@ -1,88 +0,0 @@
-#include <iostream>
-#include <signal.h>
-#include <stdlib.h>     /* srand, rand */
-#include <unistd.h>
-#include <mutex>
-#include "utils.h"
-
-#include <opencv2/core/core.hpp>
-#include <opencv2/highgui/highgui.hpp>
-#include <opencv2/videoio.hpp>
-#include <opencv2/imgproc/imgproc.hpp>
-
-#include "Yolo3Detection.h"
-
-bool 			gRun;
-bool SAVE_RESULT = false;
-
-void sig_handler(int signo) {
-    std::cout<<"request gateway stop\n";
-    gRun = false;
-}
-
-int main(int argc, char *argv[]) {
-
-    std::cout<<"detection\n";
-    signal(SIGINT, sig_handler);
-
-
-    char *net = "yolo3_berkeley.rt";
-    if(argc > 1)
-        net = argv[1]; 
-    char *input = "../demo/yolo_test.mp4";
-    if(argc > 2)
-        input = argv[2]; 
-
-    tk::dnn::Yolo3Detection yolo;
-    yolo.init(net);
-
-    gRun = true;
-
-    cv::VideoCapture cap(input);
-    if(!cap.isOpened())
-        gRun = false; 
-    else
-        std::cout<<"camera started\n";
-
-
-    cv::VideoWriter resultVideo;
-    if(SAVE_RESULT) {
-        int w = cap.get(cv::CAP_PROP_FRAME_WIDTH);
-        int h = cap.get(cv::CAP_PROP_FRAME_HEIGHT);
-        resultVideo.open("result.mp4", cv::VideoWriter::fourcc('M','P','4','V'), 30, cv::Size(w, h));
-    }
-
-    cv::Mat frame;
-    cv::Mat dnn_input;
-    cv::namedWindow("detection", cv::WINDOW_NORMAL);
-    
-    while(gRun) {
-        cap >> frame; 
-        if(!frame.data) {
-            break;
-        }  
- 
-        // this will be resized to the net format
-        dnn_input = frame.clone();
-        // TODO: async infer
-        yolo.update(dnn_input);
-
-        frame = yolo.draw(frame);
-        
-        cv::imshow("detection", frame);
-        cv::waitKey(1);
-        if(SAVE_RESULT)
-            resultVideo << frame;
-    }
-
-    std::cout<<"detection end\n";   
-
-
-    std::cout<<COL_GREENB<<"\n\nTime stats:\n";
-    std::cout<<"Min: "<<*std::min_element(yolo.stats.begin(), yolo.stats.end())<<" ms\n";    
-    std::cout<<"Max: "<<*std::max_element(yolo.stats.begin(), yolo.stats.end())<<" ms\n";    
-    double mean = 0; for(int i=0; i<yolo.stats.size(); i++) mean += yolo.stats[i]; mean /= yolo.stats.size();
-    std::cout<<"Avg: "<<mean<<" ms\n"<<COL_END;    
-    return 0;
-}
-

From 7dbbbe4d9aac87ab9376459fe79b4046ea37525a Mon Sep 17 00:00:00 2001
From: Davide Sapienza <sapienza.dav@gmail.com>
Date: Tue, 3 Mar 2020 17:34:01 +0100
Subject: [PATCH 4/7] Update README

Signed-off-by: Davide Sapienza <sapienza.dav@gmail.com>
---
 README.md | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/README.md b/README.md
index f8a3262..d04d52f 100644
--- a/README.md
+++ b/README.md
@@ -82,8 +82,8 @@ rm yolo3_berkeley.rt		 # be sure to delete(or move) old tensorRT files
 ```
 this will genereate a yolo3_berkeley.rt file that can be used for live detection:
 ```
-./yolo3_demo                               # launch detection on a demo video
-./yolo3_demo yolo3_berkeley.rt /dev/video0 # launch detection on device 0
+./demo                                 # launch detection on a demo video
+./demo yolo3_berkeley.rt /dev/video0 y # launch detection on device 0
 ```
 ![demo](https://user-images.githubusercontent.com/11562617/72547657-540e7800-388d-11ea-83c6-49dfea2a0607.gif)
 
@@ -110,9 +110,9 @@ rm dla34_cnet.rt		     # be sure to delete(or move) old tensorRT files
 
 this will genereate resnet101_cnet.rt and dla34_cnet.rt file that can be used for live detection:
 ```
-./centernet_demo                               # launch detection on a demo video
-./centernet_demo resnet101_cnet.rt /dev/video0 # launch detection on device 0
-./centernet_demo dla34_cnet.rt /dev/video0     # launch detection on device 0
+./demo dla34_cnet.rt ../demo/yolo_test.mp4 c    # launch detection on a demo video
+./demo resnet101_cnet.rt /dev/video0 c          # launch detection on device 0
+./demo dla34_cnet.rt /dev/video0 c              # launch detection on device 0
 ```
 
 ## mAP demo

From 7ea31d123a1f6f1d80b2b2dd1557dbefeb2ef201 Mon Sep 17 00:00:00 2001
From: Micaela Verucchi <micaela.verucchi@unimore.it>
Date: Sun, 8 Mar 2020 18:12:08 +0100
Subject: [PATCH 5/7] Update README

Signed-off-by: Micaela Verucchi <micaela.verucchi@unimore.it>
---
 README.md | 33 +++++++++++++++++++++++++++++++++
 1 file changed, 33 insertions(+)

diff --git a/README.md b/README.md
index d04d52f..7fc1848 100644
--- a/README.md
+++ b/README.md
@@ -139,3 +139,36 @@ Example:
 cd build
 ./map_demo dla34_cnet.rt c ../demo/COCO_val2017/all_labels.txt ../demo/config.yaml
 ```
+
+## Supported networks
+
+| Test Name         | Network                                       | Dataset                                                       | N Classes | Input size    | Weights                                                                   |
+| :---------------- | :-------------------------------------------- | :-----------------------------------------------------------: | :-------: | :-----------: | :------------------------------------------------------------------------ |
+| yolo              | YOLO v2<sup>1</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 80        | 608x608       | weights                                                                   |
+| yolo_224          | YOLO v2<sup>1</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 80        | 224x224       | weights                                                                   |
+| yolo_berkeley     | YOLO v2<sup>1</sup>                           | [BDD100K  ](https://bair.berkeley.edu/blog/2018/05/30/bdd/)   | 10        | 416x736       | weights                                                                   |
+| yolo_relu         | YOLO v2 (with ReLU, not Leaky)<sup>1</sup>    | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | weights                                                                   |
+| yolo_tiny         | YOLO v2 tiny<sup>1</sup>                      | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | weights                                                                   |
+| yolo_voc          | YOLO v2<sup>1</sup>                           | [VOC      ](http://host.robots.ox.ac.uk/pascal/VOC/)          | 21        | 416x416       | weights                                                                   |
+| yolo3             | YOLO v3<sup>2</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/jPXmHyptpLoNdNR/download)     |
+| yolo3_berkeley    | YOLO v3<sup>2</sup>                           | [BDD100K  ](https://bair.berkeley.edu/blog/2018/05/30/bdd/)   | 10        | 320x544       | weights                                                                   |
+| yolo3_coco4       | YOLO v3<sup>2</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 4         | 416x416       | weights                                                                   |
+| yolo3_flir        | YOLO v3<sup>2</sup>                           | [FREE FLIR](https://www.flir.com/oem/adas/adas-dataset-form/) | 3         | 320x544       | weights                                                                   |
+| yolo3_tiny        | YOLO v3 tiny<sup>2</sup>                      | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/LMcSHtWaLeps8yN/download)     |
+| yolo3_tiny512     | YOLO v3 tiny<sup>2</sup>                      | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/pjooA5DMrrEbrmA/download)     |
+| dla34             | Deep Leayer Aggreagtion (DLA) 34<sup>3</sup>  | [COCO 2014](http://cocodataset.org/)                          | 80        | 224x224       | weights                                                                   |
+| dla34_cnet        | Centernet (DLA34 backend)<sup>4</sup>         | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/8AjXdgCeRzCa5AF/download)     |
+| mobilenetv2ssd    | Mobilnet v2 SSD Lite<sup>5</sup>              | [VOC      ](http://host.robots.ox.ac.uk/pascal/VOC/)          | 21        | 300x300       | [weights](https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download)     |
+| resnet101         | Resnet 101<sup>6</sup>                        | [COCO 2014](http://cocodataset.org/)                          | 80        | 224x224       | weights                                                                   |
+| resnet101_cnet    | Centernet (Resnet101 backend)<sup>4</sup>     | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/B6mj33k7beECXsY/download)     |
+
+
+
+## References
+
+1. Redmon, Joseph, and Ali Farhadi. "YOLO9000: better, faster, stronger." Proceedings of the IEEE conference on computer vision and pattern recognition. 2017.
+2. Redmon, Joseph, and Ali Farhadi. "Yolov3: An incremental improvement." arXiv preprint arXiv:1804.02767 (2018).
+3. Yu, Fisher, et al. "Deep layer aggregation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
+4. Zhou, Xingyi, Dequan Wang, and Philipp Krähenbühl. "Objects as points." arXiv preprint arXiv:1904.07850 (2019).
+5. Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
+6. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
\ No newline at end of file

From 4b85a2238aec4319c474ab4c28672b971ded81d0 Mon Sep 17 00:00:00 2001
From: Micaela Verucchi <micaelaverucchi@gmail.com>
Date: Tue, 10 Mar 2020 14:35:25 +0100
Subject: [PATCH 6/7] Add writing results on file for map demo

Signed-off-by: Micaela Verucchi <micaelaverucchi@gmail.com>
---
 CMakeLists.txt                 |  3 +++
 demo/demo/map.cpp              | 22 +++++++++++++--
 include/evaluation.h           |  6 ++---
 include/tkDNN/Yolo3Detection.h |  4 +--
 src/evaluation.cpp             | 49 +++++++++++++++++++++++++++++-----
 5 files changed, 71 insertions(+), 13 deletions(-)

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
+}

From e6aa73d7bcc01138ee37b333df48418d0ad11bc9 Mon Sep 17 00:00:00 2001
From: Micaela Verucchi <micaelaverucchi@gmail.com>
Date: Tue, 10 Mar 2020 15:26:57 +0100
Subject: [PATCH 7/7] Update README.md

---
 README.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/README.md b/README.md
index 7fc1848..93af488 100644
--- a/README.md
+++ b/README.md
@@ -155,7 +155,7 @@ cd build
 | yolo3_coco4       | YOLO v3<sup>2</sup>                           | [COCO 2014](http://cocodataset.org/)                          | 4         | 416x416       | weights                                                                   |
 | yolo3_flir        | YOLO v3<sup>2</sup>                           | [FREE FLIR](https://www.flir.com/oem/adas/adas-dataset-form/) | 3         | 320x544       | weights                                                                   |
 | yolo3_tiny        | YOLO v3 tiny<sup>2</sup>                      | [COCO 2014](http://cocodataset.org/)                          | 80        | 416x416       | [weights](https://cloud.hipert.unimore.it/s/LMcSHtWaLeps8yN/download)     |
-| yolo3_tiny512     | YOLO v3 tiny<sup>2</sup>                      | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/pjooA5DMrrEbrmA/download)     |
+| yolo3_tiny512     | YOLO v3 tiny<sup>2</sup>                      | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/njnYACnQfWQFKrn/download)     |
 | dla34             | Deep Leayer Aggreagtion (DLA) 34<sup>3</sup>  | [COCO 2014](http://cocodataset.org/)                          | 80        | 224x224       | weights                                                                   |
 | dla34_cnet        | Centernet (DLA34 backend)<sup>4</sup>         | [COCO 2017](http://cocodataset.org/)                          | 80        | 512x512       | [weights](https://cloud.hipert.unimore.it/s/8AjXdgCeRzCa5AF/download)     |
 | mobilenetv2ssd    | Mobilnet v2 SSD Lite<sup>5</sup>              | [VOC      ](http://host.robots.ox.ac.uk/pascal/VOC/)          | 21        | 300x300       | [weights](https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download)     |
@@ -171,4 +171,4 @@ cd build
 3. Yu, Fisher, et al. "Deep layer aggregation." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
 4. Zhou, Xingyi, Dequan Wang, and Philipp Krähenbühl. "Objects as points." arXiv preprint arXiv:1904.07850 (2019).
 5. Sandler, Mark, et al. "Mobilenetv2: Inverted residuals and linear bottlenecks." Proceedings of the IEEE conference on computer vision and pattern recognition. 2018.
-6. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.
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+6. He, Kaiming, et al. "Deep residual learning for image recognition." Proceedings of the IEEE conference on computer vision and pattern recognition. 2016.