diff --git a/CMakeLists.txt b/CMakeLists.txt
index f305500..83ffd58 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -62,6 +62,9 @@ target_link_libraries(test_mnistRT tkDNN)
 add_executable(test_yolo tests/yolo/yolo.cpp)
 target_link_libraries(test_yolo tkDNN)
 
+add_executable(test_yolo_voc tests/yolo_voc/yolo_voc.cpp)
+target_link_libraries(test_yolo_voc tkDNN)
+
 add_executable(test_yolo_tiny tests/yolo_tiny/yolo_tiny.cpp)
 target_link_libraries(test_yolo_tiny tkDNN)
 
diff --git a/demo/live/live.cpp b/demo/live/live.cpp
index 5c1a833..d08ac2e 100644
--- a/demo/live/live.cpp
+++ b/demo/live/live.cpp
@@ -7,7 +7,15 @@
 #include <opencv2/highgui/highgui.hpp>
 #include <opencv2/imgproc/imgproc.hpp>
 
+#define VOC
+
+#ifdef VOC
+const char *reg_bias = "../tests/yolo_voc/layers/g31.bin";
+#define CLASS 20
+#else
 const char *reg_bias = "../tests/yolo/layers/g31.bin";
+#define CLASS 80
+#endif
 
 int prob_sort(const void *pa, const void *pb) {
     tkDNN::box a = *(tkDNN::box *)pa;
@@ -126,18 +134,26 @@ int main(int argc, char *argv[]) {
     }
     //end parsing
 
-    std::cout<<"open video stream on device: "<<device<<"\n";
-    cv::VideoCapture cap(device); 
+    //std::cout<<"open video stream on device: "<<device<<"\n";
+    //cv::VideoCapture cap(device);
+		const char* pipe = "nvcamerasrc ! video/x-raw(memory:NVMM), width=(int)640, height=(int)480, format=(string)I420, framerate=(fraction)30/1 ! nvvidconv ! video/x-raw, format=(string)I420 ! videoconvert ! video/x-raw, format=(string)BGR ! appsink";
+		/*const char* pipe = "nvcamerasrc ! "
+		"video/x-raw(memory:NVMM), width=(int)2592, height=(int)1458, format=(string)I420, framerate=(fraction)30/1 ! "
+		"nvvidconv ! video/x-raw, width=(int)1280, height=(int)720, format=(string)BGRx ! "
+		"videoconvert ! appsink";*/
+
+		cv::VideoCapture cap(pipe);
+
     if(!cap.isOpened())
         FatalError("unable to open video stream");
-    cap.set(CV_CAP_PROP_BUFFERSIZE, 1); // process only last frame
+    //cap.set(CV_CAP_PROP_BUFFERSIZE, 1); // process only last frame
 
     if(!fileExist(tensor_path))
         FatalError("unable to read serialRT file");
 
     //convert network to tensorRT
     tkDNN::NetworkRT netRT(NULL, tensor_path);
-    tkDNN::RegionInterpret rI(netRT.input_dim, netRT.output_dim, 80, 4, 5, thresh, reg_bias);
+    tkDNN::RegionInterpret rI(netRT.input_dim, netRT.output_dim, CLASS, 4, 5, thresh, reg_bias);
 
     dnnType *input = new float[netRT.input_dim.tot()];
     dnnType *output = new float[netRT.output_dim.tot()];
diff --git a/tests/yolo_voc/yolo_voc.cfg b/tests/yolo_voc/yolo_voc.cfg
new file mode 100644
index 0000000..dbf2de2
--- /dev/null
+++ b/tests/yolo_voc/yolo_voc.cfg
@@ -0,0 +1,258 @@
+[net]
+# Testing
+batch=1
+subdivisions=1
+# Training
+# batch=64
+# subdivisions=8
+height=416
+width=416
+channels=3
+momentum=0.9
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 80200
+policy=steps
+steps=40000,60000
+scales=.1,.1
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[maxpool]
+size=2
+stride=2
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=1
+pad=1
+activation=leaky
+
+
+#######
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[route]
+layers=-9
+
+[convolutional]
+batch_normalize=1
+size=1
+stride=1
+pad=1
+filters=64
+activation=leaky
+
+[reorg]
+stride=2
+
+[route]
+layers=-1,-4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=125
+activation=linear
+
+
+[region]
+anchors =  1.3221, 1.73145, 3.19275, 4.00944, 5.05587, 8.09892, 9.47112, 4.84053, 11.2364, 10.0071
+bias_match=1
+classes=20
+coords=4
+num=5
+softmax=1
+jitter=.3
+rescore=1
+
+object_scale=5
+noobject_scale=1
+class_scale=1
+coord_scale=1
+
+absolute=1
+thresh = .6
+random=1
diff --git a/tests/yolo_voc/yolo_voc.cpp b/tests/yolo_voc/yolo_voc.cpp
new file mode 100644
index 0000000..843c981
--- /dev/null
+++ b/tests/yolo_voc/yolo_voc.cpp
@@ -0,0 +1,150 @@
+#include<iostream>
+#include "tkdnn.h"
+
+const char *input_bin  = "../tests/yolo_voc/layers/input.bin";
+const char *c0_bin     = "../tests/yolo_voc/layers/c0.bin";
+const char *c2_bin     = "../tests/yolo_voc/layers/c2.bin";
+const char *c4_bin     = "../tests/yolo_voc/layers/c4.bin";
+const char *c5_bin     = "../tests/yolo_voc/layers/c5.bin";
+const char *c6_bin     = "../tests/yolo_voc/layers/c6.bin";
+const char *c8_bin     = "../tests/yolo_voc/layers/c8.bin";
+const char *c9_bin     = "../tests/yolo_voc/layers/c9.bin";
+const char *c10_bin    = "../tests/yolo_voc/layers/c10.bin";
+const char *c12_bin    = "../tests/yolo_voc/layers/c12.bin";
+const char *c13_bin    = "../tests/yolo_voc/layers/c13.bin";
+const char *c14_bin    = "../tests/yolo_voc/layers/c14.bin";
+const char *c15_bin    = "../tests/yolo_voc/layers/c15.bin";
+const char *c16_bin    = "../tests/yolo_voc/layers/c16.bin";
+const char *c18_bin    = "../tests/yolo_voc/layers/c18.bin";
+const char *c19_bin    = "../tests/yolo_voc/layers/c19.bin";
+const char *c20_bin    = "../tests/yolo_voc/layers/c20.bin";
+const char *c21_bin    = "../tests/yolo_voc/layers/c21.bin";
+const char *c22_bin    = "../tests/yolo_voc/layers/c22.bin";
+const char *c23_bin    = "../tests/yolo_voc/layers/c23.bin";
+const char *c24_bin    = "../tests/yolo_voc/layers/c24.bin";
+const char *c26_bin    = "../tests/yolo_voc/layers/c26.bin";
+const char *c29_bin    = "../tests/yolo_voc/layers/c29.bin";
+const char *c30_bin    = "../tests/yolo_voc/layers/c30.bin";
+const char *g31_bin    = "../tests/yolo_voc/layers/g31.bin";
+const char *output_bin = "../tests/yolo_voc/layers/output.bin";
+
+int main() {
+
+    // Network layout
+    tkDNN::dataDim_t dim(1, 3, 416, 416, 1);
+    tkDNN::Network net(dim);
+
+    tkDNN::Conv2d     c0 (&net, 32, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tkDNN::Activation a0 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p1 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c2 (&net, 64, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tkDNN::Activation a2 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p3 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c4 (&net, 128, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tkDNN::Activation a4 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c5 (&net, 64, 1, 1, 1, 1, 0, 0,   c5_bin, true);
+    tkDNN::Activation a5 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tkDNN::Activation a6 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p7 (&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c8 (&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tkDNN::Activation a8 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c9 (&net, 128, 1, 1, 1, 1, 0, 0,  c9_bin, true);
+    tkDNN::Activation a9 (&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c10(&net, 256, 3, 3, 1, 1, 1, 1,  c10_bin, true);
+    tkDNN::Activation a10(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p11(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c12(&net, 512, 3, 3, 1, 1, 1, 1,  c12_bin, true);
+    tkDNN::Activation a12(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0,  c13_bin, true);
+    tkDNN::Activation a13(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1,  c14_bin, true);
+    tkDNN::Activation a14(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c15(&net, 256, 1, 1, 1, 1, 0, 0,  c15_bin, true);
+    tkDNN::Activation a15(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c16(&net, 512, 3, 3, 1, 1, 1, 1,  c16_bin, true); 
+    tkDNN::Activation a16(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Pooling    p17(&net, 2, 2, 2, 2, tkDNN::POOLING_MAX);
+
+    tkDNN::Conv2d     c18(&net, 1024, 3, 3, 1, 1, 1, 1, c18_bin, true);
+    tkDNN::Activation a18(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c19(&net, 512, 1, 1, 1, 1, 0, 0,  c19_bin, true);
+    tkDNN::Activation a19(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c20(&net, 1024, 3, 3, 1, 1, 1, 1, c20_bin, true);
+    tkDNN::Activation a20(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c21(&net, 512, 1, 1, 1, 1, 0, 0,  c21_bin, true);
+    tkDNN::Activation a21(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c22(&net, 1024, 3, 3, 1, 1, 1, 1, c22_bin, true);
+    tkDNN::Activation a22(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c23(&net, 1024, 3, 3, 1, 1, 1, 1, c23_bin, true);
+    tkDNN::Activation a23(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Conv2d     c24(&net, 1024, 3, 3, 1, 1, 1, 1, c24_bin, true);
+    tkDNN::Activation a24(&net, tkDNN::ACTIVATION_LEAKY);
+
+    tkDNN::Layer *m25_layers[1] = { &a16 };
+    tkDNN::Route      m25(&net, m25_layers, 1);
+    tkDNN::Conv2d     c26(&net, 64, 1, 1, 1, 1, 0, 0,   c26_bin, true);
+    tkDNN::Activation a26(&net, tkDNN::ACTIVATION_LEAKY);
+    tkDNN::Reorg      r27(&net, 2);
+
+    tkDNN::Layer *m28_layers[2] = { &r27, &a24 };
+    tkDNN::Route      m28(&net, m28_layers, 2);
+
+    tkDNN::Conv2d     c29(&net, 1024, 3, 3, 1, 1, 1, 1, c29_bin, true);
+    tkDNN::Activation a29(&net, tkDNN::ACTIVATION_LEAKY);   
+    tkDNN::Conv2d     c30(&net, 125, 1, 1, 1, 1, 0, 0,  c30_bin, false);
+    tkDNN::Region     g31(&net, 20, 4, 5);
+    
+    tkDNN::RegionInterpret rI(dim, g31.output_dim, 20, 4, 5, 0.6f, g31_bin);
+
+    // Load input
+    dnnType *data;
+    dnnType *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+    
+    //print network model
+    net.print();
+
+    //convert network to tensorRT
+    tkDNN::NetworkRT netRT(&net, "yolo_voc.rt");
+
+    dnnType *out_data, *out_data2; // cudnn output, tensorRT output
+
+    tkDNN::dataDim_t dim1 = dim; //input dim
+    printCenteredTitle(" CUDNN inference ", '=', 30); {
+        dim1.print();
+        TIMER_START
+        out_data = net.infer(dim1, data);    
+        TIMER_STOP
+        dim1.print();   
+    }
+    
+    tkDNN::dataDim_t dim2 = dim;
+    printCenteredTitle(" TENSORRT inference ", '=', 30); {
+        dim2.print();
+        TIMER_START
+        out_data2 = netRT.infer(dim2, data);
+        TIMER_STOP
+        dim2.print();
+    }
+
+    printCenteredTitle(" CHECK RESULTS ", '=', 30);
+    dnnType *out, *out_h;
+    int out_dim = net.getOutputDim().tot();
+    readBinaryFile(output_bin, out_dim, &out_h, &out);
+    std::cout<<"CUDNN vs correct"; checkResult(out_dim, out_data, out);
+    std::cout<<"TRT   vs correct"; checkResult(out_dim, out_data2, out);
+    std::cout<<"CUDNN vs TRT    "; checkResult(out_dim, out_data, out_data2);
+    
+    std::cout<<"\n\nDetected objects: \n";
+    dnnType *output_h = new dnnType[rI.output_dim.tot()];
+    checkCuda(cudaMemcpy(output_h, out_data2, 
+              rI.output_dim.tot()*sizeof(dnnType), cudaMemcpyDeviceToHost));
+    rI.interpretData(output_h);
+    rI.showImageResult(input_h);
+    return 0;
+}