diff --git a/tests/yolo3_tiny512/yolo3_tiny512.cpp b/tests/yolo3_tiny512/yolo3_tiny512.cpp
new file mode 100644
index 0000000..b3a6d03
--- /dev/null
+++ b/tests/yolo3_tiny512/yolo3_tiny512.cpp
@@ -0,0 +1,123 @@
+#include<iostream>
+#include "tkdnn.h"
+
+const char *input_bin  = "../tests/yolo3_tiny512/layers/input.bin";
+const char *c0_bin     = "../tests/yolo3_tiny512/layers/c0.bin";
+const char *c2_bin     = "../tests/yolo3_tiny512/layers/c2.bin";
+const char *c4_bin     = "../tests/yolo3_tiny512/layers/c4.bin";
+const char *c6_bin     = "../tests/yolo3_tiny512/layers/c6.bin";
+const char *c8_bin     = "../tests/yolo3_tiny512/layers/c8.bin";
+const char *c10_bin    = "../tests/yolo3_tiny512/layers/c10.bin";
+const char *c12_bin    = "../tests/yolo3_tiny512/layers/c12.bin";
+const char *c13_bin    = "../tests/yolo3_tiny512/layers/c13.bin";
+const char *c14_bin    = "../tests/yolo3_tiny512/layers/c14.bin";
+const char *c15_bin    = "../tests/yolo3_tiny512/layers/c15.bin";
+const char *c18_bin    = "../tests/yolo3_tiny512/layers/c18.bin";
+const char *c21_bin    = "../tests/yolo3_tiny512/layers/c21.bin";
+const char *c22_bin    = "../tests/yolo3_tiny512/layers/c22.bin";
+const char *g16_bin    = "../tests/yolo3_tiny512/layers/g16.bin";
+const char *g23_bin    = "../tests/yolo3_tiny512/layers/g23.bin";
+// const char *output_bin = "../tests/yolo3_tiny512/layers/output.bin";
+
+const char *output_bin = "../tests/yolo3_tiny512/debug/layer23_out.bin";
+
+int main() {
+
+    int classes = 80;
+
+    // Network layout
+    tk::dnn::dataDim_t dim(1, 3, 512, 512, 1);
+    tk::dnn::Network net(dim);
+        
+
+    tk::dnn::Conv2d     c0 (&net, 16, 3, 3, 1, 1, 1, 1,   c0_bin, true);
+    tk::dnn::Activation a0 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p1 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+
+    tk::dnn::Conv2d     c2 (&net, 32, 3, 3, 1, 1, 1, 1,   c2_bin, true);
+    tk::dnn::Activation a2 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p3 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+
+    tk::dnn::Conv2d     c4 (&net, 64, 3, 3, 1, 1, 1, 1,  c4_bin, true);
+    tk::dnn::Activation a4 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p5 (&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+
+    tk::dnn::Conv2d     c6 (&net, 128, 3, 3, 1, 1, 1, 1,  c6_bin, true);
+    tk::dnn::Activation a6 (&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p7(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+
+    tk::dnn::Conv2d     c8(&net, 256, 3, 3, 1, 1, 1, 1,  c8_bin, true);
+    tk::dnn::Activation a8(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p9(&net, 2, 2, 2, 2, tk::dnn::POOLING_MAX);
+
+    tk::dnn::Conv2d     c10(&net, 512, 3, 3, 1, 1, 1, 1, c10_bin, true);
+    tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Pooling    p11(&net, 2, 2, 1, 1,0,0, tk::dnn::POOLING_MAX);
+
+    tk::dnn::Conv2d     c12(&net, 1024, 3, 3, 1, 1, 1, 1, c12_bin, true);
+    tk::dnn::Activation a12(&net, tk::dnn::ACTIVATION_LEAKY);
+
+    tk::dnn::Conv2d     c13(&net, 256, 1, 1, 1, 1, 0, 0, c13_bin, true);
+    tk::dnn::Activation a13(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c14(&net, 512, 3, 3, 1, 1, 1, 1, c14_bin, true);
+    tk::dnn::Activation a14(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c15(&net, 255, 1, 1, 1, 1, 0, 0, c15_bin, false);
+
+    tk::dnn::Yolo     yolo0  (&net, classes, 2, g16_bin);
+
+    tk::dnn::Layer *m17_layers[1] = { &a13 };
+    tk::dnn::Route      m17  (&net, m17_layers, 1);
+    tk::dnn::Conv2d     c18(&net, 128, 1, 1, 1, 1, 0, 0, c18_bin, true);
+    tk::dnn::Activation a18(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Upsample   u19  (&net, 2);
+
+    tk::dnn::Layer *m20_layers[2] = { &u19, &a8 };
+    tk::dnn::Route      m20  (&net, m20_layers, 2);
+
+    tk::dnn::Conv2d     c21(&net, 256, 3, 3, 1, 1, 1, 1, c21_bin, true);
+    tk::dnn::Activation a21(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d     c22(&net, 255, 1, 1, 1, 1, 0, 0, c22_bin, false);
+
+    tk::dnn::Yolo     yolo1  (&net, classes, 2, g23_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
+    tk::dnn::NetworkRT netRT(&net, "yolo3_tiny512.rt");
+
+    dnnType *out_data, *out_data2; // cudnn output, tensorRT output
+
+    tk::dnn::dataDim_t dim1 = dim; //input dim
+    printCenteredTitle(" CUDNN inference ", '=', 30); {
+        dim1.print();
+        TIMER_START
+        out_data = net.infer(dim1, data);    
+        TIMER_STOP
+        dim1.print();   
+    }
+ 
+    tk::dnn::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);
+    
+    return 0;
+}