diff --git a/CMakeLists.txt b/CMakeLists.txt
index 88e94e3..9e9c27c 100644
--- a/CMakeLists.txt
+++ b/CMakeLists.txt
@@ -107,6 +107,9 @@ target_link_libraries(test_dla34_cnet tkDNN)
 add_executable(test_shelfnet tests/shelfnet/shelfnet.cpp)
 target_link_libraries(test_shelfnet tkDNN)
 
+add_executable(test_shelfnet_berkeley tests/shelfnet/shelfnet_berkeley.cpp)
+target_link_libraries(test_shelfnet_berkeley tkDNN)
+
 # DEMOS
 add_executable(test_rtinference tests/test_rtinference/rtinference.cpp)
 target_link_libraries(test_rtinference tkDNN)
diff --git a/demo/demo/seg_demo.cpp b/demo/demo/seg_demo.cpp
index c1ee43b..0737ad7 100644
--- a/demo/demo/seg_demo.cpp
+++ b/demo/demo/seg_demo.cpp
@@ -29,9 +29,12 @@ int main(int argc, char *argv[]) {
     int n_batch = 1;
     if(argc > 3)
         n_batch = atoi(argv[3]); 
-    bool show = false;
+    int n_classes = 19;
     if(argc > 4)
-        show = atoi(argv[4]); 
+        n_classes = atoi(argv[4]); 
+    bool show = false;
+    if(argc > 5)
+        show = atoi(argv[5]); 
 
     if(n_batch < 1 || n_batch > 64)
         FatalError("Batch dim not supported");
@@ -39,7 +42,7 @@ int main(int argc, char *argv[]) {
     if(!show)
         SAVE_RESULT = true;
 
-    int n_classes = 19;
+    
 
     tk::dnn::SegmentationNN segNN;
     segNN.init(net, n_classes, n_batch);
diff --git a/tests/shelfnet/shelfnet.cpp b/tests/shelfnet/shelfnet.cpp
index 7cc1b42..48cad04 100644
--- a/tests/shelfnet/shelfnet.cpp
+++ b/tests/shelfnet/shelfnet.cpp
@@ -83,7 +83,7 @@ const char *trans[] = {
 int main()
 {
 
-    // downloadWeightsifDoNotExist(input_bin, "shelfnet", "https://cloud.hipert.unimore.it/s/x4ZfxBKN23zAJQp/download");
+    downloadWeightsifDoNotExist(input_bin, "shelfnet", "https://cloud.hipert.unimore.it/s/mEDZMRJaGCFWSJF/download");
 
     int classes = 19;
 
diff --git a/tests/shelfnet/shelfnet_berkeley.cpp b/tests/shelfnet/shelfnet_berkeley.cpp
new file mode 100644
index 0000000..fe81191
--- /dev/null
+++ b/tests/shelfnet/shelfnet_berkeley.cpp
@@ -0,0 +1,295 @@
+#include <iostream>
+#include <opencv2/highgui/highgui.hpp>
+#include <opencv2/imgproc/imgproc.hpp>
+
+#include "tkdnn.h"
+#include "NetworkViz.h"
+
+
+const char *input_bin = "shelfnet_berkeley/debug/input.bin";
+
+const char *backbone[] = {
+    "shelfnet_berkeley/layers/backbone-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer1-0-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer1-0-conv2.bin",
+    "shelfnet_berkeley/layers/backbone-layer1-1-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer1-1-conv2.bin",
+    "shelfnet_berkeley/layers/backbone-layer2-0-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer2-0-conv2.bin",
+    "shelfnet_berkeley/layers/backbone-layer2-0-downsample-0.bin",
+    "shelfnet_berkeley/layers/backbone-layer2-1-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer2-1-conv2.bin",
+    "shelfnet_berkeley/layers/backbone-layer3-0-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer3-0-conv2.bin",
+    "shelfnet_berkeley/layers/backbone-layer3-0-downsample-0.bin",
+    "shelfnet_berkeley/layers/backbone-layer3-1-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer3-1-conv2.bin",
+    "shelfnet_berkeley/layers/backbone-layer4-0-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer4-0-conv2.bin",
+    "shelfnet_berkeley/layers/backbone-layer4-0-downsample-0.bin",
+    "shelfnet_berkeley/layers/backbone-layer4-1-conv1.bin",
+    "shelfnet_berkeley/layers/backbone-layer4-1-conv2.bin"};
+
+const char *conv_out[] = {
+    "shelfnet_berkeley/layers/conv_out-conv-conv.bin",
+    "shelfnet_berkeley/layers/conv_out-conv_out.bin",
+    "shelfnet_berkeley/layers/conv_out16-conv-conv.bin",
+    "shelfnet_berkeley/layers/conv_out16-conv_out.bin",
+    "shelfnet_berkeley/layers/conv_out32-conv-conv.bin",
+    "shelfnet_berkeley/layers/conv_out32-conv_out.bin"
+    };
+
+const char *decoder[] = {
+    "shelfnet_berkeley/layers/decoder-bottom-conv1.bin",
+    "shelfnet_berkeley/layers/decoder-bottom-conv12.bin",
+    "shelfnet_berkeley/layers/decoder-up_conv_list-0-conv-conv.bin",
+    "shelfnet_berkeley/layers/decoder-up_conv_list-0-conv_atten.bin",
+    "shelfnet_berkeley/layers/decoder-up_dense_list-0-conv.bin",
+    "shelfnet_berkeley/layers/decoder-up_conv_list-1-conv-conv.bin",
+    "shelfnet_berkeley/layers/decoder-up_conv_list-1-conv_atten.bin",
+    "shelfnet_berkeley/layers/decoder-up_dense_list-1-conv.bin"
+    };
+
+    
+const char *ladder[] = {
+    "shelfnet_berkeley/layers/ladder-inconv-conv1.bin",
+    "shelfnet_berkeley/layers/ladder-inconv-conv12.bin",
+    "shelfnet_berkeley/layers/ladder-down_module_list-0-conv1.bin",
+    "shelfnet_berkeley/layers/ladder-down_module_list-0-conv12.bin",
+    "shelfnet_berkeley/layers/ladder-down_conv_list-0.bin",
+
+    "shelfnet_berkeley/layers/ladder-down_module_list-1-conv1.bin",
+    "shelfnet_berkeley/layers/ladder-down_module_list-1-conv12.bin",
+    "shelfnet_berkeley/layers/ladder-down_conv_list-1.bin",
+
+    "shelfnet_berkeley/layers/ladder-bottom-conv1.bin",
+    "shelfnet_berkeley/layers/ladder-bottom-conv12.bin",
+    
+    
+    
+    "shelfnet_berkeley/layers/ladder-up_conv_list-0-conv-conv.bin",
+    "shelfnet_berkeley/layers/ladder-up_conv_list-0-conv_atten.bin",
+    "shelfnet_berkeley/layers/ladder-up_dense_list-0-conv.bin",
+
+    
+    "shelfnet_berkeley/layers/ladder-up_conv_list-1-conv-conv.bin",
+    "shelfnet_berkeley/layers/ladder-up_conv_list-1-conv_atten.bin",
+    "shelfnet_berkeley/layers/ladder-up_dense_list-1-conv.bin"};
+
+const char *trans[] = {
+    "shelfnet_berkeley/layers/trans1-conv.bin",
+    "shelfnet_berkeley/layers/trans2-conv.bin",
+    "shelfnet_berkeley/layers/trans3-conv.bin"};
+int main()
+{
+
+    downloadWeightsifDoNotExist(input_bin, "shelfnet_berkeley", "https://cloud.hipert.unimore.it/s/m92e7QdD9gYMF7f/download");
+
+    int classes = 20;
+
+    // Network layout    
+    tk::dnn::dataDim_t dim(1, 3, 1024, 1024, 1);
+    tk::dnn::Network net(dim);
+
+    int bi = 0, di = 0, li = 0, ci = 0;
+    new tk::dnn::Conv2d(&net, 64, 7, 7, 2, 2, 3, 3, backbone[bi++], true);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+    tk::dnn::Layer* last = new tk::dnn::Pooling (&net, 3, 3, 2, 2, 1, 1, tk::dnn::POOLING_MAX);
+
+
+    
+    for(int i=0; i<2; ++i){
+        new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
+        new tk::dnn::Shortcut(&net, last);
+        last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
+    }
+
+    std::vector<tk::dnn::Layer*> features;
+    for(int i=0;i<3;++i){
+        int out_channel = pow(2,7+i);
+        std::cout<<out_channel<<std::endl;
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 2, 2, 1, 1, backbone[bi++], true);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        tk::dnn::Layer* bn2 = new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
+        new tk::dnn::Route(&net, &last, 1);
+        new tk::dnn::Conv2d (&net, out_channel, 1, 1, 2, 2, 0, 0, backbone[bi++], true);
+        new tk::dnn::Shortcut(&net, bn2);
+        last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
+
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, backbone[bi++], true);
+        
+        new tk::dnn::Shortcut(&net, last);
+        last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
+        features.push_back(last);
+    }
+
+    for(int i=0; i<features.size(); ++i){
+        new tk::dnn::Route(&net, &features[i], 1);
+        int out_channel = pow(2,6+i);
+        new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, trans[i], true);
+        features[i] = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+    }
+
+    //DECODER
+    
+    last = features[2];
+    std::vector<tk::dnn::Layer*> up_out;
+    //bottom
+    new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+    new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, decoder[di++], true, false, 1, true);
+    new tk::dnn::Shortcut(&net, last);
+    last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
+    up_out.push_back(last);
+
+    for(int i=0; i<2; ++i){
+        int out_channel = pow(2,7-i);
+        //up-conv
+        std::cout<<out_channel<<std::endl;
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        
+        new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
+        new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, decoder[di++], true);
+        
+        tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
+        new tk::dnn::Route(&net, &last, 1);
+        new tk::dnn::Shortcut(&net, act, true);
+
+        //interpolate
+        new tk::dnn::Resize(&net, 1,2,2);
+        new tk::dnn::Shortcut(&net, features[1-i]);
+
+        //up-dense
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, decoder[di++], true);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        up_out.push_back(last);
+    }
+
+    //LADDER
+
+    std::vector<tk::dnn::Layer*> down_out;
+    new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+    new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
+    new tk::dnn::Shortcut(&net, last);
+    new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
+    
+    for(int i=0; i<2;++i){
+        int out_channel = pow(2,6+i);
+        tk::dnn::Layer* l_last = new tk::dnn::Shortcut(&net, up_out[2-i]);
+    
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
+        new tk::dnn::Shortcut(&net, l_last);
+        l_last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
+        down_out.push_back(l_last);
+
+        new tk::dnn::Conv2d (&net, out_channel*2, 3, 3, 2, 2, 1, 1, ladder[li++], false);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.0f); //should be ReLU
+    }
+
+    new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
+    new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+    new tk::dnn::Conv2d (&net, 256, 3, 3, 1, 1, 1, 1, ladder[li++], true, false, 1, true);
+    new tk::dnn::Shortcut(&net, last);
+    last = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_RELU);
+    up_out.clear();
+    up_out.push_back(last);
+
+    for(int i=0; i<2; ++i){
+        int out_channel = pow(2,7-i);
+        //up-conv
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        
+        new tk::dnn::Pooling(&net, last->output_dim.w, last->output_dim.h, last->output_dim.w, last->output_dim.h, 0, 0, tk::dnn::POOLING_AVERAGE);
+        new tk::dnn::Conv2d (&net, out_channel, 1, 1, 1, 1, 0, 0, ladder[li++], true);
+        
+        tk::dnn::Layer* act = new tk::dnn::Activation (&net, CUDNN_ACTIVATION_SIGMOID);
+        new tk::dnn::Route(&net, &last, 1);
+        new tk::dnn::Shortcut(&net, act, true);
+
+        //interpolate
+        new tk::dnn::Resize(&net, 1,2,2);
+        new tk::dnn::Shortcut(&net, down_out[1-i]);
+
+        // //up-dense
+        new tk::dnn::Conv2d (&net, out_channel, 3, 3, 1, 1, 1, 1, ladder[li++], true);
+        last = new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        up_out.push_back(last);
+    }
+
+
+    // for(int i=2;i>=0;--i){
+        // new tk::dnn::Route(&net, &up_out[i], 1);
+        new tk::dnn::Conv2d (&net, 64, 3, 3, 1, 1, 1, 1, conv_out[ci++], true);
+        new tk::dnn::Activation (&net, tk::dnn::ACTIVATION_LEAKY, 0.0f, 0.01);
+        new tk::dnn::Conv2d (&net, classes, 3, 3, 1, 1, 1, 1, conv_out[ci++], false);
+        /*up_out[i] =*/ new tk::dnn::Resize(&net, classes, net.input_dim.h, net.input_dim.w, true, tk::dnn::ResizeMode_t::LINEAR);
+    // }
+
+    new tk::dnn::Softmax(&net);
+    
+    const char *output_bin = "shelfnet_berkeley/debug/softmax.bin";
+    
+    // Load input
+    dnnType *data;
+    dnnType *input_h;
+    readBinaryFile(input_bin, dim.tot(), &input_h, &data);
+    std::cout<<"Input:"<<std::endl;
+
+    //print network model
+    net.print();
+
+    // // convert network to tensorRT
+    tk::dnn::NetworkRT netRT(&net, net.getNetworkRTName("shelfnet_berkeley"));
+
+    tk::dnn::dataDim_t dim1 = dim; //input dim
+    dnnType *cudnn_out = nullptr;
+    printCenteredTitle(" CUDNN inference ", '=', 30);
+    {
+        dim1.print();
+        TKDNN_TSTART
+        cudnn_out = net.infer(dim1, data);
+        TKDNN_TSTOP
+        dim1.print();
+    }
+
+    tk::dnn::dataDim_t dim2 = dim;
+    printCenteredTitle(" TENSORRT inference ", '=', 30);
+    {
+        dim2.print();
+        TKDNN_TSTART
+        netRT.infer(dim2, data);
+        TKDNN_TSTOP
+        dim2.print();
+    }
+
+    dnnType *rt_out1 = (dnnType *)netRT.buffersRT[1];
+
+    printCenteredTitle(std::string(" CHECK RESULTS ").c_str(), '=', 30);
+    dnnType *out1, *out1_h;
+    int odim1 = dim1.tot();
+    readBinaryFile(output_bin, odim1, &out1_h, &out1);
+
+    int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0; 
+    std::cout << "CUDNN vs correct" << std::endl;
+    ret_cudnn |= checkResult(odim1, cudnn_out, out1, true, 20) == 0 ? 0 : ERROR_CUDNN;
+
+    std::cout << "TRT   vs correct" << std::endl;
+    ret_tensorrt |=checkResult(odim1, rt_out1, out1) == 0 ? 0 : ERROR_TENSORRT;
+
+    std::cout << "CUDNN vs TRT    " << std::endl;
+    ret_cudnn_tensorrt |= checkResult(odim1, cudnn_out, rt_out1) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
+    
+    cv::Mat viz = vizLayer2Mat(&net, net.num_layers-1);
+    cv::imwrite("test.png", viz);
+
+    return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
+}