diff --git a/tests/yolo4_berkeley/yolo4_berkeley.cfg b/tests/yolo4_berkeley/yolo4_berkeley.cfg
new file mode 100644
index 0000000..b11c0a7
--- /dev/null
+++ b/tests/yolo4_berkeley/yolo4_berkeley.cfg
@@ -0,0 +1,1159 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=16
+width=544
+height=320
+channels=3
+momentum=0.949
+decay=0.0005
+angle=0
+saturation = 1.5
+exposure = 1.5
+hue=.1
+
+learning_rate=0.001
+burn_in=1000
+max_batches = 20000
+policy=steps
+steps=16000d,18000
+scales=.1,.1
+
+#cutmix=1
+mosaic=1
+
+#:104x104 54:52x52 85:26x26 104:13x13 for 416
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=3
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=32
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-7
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=64
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-10
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-28
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+# Downsample
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=3
+stride=2
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -2
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=3
+stride=1
+pad=1
+activation=mish
+
+[shortcut]
+from=-3
+activation=linear
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=mish
+
+[route]
+layers = -1,-16
+
+[convolutional]
+batch_normalize=1
+filters=1024
+size=1
+stride=1
+pad=1
+activation=mish
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+### SPP ###
+[maxpool]
+stride=1
+size=5
+
+[route]
+layers=-2
+
+[maxpool]
+stride=1
+size=9
+
+[route]
+layers=-4
+
+[maxpool]
+stride=1
+size=13
+
+[route]
+layers=-1,-3,-5,-6
+### End SPP ###
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 85
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[route]
+layers = 54
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[route]
+layers = -1, -3
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+##########################
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=256
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=45
+activation=linear
+
+
+[yolo]
+mask = 0,1,2
+anchors =  6,  7,  14, 11,   9, 19,  26, 20,  18, 42,  48, 35,  74, 65, 126, 99, 183,169
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.2
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=256
+activation=leaky
+
+[route]
+layers = -1, -16
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=512
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=45
+activation=linear
+
+
+[yolo]
+mask = 3,4,5
+anchors =  6,  7,  14, 11,   9, 19,  26, 20,  18, 42,  48, 35,  74, 65, 126, 99, 183,169
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+scale_x_y = 1.1
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=2
+pad=1
+filters=512
+activation=leaky
+
+[route]
+layers = -1, -37
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=512
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+size=3
+stride=1
+pad=1
+filters=1024
+activation=leaky
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=45
+activation=linear
+
+
+[yolo]
+mask = 6,7,8
+anchors =  6,  7,  14, 11,   9, 19,  26, 20,  18, 42,  48, 35,  74, 65, 126, 99, 183,169
+classes=10
+num=9
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=1
+scale_x_y = 1.05
+iou_thresh=0.213
+cls_normalizer=1.0
+iou_normalizer=0.07
+iou_loss=ciou
+nms_kind=greedynms
+beta_nms=0.6
+max_delta=5
+
diff --git a/tests/yolo4_berkeley/yolo4_berkeley.cpp b/tests/yolo4_berkeley/yolo4_berkeley.cpp
new file mode 100644
index 0000000..d10ad59
--- /dev/null
+++ b/tests/yolo4_berkeley/yolo4_berkeley.cpp
@@ -0,0 +1,666 @@
+#include <iostream>
+#include <vector>
+#include "tkdnn.h"
+
+int main()
+{
+
+    // Network layout
+    tk::dnn::dataDim_t dim(1, 3, 320, 544, 1);
+    tk::dnn::Network net(dim);
+
+    // create yolo4_berkeley model
+    std::string bin_path = "yolo4_berkeley";
+    int classes = 10;
+    tk::dnn::Yolo *yolo[3];
+
+    std::string input_bin = bin_path + "/layers/input.bin";
+    
+    std::vector<std::string> output_bins = {
+        bin_path + "/debug/layer139_out.bin",
+        bin_path + "/debug/layer150_out.bin",
+        bin_path + "/debug/layer161_out.bin"};
+    std::string c0_bin = bin_path + "/layers/c0.bin";
+    std::string c1_bin = bin_path + "/layers/c1.bin";
+    std::string c2_bin = bin_path + "/layers/c2.bin";
+    std::string c3_bin = bin_path + "/layers/c3.bin";
+    std::string c4_bin = bin_path + "/layers/c4.bin";
+    std::string c5_bin = bin_path + "/layers/c5.bin";
+    std::string c6_bin = bin_path + "/layers/c6.bin";
+    std::string c7_bin = bin_path + "/layers/c7.bin";
+    std::string c8_bin = bin_path + "/layers/c8.bin";
+    std::string c10_bin = bin_path + "/layers/c10.bin";
+    std::string c11_bin = bin_path + "/layers/c11.bin";
+    std::string c12_bin = bin_path + "/layers/c12.bin";
+    std::string c13_bin = bin_path + "/layers/c13.bin";
+    std::string c14_bin = bin_path + "/layers/c14.bin";
+    std::string c15_bin = bin_path + "/layers/c15.bin";
+    std::string c16_bin = bin_path + "/layers/c16.bin";
+    std::string c17_bin = bin_path + "/layers/c17.bin";
+    std::string c18_bin = bin_path + "/layers/c18.bin";
+    std::string c19_bin = bin_path + "/layers/c19.bin";
+    std::string c20_bin = bin_path + "/layers/c20.bin";
+    std::string c21_bin = bin_path + "/layers/c21.bin";
+    std::string c23_bin = bin_path + "/layers/c23.bin";
+    std::string c24_bin = bin_path + "/layers/c24.bin";
+    std::string c25_bin = bin_path + "/layers/c25.bin";
+    std::string c26_bin = bin_path + "/layers/c26.bin";
+    std::string c27_bin = bin_path + "/layers/c27.bin";
+    std::string c28_bin = bin_path + "/layers/c28.bin";
+    std::string c29_bin = bin_path + "/layers/c29.bin";
+    std::string c30_bin = bin_path + "/layers/c30.bin";
+    std::string c31_bin = bin_path + "/layers/c31.bin";
+    std::string c32_bin = bin_path + "/layers/c32.bin";
+    std::string c33_bin = bin_path + "/layers/c33.bin";
+    std::string c34_bin = bin_path + "/layers/c34.bin";
+    std::string c35_bin = bin_path + "/layers/c35.bin";
+    std::string c36_bin = bin_path + "/layers/c36.bin";
+    std::string c37_bin = bin_path + "/layers/c37.bin";
+    std::string c38_bin = bin_path + "/layers/c38.bin";
+    std::string c39_bin = bin_path + "/layers/c39.bin";
+    std::string c40_bin = bin_path + "/layers/c40.bin";
+    std::string c41_bin = bin_path + "/layers/c41.bin";
+    std::string c42_bin = bin_path + "/layers/c42.bin";
+    std::string c43_bin = bin_path + "/layers/c43.bin";
+    std::string c44_bin = bin_path + "/layers/c44.bin";
+    std::string c45_bin = bin_path + "/layers/c45.bin";
+    std::string c46_bin = bin_path + "/layers/c46.bin";
+    std::string c47_bin = bin_path + "/layers/c47.bin";
+    std::string c48_bin = bin_path + "/layers/c48.bin";
+    std::string c49_bin = bin_path + "/layers/c49.bin";
+    std::string c50_bin = bin_path + "/layers/c50.bin";
+    std::string c51_bin = bin_path + "/layers/c51.bin";
+    std::string c52_bin = bin_path + "/layers/c52.bin";
+    std::string c53_bin = bin_path + "/layers/c53.bin";
+    std::string c54_bin = bin_path + "/layers/c54.bin";
+    std::string c55_bin = bin_path + "/layers/c55.bin";
+    std::string c56_bin = bin_path + "/layers/c56.bin";
+    std::string c57_bin = bin_path + "/layers/c57.bin";
+    std::string c58_bin = bin_path + "/layers/c58.bin";
+    std::string c59_bin = bin_path + "/layers/c59.bin";
+    std::string c60_bin = bin_path + "/layers/c60.bin";
+    std::string c61_bin = bin_path + "/layers/c61.bin";
+    std::string c62_bin = bin_path + "/layers/c62.bin";
+    std::string c63_bin = bin_path + "/layers/c63.bin";
+    std::string c65_bin = bin_path + "/layers/c65.bin";
+    std::string c66_bin = bin_path + "/layers/c66.bin";
+    std::string c67_bin = bin_path + "/layers/c67.bin";
+    std::string c68_bin = bin_path + "/layers/c68.bin";
+    std::string c69_bin = bin_path + "/layers/c69.bin";
+    std::string c70_bin = bin_path + "/layers/c70.bin";
+    std::string c71_bin = bin_path + "/layers/c71.bin";
+    std::string c72_bin = bin_path + "/layers/c72.bin";
+    std::string c74_bin = bin_path + "/layers/c74.bin";
+    std::string c75_bin = bin_path + "/layers/c75.bin";
+    std::string c76_bin = bin_path + "/layers/c76.bin";
+    std::string c77_bin = bin_path + "/layers/c77.bin";
+    std::string c78_bin = bin_path + "/layers/c78.bin";
+    std::string c80_bin = bin_path + "/layers/c80.bin";
+    std::string c81_bin = bin_path + "/layers/c81.bin";
+    std::string c82_bin = bin_path + "/layers/c82.bin";
+    std::string c83_bin = bin_path + "/layers/c83.bin";
+    std::string c85_bin = bin_path + "/layers/c85.bin";
+    std::string c86_bin = bin_path + "/layers/c86.bin";
+    std::string c87_bin = bin_path + "/layers/c87.bin";
+    std::string c89_bin = bin_path + "/layers/c89.bin";
+    std::string c90_bin = bin_path + "/layers/c90.bin";
+    std::string c91_bin = bin_path + "/layers/c91.bin";
+    std::string c92_bin = bin_path + "/layers/c92.bin";
+    std::string c93_bin = bin_path + "/layers/c93.bin";
+    std::string c94_bin = bin_path + "/layers/c94.bin";
+    std::string c96_bin = bin_path + "/layers/c96.bin";
+    std::string c97_bin = bin_path + "/layers/c97.bin";
+    std::string c98_bin = bin_path + "/layers/c98.bin";
+    std::string c99_bin = bin_path + "/layers/c99.bin";
+    std::string c100_bin = bin_path + "/layers/c100.bin";
+    std::string c101_bin = bin_path + "/layers/c101.bin";
+    std::string c102_bin = bin_path + "/layers/c102.bin";
+    std::string c103_bin = bin_path + "/layers/c103.bin";
+    std::string c104_bin = bin_path + "/layers/c104.bin";
+    std::string c105_bin = bin_path + "/layers/c105.bin";
+    std::string c106_bin = bin_path + "/layers/c106.bin";
+    std::string c107_bin = bin_path + "/layers/c107.bin";
+    std::string c108_bin = bin_path + "/layers/c108.bin";
+    std::string c109_bin = bin_path + "/layers/c109.bin";
+    std::string c110_bin = bin_path + "/layers/c110.bin";
+    std::string c111_bin = bin_path + "/layers/c111.bin";
+    std::string c112_bin = bin_path + "/layers/c112.bin";
+    std::string c113_bin = bin_path + "/layers/c113.bin";
+    std::string c114_bin = bin_path + "/layers/c114.bin";
+    std::string c115_bin = bin_path + "/layers/c115.bin";
+    std::string c116_bin = bin_path + "/layers/c116.bin";
+    std::string c117_bin = bin_path + "/layers/c117.bin";
+    std::string c119_bin = bin_path + "/layers/c119.bin";
+    std::string c120_bin = bin_path + "/layers/c120.bin";
+    std::string c121_bin = bin_path + "/layers/c121.bin";
+    std::string c122_bin = bin_path + "/layers/c122.bin";
+    std::string c123_bin = bin_path + "/layers/c123.bin";
+    std::string c124_bin = bin_path + "/layers/c124.bin";
+    std::string c125_bin = bin_path + "/layers/c125.bin";
+    std::string c126_bin = bin_path + "/layers/c126.bin";
+    std::string c127_bin = bin_path + "/layers/c127.bin";
+    std::string c128_bin = bin_path + "/layers/c128.bin";
+    std::string c130_bin = bin_path + "/layers/c130.bin";
+    std::string c131_bin = bin_path + "/layers/c131.bin";
+    std::string c132_bin = bin_path + "/layers/c132.bin";
+    std::string c133_bin = bin_path + "/layers/c133.bin";
+    std::string c134_bin = bin_path + "/layers/c134.bin";
+    std::string c135_bin = bin_path + "/layers/c135.bin";
+    std::string c136_bin = bin_path + "/layers/c136.bin";
+    std::string c137_bin = bin_path + "/layers/c137.bin";
+    std::string c138_bin = bin_path + "/layers/c138.bin";
+    std::string c141_bin = bin_path + "/layers/c141.bin";
+    std::string c142_bin = bin_path + "/layers/c142.bin";
+    std::string c143_bin = bin_path + "/layers/c143.bin";
+    std::string c144_bin = bin_path + "/layers/c144.bin";
+    std::string c145_bin = bin_path + "/layers/c145.bin";
+    std::string c146_bin = bin_path + "/layers/c146.bin";
+    std::string c147_bin = bin_path + "/layers/c147.bin";
+    std::string c148_bin = bin_path + "/layers/c148.bin";
+    std::string c149_bin = bin_path + "/layers/c149.bin";
+    std::string c150_bin = bin_path + "/layers/c150.bin";
+    std::string c151_bin = bin_path + "/layers/c151.bin";
+    std::string c152_bin = bin_path + "/layers/c152.bin";
+    std::string c153_bin = bin_path + "/layers/c153.bin";
+    std::string c154_bin = bin_path + "/layers/c154.bin";
+    std::string c155_bin = bin_path + "/layers/c155.bin";
+    std::string c156_bin = bin_path + "/layers/c156.bin";
+    std::string c157_bin = bin_path + "/layers/c157.bin";
+    std::string c158_bin = bin_path + "/layers/c158.bin";
+    std::string c159_bin = bin_path + "/layers/c159.bin";
+    std::string c160_bin = bin_path + "/layers/c160.bin";    
+    std::string g139_bin = bin_path + "/layers/g139.bin";
+    std::string g150_bin = bin_path + "/layers/g150.bin";
+    std::string g161_bin = bin_path + "/layers/g161.bin";
+    
+
+    downloadWeightsifDoNotExist(input_bin, bin_path, "https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download");
+
+    tk::dnn::Conv2d c0(&net, 32, 3, 3, 1, 1, 1, 1, c0_bin, true);
+    tk::dnn::Activation a0(&net, tk::dnn::ACTIVATION_MISH);
+
+    // downsample
+    tk::dnn::Conv2d c1(&net, 64, 3, 3, 2, 2, 1, 1, c1_bin, true);
+    tk::dnn::Activation a1(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c2(&net, 64, 1, 1, 1, 1, 0, 0, c2_bin, true);
+    tk::dnn::Activation a2(&net, tk::dnn::ACTIVATION_MISH);  
+
+    tk::dnn::Layer *r3_layers[1] = {&a1};
+    tk::dnn::Route r3(&net, r3_layers, 1);
+    
+    tk::dnn::Conv2d c4(&net, 64, 1, 1, 1, 1, 0, 0, c4_bin, true);
+    tk::dnn::Activation a4(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c5(&net, 32, 1, 1, 1, 1, 0, 0, c5_bin, true);
+    tk::dnn::Activation a5(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c6(&net, 64, 3, 3, 1, 1, 1, 1, c6_bin, true);
+    tk::dnn::Activation a6(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s7(&net, &a4);
+
+    tk::dnn::Conv2d c8(&net, 64, 1, 1, 1, 1, 0, 0, c8_bin, true);
+    tk::dnn::Activation a8(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Layer *r9_layers[2] = {&a8, &a2};
+    tk::dnn::Route r9(&net, r9_layers, 2);
+
+    tk::dnn::Conv2d c10(&net, 64, 1, 1, 1, 1, 0, 0, c10_bin, true);
+    tk::dnn::Activation a10(&net, tk::dnn::ACTIVATION_MISH);
+
+    // downsample
+    tk::dnn::Conv2d c11(&net, 128, 3, 3, 2, 2, 1, 1, c11_bin, true);
+    tk::dnn::Activation a11(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c12(&net, 64, 1, 1, 1, 1, 0, 0, c12_bin, true);
+    tk::dnn::Activation a12(&net, tk::dnn::ACTIVATION_MISH);  
+
+    tk::dnn::Layer *r13_layers[1] = {&a11};
+    tk::dnn::Route r13(&net, r13_layers, 1);
+    
+    tk::dnn::Conv2d c14(&net, 64, 1, 1, 1, 1, 0, 0, c14_bin, true);
+    tk::dnn::Activation a14(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c15(&net, 64, 1, 1, 1, 1, 0, 0, c15_bin, true);
+    tk::dnn::Activation a15(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c16(&net, 64, 3, 3, 1, 1, 1, 1, c16_bin, true);
+    tk::dnn::Activation a16(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s17(&net, &a14);
+
+    tk::dnn::Conv2d c18(&net, 64, 1, 1, 1, 1, 0, 0, c18_bin, true);
+    tk::dnn::Activation a18(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c19(&net, 64, 3, 3, 1, 1, 1, 1, c19_bin, true);
+    tk::dnn::Activation a19(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s20(&net, &s17);
+
+    tk::dnn::Conv2d c21(&net, 64, 1, 1, 1, 1, 0, 0, c21_bin, true);
+    tk::dnn::Activation a21(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Layer *r22_layers[2] = {&a21, &a12};
+    tk::dnn::Route r22(&net, r22_layers, 2);
+
+    tk::dnn::Conv2d c23(&net, 128, 1, 1, 1, 1, 0, 0, c23_bin, true);
+    tk::dnn::Activation a23(&net, tk::dnn::ACTIVATION_MISH);
+
+    //downsample
+    tk::dnn::Conv2d c24(&net, 256, 3, 3, 2, 2, 1, 1, c24_bin, true);
+    tk::dnn::Activation a24(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c25(&net, 128, 1, 1, 1, 1, 0, 0, c25_bin, true);
+    tk::dnn::Activation a25(&net, tk::dnn::ACTIVATION_MISH);  
+
+    tk::dnn::Layer *r26_layers[1] = {&a24};
+    tk::dnn::Route r26(&net, r26_layers, 1);
+    
+    tk::dnn::Conv2d c27(&net, 128, 1, 1, 1, 1, 0, 0, c27_bin, true);
+    tk::dnn::Activation a27(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c28(&net, 128, 1, 1, 1, 1, 0, 0, c28_bin, true);
+    tk::dnn::Activation a28(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c29(&net, 128, 3, 3, 1, 1, 1, 1, c29_bin, true);
+    tk::dnn::Activation a29(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s30(&net, &a27);
+
+    tk::dnn::Conv2d c31(&net, 128, 1, 1, 1, 1, 0, 0, c31_bin, true);
+    tk::dnn::Activation a31(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c32(&net, 128, 3, 3, 1, 1, 1, 1, c32_bin, true);
+    tk::dnn::Activation a32(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s33(&net, &s30);
+
+    tk::dnn::Conv2d c34(&net, 128, 1, 1, 1, 1, 0, 0, c34_bin, true);
+    tk::dnn::Activation a34(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c35(&net, 128, 3, 3, 1, 1, 1, 1, c35_bin, true);
+    tk::dnn::Activation a35(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s36(&net, &s33);
+
+    tk::dnn::Conv2d c37(&net, 128, 1, 1, 1, 1, 0, 0, c37_bin, true);
+    tk::dnn::Activation a37(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c38(&net, 128, 3, 3, 1, 1, 1, 1, c38_bin, true);
+    tk::dnn::Activation a38(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s39(&net, &s36);
+    
+    tk::dnn::Conv2d c40(&net, 128, 1, 1, 1, 1, 0, 0, c40_bin, true);
+    tk::dnn::Activation a40(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c41(&net, 128, 3, 3, 1, 1, 1, 1, c41_bin, true);
+    tk::dnn::Activation a41(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s42(&net, &s39);
+
+    tk::dnn::Conv2d c43(&net, 128, 1, 1, 1, 1, 0, 0, c43_bin, true);
+    tk::dnn::Activation a43(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c44(&net, 128, 3, 3, 1, 1, 1, 1, c44_bin, true);
+    tk::dnn::Activation a44(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s45(&net, &s42);
+
+    tk::dnn::Conv2d c46(&net, 128, 1, 1, 1, 1, 0, 0, c46_bin, true);
+    tk::dnn::Activation a46(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c47(&net, 128, 3, 3, 1, 1, 1, 1, c47_bin, true);
+    tk::dnn::Activation a47(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s48(&net, &s45);
+
+    tk::dnn::Conv2d c49(&net, 128, 1, 1, 1, 1, 0, 0, c49_bin, true);
+    tk::dnn::Activation a49(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c50(&net, 128, 3, 3, 1, 1, 1, 1, c50_bin, true);
+    tk::dnn::Activation a50(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s51(&net, &s48);
+
+    tk::dnn::Conv2d c52(&net, 128, 1, 1, 1, 1, 0, 0, c52_bin, true);
+    tk::dnn::Activation a52(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Layer *r53_layers[2] = {&a52, &a25};
+    tk::dnn::Route r53(&net, r53_layers, 2);
+
+    tk::dnn::Conv2d c54(&net, 256, 1, 1, 1, 1, 0, 0, c54_bin, true);
+    tk::dnn::Activation a54(&net, tk::dnn::ACTIVATION_MISH);
+
+    //downsample
+    tk::dnn::Conv2d c55(&net, 512, 3, 3, 2, 2, 1, 1, c55_bin, true);
+    tk::dnn::Activation a55(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c56(&net, 256, 1, 1, 1, 1, 0, 0, c56_bin, true);
+    tk::dnn::Activation a56(&net, tk::dnn::ACTIVATION_MISH);  
+
+    tk::dnn::Layer *r57_layers[1] = {&a55};
+    tk::dnn::Route r57(&net, r57_layers, 1);
+    
+    tk::dnn::Conv2d c58(&net, 256, 1, 1, 1, 1, 0, 0, c58_bin, true);
+    tk::dnn::Activation a58(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c59(&net, 256, 1, 1, 1, 1, 0, 0, c59_bin, true);
+    tk::dnn::Activation a59(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c60(&net, 256, 3, 3, 1, 1, 1, 1, c60_bin, true);
+    tk::dnn::Activation a60(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s61(&net, &a58);
+
+    tk::dnn::Conv2d c62(&net, 256, 1, 1, 1, 1, 0, 0, c62_bin, true);
+    tk::dnn::Activation a62(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c63(&net, 256, 3, 3, 1, 1, 1, 1, c63_bin, true);
+    tk::dnn::Activation a63(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s64(&net, &s61);
+
+    tk::dnn::Conv2d c65(&net, 256, 1, 1, 1, 1, 0, 0, c65_bin, true);
+    tk::dnn::Activation a65(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c66(&net, 256, 3, 3, 1, 1, 1, 1, c66_bin, true);
+    tk::dnn::Activation a66(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s67(&net, &s64);
+
+    tk::dnn::Conv2d c68(&net, 256, 1, 1, 1, 1, 0, 0, c68_bin, true);
+    tk::dnn::Activation a68(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c69(&net, 256, 3, 3, 1, 1, 1, 1, c69_bin, true);
+    tk::dnn::Activation a69(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s70(&net, &s67);
+    
+    tk::dnn::Conv2d c71(&net, 256, 1, 1, 1, 1, 0, 0, c71_bin, true);
+    tk::dnn::Activation a71(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c72(&net, 256, 3, 3, 1, 1, 1, 1, c72_bin, true);
+    tk::dnn::Activation a72(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s73(&net, &s70);
+
+    tk::dnn::Conv2d c74(&net, 256, 1, 1, 1, 1, 0, 0, c74_bin, true);
+    tk::dnn::Activation a74(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c75(&net, 256, 3, 3, 1, 1, 1, 1, c75_bin, true);
+    tk::dnn::Activation a75(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s76(&net, &s73);
+
+    tk::dnn::Conv2d c77(&net, 256, 1, 1, 1, 1, 0, 0, c77_bin, true);
+    tk::dnn::Activation a77(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c78(&net, 256, 3, 3, 1, 1, 1, 1, c78_bin, true);
+    tk::dnn::Activation a78(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s79(&net, &s76);
+
+    tk::dnn::Conv2d c80(&net, 256, 1, 1, 1, 1, 0, 0, c80_bin, true);
+    tk::dnn::Activation a80(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c81(&net, 256, 3, 3, 1, 1, 1, 1, c81_bin, true);
+    tk::dnn::Activation a81(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s82(&net, &s79);
+
+    tk::dnn::Conv2d c83(&net, 256, 1, 1, 1, 1, 0, 0, c83_bin, true);
+    tk::dnn::Activation a83(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Layer *r84_layers[2] = {&a83, &a56};
+    tk::dnn::Route r84(&net, r84_layers, 2);
+
+    tk::dnn::Conv2d c85(&net, 512, 1, 1, 1, 1, 0, 0, c85_bin, true);
+    tk::dnn::Activation a85(&net, tk::dnn::ACTIVATION_MISH);
+
+    //downsample
+    tk::dnn::Conv2d c86(&net, 1024, 3, 3, 2, 2, 1, 1, c86_bin, true);
+    tk::dnn::Activation a86(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c87(&net, 512, 1, 1, 1, 1, 0, 0, c87_bin, true);
+    tk::dnn::Activation a87(&net, tk::dnn::ACTIVATION_MISH);  
+
+    tk::dnn::Layer *r88_layers[1] = {&a86};
+    tk::dnn::Route r88(&net, r88_layers, 1);
+    
+    tk::dnn::Conv2d c89(&net, 512, 1, 1, 1, 1, 0, 0, c89_bin, true);
+    tk::dnn::Activation a89(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c90(&net, 512, 1, 1, 1, 1, 0, 0, c90_bin, true);
+    tk::dnn::Activation a90(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c91(&net, 512, 3, 3, 1, 1, 1, 1, c91_bin, true);
+    tk::dnn::Activation a91(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s92(&net, &a89);
+
+    tk::dnn::Conv2d c93(&net, 512, 1, 1, 1, 1, 0, 0, c93_bin, true);
+    tk::dnn::Activation a93(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c94(&net, 512, 3, 3, 1, 1, 1, 1, c94_bin, true);
+    tk::dnn::Activation a94(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s95(&net, &s92);
+
+    tk::dnn::Conv2d c96(&net, 512, 1, 1, 1, 1, 0, 0, c96_bin, true);
+    tk::dnn::Activation a96(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c97(&net, 512, 3, 3, 1, 1, 1, 1, c97_bin, true);
+    tk::dnn::Activation a97(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s98(&net, &s95);
+
+    tk::dnn::Conv2d c99(&net, 512, 1, 1, 1, 1, 0, 0, c99_bin, true);
+    tk::dnn::Activation a99(&net, tk::dnn::ACTIVATION_MISH);
+    tk::dnn::Conv2d c100(&net, 512, 3, 3, 1, 1, 1, 1, c100_bin, true);
+    tk::dnn::Activation a100(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Shortcut s101(&net, &s98);
+    
+    tk::dnn::Conv2d c102(&net, 512, 1, 1, 1, 1, 0, 0, c102_bin, true);
+    tk::dnn::Activation a102(&net, tk::dnn::ACTIVATION_MISH);
+
+    tk::dnn::Layer *r103_layers[2] = {&a102, &a87};
+    tk::dnn::Route r103(&net, r103_layers, 2);
+
+    tk::dnn::Conv2d c104(&net, 1024, 1, 1, 1, 1, 0, 0, c104_bin, true);
+    tk::dnn::Activation a104(&net, tk::dnn::ACTIVATION_MISH);
+
+
+    //################
+    tk::dnn::Conv2d c105(&net, 512, 1, 1, 1, 1, 0, 0, c105_bin, true);
+    tk::dnn::Activation a105(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c106(&net, 1024, 3, 3, 1, 1, 1, 1, c106_bin, true);
+    tk::dnn::Activation a106(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c107(&net, 512, 1, 1, 1, 1, 0, 0, c107_bin, true);
+    tk::dnn::Activation a107(&net, tk::dnn::ACTIVATION_LEAKY);
+
+    //SPP
+    tk::dnn::Pooling p108(&net, 5, 5, 1, 1, 0, 0, tk::dnn::POOLING_MAX_FIXEDSIZE);
+    tk::dnn::Layer *r109_layers[1] = {&a107};
+    tk::dnn::Route r109(&net, r109_layers, 1);
+
+    tk::dnn::Pooling p110(&net, 9, 9, 1, 1, 0, 0, tk::dnn::POOLING_MAX_FIXEDSIZE);
+    tk::dnn::Layer *r111_layers[1] = {&a107};
+    tk::dnn::Route r111(&net, r111_layers, 1);
+
+    tk::dnn::Pooling p112(&net, 13, 13, 1, 1, 12, 12, tk::dnn::POOLING_MAX_FIXEDSIZE);
+    tk::dnn::Layer *r113_layers[4] = {&p112, &p110, &p108, &a107};
+    tk::dnn::Route r113(&net, r113_layers, 4);
+    //END SPP
+
+    tk::dnn::Conv2d c114(&net, 512, 1, 1, 1, 1, 0, 0, c114_bin, true);
+    tk::dnn::Activation a114(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c115(&net, 1024, 3, 3, 1, 1, 1, 1, c115_bin, true);
+    tk::dnn::Activation a115(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c116(&net, 512, 1, 1, 1, 1, 0, 0, c116_bin, true);
+    tk::dnn::Activation a116(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c117(&net, 256, 1, 1, 1, 1, 0, 0, c117_bin, true);
+    tk::dnn::Activation a117(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Upsample u118(&net, 2);
+    tk::dnn::Layer *r119_layers[1] = {&a85};
+    tk::dnn::Route r119(&net, r119_layers, 1);
+    tk::dnn::Conv2d c120(&net, 256, 1, 1, 1, 1, 0, 0, c120_bin, true);
+    tk::dnn::Activation a120(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Layer *r121_layers[2] = {&a120,&u118};
+    tk::dnn::Route r121(&net, r121_layers, 2);
+
+    tk::dnn::Conv2d c122(&net, 256, 1, 1, 1, 1, 0, 0, c122_bin, true);
+    tk::dnn::Activation a122(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c123(&net, 512, 3, 3, 1, 1, 1, 1, c123_bin, true);
+    tk::dnn::Activation a123(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c124(&net, 256, 1, 1, 1, 1, 0, 0, c124_bin, true);
+    tk::dnn::Activation a124(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c125(&net, 512, 3, 3, 1, 1, 1, 1, c125_bin, true);
+    tk::dnn::Activation a125(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c126(&net, 256, 1, 1, 1, 1, 0, 0, c126_bin, true);
+    tk::dnn::Activation a126(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c127(&net, 128, 1, 1, 1, 1, 0, 0, c127_bin, true);
+    tk::dnn::Activation a127(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Upsample u128(&net, 2);
+    tk::dnn::Layer *r129_layers[1] = {&a54};
+    tk::dnn::Route r129(&net, r129_layers, 1);
+    tk::dnn::Conv2d c130(&net, 128, 1, 1, 1, 1, 0, 0, c130_bin, true);
+    tk::dnn::Activation a130(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Layer *r131_layers[2] = {&a130,&u128};
+    tk::dnn::Route r131(&net, r131_layers, 2);
+
+
+    tk::dnn::Conv2d c132(&net, 128, 1, 1, 1, 1, 0, 0, c132_bin, true);
+    tk::dnn::Activation a132(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c133(&net, 256, 3, 3, 1, 1, 1, 1, c133_bin, true);
+    tk::dnn::Activation a133(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c134(&net, 128, 1, 1, 1, 1, 0, 0, c134_bin, true);
+    tk::dnn::Activation a134(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c135(&net, 256, 3, 3, 1, 1, 1, 1, c135_bin, true);
+    tk::dnn::Activation a135(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c136(&net, 128, 1, 1, 1, 1, 0, 0, c136_bin, true);
+    tk::dnn::Activation a136(&net, tk::dnn::ACTIVATION_LEAKY);
+
+
+    tk::dnn::Conv2d c137(&net, 256, 3, 3, 1, 1, 1, 1, c137_bin, true);
+    tk::dnn::Activation a137(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c138(&net, 45, 1, 1, 1, 1, 0, 0, c138_bin, false);
+    tk::dnn::Yolo yolo139(&net, classes, 3, g139_bin, 3, 1.2);
+
+    tk::dnn::Layer *r140_layers[1] = {&a136};
+    tk::dnn::Route r140(&net, r140_layers, 1);
+    tk::dnn::Conv2d c141(&net, 256, 3, 3, 2, 2, 1, 1, c141_bin, true);
+    tk::dnn::Activation a141(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Layer *r142_layers[2] = {&a141,&a126};
+    tk::dnn::Route r142(&net, r142_layers, 2);
+
+    tk::dnn::Conv2d c143(&net, 256, 1, 1, 1, 1, 0, 0, c143_bin, true);
+    tk::dnn::Activation a143(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c144(&net, 512, 3, 3, 1, 1, 1, 1, c144_bin, true);
+    tk::dnn::Activation a144(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c145(&net, 256, 1, 1, 1, 1, 0, 0, c145_bin, true);
+    tk::dnn::Activation a145(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c146(&net, 512, 3, 3, 1, 1, 1, 1, c146_bin, true);
+    tk::dnn::Activation a146(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c147(&net, 256, 1, 1, 1, 1, 0, 0, c147_bin, true);
+    tk::dnn::Activation a147(&net, tk::dnn::ACTIVATION_LEAKY);
+
+    tk::dnn::Conv2d c148(&net, 512, 3, 3, 1, 1, 1, 1, c148_bin, true);
+    tk::dnn::Activation a148(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c149(&net, 45, 1, 1, 1, 1, 0, 0, c149_bin, false);
+    tk::dnn::Yolo yolo150(&net, classes, 3, g150_bin, 3, 1.1);
+
+    tk::dnn::Layer *r151_layers[1] = {&a147};
+    tk::dnn::Route r151(&net, r151_layers, 1);
+    tk::dnn::Conv2d c152(&net, 512, 3, 3, 2, 2, 1, 1, c152_bin, true);
+    tk::dnn::Activation a152(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Layer *r153_layers[2] = {&a152,&a116};
+    tk::dnn::Route r153(&net, r153_layers, 2);
+
+    tk::dnn::Conv2d c154(&net, 512, 1, 1, 1, 1, 0, 0, c154_bin, true);
+    tk::dnn::Activation a154(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c155(&net, 1024, 3, 3, 1, 1, 1, 1, c155_bin, true);
+    tk::dnn::Activation a155(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c156(&net, 512, 1, 1, 1, 1, 0, 0, c156_bin, true);
+    tk::dnn::Activation a156(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c157(&net, 1024, 3, 3, 1, 1, 1, 1, c157_bin, true);
+    tk::dnn::Activation a157(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c158(&net, 512, 1, 1, 1, 1, 0, 0, c158_bin, true);
+    tk::dnn::Activation a158(&net, tk::dnn::ACTIVATION_LEAKY);
+
+    tk::dnn::Conv2d c159(&net, 1024, 3, 3, 1, 1, 1, 1, c159_bin, true);
+    tk::dnn::Activation a159(&net, tk::dnn::ACTIVATION_LEAKY);
+    tk::dnn::Conv2d c160(&net, 45, 1, 1, 1, 1, 0, 0, c160_bin, false);
+    tk::dnn::Yolo yolo161(&net, classes, 3, g161_bin, 3, 1.05);
+
+
+    
+
+    
+
+    yolo[0] = &yolo139;
+    yolo[1] = &yolo150;
+    yolo[2] = &yolo161;
+
+    // fill classes names
+    for (int i = 0; i < 3; i++)
+    {
+        yolo[i]->classesNames = {"person","car","truck","bus","motor","bike","rider","traffic light","traffic sign","train"};
+    }
+
+    // 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, net.getNetworkRTName("yolo4_berkeley"));
+
+    // the network have 3 outputs
+    tk::dnn::dataDim_t out_dim[3];
+    for (int i = 0; i < 3; i++)
+        out_dim[i] = yolo[i]->output_dim;
+    dnnType *cudnn_out[3], *rt_out[3];
+
+    tk::dnn::dataDim_t dim1 = dim; //input dim
+    printCenteredTitle(" CUDNN inference ", '=', 30);
+    {
+        dim1.print();
+        TIMER_START
+        net.infer(dim1, data);
+        TIMER_STOP
+        dim1.print();
+    }
+    
+    for (int i = 0; i < 3; i++)
+        cudnn_out[i] = yolo[i]->dstData;
+
+    printCenteredTitle(" compute detections ", '=', 30);
+    TIMER_START
+    int ndets = 0;
+    tk::dnn::Yolo::detection *dets = tk::dnn::Yolo::allocateDetections(tk::dnn::Yolo::MAX_DETECTIONS, classes);
+    for (int i = 0; i < 3; i++)
+        yolo[i]->computeDetections(dets, ndets, net.input_dim.w, net.input_dim.h, 0.5);
+    tk::dnn::Yolo::mergeDetections(dets, ndets, classes);
+
+    for (int j = 0; j < ndets; j++)
+    {
+        tk::dnn::Yolo::box b = dets[j].bbox;
+        int x0 = (b.x - b.w / 2.);
+        int x1 = (b.x + b.w / 2.);
+        int y0 = (b.y - b.h / 2.);
+        int y1 = (b.y + b.h / 2.);
+
+        int cl = 0;
+        for (int c = 0; c < classes; ++c)
+        {
+            float prob = dets[j].prob[c];
+            if (prob > 0)
+                cl = c;
+        }
+        std::cout << cl << ": " << x0 << " " << y0 << " " << x1 << " " << y1 << "\n";
+    }
+    TIMER_STOP
+
+    tk::dnn::dataDim_t dim2 = dim;
+    printCenteredTitle(" TENSORRT inference ", '=', 30);
+    {
+        dim2.print();
+        TIMER_START
+        netRT.infer(dim2, data);
+        TIMER_STOP
+        dim2.print();
+    }
+
+    for (int i = 0; i < 3; i++)
+        rt_out[i] = (dnnType *)netRT.buffersRT[i + 1];
+
+    int ret_cudnn = 0, ret_tensorrt = 0, ret_cudnn_tensorrt = 0; 
+    for (int i = 0; i < 3; i++)
+    {
+        printCenteredTitle((std::string(" YOLO ") + std::to_string(i) + " CHECK RESULTS ").c_str(), '=', 30);
+        dnnType *out, *out_h;
+        int odim = out_dim[i].tot();
+        readBinaryFile(output_bins[i], odim, &out_h, &out);
+        std::cout<<"CUDNN vs correct"; 
+        ret_cudnn |= checkResult(odim, cudnn_out[i], out) == 0 ? 0: ERROR_CUDNN;
+        std::cout<<"TRT   vs correct"; 
+        ret_tensorrt |= checkResult(odim, rt_out[i], out) == 0 ? 0 : ERROR_TENSORRT;
+        std::cout<<"CUDNN vs TRT    "; 
+        ret_cudnn_tensorrt |= checkResult(odim, cudnn_out[i], rt_out[i]) == 0 ? 0 : ERROR_CUDNNvsTENSORRT;
+    }
+    return ret_cudnn | ret_tensorrt | ret_cudnn_tensorrt;
+}