diff --git a/tests/darknet/cfg/enet-coco-wo-dropout.cfg b/tests/darknet/cfg/enet-coco-wo-dropout.cfg
new file mode 100644
index 0000000..8824fd9
--- /dev/null
+++ b/tests/darknet/cfg/enet-coco-wo-dropout.cfg
@@ -0,0 +1,1072 @@
+[net]
+# Testing
+#batch=1
+#subdivisions=1
+# Training
+batch=64
+subdivisions=8
+width=416
+height=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 = 500200
+policy=steps
+steps=400000,450000
+scales=.1,.1
+
+### CONV1 - 1 (1)
+# conv1
+[convolutional]
+filters=32
+size=3
+pad=1
+stride=2
+batch_normalize=1
+activation=swish
+
+
+### CONV2 - MBConv1 - 1 (1)
+# conv2_1_expand
+[convolutional]
+filters=32
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv2_1_dwise
+[convolutional]
+groups=32
+filters=32
+size=3
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=4 (recommended r=16)
+[convolutional]
+filters=8
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=32
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv2_1_linear
+[convolutional]
+filters=16
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+
+### CONV3 - MBConv6 - 1 (2)
+# conv2_2_expand
+[convolutional]
+filters=96
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv2_2_dwise
+[convolutional]
+groups=96
+filters=96
+size=3
+pad=1
+stride=2
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=8 (recommended r=16)
+[convolutional]
+filters=16
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=96
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv2_2_linear
+[convolutional]
+filters=24
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV3 - MBConv6 - 2 (2)
+# conv3_1_expand
+[convolutional]
+filters=144
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv3_1_dwise
+[convolutional]
+groups=144
+filters=144
+size=3
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=8
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=144
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv3_1_linear
+[convolutional]
+filters=24
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+
+### CONV4 - MBConv6 - 1 (2)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_3_1
+[shortcut]
+from=-8
+activation=linear
+
+# conv_3_2_expand
+[convolutional]
+filters=144
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_3_2_dwise
+[convolutional]
+groups=144
+filters=144
+size=5
+pad=1
+stride=2
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=8
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=144
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_3_2_linear
+[convolutional]
+filters=40
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV4 - MBConv6 - 2 (2)
+# conv_4_1_expand
+[convolutional]
+filters=192
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_4_1_dwise
+[convolutional]
+groups=192
+filters=192
+size=5
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=16
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=192
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_4_1_linear
+[convolutional]
+filters=40
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+
+
+### CONV5 - MBConv6 - 1 (3)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_4_2
+[shortcut]
+from=-8
+activation=linear
+
+# conv_4_3_expand
+[convolutional]
+filters=192
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_4_3_dwise
+[convolutional]
+groups=192
+filters=192
+size=3
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=16
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=192
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_4_3_linear
+[convolutional]
+filters=80
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV5 - MBConv6 - 2 (3)
+# conv_4_4_expand
+[convolutional]
+filters=384
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_4_4_dwise
+[convolutional]
+groups=384
+filters=384
+size=3
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=24
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=384
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_4_4_linear
+[convolutional]
+filters=80
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV5 - MBConv6 - 3 (3)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_4_4
+[shortcut]
+from=-8
+activation=linear
+
+# conv_4_5_expand
+[convolutional]
+filters=384
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_4_5_dwise
+[convolutional]
+groups=384
+filters=384
+size=3
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=24
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=384
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_4_5_linear
+[convolutional]
+filters=80
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+
+### CONV6 - MBConv6 - 1 (3)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_4_6
+[shortcut]
+from=-8
+activation=linear
+
+# conv_4_7_expand
+[convolutional]
+filters=384
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_4_7_dwise
+[convolutional]
+groups=384
+filters=384
+size=5
+pad=1
+stride=2
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=24
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=384
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_4_7_linear
+[convolutional]
+filters=112
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV6 - MBConv6 - 2 (3)
+# conv_5_1_expand
+[convolutional]
+filters=576
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_5_1_dwise
+[convolutional]
+groups=576
+filters=576
+size=5
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=32
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=576
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_5_1_linear
+[convolutional]
+filters=112
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV6 - MBConv6 - 3 (3)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_5_1
+[shortcut]
+from=-8
+activation=linear
+
+# conv_5_2_expand
+[convolutional]
+filters=576
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_5_2_dwise
+[convolutional]
+groups=576
+filters=576
+size=5
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=32
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=576
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_5_2_linear
+[convolutional]
+filters=112
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV7 - MBConv6 - 1 (4)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_5_2
+[shortcut]
+from=-8
+activation=linear
+
+# conv_5_3_expand
+[convolutional]
+filters=576
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_5_3_dwise
+[convolutional]
+groups=576
+filters=576
+size=5
+pad=1
+stride=2
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=32
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=576
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_5_3_linear
+[convolutional]
+filters=192
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV7 - MBConv6 - 2 (4)
+# conv_6_1_expand
+[convolutional]
+filters=960
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_6_1_dwise
+[convolutional]
+groups=960
+filters=960
+size=5
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=64
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=960
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_6_1_linear
+[convolutional]
+filters=192
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV7 - MBConv6 - 3 (4)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_6_1
+[shortcut]
+from=-8
+activation=linear
+
+# conv_6_2_expand
+[convolutional]
+filters=960
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_6_2_dwise
+[convolutional]
+groups=960
+filters=960
+size=5
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=64
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=960
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_6_2_linear
+[convolutional]
+filters=192
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV7 - MBConv6 - 4 (4)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_6_1
+[shortcut]
+from=-8
+activation=linear
+
+# conv_6_2_expand
+[convolutional]
+filters=960
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_6_2_dwise
+[convolutional]
+groups=960
+filters=960
+size=5
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=64
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=960
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_6_2_linear
+[convolutional]
+filters=192
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+
+### CONV8 - MBConv6 - 1 (1)
+# dropout only before residual connection
+#[dropout]
+#probability=.0
+
+# block_6_2
+[shortcut]
+from=-8
+activation=linear
+
+# conv_6_3_expand
+[convolutional]
+filters=960
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+# conv_6_3_dwise
+[convolutional]
+groups=960
+filters=960
+size=3
+stride=1
+pad=1
+batch_normalize=1
+activation=swish
+
+
+#squeeze-n-excitation
+[avgpool]
+
+# squeeze ratio r=16 (recommended r=16)
+[convolutional]
+filters=64
+size=1
+stride=1
+activation=swish
+
+# excitation
+[convolutional]
+filters=960
+size=1
+stride=1
+activation=logistic
+
+# multiply channels
+[scale_channels]
+from=-4
+
+
+# conv_6_3_linear
+[convolutional]
+filters=320
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=linear
+
+
+### CONV9 - Conv2d 1x1
+# conv_6_4
+[convolutional]
+filters=1280
+size=1
+stride=1
+pad=0
+batch_normalize=1
+activation=swish
+
+##########################
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[convolutional]
+batch_normalize=1
+filters=256
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+activation=leaky
+from=-2
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+
+
+[yolo]
+mask = 3,4,5
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+
+[route]
+layers = -4
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=1
+stride=1
+pad=1
+activation=leaky
+
+[upsample]
+stride=2
+
+[shortcut]
+activation=leaky
+from=84
+
+[convolutional]
+batch_normalize=1
+filters=128
+size=3
+stride=1
+pad=1
+activation=leaky
+
+[shortcut]
+activation=leaky
+from=-3
+
+[shortcut]
+activation=leaky
+from=84
+
+[convolutional]
+size=1
+stride=1
+pad=1
+filters=255
+activation=linear
+
+[yolo]
+mask = 1,2,3
+anchors = 10,14,  23,27,  37,58,  81,82,  135,169,  344,319
+classes=80
+num=6
+jitter=.3
+ignore_thresh = .7
+truth_thresh = 1
+random=0
+
diff --git a/tests/darknet/enet_coco_wo_dropout.cpp b/tests/darknet/enet_coco_wo_dropout.cpp
new file mode 100644
index 0000000..af2c17f
--- /dev/null
+++ b/tests/darknet/enet_coco_wo_dropout.cpp
@@ -0,0 +1,33 @@
+#include<iostream>
+#include<vector>
+#include "tkdnn.h"
+#include "test.h"
+#include "DarknetParser.h"
+
+int main() {
+    std::string bin_path  = "enet_coco_wo_dropout";
+    std::vector<std::string> input_bins = { 
+        bin_path + "/layers/input.bin"
+    };
+    std::vector<std::string> output_bins = {
+        bin_path + "/debug/layer127_out.bin",
+        bin_path + "/debug/layer136_out.bin"
+    };
+    std::string wgs_path  = bin_path + "/layers";
+    std::string cfg_path  = std::string(TKDNN_PATH) + "/tests/darknet/cfg/enet-coco-wo-dropout.cfg";
+    std::string name_path = std::string(TKDNN_PATH) + "/tests/darknet/names/coco.names";
+    // downloadWeightsifDoNotExist(input_bins[0], bin_path, "https://cloud.hipert.unimore.it/s/d97CFzYqCPCp5Hg/download");
+
+    // parse darknet network
+    tk::dnn::Network *net = tk::dnn::darknetParser(cfg_path, wgs_path, name_path);
+    net->print();
+
+    //convert network to tensorRT
+    tk::dnn::NetworkRT *netRT = new tk::dnn::NetworkRT(net, net->getNetworkRTName(bin_path.c_str()));
+    
+    int ret = testInference(input_bins, output_bins, net, netRT);
+    net->releaseLayers();
+    delete net;
+    delete netRT;
+    return ret;
+}