From a63782ca8937f412e943b6c841a5671ce39c60fc Mon Sep 17 00:00:00 2001
From: AlexeyAB <alexeyab84@gmail.com>
Date: Mon, 2 Sep 2019 15:55:05 +0300
Subject: [PATCH] Added: efficientnet_b0.cfg

---
 build/darknet/x64/cfg/efficientnet_b0.cfg | 1005 +++++++++++++++++++++
 cfg/efficientnet_b0.cfg                   | 1005 +++++++++++++++++++++
 2 files changed, 2010 insertions(+)
 create mode 100644 build/darknet/x64/cfg/efficientnet_b0.cfg
 create mode 100644 cfg/efficientnet_b0.cfg

diff --git a/build/darknet/x64/cfg/efficientnet_b0.cfg b/build/darknet/x64/cfg/efficientnet_b0.cfg
new file mode 100644
index 00000000..3bd3e895
--- /dev/null
+++ b/build/darknet/x64/cfg/efficientnet_b0.cfg
@@ -0,0 +1,1005 @@
+[net]
+# Training
+batch=120
+subdivisions=4
+# Testing
+#batch=1
+#subdivisions=1
+height=224
+width=224
+channels=3
+momentum=0.9
+decay=0.0005
+max_crop=256
+
+burn_in=1000
+#burn_in=100
+learning_rate=0.256
+policy=poly
+power=4
+max_batches=800000
+momentum=0.9
+decay=0.00005
+
+angle=7
+hue=.1
+saturation=.75
+exposure=.75
+aspect=.75
+
+
+### 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=.2
+
+# block_3_1
+[shortcut]
+from=-9
+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=.2
+
+# block_4_2
+[shortcut]
+from=-9
+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=.2
+
+# block_4_4
+[shortcut]
+from=-9
+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=.2
+
+# block_4_6
+[shortcut]
+from=-9
+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=.2
+
+# block_5_1
+[shortcut]
+from=-9
+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=.2
+
+# block_5_2
+[shortcut]
+from=-9
+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=.2
+
+# block_6_1
+[shortcut]
+from=-9
+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=.2
+
+# block_6_1
+[shortcut]
+from=-9
+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=.2
+
+# block_6_2
+[shortcut]
+from=-9
+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
+
+
+[avgpool]
+
+[dropout]
+probability=.2
+
+[convolutional]
+filters=1000
+size=1
+stride=1
+pad=0
+activation=linear
+
+[softmax]
+groups=1
+
+#[cost]
+#type=sse
+
diff --git a/cfg/efficientnet_b0.cfg b/cfg/efficientnet_b0.cfg
new file mode 100644
index 00000000..3bd3e895
--- /dev/null
+++ b/cfg/efficientnet_b0.cfg
@@ -0,0 +1,1005 @@
+[net]
+# Training
+batch=120
+subdivisions=4
+# Testing
+#batch=1
+#subdivisions=1
+height=224
+width=224
+channels=3
+momentum=0.9
+decay=0.0005
+max_crop=256
+
+burn_in=1000
+#burn_in=100
+learning_rate=0.256
+policy=poly
+power=4
+max_batches=800000
+momentum=0.9
+decay=0.00005
+
+angle=7
+hue=.1
+saturation=.75
+exposure=.75
+aspect=.75
+
+
+### 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=.2
+
+# block_3_1
+[shortcut]
+from=-9
+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=.2
+
+# block_4_2
+[shortcut]
+from=-9
+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=.2
+
+# block_4_4
+[shortcut]
+from=-9
+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=.2
+
+# block_4_6
+[shortcut]
+from=-9
+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=.2
+
+# block_5_1
+[shortcut]
+from=-9
+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=.2
+
+# block_5_2
+[shortcut]
+from=-9
+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=.2
+
+# block_6_1
+[shortcut]
+from=-9
+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=.2
+
+# block_6_1
+[shortcut]
+from=-9
+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=.2
+
+# block_6_2
+[shortcut]
+from=-9
+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
+
+
+[avgpool]
+
+[dropout]
+probability=.2
+
+[convolutional]
+filters=1000
+size=1
+stride=1
+pad=0
+activation=linear
+
+[softmax]
+groups=1
+
+#[cost]
+#type=sse
+