mirror of https://github.com/AlexeyAB/darknet.git
1073 lines
12 KiB
INI
1073 lines
12 KiB
INI
[net]
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# Testing
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#batch=1
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#subdivisions=1
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# Training
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batch=64
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subdivisions=8
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width=416
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height=416
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channels=3
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momentum=0.9
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decay=0.0005
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angle=0
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saturation = 1.5
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exposure = 1.5
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hue=.1
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learning_rate=0.001
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burn_in=1000
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max_batches = 500200
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policy=steps
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steps=400000,450000
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scales=.1,.1
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### CONV1 - 1 (1)
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# conv1
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[convolutional]
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filters=32
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size=3
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pad=1
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stride=2
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batch_normalize=1
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activation=swish
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### CONV2 - MBConv1 - 1 (1)
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# conv2_1_expand
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[convolutional]
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filters=32
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv2_1_dwise
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[convolutional]
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groups=32
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filters=32
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size=3
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stride=1
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pad=1
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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# squeeze ratio r=4 (recommended r=16)
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[convolutional]
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filters=8
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=32
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv2_1_linear
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[convolutional]
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filters=16
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV3 - MBConv6 - 1 (2)
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# conv2_2_expand
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[convolutional]
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filters=96
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv2_2_dwise
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[convolutional]
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groups=96
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filters=96
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size=3
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pad=1
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stride=2
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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# squeeze ratio r=8 (recommended r=16)
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[convolutional]
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filters=16
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=96
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv2_2_linear
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[convolutional]
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filters=24
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV3 - MBConv6 - 2 (2)
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# conv3_1_expand
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[convolutional]
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filters=144
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv3_1_dwise
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[convolutional]
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groups=144
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filters=144
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size=3
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stride=1
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pad=1
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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# squeeze ratio r=16 (recommended r=16)
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[convolutional]
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filters=8
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=144
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv3_1_linear
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[convolutional]
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filters=24
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV4 - MBConv6 - 1 (2)
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# dropout only before residual connection
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[dropout]
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probability=.0
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# block_3_1
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[shortcut]
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from=-9
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activation=linear
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# conv_3_2_expand
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[convolutional]
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filters=144
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv_3_2_dwise
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[convolutional]
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groups=144
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filters=144
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size=5
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pad=1
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stride=2
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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# squeeze ratio r=16 (recommended r=16)
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[convolutional]
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filters=8
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=144
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv_3_2_linear
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[convolutional]
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filters=40
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV4 - MBConv6 - 2 (2)
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# conv_4_1_expand
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[convolutional]
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filters=192
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv_4_1_dwise
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[convolutional]
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groups=192
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filters=192
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size=5
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stride=1
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pad=1
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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# squeeze ratio r=16 (recommended r=16)
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[convolutional]
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filters=16
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=192
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv_4_1_linear
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[convolutional]
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filters=40
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV5 - MBConv6 - 1 (3)
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# dropout only before residual connection
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[dropout]
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probability=.0
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# block_4_2
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[shortcut]
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from=-9
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activation=linear
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# conv_4_3_expand
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[convolutional]
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filters=192
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv_4_3_dwise
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[convolutional]
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groups=192
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filters=192
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size=3
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stride=1
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pad=1
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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# squeeze ratio r=16 (recommended r=16)
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[convolutional]
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filters=16
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=192
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv_4_3_linear
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[convolutional]
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filters=80
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV5 - MBConv6 - 2 (3)
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# conv_4_4_expand
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[convolutional]
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filters=384
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv_4_4_dwise
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[convolutional]
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groups=384
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filters=384
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size=3
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stride=1
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pad=1
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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# squeeze ratio r=16 (recommended r=16)
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[convolutional]
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filters=24
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=384
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv_4_4_linear
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[convolutional]
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filters=80
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV5 - MBConv6 - 3 (3)
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# dropout only before residual connection
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[dropout]
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probability=.0
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# block_4_4
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[shortcut]
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from=-9
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activation=linear
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# conv_4_5_expand
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[convolutional]
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filters=384
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv_4_5_dwise
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[convolutional]
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groups=384
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filters=384
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size=3
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stride=1
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pad=1
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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|
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# squeeze ratio r=16 (recommended r=16)
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[convolutional]
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filters=24
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size=1
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stride=1
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activation=swish
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# excitation
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[convolutional]
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filters=384
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv_4_5_linear
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[convolutional]
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filters=80
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV6 - MBConv6 - 1 (3)
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# dropout only before residual connection
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[dropout]
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probability=.0
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# block_4_6
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[shortcut]
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from=-9
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activation=linear
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# conv_4_7_expand
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[convolutional]
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filters=384
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv_4_7_dwise
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[convolutional]
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groups=384
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filters=384
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size=5
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pad=1
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stride=2
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batch_normalize=1
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activation=swish
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#squeeze-n-excitation
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[avgpool]
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|
|
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# squeeze ratio r=16 (recommended r=16)
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[convolutional]
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filters=24
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|
size=1
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stride=1
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activation=swish
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|
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# excitation
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[convolutional]
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filters=384
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size=1
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stride=1
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activation=logistic
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# multiply channels
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[scale_channels]
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from=-4
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# conv_4_7_linear
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[convolutional]
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filters=112
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=linear
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### CONV6 - MBConv6 - 2 (3)
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# conv_5_1_expand
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[convolutional]
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filters=576
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size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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# conv_5_1_dwise
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[convolutional]
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groups=576
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filters=576
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size=5
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stride=1
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pad=1
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batch_normalize=1
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activation=swish
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|
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|
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#squeeze-n-excitation
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|
[avgpool]
|
|
|
|
# squeeze ratio r=16 (recommended r=16)
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|
[convolutional]
|
|
filters=32
|
|
size=1
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|
stride=1
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activation=swish
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|
|
|
# excitation
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[convolutional]
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|
filters=576
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|
size=1
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|
stride=1
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|
activation=logistic
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|
|
|
# multiply channels
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[scale_channels]
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|
from=-4
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|
|
|
|
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# conv_5_1_linear
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[convolutional]
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|
filters=112
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|
size=1
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|
stride=1
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|
pad=0
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|
batch_normalize=1
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activation=linear
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|
|
|
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### CONV6 - MBConv6 - 3 (3)
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# dropout only before residual connection
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[dropout]
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probability=.0
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|
|
|
# block_5_1
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[shortcut]
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|
from=-9
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|
activation=linear
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|
|
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# conv_5_2_expand
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[convolutional]
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filters=576
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|
size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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|
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# conv_5_2_dwise
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[convolutional]
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groups=576
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filters=576
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size=5
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stride=1
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pad=1
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|
batch_normalize=1
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activation=swish
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|
|
|
|
|
#squeeze-n-excitation
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|
[avgpool]
|
|
|
|
# squeeze ratio r=16 (recommended r=16)
|
|
[convolutional]
|
|
filters=32
|
|
size=1
|
|
stride=1
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|
activation=swish
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|
|
|
# excitation
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|
[convolutional]
|
|
filters=576
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|
size=1
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|
stride=1
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|
activation=logistic
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|
|
|
# multiply channels
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[scale_channels]
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|
from=-4
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|
|
|
|
|
# conv_5_2_linear
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|
[convolutional]
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|
filters=112
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|
size=1
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|
stride=1
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|
pad=0
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|
batch_normalize=1
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activation=linear
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|
|
|
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### CONV7 - MBConv6 - 1 (4)
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|
# dropout only before residual connection
|
|
[dropout]
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probability=.0
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|
|
|
# block_5_2
|
|
[shortcut]
|
|
from=-9
|
|
activation=linear
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|
|
|
# conv_5_3_expand
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|
[convolutional]
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|
filters=576
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|
size=1
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stride=1
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pad=0
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batch_normalize=1
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activation=swish
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|
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# conv_5_3_dwise
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|
[convolutional]
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groups=576
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filters=576
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size=5
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pad=1
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stride=2
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batch_normalize=1
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|
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
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|
stride=1
|
|
activation=logistic
|
|
|
|
# multiply channels
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|
[scale_channels]
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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=-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=.0
|
|
|
|
# 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=.0
|
|
|
|
# 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
|
|
|
|
##########################
|
|
|
|
[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=90
|
|
|
|
[convolutional]
|
|
batch_normalize=1
|
|
filters=128
|
|
size=3
|
|
stride=1
|
|
pad=1
|
|
activation=leaky
|
|
|
|
[shortcut]
|
|
activation=leaky
|
|
from=-3
|
|
|
|
[shortcut]
|
|
activation=leaky
|
|
from=90
|
|
|
|
[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
|
|
|