mirror of https://github.com/AlexeyAB/darknet.git
1010 lines
12 KiB
INI
1010 lines
12 KiB
INI
# https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/lite/efficientnet_lite_builder.py
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# (width_coefficient, depth_coefficient, resolution, dropout_rate)
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# 'efficientnet-lite3': (1.2, 1.4, 280, 0.3),
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#
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#_DEFAULT_BLOCKS_ARGS = [
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# 'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25',
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# 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25',
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# 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25',
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# 'r1_k3_s11_e6_i192_o320_se0.25',
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#]
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[net]
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# Training
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batch=120
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subdivisions=6
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height=288
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width=288
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channels=3
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momentum=0.9
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decay=0.0005
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max_crop=320
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cutmix=1
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mosaic=1
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label_smooth_eps=0.1
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burn_in=1000
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learning_rate=0.256
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policy=step
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step=10000
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scale=0.96
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max_batches=1600000
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momentum=0.9
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decay=0.00005
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angle=7
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hue=.1
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saturation=.75
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exposure=.75
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aspect=.75
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### CONV1 - 1 (1)
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# conv1
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[convolutional]
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filters=40 #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=relu6
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### CONV2 - MBConv1 - 1 (2)
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# conv2_1_expand
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[convolutional]
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filters=40 #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=relu6
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# conv2_1_dwise
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[convolutional]
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groups=40 #32
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filters=40 #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=relu6
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# conv2_1_linear
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[convolutional]
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filters=16 #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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### CONV2 - MBConv1 - 2 (2)
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# conv2_1_expand
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[convolutional]
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filters=40 #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=relu6
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# conv2_1_dwise
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[convolutional]
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groups=40 #32
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filters=40 #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=relu6
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# conv2_1_linear
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[convolutional]
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filters=16 #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 (3)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_3_1
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[shortcut]
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from=-5
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activation=linear
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# conv2_2_expand
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[convolutional]
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filters=112 #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=relu6
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# conv2_2_dwise
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[convolutional]
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groups=112 #96
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filters=112 #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=relu6
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# conv2_2_linear
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[convolutional]
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filters=32 #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 (3)
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# conv3_1_expand
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[convolutional]
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filters=176 #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=relu6
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# conv3_1_dwise
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[convolutional]
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groups=176 #144
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filters=176 #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=relu6
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# conv3_1_linear
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[convolutional]
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filters=32 #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 - 3 (3)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_3_1
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[shortcut]
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from=-5
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activation=linear
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# conv3_1_expand
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[convolutional]
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filters=176 #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=relu6
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# conv3_1_dwise
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[convolutional]
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groups=176 #144
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filters=176 #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=relu6
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# conv3_1_linear
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[convolutional]
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filters=32 #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 (3)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_3_1
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[shortcut]
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from=-5
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activation=linear
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# conv_3_2_expand
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[convolutional]
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filters=176 #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=relu6
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# conv_3_2_dwise
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[convolutional]
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groups=176 #144
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filters=176 #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=relu6
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# conv_3_2_linear
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[convolutional]
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filters=48 #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 (3)
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# conv_4_1_expand
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[convolutional]
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filters=232 #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=relu6
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# conv_4_1_dwise
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[convolutional]
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groups=232 #192
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filters=232 #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=relu6
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# conv_4_1_linear
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[convolutional]
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filters=48 #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 - 3 (3)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_4_2
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[shortcut]
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from=-5
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activation=linear
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# conv_4_1_expand
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[convolutional]
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filters=232 #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=relu6
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# conv_4_1_dwise
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[convolutional]
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groups=232 #192
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filters=232 #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=relu6
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# conv_4_1_linear
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[convolutional]
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filters=48 #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 (5)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_4_2
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[shortcut]
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from=-5
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activation=linear
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# conv_4_3_expand
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[convolutional]
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filters=232 #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=relu6
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# conv_4_3_dwise
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[convolutional]
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groups=232 #192
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filters=232 #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=relu6
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# conv_4_3_linear
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[convolutional]
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filters=96 #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 (5)
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# conv_4_4_expand
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[convolutional]
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filters=464 #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=relu6
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# conv_4_4_dwise
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[convolutional]
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groups=464 #384
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filters=464 #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=relu6
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# conv_4_4_linear
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[convolutional]
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filters=96 #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 (5)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_4_4
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[shortcut]
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from=-5
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activation=linear
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# conv_4_5_expand
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[convolutional]
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filters=464 #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=relu6
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# conv_4_5_dwise
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[convolutional]
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groups=464 #384
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filters=464 #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=relu6
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# conv_4_5_linear
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[convolutional]
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filters=96 #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 - 4 (5)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_4_4
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[shortcut]
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from=-5
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activation=linear
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# conv_4_5_expand
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[convolutional]
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filters=464 #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=relu6
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# conv_4_5_dwise
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[convolutional]
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groups=464 #384
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filters=464 #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=relu6
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# conv_4_5_linear
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[convolutional]
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filters=96 #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 - 5 (5)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_4_4
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[shortcut]
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from=-5
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activation=linear
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# conv_4_5_expand
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[convolutional]
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filters=464 #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=relu6
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# conv_4_5_dwise
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[convolutional]
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groups=464 #384
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filters=464 #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=relu6
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# conv_4_5_linear
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[convolutional]
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filters=96 #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 (5)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_4_6
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[shortcut]
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from=-5
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activation=linear
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# conv_4_7_expand
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[convolutional]
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filters=464 #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=relu6
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# conv_4_7_dwise
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[convolutional]
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groups=464 #384
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filters=464 #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=relu6
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# conv_4_7_linear
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[convolutional]
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filters=136 #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 (5)
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# conv_5_1_expand
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[convolutional]
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filters=688 #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=relu6
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# conv_5_1_dwise
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[convolutional]
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groups=688 #576
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filters=688 #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=relu6
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# conv_5_1_linear
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[convolutional]
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filters=136 #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 - 3 (5)
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# dropout only before residual connection
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[dropout]
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probability=.3
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# block_5_1
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[shortcut]
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from=-5
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activation=linear
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# conv_5_2_expand
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[convolutional]
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filters=688 #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=relu6
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# conv_5_2_dwise
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[convolutional]
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groups=688 #576
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filters=688 #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=relu6
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|
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# conv_5_2_linear
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[convolutional]
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filters=136 #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 - 4 (5)
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# dropout only before residual connection
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[dropout]
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probability=.3
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|
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# block_5_1
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[shortcut]
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from=-5
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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=688 #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=relu6
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|
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# conv_5_2_dwise
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[convolutional]
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groups=688 #576
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filters=688 #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=relu6
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|
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# conv_5_2_linear
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[convolutional]
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filters=136 #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 - 5 (5)
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|
# dropout only before residual connection
|
|
[dropout]
|
|
probability=.3
|
|
|
|
# block_5_1
|
|
[shortcut]
|
|
from=-5
|
|
activation=linear
|
|
|
|
# conv_5_2_expand
|
|
[convolutional]
|
|
filters=688 #576
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_5_2_dwise
|
|
[convolutional]
|
|
groups=688 #576
|
|
filters=688 #576
|
|
size=5
|
|
stride=1
|
|
pad=1
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_5_2_linear
|
|
[convolutional]
|
|
filters=136 #112
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
|
|
### CONV7 - MBConv6 - 1 (6)
|
|
# dropout only before residual connection
|
|
[dropout]
|
|
probability=.3
|
|
|
|
# block_5_2
|
|
[shortcut]
|
|
from=-5
|
|
activation=linear
|
|
|
|
# conv_5_3_expand
|
|
[convolutional]
|
|
filters=688 #576
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_5_3_dwise
|
|
[convolutional]
|
|
groups=688 #576
|
|
filters=688 #576
|
|
size=5
|
|
pad=1
|
|
stride=2
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
|
|
# conv_5_3_linear
|
|
[convolutional]
|
|
filters=232 #192
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
### CONV7 - MBConv6 - 2 (6)
|
|
# conv_6_1_expand
|
|
[convolutional]
|
|
filters=1152 #960
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_1_dwise
|
|
[convolutional]
|
|
groups=1152 #960
|
|
filters=1152 #960
|
|
size=5
|
|
stride=1
|
|
pad=1
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_1_linear
|
|
[convolutional]
|
|
filters=232 #192
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
### CONV7 - MBConv6 - 3 (6)
|
|
# dropout only before residual connection
|
|
[dropout]
|
|
probability=.3
|
|
|
|
# block_6_1
|
|
[shortcut]
|
|
from=-5
|
|
activation=linear
|
|
|
|
# conv_6_2_expand
|
|
[convolutional]
|
|
filters=1152 #960
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_2_dwise
|
|
[convolutional]
|
|
groups=1152 #960
|
|
filters=1152 #960
|
|
size=5
|
|
stride=1
|
|
pad=1
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_2_linear
|
|
[convolutional]
|
|
filters=232 #192
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
### CONV7 - MBConv6 - 4 (6)
|
|
# dropout only before residual connection
|
|
[dropout]
|
|
probability=.3
|
|
|
|
# block_6_1
|
|
[shortcut]
|
|
from=-5
|
|
activation=linear
|
|
|
|
# conv_6_2_expand
|
|
[convolutional]
|
|
filters=1152 #960
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_2_dwise
|
|
[convolutional]
|
|
groups=1152 #960
|
|
filters=1152 #960
|
|
size=5
|
|
stride=1
|
|
pad=1
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_2_linear
|
|
[convolutional]
|
|
filters=232 #192
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
### CONV7 - MBConv6 - 5 (6)
|
|
# dropout only before residual connection
|
|
[dropout]
|
|
probability=.3
|
|
|
|
# block_6_1
|
|
[shortcut]
|
|
from=-5
|
|
activation=linear
|
|
|
|
# conv_6_2_expand
|
|
[convolutional]
|
|
filters=1152 #960
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_2_dwise
|
|
[convolutional]
|
|
groups=1152 #960
|
|
filters=1152 #960
|
|
size=5
|
|
stride=1
|
|
pad=1
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_2_linear
|
|
[convolutional]
|
|
filters=232 #192
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
### CONV7 - MBConv6 - 6 (6)
|
|
# dropout only before residual connection
|
|
[dropout]
|
|
probability=.3
|
|
|
|
# block_6_1
|
|
[shortcut]
|
|
from=-5
|
|
activation=linear
|
|
|
|
# conv_6_2_expand
|
|
[convolutional]
|
|
filters=1152 #960
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_2_dwise
|
|
[convolutional]
|
|
groups=1152 #960
|
|
filters=1152 #960
|
|
size=5
|
|
stride=1
|
|
pad=1
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
|
|
|
|
# conv_6_2_linear
|
|
[convolutional]
|
|
filters=232 #192
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
|
|
### CONV8 - MBConv6 - 1 (1)
|
|
# dropout only before residual connection
|
|
[dropout]
|
|
probability=.3
|
|
|
|
# block_6_2
|
|
[shortcut]
|
|
from=-5
|
|
activation=linear
|
|
|
|
# conv_6_3_expand
|
|
[convolutional]
|
|
filters=1152 #960
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
# conv_6_3_dwise
|
|
[convolutional]
|
|
groups=1152 #960
|
|
filters=1152 #960
|
|
size=3
|
|
stride=1
|
|
pad=1
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
|
|
|
|
# conv_6_3_linear
|
|
[convolutional]
|
|
filters=384 #320
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=linear
|
|
|
|
|
|
|
|
|
|
### CONV9 - Conv2d 1x1
|
|
# conv_6_4
|
|
[convolutional]
|
|
filters=1536 #1280
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
batch_normalize=1
|
|
activation=relu6
|
|
|
|
|
|
[avgpool]
|
|
|
|
[dropout]
|
|
probability=.3
|
|
|
|
[convolutional]
|
|
filters=1000
|
|
size=1
|
|
stride=1
|
|
pad=0
|
|
activation=linear
|
|
|
|
[softmax]
|
|
groups=1
|
|
|
|
#[cost]
|
|
#type=sse
|
|
|