# https://github.com/tensorflow/tpu/blob/master/models/official/efficientnet/lite/efficientnet_lite_builder.py # (width_coefficient, depth_coefficient, resolution, dropout_rate) # 'efficientnet-lite3': (1.2, 1.4, 280, 0.3), # #_DEFAULT_BLOCKS_ARGS = [ # 'r1_k3_s11_e1_i32_o16_se0.25', 'r2_k3_s22_e6_i16_o24_se0.25', # 'r2_k5_s22_e6_i24_o40_se0.25', 'r3_k3_s22_e6_i40_o80_se0.25', # 'r3_k5_s11_e6_i80_o112_se0.25', 'r4_k5_s22_e6_i112_o192_se0.25', # 'r1_k3_s11_e6_i192_o320_se0.25', #] [net] # Training batch=120 subdivisions=6 height=288 width=288 channels=3 momentum=0.9 decay=0.0005 max_crop=320 cutmix=1 mosaic=1 label_smooth_eps=0.1 burn_in=1000 learning_rate=0.256 policy=step step=10000 scale=0.96 max_batches=1600000 momentum=0.9 decay=0.00005 angle=7 hue=.1 saturation=.75 exposure=.75 aspect=.75 ### CONV1 - 1 (1) # conv1 [convolutional] filters=40 #32 size=3 pad=1 stride=2 batch_normalize=1 activation=relu6 ### CONV2 - MBConv1 - 1 (2) # conv2_1_expand [convolutional] filters=40 #32 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv2_1_dwise [convolutional] groups=40 #32 filters=40 #32 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv2_1_linear [convolutional] filters=16 #16 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV2 - MBConv1 - 2 (2) # conv2_1_expand [convolutional] filters=40 #32 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv2_1_dwise [convolutional] groups=40 #32 filters=40 #32 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv2_1_linear [convolutional] filters=16 #16 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV3 - MBConv6 - 1 (3) # dropout only before residual connection [dropout] probability=.3 # block_3_1 [shortcut] from=-5 activation=linear # conv2_2_expand [convolutional] filters=112 #96 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv2_2_dwise [convolutional] groups=112 #96 filters=112 #96 size=3 pad=1 stride=2 batch_normalize=1 activation=relu6 # conv2_2_linear [convolutional] filters=32 #24 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV3 - MBConv6 - 2 (3) # conv3_1_expand [convolutional] filters=176 #144 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv3_1_dwise [convolutional] groups=176 #144 filters=176 #144 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv3_1_linear [convolutional] filters=32 #24 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV3 - MBConv6 - 3 (3) # dropout only before residual connection [dropout] probability=.3 # block_3_1 [shortcut] from=-5 activation=linear # conv3_1_expand [convolutional] filters=176 #144 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv3_1_dwise [convolutional] groups=176 #144 filters=176 #144 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv3_1_linear [convolutional] filters=32 #24 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV4 - MBConv6 - 1 (3) # dropout only before residual connection [dropout] probability=.3 # block_3_1 [shortcut] from=-5 activation=linear # conv_3_2_expand [convolutional] filters=176 #144 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_3_2_dwise [convolutional] groups=176 #144 filters=176 #144 size=5 pad=1 stride=2 batch_normalize=1 activation=relu6 # conv_3_2_linear [convolutional] filters=48 #40 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV4 - MBConv6 - 2 (3) # conv_4_1_expand [convolutional] filters=232 #192 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_1_dwise [convolutional] groups=232 #192 filters=232 #192 size=5 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_4_1_linear [convolutional] filters=48 #40 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV4 - MBConv6 - 3 (3) # dropout only before residual connection [dropout] probability=.3 # block_4_2 [shortcut] from=-5 activation=linear # conv_4_1_expand [convolutional] filters=232 #192 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_1_dwise [convolutional] groups=232 #192 filters=232 #192 size=5 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_4_1_linear [convolutional] filters=48 #40 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV5 - MBConv6 - 1 (5) # dropout only before residual connection [dropout] probability=.3 # block_4_2 [shortcut] from=-5 activation=linear # conv_4_3_expand [convolutional] filters=232 #192 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_3_dwise [convolutional] groups=232 #192 filters=232 #192 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_4_3_linear [convolutional] filters=96 #80 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV5 - MBConv6 - 2 (5) # conv_4_4_expand [convolutional] filters=464 #384 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_4_dwise [convolutional] groups=464 #384 filters=464 #384 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_4_4_linear [convolutional] filters=96 #80 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV5 - MBConv6 - 3 (5) # dropout only before residual connection [dropout] probability=.3 # block_4_4 [shortcut] from=-5 activation=linear # conv_4_5_expand [convolutional] filters=464 #384 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_5_dwise [convolutional] groups=464 #384 filters=464 #384 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_4_5_linear [convolutional] filters=96 #80 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV5 - MBConv6 - 4 (5) # dropout only before residual connection [dropout] probability=.3 # block_4_4 [shortcut] from=-5 activation=linear # conv_4_5_expand [convolutional] filters=464 #384 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_5_dwise [convolutional] groups=464 #384 filters=464 #384 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_4_5_linear [convolutional] filters=96 #80 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV5 - MBConv6 - 5 (5) # dropout only before residual connection [dropout] probability=.3 # block_4_4 [shortcut] from=-5 activation=linear # conv_4_5_expand [convolutional] filters=464 #384 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_5_dwise [convolutional] groups=464 #384 filters=464 #384 size=3 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_4_5_linear [convolutional] filters=96 #80 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV6 - MBConv6 - 1 (5) # dropout only before residual connection [dropout] probability=.3 # block_4_6 [shortcut] from=-5 activation=linear # conv_4_7_expand [convolutional] filters=464 #384 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_4_7_dwise [convolutional] groups=464 #384 filters=464 #384 size=5 pad=1 stride=2 batch_normalize=1 activation=relu6 # conv_4_7_linear [convolutional] filters=136 #112 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV6 - MBConv6 - 2 (5) # conv_5_1_expand [convolutional] filters=688 #576 size=1 stride=1 pad=0 batch_normalize=1 activation=relu6 # conv_5_1_dwise [convolutional] groups=688 #576 filters=688 #576 size=5 stride=1 pad=1 batch_normalize=1 activation=relu6 # conv_5_1_linear [convolutional] filters=136 #112 size=1 stride=1 pad=0 batch_normalize=1 activation=linear ### CONV6 - MBConv6 - 3 (5) # 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 ### CONV6 - MBConv6 - 4 (5) # 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 ### CONV6 - MBConv6 - 5 (5) # 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