Backbone

ResNet#

Res: Residual

ResBlock#

[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=mish
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=mish
[shortcut]
from=-3
activation=linear

ResNet#

CSPResNet#

CSP: Cross Stage Partial

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish
[route]
layers = -2
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish
... resnet -> 3 x n layers ...
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish
[route]
layers = -1,-7 # -(3 x n + 4)
[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=mish

CSPDarknet#

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=mish
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=mish
... CSPResNet -> ResBlock x 1 ...
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
pad=1
activation=mish
... CSPResNet -> ResBlock x 2 ...
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
pad=1
activation=mish
... CSPResNet -> ResBlock x 8 ...
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
pad=1
activation=mish
... CSPResNet -> ResBlock x 8 ...
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
pad=1
activation=mish
... CSPResNet -> ResBlock x 4 ...

Reference#

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