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vgg16.yaml
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vgg16.yaml
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# parameters
number_classes: 20 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
# VGG16 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [64, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 2-P1/2
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 5-P2/4
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 8-P3/8
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 13-P4/16
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, Maxpool, [2, 2]], # 16-P5/32
]
# YOLOv3 head
head:
[[-1, 1, Bottleneck, [1024, False]],
[-1, 2, Bottleneck, [1024, False]], # 18
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
[[-1, 14], 1, Concat, [1]], # concat backbone P4
[-1, 1, Bottleneck, [512, False]],
[-1, 2, Bottleneck, [512, False]], # 23
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, "nearest"]],
[[-1, 9], 1, Concat, [1]], # concat backbone P3
[-1, 1, Bottleneck, [256, False]],
[-1, 2, Bottleneck, [256, False]], # 28
[[28, 23, 18], 1, Detect, [number_classes, anchors]], # Detect(P3, P4, P5)
]