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# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [12,16, 19,36, 40,28] # P3/8
- [36,75, 76,55, 72,146] # P4/16
- [142,110, 192,243, 459,401] # P5/32
# CSP-Darknet backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, BottleneckCSPC, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, BottleneckCSPC, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, BottleneckCSPC, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, BottleneckCSPC, [1024]], # 10
]
# CSP-Dark-PAN head
head:
[[-1, 1, SPPCSPC, [512]], # 11
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[8, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [256]], # 16
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[6, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 2, BottleneckCSPB, [128]], # 21
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 16], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [256]], # 25
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 11], 1, Concat, [1]], # cat
[-1, 2, BottleneckCSPB, [512]], # 29
[-1, 1, Conv, [1024, 3, 1]],
[[22,26,30], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
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