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# parameters
nc: 1 # number of classes
depth_multiple: 0.67 # model depth multiple
width_multiple: 0.75 # layer channel multiple
# anchors
anchors:
- [4,5, 8,10, 13,16] # P3/8
- [23,29, 43,55, 73,105] # P4/16
- [146,217, 231,300, 335,433] # P5/32
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, StemBlock, [64, 3, 2]], # 0-P1/2
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 2-P3/8
[-1, 9, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 4-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 6-P5/32
[-1, 1, SPP, [1024, [3,5,7]]],
[-1, 3, C3, [1024, False]], # 8
]
# YOLOv5 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 5], 1, Concat, [1]], # cat backbone P4
[-1, 3, C3, [512, False]], # 12
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 3], 1, Concat, [1]], # cat backbone P3
[-1, 3, C3, [256, False]], # 16 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]],
[[-1, 13], 1, Concat, [1]], # cat head P4
[-1, 3, C3, [512, False]], # 19 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]],
[[-1, 9], 1, Concat, [1]], # cat head P5
[-1, 3, C3, [1024, False]], # 22 (P5/32-large)
[[16, 19, 22], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
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