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# parameters
nc: 1 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
# anchors
anchors:
- [6,7, 9,11, 13,16] # P3/8
- [18,23, 26,33, 37,47] # P4/16
- [54,67, 77,104, 112,154] # P5/32
- [174,238, 258,355, 445,568] # P6/64
# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, StemBlock, [32, 3, 2]], # 0-P2/4
[-1, 1, ShuffleV2Block, [128, 2]], # 1-P3/8
[-1, 3, ShuffleV2Block, [128, 1]], # 2
[-1, 1, ShuffleV2Block, [256, 2]], # 3-P4/16
[-1, 7, ShuffleV2Block, [256, 1]], # 4
[-1, 1, ShuffleV2Block, [384, 2]], # 5-P5/32
[-1, 3, ShuffleV2Block, [384, 1]], # 6
[-1, 1, ShuffleV2Block, [512, 2]], # 7-P6/64
[-1, 3, ShuffleV2Block, [512, 1]], # 8
]
# YOLOv5 head
head:
[[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P5
[-1, 1, C3, [128, False]], # 12
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P4
[-1, 1, C3, [128, False]], # 16 (P4/8-small)
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 2], 1, Concat, [1]], # cat backbone P3
[-1, 1, C3, [128, False]], # 20 (P3/8-small)
[-1, 1, Conv, [128, 3, 2]],
[[-1, 17], 1, Concat, [1]], # cat head P4
[-1, 1, C3, [128, False]], # 23 (P4/16-medium)
[-1, 1, Conv, [128, 3, 2]],
[[-1, 13], 1, Concat, [1]], # cat head P5
[-1, 1, C3, [128, False]], # 26 (P5/32-large)
[-1, 1, Conv, [128, 3, 2]],
[[-1, 9], 1, Concat, [1]], # cat head P6
[-1, 1, C3, [128, False]], # 29 (P6/64-large)
[[20, 23, 26, 29], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
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