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# parameters
nc: 1 # number of classes
depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # 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, [ 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, [ 768, 3, 2 ] ], # 6-P5/32
[ -1, 3, C3, [ 768 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 8-P6/64
[ -1, 1, SPP, [ 1024, [ 3, 5, 7 ] ] ],
[ -1, 3, C3, [ 1024, False ] ], # 10
]
# YOLOv5 head
head:
[ [ -1, 1, Conv, [ 768, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 7 ], 1, Concat, [ 1 ] ], # cat backbone P5
[ -1, 3, C3, [ 768, False ] ], # 14
[ -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 ] ], # 18
[ -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 ] ], # 22 (P3/8-small)
[ -1, 1, Conv, [ 256, 3, 2 ] ],
[ [ -1, 19 ], 1, Concat, [ 1 ] ], # cat head P4
[ -1, 3, C3, [ 512, False ] ], # 25 (P4/16-medium)
[ -1, 1, Conv, [ 512, 3, 2 ] ],
[ [ -1, 15 ], 1, Concat, [ 1 ] ], # cat head P5
[ -1, 3, C3, [ 768, False ] ], # 28 (P5/32-large)
[ -1, 1, Conv, [ 768, 3, 2 ] ],
[ [ -1, 11 ], 1, Concat, [ 1 ] ], # cat head P6
[ -1, 3, C3, [ 1024, False ] ], # 31 (P6/64-xlarge)
[ [ 22, 25, 28, 31 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5, P6)
]
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