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Create yolov5-bifpn.yaml (#4195)
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glenn-jocher authored Jul 28, 2021
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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:
- [ 10,13, 16,30, 33,23 ] # P3/8
- [ 30,61, 62,45, 59,119 ] # P4/16
- [ 116,90, 156,198, 373,326 ] # P5/32

# YOLOv5 backbone
backbone:
# [from, number, module, args]
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[ -1, 3, Bottleneck, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[ -1, 9, BottleneckCSP, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[ -1, 9, BottleneckCSP, [ 512 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
[ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
[ -1, 6, BottleneckCSP, [ 1024 ] ], # 9
]

# YOLOv5 BiFPN head
head:
[ [ -1, 3, BottleneckCSP, [ 1024, False ] ], # 10 (P5/32-large)

[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 20, 6 ], 1, Concat, [ 1 ] ], # cat P4
[ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 3, BottleneckCSP, [ 512, False ] ], # 14 (P4/16-medium)

[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 3, BottleneckCSP, [ 256, False ] ], # 18 (P3/8-small)

[ [ 18, 14, 10 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
]

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