ScaledYOLOv4/models/yolov4-p6.yaml

65 lines
2.1 KiB
YAML

# parameters
nc: 80 # number of classes
depth_multiple: 1.0 # expand model depth
width_multiple: 1.0 # expand layer channels
# anchors
anchors:
- [13,17, 31,25, 24,51, 61,45] # P3/8
- [61,45, 48,102, 119,96, 97,189] # P4/16
- [97,189, 217,184, 171,384, 324,451] # P5/32
- [324,451, 545,357, 616,618, 1024,1024] # P6/64
# csp-p6 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, BottleneckCSP, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 15, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 15, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 7, BottleneckCSP, [1024]],
[-1, 1, Conv, [1024, 3, 2]], # 11-P6/64
[-1, 7, BottleneckCSP, [1024]], # 12
]
# yolov4-p6 head
# na = len(anchors[0])
head:
[[-1, 1, SPPCSP, [512]], # 13
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-6, 1, Conv, [512, 1, 1]], # route backbone P5
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSP2, [512]], # 18
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-13, 1, Conv, [256, 1, 1]], # route backbone P4
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSP2, [256]], # 23
[-1, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[-20, 1, Conv, [128, 1, 1]], # route backbone P3
[[-1, -2], 1, Concat, [1]],
[-1, 3, BottleneckCSP2, [128]], # 28
[-1, 1, Conv, [256, 3, 1]],
[-2, 1, Conv, [256, 3, 2]],
[[-1, 23], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSP2, [256]], # 32
[-1, 1, Conv, [512, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 18], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSP2, [512]], # 36
[-1, 1, Conv, [1024, 3, 1]],
[-2, 1, Conv, [512, 3, 2]],
[[-1, 13], 1, Concat, [1]], # cat
[-1, 3, BottleneckCSP2, [512]], # 40
[-1, 1, Conv, [1024, 3, 1]],
[[29,33,37,41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]