66 lines
2.3 KiB
YAML
66 lines
2.3 KiB
YAML
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# parameters
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#nc: 80 # number of classes
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nc: 3 # number of classes
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depth_multiple: 0.67 # model depth multiple
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width_multiple: 0.75 # layer channel multiple 使卷积核个数变化
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# anchors
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anchors:
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- [5,6, 8,14, 15,11] #P2/4 增加的锚点？？？？？ 增减检测层之后需要增加的 先验框的大小（4个尺度上的）
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- [10,13, 16,30, 33,23] # P3/8 表示8倍下采样后的结果
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- [30,61, 62,45, 59,119] # P4/16
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- [116,90, 156,198, 373,326] # P5/32
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# YOLOv5 backbone
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backbone:
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# [from, number, module, args]
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#-1表示来自上一层输入；number表示本模块重复次数；
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[[-1, 1, Focus, [64, 3]], # 0-P1/2 功能层参数
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 功能层参数 128表示128个卷积核，3表示3x3卷积核，2表示步长是2
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[-1, 3, C3, [128]], #160*160 瓶颈层是主要学习到特征，可增减瓶颈层的深度实现模型深度变化
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 9, C3, [256]], #80*80
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]], #40*40
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 1, SPP, [1024, [5, 9, 13]]], #spp也是功能层的参数
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[-1, 3, C3, [1024, False]], # 9 20*20
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]
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# YOLOv5 head
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head:
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[[-1, 1, Conv, [512, 1, 1]], #20*20
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[-1, 1, nn.Upsample, [None, 2, 'nearest']], #40*40
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[[-1, 6], 1, Concat, [1]], # cat backbone P4 40*40
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[-1, 3, C3, [512, False]], # 13 40*40
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[-1, 1, Conv, [512, 1, 1]], #40*40
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3 80*80
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[-1, 3, C3, [512, False]], # 17 (P3/8-small) 80*80
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[-1, 1, Conv, [256, 1, 1]], #18 80*80
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[-1, 1, nn.Upsample, [None, 2, 'nearest']], #19 160*160
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[[-1, 2], 1, Concat, [1]], #20 cat backbone p2 160*160
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[-1, 3, C3, [256, False]], #21 160*160
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[-1, 1, Conv, [256, 3, 2]], #22 80*80
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[[-1, 18], 1, Concat, [1]], #23 80*80
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[-1, 3, C3, [256, False]], #24 80*80
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[-1, 1, Conv, [256, 3, 2]], #25 40*40
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[[-1, 14], 1, Concat, [1]], # 26 cat head P4 40*40
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[-1, 3, C3, [512, False]], # 27 (P4/16-medium) 40*40
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[-1, 1, Conv, [512, 3, 2]], #28 20*20
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[[-1, 10], 1, Concat, [1]], #29 cat head P5 #20*20
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[-1, 3, C3, [1024, False]], # 30 (P5/32-large) 20*20
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[[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(p2, P3, P4, P5)
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]
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