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# parameters
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nc: 80 # number of classes
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depth_multiple: 1.0 # model depth multiple
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width_multiple: 1.0 # layer channel multiple
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# anchors
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anchors:
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- [10,13, 16,30, 33,23] # P3/8
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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, 1, Focus, [64, 3]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, BottleneckCSP, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 9, BottleneckCSP, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, BottleneckCSP, [512]],
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 1, SPP, [1024, [5, 9, 13]]],
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[-1, 3, BottleneckCSP, [1024, False]], # 9
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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]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 6], 1, Concat, [1]], # cat backbone P4
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[-1, 3, BottleneckCSP, [512, False]], # 13
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 4], 1, Concat, [1]], # cat backbone P3
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[-1, 3, BottleneckCSP, [256, False]], # 17
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[-1, 1, Conv, [256, 3, 2]],
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[[-1, 14], 1, Concat, [1]], # cat head P4
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[-1, 3, BottleneckCSP, [512, False]], # 20
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[-1, 1, Conv, [512, 3, 2]],
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[[-1, 10], 1, Concat, [1]], # cat head P5
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[-1, 3, BottleneckCSP, [1024, False]], # 23
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[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
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]
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