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50 lines
1.4 KiB
50 lines
1.4 KiB
# Ultralytics YOLO 🚀, GPL-3.0 license
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# Parameters
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov5n.yaml' will call yolov5.yaml with scale 'n'
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# [depth, width, max_channels]
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n: [0.33, 0.25, 1024]
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s: [0.33, 0.50, 1024]
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m: [0.67, 0.75, 1024]
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l: [1.00, 1.00, 1024]
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x: [1.33, 1.25, 1024]
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# YOLOv5 v6.0 backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
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[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
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[-1, 3, C3, [128]],
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[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
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[-1, 6, C3, [256]],
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[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
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[-1, 9, C3, [512]],
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[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
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[-1, 3, C3, [1024]],
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[-1, 1, SPPF, [1024, 5]], # 9
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]
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# YOLOv5 v6.0 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, C3, [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, C3, [256, False]], # 17 (P3/8-small)
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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, C3, [512, False]], # 20 (P4/16-medium)
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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, C3, [1024, False]], # 23 (P5/32-large)
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[[17, 20, 23], 1, Detect, [nc]], # Detect(P3, P4, P5)
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]
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