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40 lines
1.2 KiB
40 lines
1.2 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv3-tiny object detection model with P4-P5 outputs. For details see https://docs.ultralytics.com/models/yolov3
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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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# YOLOv3-tiny backbone
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backbone:
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# [from, number, module, args]
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[[-1, 1, Conv, [16, 3, 1]], # 0
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[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
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[-1, 1, Conv, [32, 3, 1]],
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[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
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[-1, 1, Conv, [64, 3, 1]],
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[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
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[-1, 1, Conv, [128, 3, 1]],
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[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
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[-1, 1, Conv, [256, 3, 1]],
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[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
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[-1, 1, Conv, [512, 3, 1]],
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[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
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[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
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]
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# YOLOv3-tiny head
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head:
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[[-1, 1, Conv, [1024, 3, 1]],
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[-1, 1, Conv, [256, 1, 1]],
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[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
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[-2, 1, Conv, [128, 1, 1]],
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[-1, 1, nn.Upsample, [None, 2, 'nearest']],
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[[-1, 8], 1, Concat, [1]], # cat backbone P4
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[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
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[[19, 15], 1, Detect, [nc]], # Detect(P4, P5)
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]
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