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52 lines
1.7 KiB
52 lines
1.7 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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# YOLOv6 object detection model with P3-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
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# Parameters
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act: nn.ReLU()
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nc: 80 # number of classes
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scales: # model compound scaling constants, i.e. 'model=yolov6n.yaml' will call yolov8.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, 768 ]
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l: [ 1.00, 1.00, 512 ]
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x: [ 1.00, 1.25, 512 ]
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# YOLOv6-3.0s backbone
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backbone:
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# [from, repeats, module, args]
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- [ -1, 1, Conv, [ 64, 3, 2 ] ] # 0-P1/2
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- [ -1, 1, Conv, [ 128, 3, 2 ] ] # 1-P2/4
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- [ -1, 6, Conv, [ 128, 3, 1 ] ]
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- [ -1, 1, Conv, [ 256, 3, 2 ] ] # 3-P3/8
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- [ -1, 12, Conv, [ 256, 3, 1 ] ]
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- [ -1, 1, Conv, [ 512, 3, 2 ] ] # 5-P4/16
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- [ -1, 18, Conv, [ 512, 3, 1 ] ]
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- [ -1, 1, Conv, [ 1024, 3, 2 ] ] # 7-P5/32
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- [ -1, 9, Conv, [ 1024, 3, 1 ] ]
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- [ -1, 1, SPPF, [ 1024, 5 ] ] # 9
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# YOLOv6-3.0s head
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head:
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- [ -1, 1, nn.ConvTranspose2d, [ 256, 2, 2, 0 ] ]
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- [ [ -1, 6 ], 1, Concat, [ 1 ] ] # cat backbone P4
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- [ -1, 1, Conv, [ 256, 3, 1 ] ]
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- [ -1, 9, Conv, [ 256, 3, 1 ] ] # 13
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- [ -1, 1, nn.ConvTranspose2d, [ 128, 2, 2, 0 ] ]
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- [ [ -1, 4 ], 1, Concat, [ 1 ] ] # cat backbone P3
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- [ -1, 1, Conv, [ 128, 3, 1 ] ]
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- [ -1, 9, Conv, [ 128, 3, 1 ] ] # 17
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- [ -1, 1, Conv, [ 128, 3, 2 ] ]
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- [ [ -1, 12 ], 1, Concat, [ 1 ] ] # cat head P4
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- [ -1, 1, Conv, [ 256, 3, 1 ] ]
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- [ -1, 9, Conv, [ 256, 3, 1 ] ] # 21
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- [ -1, 1, Conv, [ 256, 3, 2 ] ]
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- [ [ -1, 9 ], 1, Concat, [ 1 ] ] # cat head P5
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- [ -1, 1, Conv, [ 512, 3, 1 ] ]
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- [ -1, 9, Conv, [ 512, 3, 1 ] ] # 25
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- [ [ 17, 21, 25 ], 1, Detect, [ nc ] ] # Detect(P3, P4, P5)
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