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Models `*.yaml` reformat (#3875)

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Glenn Jocher 2021-07-04 16:55:08 +02:00 committed by GitHub
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68
models/hub/yolov3-spp.yaml
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@ -1,51 +1,49 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [ 10,13, 16,30, 33,23 ] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [ 30,61, 62,45, 59,119 ] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [ 116,90, 156,198, 373,326 ] # P5/32
# darknet53 backbone # darknet53 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0 [ [ -1, 1, Conv, [ 32, 3, 1 ] ], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2 [ -1, 1, Conv, [ 64, 3, 2 ] ], # 1-P1/2
[-1, 1, Bottleneck, [64]], [ -1, 1, Bottleneck, [ 64 ] ],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 3-P2/4
[-1, 2, Bottleneck, [128]], [ -1, 2, Bottleneck, [ 128 ] ],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8 [ -1, 1, Conv, [ 256, 3, 2 ] ], # 5-P3/8
[-1, 8, Bottleneck, [256]], [ -1, 8, Bottleneck, [ 256 ] ],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16 [ -1, 1, Conv, [ 512, 3, 2 ] ], # 7-P4/16
[-1, 8, Bottleneck, [512]], [ -1, 8, Bottleneck, [ 512 ] ],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10 [ -1, 4, Bottleneck, [ 1024 ] ], # 10
] ]
# YOLOv3-SPP head # YOLOv3-SPP head
head: head:
[[-1, 1, Bottleneck, [1024, False]], [ [ -1, 1, Bottleneck, [ 1024, False ] ],
[-1, 1, SPP, [512, [5, 9, 13]]], [ -1, 1, SPP, [ 512, [ 5, 9, 13 ] ] ],
[-1, 1, Conv, [1024, 3, 1]], [ -1, 1, Conv, [ 1024, 3, 1 ] ],
[-1, 1, Conv, [512, 1, 1]], [ -1, 1, Conv, [ 512, 1, 1 ] ],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) [ -1, 1, Conv, [ 1024, 3, 1 ] ], # 15 (P5/32-large)
[-2, 1, Conv, [256, 1, 1]], [ -2, 1, Conv, [ 256, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 8], 1, Concat, [1]], # cat backbone P4 [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4
[-1, 1, Bottleneck, [512, False]], [ -1, 1, Bottleneck, [ 512, False ] ],
[-1, 1, Bottleneck, [512, False]], [ -1, 1, Bottleneck, [ 512, False ] ],
[-1, 1, Conv, [256, 1, 1]], [ -1, 1, Conv, [ 256, 1, 1 ] ],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 1 ] ], # 22 (P4/16-medium)
[-2, 1, Conv, [128, 1, 1]], [ -2, 1, Conv, [ 128, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 6], 1, Concat, [1]], # cat backbone P3 [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P3
[-1, 1, Bottleneck, [256, False]], [ -1, 1, Bottleneck, [ 256, False ] ],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) [ -1, 2, Bottleneck, [ 256, False ] ], # 27 (P3/8-small)
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [ [ 27, 22, 15 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
] ]

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models/hub/yolov3-tiny.yaml
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@ -1,41 +1,39 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: anchors:
- [10,14, 23,27, 37,58] # P4/16 - [ 10,14, 23,27, 37,58 ] # P4/16
- [81,82, 135,169, 344,319] # P5/32 - [ 81,82, 135,169, 344,319 ] # P5/32
# YOLOv3-tiny backbone # YOLOv3-tiny backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [16, 3, 1]], # 0 [ [ -1, 1, Conv, [ 16, 3, 1 ] ], # 0
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 1-P1/2
[-1, 1, Conv, [32, 3, 1]], [ -1, 1, Conv, [ 32, 3, 1 ] ],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 3-P2/4
[-1, 1, Conv, [64, 3, 1]], [ -1, 1, Conv, [ 64, 3, 1 ] ],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 5-P3/8
[-1, 1, Conv, [128, 3, 1]], [ -1, 1, Conv, [ 128, 3, 1 ] ],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 7-P4/16
[-1, 1, Conv, [256, 3, 1]], [ -1, 1, Conv, [ 256, 3, 1 ] ],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 [ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 9-P5/32
[-1, 1, Conv, [512, 3, 1]], [ -1, 1, Conv, [ 512, 3, 1 ] ],
[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 [ -1, 1, nn.ZeroPad2d, [ [ 0, 1, 0, 1 ] ] ], # 11
[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 [ -1, 1, nn.MaxPool2d, [ 2, 1, 0 ] ], # 12
] ]
# YOLOv3-tiny head # YOLOv3-tiny head
head: head:
[[-1, 1, Conv, [1024, 3, 1]], [ [ -1, 1, Conv, [ 1024, 3, 1 ] ],
[-1, 1, Conv, [256, 1, 1]], [ -1, 1, Conv, [ 256, 1, 1 ] ],
[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) [ -1, 1, Conv, [ 512, 3, 1 ] ], # 15 (P5/32-large)
[-2, 1, Conv, [128, 1, 1]], [ -2, 1, Conv, [ 128, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 8], 1, Concat, [1]], # cat backbone P4 [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4
[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) [ -1, 1, Conv, [ 256, 3, 1 ] ], # 19 (P4/16-medium)
[[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) [ [ 19, 15 ], 1, Detect, [ nc, anchors ] ], # Detect(P4, P5)
] ]

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models/hub/yolov3.yaml
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@ -1,51 +1,49 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [ 10,13, 16,30, 33,23 ] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [ 30,61, 62,45, 59,119 ] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [ 116,90, 156,198, 373,326 ] # P5/32
# darknet53 backbone # darknet53 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0 [ [ -1, 1, Conv, [ 32, 3, 1 ] ], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2 [ -1, 1, Conv, [ 64, 3, 2 ] ], # 1-P1/2
[-1, 1, Bottleneck, [64]], [ -1, 1, Bottleneck, [ 64 ] ],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 3-P2/4
[-1, 2, Bottleneck, [128]], [ -1, 2, Bottleneck, [ 128 ] ],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8 [ -1, 1, Conv, [ 256, 3, 2 ] ], # 5-P3/8
[-1, 8, Bottleneck, [256]], [ -1, 8, Bottleneck, [ 256 ] ],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16 [ -1, 1, Conv, [ 512, 3, 2 ] ], # 7-P4/16
[-1, 8, Bottleneck, [512]], [ -1, 8, Bottleneck, [ 512 ] ],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10 [ -1, 4, Bottleneck, [ 1024 ] ], # 10
] ]
# YOLOv3 head # YOLOv3 head
head: head:
[[-1, 1, Bottleneck, [1024, False]], [ [ -1, 1, Bottleneck, [ 1024, False ] ],
[-1, 1, Conv, [512, [1, 1]]], [ -1, 1, Conv, [ 512, [ 1, 1 ] ] ],
[-1, 1, Conv, [1024, 3, 1]], [ -1, 1, Conv, [ 1024, 3, 1 ] ],
[-1, 1, Conv, [512, 1, 1]], [ -1, 1, Conv, [ 512, 1, 1 ] ],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) [ -1, 1, Conv, [ 1024, 3, 1 ] ], # 15 (P5/32-large)
[-2, 1, Conv, [256, 1, 1]], [ -2, 1, Conv, [ 256, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 8], 1, Concat, [1]], # cat backbone P4 [ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4
[-1, 1, Bottleneck, [512, False]], [ -1, 1, Bottleneck, [ 512, False ] ],
[-1, 1, Bottleneck, [512, False]], [ -1, 1, Bottleneck, [ 512, False ] ],
[-1, 1, Conv, [256, 1, 1]], [ -1, 1, Conv, [ 256, 1, 1 ] ],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) [ -1, 1, Conv, [ 512, 3, 1 ] ], # 22 (P4/16-medium)
[-2, 1, Conv, [128, 1, 1]], [ -2, 1, Conv, [ 128, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 6], 1, Concat, [1]], # cat backbone P3 [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P3
[-1, 1, Bottleneck, [256, False]], [ -1, 1, Bottleneck, [ 256, False ] ],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) [ -1, 2, Bottleneck, [ 256, False ] ], # 27 (P3/8-small)
[[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [ [ 27, 22, 15 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
] ]

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models/hub/yolov5-fpn.yaml
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# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [ 10,13, 16,30, 33,23 ] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [ 30,61, 62,45, 59,119 ] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [ 116,90, 156,198, 373,326 ] # P5/32
# YOLOv5 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[-1, 3, Bottleneck, [128]], [ -1, 3, Bottleneck, [ 128 ] ],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[-1, 9, BottleneckCSP, [256]], [ -1, 9, BottleneckCSP, [ 256 ] ],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[-1, 9, BottleneckCSP, [512]], [ -1, 9, BottleneckCSP, [ 512 ] ],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
[-1, 6, BottleneckCSP, [1024]], # 9 [ -1, 6, BottleneckCSP, [ 1024 ] ], # 9
] ]
# YOLOv5 FPN head # YOLOv5 FPN head
head: head:
[[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large) [ [ -1, 3, BottleneckCSP, [ 1024, False ] ], # 10 (P5/32-large)
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[-1, 1, Conv, [512, 1, 1]], [ -1, 1, Conv, [ 512, 1, 1 ] ],
[-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium) [ -1, 3, BottleneckCSP, [ 512, False ] ], # 14 (P4/16-medium)
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[-1, 1, Conv, [256, 1, 1]], [ -1, 1, Conv, [ 256, 1, 1 ] ],
[-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small) [ -1, 3, BottleneckCSP, [ 256, False ] ], # 18 (P3/8-small)
[[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [ [ 18, 14, 10 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
] ]

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models/hub/yolov5-p2.yaml
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# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: 3 anchors: 3
# YOLOv5 backbone # YOLOv5 backbone

4
models/hub/yolov5-p6.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: 3 anchors: 3
# YOLOv5 backbone # YOLOv5 backbone

4
models/hub/yolov5-p7.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: 3 anchors: 3
# YOLOv5 backbone # YOLOv5 backbone

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models/hub/yolov5-panet.yaml
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@ -1,48 +1,46 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [ 10,13, 16,30, 33,23 ] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [ 30,61, 62,45, 59,119 ] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [ 116,90, 156,198, 373,326 ] # P5/32
# YOLOv5 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[-1, 3, BottleneckCSP, [128]], [ -1, 3, BottleneckCSP, [ 128 ] ],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[-1, 9, BottleneckCSP, [256]], [ -1, 9, BottleneckCSP, [ 256 ] ],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[-1, 9, BottleneckCSP, [512]], [ -1, 9, BottleneckCSP, [ 512 ] ],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
[-1, 3, BottleneckCSP, [1024, False]], # 9 [ -1, 3, BottleneckCSP, [ 1024, False ] ], # 9
] ]
# YOLOv5 PANet head # YOLOv5 PANet head
head: head:
[[-1, 1, Conv, [512, 1, 1]], [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[-1, 3, BottleneckCSP, [512, False]], # 13 [ -1, 3, BottleneckCSP, [ 512, False ] ], # 13
[-1, 1, Conv, [256, 1, 1]], [ -1, 1, Conv, [ 256, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[-1, 3, BottleneckCSP, [256, False]], # 17 (P3/8-small) [ -1, 3, BottleneckCSP, [ 256, False ] ], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [ -1, 1, Conv, [ 256, 3, 2 ] ],
[[-1, 14], 1, Concat, [1]], # cat head P4 [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4
[-1, 3, BottleneckCSP, [512, False]], # 20 (P4/16-medium) [ -1, 3, BottleneckCSP, [ 512, False ] ], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [ -1, 1, Conv, [ 512, 3, 2 ] ],
[[-1, 10], 1, Concat, [1]], # cat head P5 [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5
[-1, 3, BottleneckCSP, [1024, False]], # 23 (P5/32-large) [ -1, 3, BottleneckCSP, [ 1024, False ] ], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [ [ 17, 20, 23 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
] ]

4
models/hub/yolov5l6.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8 - [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16 - [ 96,68, 86,152, 180,137 ] # P4/16

4
models/hub/yolov5m6.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 0.67 # model depth multiple depth_multiple: 0.67 # model depth multiple
width_multiple: 0.75 # layer channel multiple width_multiple: 0.75 # layer channel multiple
# anchors
anchors: anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8 - [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16 - [ 96,68, 86,152, 180,137 ] # P4/16

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models/hub/yolov5s-transformer.yaml
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@ -1,48 +1,46 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple width_multiple: 0.50 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [ 10,13, 16,30, 33,23 ] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [ 30,61, 62,45, 59,119 ] # P4/16
- [116,90, 156,198, 373,326] # P5/32 - [ 116,90, 156,198, 373,326 ] # P5/32
# YOLOv5 backbone # YOLOv5 backbone
backbone: backbone:
# [from, number, module, args] # [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2 [ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4 [ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[-1, 3, C3, [128]], [ -1, 3, C3, [ 128 ] ],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8 [ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[-1, 9, C3, [256]], [ -1, 9, C3, [ 256 ] ],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16 [ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[-1, 9, C3, [512]], [ -1, 9, C3, [ 512 ] ],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 [ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]], [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
[-1, 3, C3TR, [1024, False]], # 9 <-------- C3TR() Transformer module [ -1, 3, C3TR, [ 1024, False ] ], # 9 <-------- C3TR() Transformer module
] ]
# YOLOv5 head # YOLOv5 head
head: head:
[[-1, 1, Conv, [512, 1, 1]], [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 6], 1, Concat, [1]], # cat backbone P4 [ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[-1, 3, C3, [512, False]], # 13 [ -1, 3, C3, [ 512, False ] ], # 13
[-1, 1, Conv, [256, 1, 1]], [ -1, 1, Conv, [ 256, 1, 1 ] ],
[-1, 1, nn.Upsample, [None, 2, 'nearest']], [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[[-1, 4], 1, Concat, [1]], # cat backbone P3 [ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[-1, 3, C3, [256, False]], # 17 (P3/8-small) [ -1, 3, C3, [ 256, False ] ], # 17 (P3/8-small)
[-1, 1, Conv, [256, 3, 2]], [ -1, 1, Conv, [ 256, 3, 2 ] ],
[[-1, 14], 1, Concat, [1]], # cat head P4 [ [ -1, 14 ], 1, Concat, [ 1 ] ], # cat head P4
[-1, 3, C3, [512, False]], # 20 (P4/16-medium) [ -1, 3, C3, [ 512, False ] ], # 20 (P4/16-medium)
[-1, 1, Conv, [512, 3, 2]], [ -1, 1, Conv, [ 512, 3, 2 ] ],
[[-1, 10], 1, Concat, [1]], # cat head P5 [ [ -1, 10 ], 1, Concat, [ 1 ] ], # cat head P5
[-1, 3, C3, [1024, False]], # 23 (P5/32-large) [ -1, 3, C3, [ 1024, False ] ], # 23 (P5/32-large)
[[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) [ [ 17, 20, 23 ], 1, Detect, [ nc, anchors ] ], # Detect(P3, P4, P5)
] ]

4
models/hub/yolov5s6.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple width_multiple: 0.50 # layer channel multiple
# anchors
anchors: anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8 - [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16 - [ 96,68, 86,152, 180,137 ] # P4/16

4
models/hub/yolov5x6.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.33 # model depth multiple depth_multiple: 1.33 # model depth multiple
width_multiple: 1.25 # layer channel multiple width_multiple: 1.25 # layer channel multiple
# anchors
anchors: anchors:
- [ 19,27, 44,40, 38,94 ] # P3/8 - [ 19,27, 44,40, 38,94 ] # P3/8
- [ 96,68, 86,152, 180,137 ] # P4/16 - [ 96,68, 86,152, 180,137 ] # P4/16

2
models/yolo.py
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@ -154,7 +154,7 @@ class Model(nn.Module):
x = m(x) # run x = m(x) # run
y.append(x if m.i in self.save else None) # save output y.append(x if m.i in self.save else None) # save output
if feature_vis and m.type == 'models.common.SPP': if feature_vis and m.type == 'models.common.SPP':
feature_visualization(x, m.type, m.i) feature_visualization(x, m.type, m.i)

4
models/yolov5l.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple width_multiple: 1.0 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16

4
models/yolov5m.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 0.67 # model depth multiple depth_multiple: 0.67 # model depth multiple
width_multiple: 0.75 # layer channel multiple width_multiple: 0.75 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16

4
models/yolov5s.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 0.33 # model depth multiple depth_multiple: 0.33 # model depth multiple
width_multiple: 0.50 # layer channel multiple width_multiple: 0.50 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16

4
models/yolov5x.yaml
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@ -1,9 +1,7 @@
# parameters # Parameters
nc: 80 # number of classes nc: 80 # number of classes
depth_multiple: 1.33 # model depth multiple depth_multiple: 1.33 # model depth multiple
width_multiple: 1.25 # layer channel multiple width_multiple: 1.25 # layer channel multiple
# anchors
anchors: anchors:
- [10,13, 16,30, 33,23] # P3/8 - [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16 - [30,61, 62,45, 59,119] # P4/16