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Models *.yaml reformat #3875

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Jul 4, 2021
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Models *.yaml reformat #3875

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68 changes: 33 additions & 35 deletions models/hub/yolov3-spp.yaml
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@@ -1,51 +1,49 @@
# parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
- [ 10,13, 16,30, 33,23 ] # P3/8
- [ 30,61, 62,45, 59,119 ] # P4/16
- [ 116,90, 156,198, 373,326 ] # P5/32

# darknet53 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, Bottleneck, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, Bottleneck, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10
[ [ -1, 1, Conv, [ 32, 3, 1 ] ], # 0
[ -1, 1, Conv, [ 64, 3, 2 ] ], # 1-P1/2
[ -1, 1, Bottleneck, [ 64 ] ],
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 3-P2/4
[ -1, 2, Bottleneck, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 5-P3/8
[ -1, 8, Bottleneck, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 7-P4/16
[ -1, 8, Bottleneck, [ 512 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P5/32
[ -1, 4, Bottleneck, [ 1024 ] ], # 10
]

# YOLOv3-SPP head
head:
[[-1, 1, Bottleneck, [1024, False]],
[-1, 1, SPP, [512, [5, 9, 13]]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
[ [ -1, 1, Bottleneck, [ 1024, False ] ],
[ -1, 1, SPP, [ 512, [ 5, 9, 13 ] ] ],
[ -1, 1, Conv, [ 1024, 3, 1 ] ],
[ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 1, Conv, [ 1024, 3, 1 ] ], # 15 (P5/32-large)

[-2, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
[ -2, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -1, 1, Bottleneck, [ 512, False ] ],
[ -1, 1, Bottleneck, [ 512, False ] ],
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, Conv, [ 512, 3, 1 ] ], # 22 (P4/16-medium)

[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P3
[-1, 1, Bottleneck, [256, False]],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
[ -2, 1, Conv, [ 128, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 1, Bottleneck, [ 256, False ] ],
[ -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)
]
50 changes: 24 additions & 26 deletions models/hub/yolov3-tiny.yaml
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@@ -1,41 +1,39 @@
# parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

# anchors
anchors:
- [10,14, 23,27, 37,58] # P4/16
- [81,82, 135,169, 344,319] # P5/32
- [ 10,14, 23,27, 37,58 ] # P4/16
- [ 81,82, 135,169, 344,319 ] # P5/32

# YOLOv3-tiny backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [16, 3, 1]], # 0
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2
[-1, 1, Conv, [32, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4
[-1, 1, Conv, [64, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8
[-1, 1, Conv, [128, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16
[-1, 1, Conv, [256, 3, 1]],
[-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32
[-1, 1, Conv, [512, 3, 1]],
[-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11
[-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12
[ [ -1, 1, Conv, [ 16, 3, 1 ] ], # 0
[ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 1-P1/2
[ -1, 1, Conv, [ 32, 3, 1 ] ],
[ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 3-P2/4
[ -1, 1, Conv, [ 64, 3, 1 ] ],
[ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 5-P3/8
[ -1, 1, Conv, [ 128, 3, 1 ] ],
[ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 7-P4/16
[ -1, 1, Conv, [ 256, 3, 1 ] ],
[ -1, 1, nn.MaxPool2d, [ 2, 2, 0 ] ], # 9-P5/32
[ -1, 1, Conv, [ 512, 3, 1 ] ],
[ -1, 1, nn.ZeroPad2d, [ [ 0, 1, 0, 1 ] ] ], # 11
[ -1, 1, nn.MaxPool2d, [ 2, 1, 0 ] ], # 12
]

# YOLOv3-tiny head
head:
[[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large)
[ [ -1, 1, Conv, [ 1024, 3, 1 ] ],
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, Conv, [ 512, 3, 1 ] ], # 15 (P5/32-large)

[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium)
[ -2, 1, Conv, [ 128, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -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)
]
68 changes: 33 additions & 35 deletions models/hub/yolov3.yaml
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Original file line number Diff line number Diff line change
@@ -1,51 +1,49 @@
# parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
- [ 10,13, 16,30, 33,23 ] # P3/8
- [ 30,61, 62,45, 59,119 ] # P4/16
- [ 116,90, 156,198, 373,326 ] # P5/32

# darknet53 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Conv, [32, 3, 1]], # 0
[-1, 1, Conv, [64, 3, 2]], # 1-P1/2
[-1, 1, Bottleneck, [64]],
[-1, 1, Conv, [128, 3, 2]], # 3-P2/4
[-1, 2, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 5-P3/8
[-1, 8, Bottleneck, [256]],
[-1, 1, Conv, [512, 3, 2]], # 7-P4/16
[-1, 8, Bottleneck, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P5/32
[-1, 4, Bottleneck, [1024]], # 10
[ [ -1, 1, Conv, [ 32, 3, 1 ] ], # 0
[ -1, 1, Conv, [ 64, 3, 2 ] ], # 1-P1/2
[ -1, 1, Bottleneck, [ 64 ] ],
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 3-P2/4
[ -1, 2, Bottleneck, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 5-P3/8
[ -1, 8, Bottleneck, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 7-P4/16
[ -1, 8, Bottleneck, [ 512 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 9-P5/32
[ -1, 4, Bottleneck, [ 1024 ] ], # 10
]

# YOLOv3 head
head:
[[-1, 1, Bottleneck, [1024, False]],
[-1, 1, Conv, [512, [1, 1]]],
[-1, 1, Conv, [1024, 3, 1]],
[-1, 1, Conv, [512, 1, 1]],
[-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large)
[ [ -1, 1, Bottleneck, [ 1024, False ] ],
[ -1, 1, Conv, [ 512, [ 1, 1 ] ] ],
[ -1, 1, Conv, [ 1024, 3, 1 ] ],
[ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 1, Conv, [ 1024, 3, 1 ] ], # 15 (P5/32-large)

[-2, 1, Conv, [256, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 8], 1, Concat, [1]], # cat backbone P4
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Bottleneck, [512, False]],
[-1, 1, Conv, [256, 1, 1]],
[-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium)
[ -2, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 8 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -1, 1, Bottleneck, [ 512, False ] ],
[ -1, 1, Bottleneck, [ 512, False ] ],
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -1, 1, Conv, [ 512, 3, 1 ] ], # 22 (P4/16-medium)

[-2, 1, Conv, [128, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 6], 1, Concat, [1]], # cat backbone P3
[-1, 1, Bottleneck, [256, False]],
[-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small)
[ -2, 1, Conv, [ 128, 1, 1 ] ],
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 1, Bottleneck, [ 256, False ] ],
[ -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)
]
50 changes: 24 additions & 26 deletions models/hub/yolov5-fpn.yaml
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@@ -1,42 +1,40 @@
# parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

# anchors
anchors:
- [10,13, 16,30, 33,23] # P3/8
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32
- [ 10,13, 16,30, 33,23 ] # P3/8
- [ 30,61, 62,45, 59,119 ] # P4/16
- [ 116,90, 156,198, 373,326 ] # P5/32

# YOLOv5 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, Bottleneck, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 6, BottleneckCSP, [1024]], # 9
[ [ -1, 1, Focus, [ 64, 3 ] ], # 0-P1/2
[ -1, 1, Conv, [ 128, 3, 2 ] ], # 1-P2/4
[ -1, 3, Bottleneck, [ 128 ] ],
[ -1, 1, Conv, [ 256, 3, 2 ] ], # 3-P3/8
[ -1, 9, BottleneckCSP, [ 256 ] ],
[ -1, 1, Conv, [ 512, 3, 2 ] ], # 5-P4/16
[ -1, 9, BottleneckCSP, [ 512 ] ],
[ -1, 1, Conv, [ 1024, 3, 2 ] ], # 7-P5/32
[ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
[ -1, 6, BottleneckCSP, [ 1024 ] ], # 9
]

# YOLOv5 FPN 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, 6], 1, Concat, [1]], # cat backbone P4
[-1, 1, Conv, [512, 1, 1]],
[-1, 3, BottleneckCSP, [512, False]], # 14 (P4/16-medium)
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 6 ], 1, Concat, [ 1 ] ], # cat backbone P4
[ -1, 1, Conv, [ 512, 1, 1 ] ],
[ -1, 3, BottleneckCSP, [ 512, False ] ], # 14 (P4/16-medium)

[-1, 1, nn.Upsample, [None, 2, 'nearest']],
[[-1, 4], 1, Concat, [1]], # cat backbone P3
[-1, 1, Conv, [256, 1, 1]],
[-1, 3, BottleneckCSP, [256, False]], # 18 (P3/8-small)
[ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
[ [ -1, 4 ], 1, Concat, [ 1 ] ], # cat backbone P3
[ -1, 1, Conv, [ 256, 1, 1 ] ],
[ -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)
]
4 changes: 1 addition & 3 deletions models/hub/yolov5-p2.yaml
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Original file line number Diff line number Diff line change
@@ -1,9 +1,7 @@
# parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

# anchors
anchors: 3

# YOLOv5 backbone
Expand Down
4 changes: 1 addition & 3 deletions models/hub/yolov5-p6.yaml
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Original file line number Diff line number Diff line change
@@ -1,9 +1,7 @@
# parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

# anchors
anchors: 3

# YOLOv5 backbone
Expand Down
4 changes: 1 addition & 3 deletions models/hub/yolov5-p7.yaml
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Original file line number Diff line number Diff line change
@@ -1,9 +1,7 @@
# parameters
# Parameters
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple

# anchors
anchors: 3

# YOLOv5 backbone
Expand Down
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