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Models *.yaml reformat (#3875)

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Glenn Jocher authored and GitHub committed 2021-07-04 16:55:08 +02:00
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models/hub/yolov3-spp.yaml
+33 -35
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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)
]