Models `*.yaml` reformat (#3875)

models/hub/yolov3-spp.yaml

@@ -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)
   ]

models/hub/yolov3-tiny.yaml

@@ -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)
   ]

models/hub/yolov3.yaml

@@ -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)
   ]

models/hub/yolov5-fpn.yaml

@@ -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)
   ]

models/hub/yolov5-p2.yaml

@@ -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

models/hub/yolov5-p6.yaml

@@ -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

models/hub/yolov5-p7.yaml

@@ -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

models/hub/yolov5-panet.yaml

@@ -1,48 +1,46 @@
-# 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, BottleneckCSP, [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, 3, BottleneckCSP, [1024, False]],  # 9
+  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+    [ -1, 3, BottleneckCSP, [ 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, 3, BottleneckCSP, [ 1024, False ] ],  # 9
   ]
 
 # YOLOv5 PANet head
 head:
-  [[-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, BottleneckCSP, [512, False]],  # 13
+  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 13
 
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, BottleneckCSP, [256, False]],  # 17 (P3/8-small)
+    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+    [ -1, 3, BottleneckCSP, [ 256, False ] ],  # 17 (P3/8-small)
 
-   [-1, 1, Conv, [256, 3, 2]],
-   [[-1, 14], 1, Concat, [1]],  # cat head P4
-   [-1, 3, BottleneckCSP, [512, False]],  # 20 (P4/16-medium)
+    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
+    [ -1, 3, BottleneckCSP, [ 512, False ] ],  # 20 (P4/16-medium)
 
-   [-1, 1, Conv, [512, 3, 2]],
-   [[-1, 10], 1, Concat, [1]],  # cat head P5
-   [-1, 3, BottleneckCSP, [1024, False]],  # 23 (P5/32-large)
+    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
+    [ -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)
   ]

models/hub/yolov5l6.yaml

@@ -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:
   - [ 19,27,  44,40,  38,94 ]  # P3/8
   - [ 96,68,  86,152,  180,137 ]  # P4/16

models/hub/yolov5m6.yaml

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

models/hub/yolov5s-transformer.yaml

@@ -1,48 +1,46 @@
-# parameters
+# Parameters
 nc: 80  # number of classes
 depth_multiple: 0.33  # model depth multiple
 width_multiple: 0.50  # 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, C3, [128]],
-   [-1, 1, Conv, [256, 3, 2]],  # 3-P3/8
-   [-1, 9, C3, [256]],
-   [-1, 1, Conv, [512, 3, 2]],  # 5-P4/16
-   [-1, 9, C3, [512]],
-   [-1, 1, Conv, [1024, 3, 2]],  # 7-P5/32
-   [-1, 1, SPP, [1024, [5, 9, 13]]],
-   [-1, 3, C3TR, [1024, False]],  # 9  <-------- C3TR() Transformer module
+  [ [ -1, 1, Focus, [ 64, 3 ] ],  # 0-P1/2
+    [ -1, 1, Conv, [ 128, 3, 2 ] ],  # 1-P2/4
+    [ -1, 3, C3, [ 128 ] ],
+    [ -1, 1, Conv, [ 256, 3, 2 ] ],  # 3-P3/8
+    [ -1, 9, C3, [ 256 ] ],
+    [ -1, 1, Conv, [ 512, 3, 2 ] ],  # 5-P4/16
+    [ -1, 9, C3, [ 512 ] ],
+    [ -1, 1, Conv, [ 1024, 3, 2 ] ],  # 7-P5/32
+    [ -1, 1, SPP, [ 1024, [ 5, 9, 13 ] ] ],
+    [ -1, 3, C3TR, [ 1024, False ] ],  # 9  <-------- C3TR() Transformer module
   ]
 
 # YOLOv5 head
 head:
-  [[-1, 1, Conv, [512, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 6], 1, Concat, [1]],  # cat backbone P4
-   [-1, 3, C3, [512, False]],  # 13
+  [ [ -1, 1, Conv, [ 512, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 6 ], 1, Concat, [ 1 ] ],  # cat backbone P4
+    [ -1, 3, C3, [ 512, False ] ],  # 13
 
-   [-1, 1, Conv, [256, 1, 1]],
-   [-1, 1, nn.Upsample, [None, 2, 'nearest']],
-   [[-1, 4], 1, Concat, [1]],  # cat backbone P3
-   [-1, 3, C3, [256, False]],  # 17 (P3/8-small)
+    [ -1, 1, Conv, [ 256, 1, 1 ] ],
+    [ -1, 1, nn.Upsample, [ None, 2, 'nearest' ] ],
+    [ [ -1, 4 ], 1, Concat, [ 1 ] ],  # cat backbone P3
+    [ -1, 3, C3, [ 256, False ] ],  # 17 (P3/8-small)
 
-   [-1, 1, Conv, [256, 3, 2]],
-   [[-1, 14], 1, Concat, [1]],  # cat head P4
-   [-1, 3, C3, [512, False]],  # 20 (P4/16-medium)
+    [ -1, 1, Conv, [ 256, 3, 2 ] ],
+    [ [ -1, 14 ], 1, Concat, [ 1 ] ],  # cat head P4
+    [ -1, 3, C3, [ 512, False ] ],  # 20 (P4/16-medium)
 
-   [-1, 1, Conv, [512, 3, 2]],
-   [[-1, 10], 1, Concat, [1]],  # cat head P5
-   [-1, 3, C3, [1024, False]],  # 23 (P5/32-large)
+    [ -1, 1, Conv, [ 512, 3, 2 ] ],
+    [ [ -1, 10 ], 1, Concat, [ 1 ] ],  # cat head P5
+    [ -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)
   ]

models/hub/yolov5s6.yaml

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

models/hub/yolov5x6.yaml

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

models/yolo.py

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

models/yolov5l.yaml

@@ -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:
   - [10,13, 16,30, 33,23]  # P3/8
   - [30,61, 62,45, 59,119]  # P4/16

models/yolov5m.yaml

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

models/yolov5s.yaml

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

models/yolov5x.yaml

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