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Update models/hub/*.yaml files for v6.0n release #5540

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Nov 6, 2021
Merged

Update models/hub/*.yaml files for v6.0n release #5540

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706ec88
Update model yamls for v6.0
glenn-jocher Nov 6, 2021
a97ef6b
Add python models/yolo.py --test
glenn-jocher Nov 6, 2021
e58e03b
Ghost fix
glenn-jocher Nov 6, 2021
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14 changes: 7 additions & 7 deletions models/hub/yolov5-bifpn.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 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, 6, 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, C3, [1024, False]], # 9
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]

# YOLOv5 BiFPN head
# YOLOv5 v6.0 BiFPN head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
Expand All @@ -37,7 +37,7 @@ head:
[-1, 3, C3, [256, False]], # 17 (P3/8-small)

[-1, 1, Conv, [256, 3, 2]],
[[-1, 14, 6], 1, Concat, [1]], # cat P4
[[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change
[-1, 3, C3, [512, False]], # 20 (P4/16-medium)

[-1, 1, Conv, [512, 3, 2]],
Expand Down
22 changes: 11 additions & 11 deletions models/hub/yolov5-fpn.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,34 +9,34 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, Bottleneck, [128]],
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 6, BottleneckCSP, [1024]], # 9
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]

# YOLOv5 FPN head
# YOLOv5 v6.0 FPN head
head:
[[-1, 3, BottleneckCSP, [1024, False]], # 10 (P5/32-large)
[[-1, 3, C3, [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, 3, C3, [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, 3, C3, [256, False]], # 18 (P3/8-small)

[[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5)
]
14 changes: 7 additions & 7 deletions models/hub/yolov5-p2.yaml
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Expand Up @@ -4,24 +4,24 @@
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
anchors: 3
anchors: 3 # auto-anchor evolves 3 anchors per P output layer

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 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, 6, 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, C3, [1024, False]], # 9
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]

# YOLOv5 head
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
Expand Down
16 changes: 8 additions & 8 deletions models/hub/yolov5-p6.yaml
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Expand Up @@ -4,26 +4,26 @@
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
anchors: 3
anchors: 3 # auto-anchor 3 anchors per P output layer

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 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, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 1, SPP, [1024, [3, 5, 7]]],
[-1, 3, C3, [1024, False]], # 11
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 11
]

# YOLOv5 head
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [768, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
Expand All @@ -50,7 +50,7 @@ head:

[-1, 1, Conv, [768, 3, 2]],
[[-1, 12], 1, Concat, [1]], # cat head P6
[-1, 3, C3, [1024, False]], # 32 (P5/64-xlarge)
[-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge)

[[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6)
]
12 changes: 6 additions & 6 deletions models/hub/yolov5-p7.yaml
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Expand Up @@ -4,25 +4,25 @@
nc: 80 # number of classes
depth_multiple: 1.0 # model depth multiple
width_multiple: 1.0 # layer channel multiple
anchors: 3
anchors: 3 # auto-anchor 3 anchors per P output layer

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 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, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3, [512]],
[-1, 1, Conv, [768, 3, 2]], # 7-P5/32
[-1, 3, C3, [768]],
[-1, 1, Conv, [1024, 3, 2]], # 9-P6/64
[-1, 3, C3, [1024]],
[-1, 1, Conv, [1280, 3, 2]], # 11-P7/128
[-1, 1, SPP, [1280, [3, 5]]],
[-1, 3, C3, [1280, False]], # 13
[-1, 3, C3, [1280]],
[-1, 1, SPPF, [1280, 5]], # 13
]

# YOLOv5 head
Expand Down
24 changes: 12 additions & 12 deletions models/hub/yolov5-panet.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,40 +9,40 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, Conv, [128, 3, 2]], # 1-P2/4
[-1, 3, BottleneckCSP, [128]],
[-1, 3, C3, [128]],
[-1, 1, Conv, [256, 3, 2]], # 3-P3/8
[-1, 9, BottleneckCSP, [256]],
[-1, 6, C3, [256]],
[-1, 1, Conv, [512, 3, 2]], # 5-P4/16
[-1, 9, BottleneckCSP, [512]],
[-1, 9, C3, [512]],
[-1, 1, Conv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, BottleneckCSP, [1024, False]], # 9
[-1, 3, C3, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]

# YOLOv5 PANet head
# YOLOv5 v6.0 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, 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, BottleneckCSP, [256, False]], # 17 (P3/8-small)
[-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, BottleneckCSP, [512, False]], # 20 (P4/16-medium)
[-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, BottleneckCSP, [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)
]
12 changes: 6 additions & 6 deletions models/hub/yolov5s-ghost.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2
[-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4
[-1, 3, C3Ghost, [128]],
[-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8
[-1, 9, C3Ghost, [256]],
[-1, 6, C3Ghost, [256]],
[-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16
[-1, 9, C3Ghost, [512]],
[-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32
[-1, 1, SPP, [1024, [5, 9, 13]]],
[-1, 3, C3Ghost, [1024, False]], # 9
[-1, 3, C3Ghost, [1024]],
[-1, 1, SPPF, [1024, 5]], # 9
]

# YOLOv5 head
# YOLOv5 v6.0 head
head:
[[-1, 1, GhostConv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
Expand Down
12 changes: 6 additions & 6 deletions models/hub/yolov5s-transformer.yaml
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Original file line number Diff line number Diff line change
Expand Up @@ -9,22 +9,22 @@ anchors:
- [30,61, 62,45, 59,119] # P4/16
- [116,90, 156,198, 373,326] # P5/32

# YOLOv5 backbone
# YOLOv5 v6.0 backbone
backbone:
# [from, number, module, args]
[[-1, 1, Focus, [64, 3]], # 0-P1/2
[[-1, 1, Conv, [64, 6, 2, 2]], # 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, 6, 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, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module
[-1, 1, SPPF, [1024, 5]], # 9
]

# YOLOv5 head
# YOLOv5 v6.0 head
head:
[[-1, 1, Conv, [512, 1, 1]],
[-1, 1, nn.Upsample, [None, 2, 'nearest']],
Expand Down
9 changes: 9 additions & 0 deletions models/yolo.py
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Expand Up @@ -306,6 +306,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
parser.add_argument('--cfg', type=str, default='yolov5s.yaml', help='model.yaml')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--profile', action='store_true', help='profile model speed')
parser.add_argument('--test', action='store_true', help='test all yolo*.yaml')
opt = parser.parse_args()
opt.cfg = check_yaml(opt.cfg) # check YAML
print_args(FILE.stem, opt)
Expand All @@ -320,6 +321,14 @@ def parse_model(d, ch): # model_dict, input_channels(3)
img = torch.rand(8 if torch.cuda.is_available() else 1, 3, 640, 640).to(device)
y = model(img, profile=True)

# Test all models
if opt.test:
for cfg in Path(ROOT / 'models').rglob('yolo*.yaml'):
try:
_ = Model(cfg)
except Exception as e:
print(f'Error in {cfg}: {e}')

# Tensorboard (not working https://github.com/ultralytics/yolov5/issues/2898)
# from torch.utils.tensorboard import SummaryWriter
# tb_writer = SummaryWriter('.')
Expand Down