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yolov5
Commits
81b31824
Unverified
提交
81b31824
authored
7月 04, 2021
作者:
Glenn Jocher
提交者:
GitHub
7月 04, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Models `*.yaml` reformat (#3875)
上级
bd88e7f4
隐藏空白字符变更
内嵌
并排
正在显示
18 个修改的文件
包含
184 行增加
和
218 行删除
+184
-218
yolov3-spp.yaml
models/hub/yolov3-spp.yaml
+33
-35
yolov3-tiny.yaml
models/hub/yolov3-tiny.yaml
+24
-26
yolov3.yaml
models/hub/yolov3.yaml
+33
-35
yolov5-fpn.yaml
models/hub/yolov5-fpn.yaml
+24
-26
yolov5-p2.yaml
models/hub/yolov5-p2.yaml
+1
-3
yolov5-p6.yaml
models/hub/yolov5-p6.yaml
+1
-3
yolov5-p7.yaml
models/hub/yolov5-p7.yaml
+1
-3
yolov5-panet.yaml
models/hub/yolov5-panet.yaml
+29
-31
yolov5l6.yaml
models/hub/yolov5l6.yaml
+1
-3
yolov5m6.yaml
models/hub/yolov5m6.yaml
+1
-3
yolov5s-transformer.yaml
models/hub/yolov5s-transformer.yaml
+29
-31
yolov5s6.yaml
models/hub/yolov5s6.yaml
+1
-3
yolov5x6.yaml
models/hub/yolov5x6.yaml
+1
-3
yolo.py
models/yolo.py
+1
-1
yolov5l.yaml
models/yolov5l.yaml
+1
-3
yolov5m.yaml
models/yolov5m.yaml
+1
-3
yolov5s.yaml
models/yolov5s.yaml
+1
-3
yolov5x.yaml
models/yolov5x.yaml
+1
-3
没有找到文件。
models/hub/yolov3-spp.yaml
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
...
...
@@ -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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
浏览文件 @
81b31824
#
p
arameters
#
P
arameters
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
...
...
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