basic版本的yolo,在yolov3版本上增加人体跟踪
xuepengqiang
2020-05-26 5966f2b095841627d62daac0159e81f83544b85c
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
[net]
batch=64
subdivisions=2
height=448
width=448
channels=3
momentum=0.9
decay=0.0005
 
hue = .1
saturation=.75
exposure=.75
 
learning_rate=0.0005
policy=steps
steps=200,400,600,800,100000,150000
scales=2.5,2,2,2,.1,.1
max_batches = 200000
 
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
 
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
 
[connected]
output= 4655
activation=linear
 
[detection]
classes=80
coords=4
rescore=1
side=7
num=3
softmax=0
sqrt=1
jitter=.2
 
object_scale=1
noobject_scale=.5
class_scale=1
coord_scale=5