basic版本的yolo,在yolov3版本上增加人体跟踪
xuepengqiang
2020-05-26 5966f2b095841627d62daac0159e81f83544b85c
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[net]
batch=1
subdivisions=1
height=448
width=448
channels=3
momentum=0.9
decay=0.0005
 
learning_rate=0.0001
policy=steps
steps=20,40,60,80,20000,30000
scales=5,5,2,2,.1,.1
max_batches = 40000
 
[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[batchnorm]
 
[convolutional]
xnor = 1
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[batchnorm]
 
[convolutional]
xnor = 1
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[batchnorm]
 
[convolutional]
xnor = 1
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[batchnorm]
 
[convolutional]
xnor = 1
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[batchnorm]
 
[convolutional]
xnor = 1
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky
 
[maxpool]
size=2
stride=2
 
[batchnorm]
 
[convolutional]
xnor = 1
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky
 
[batchnorm]
 
[convolutional]
xnor = 1
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky
 
[connected]
output= 1470
activation=linear
 
[detection]
classes=20
coords=4
rescore=1
side=7
num=2
softmax=0
sqrt=1
jitter=.2
 
object_scale=1
noobject_scale=.5
class_scale=1
coord_scale=5