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0 0.498437 0.479861 0.117188 0.168056
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0 0.602734 0.609028 0.110156 0.181944
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0 0.498828 0.459722 0.152344 0.191667
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0 0.377734 0.401389 0.132031 0.155556
0 0.529297 0.211111 0.144531 0.205556
0 0.641406 0.393750 0.126563 0.143056
0 0.744531 0.609028 0.148438 0.193056
0 0.275000 0.642361 0.167187 0.206944
0 0.526953 0.622917 0.160156 0.184722
0 0.635156 0.807639 0.126563 0.134722
0 0.375781 0.857639 0.126563 0.145833
0 0.531641 0.949306 0.172656 0.101389
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0 0.462109 0.513889 0.186719 0.163889
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0 0.140234 0.091667 0.244531 0.127778
0 0.458594 0.102083 0.295312 0.115278
0 0.792578 0.118750 0.272656 0.193056
0 0.186328 0.302083 0.307031 0.154167
0 0.498047 0.272222 0.261719 0.119444
0 0.812500 0.316667 0.323438 0.158333
0 0.203125 0.502778 0.367188 0.172222
0 0.521094 0.487500 0.207813 0.255556
0 0.798047 0.497222 0.303906 0.155556
0 0.157031 0.714583 0.264062 0.140278
0 0.480469 0.693056 0.321875 0.155556
0 0.816406 0.688889 0.318750 0.133333
0 0.182812 0.879167 0.314063 0.125000
0 0.520312 0.882639 0.340625 0.181944
0 0.832812 0.861111 0.260938 0.141667
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1 0.657812 0.411806 0.189063 0.223611
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1 0.552344 0.384028 0.159375 0.195833
1 0.780078 0.595833 0.236719 0.133333
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1 0.508594 0.282639 0.107813 0.151389
1 0.521875 0.470833 0.081250 0.100000
1 0.527344 0.709722 0.162500 0.133333
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1 0.526953 0.502083 0.303906 0.226389
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classes= 2
train = data/train.txt
valid = data/train.txt
names = data/obj.names
backup = backup/

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air
bird
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data/img/air1.jpg
data/img/air2.jpg
data/img/air3.jpg
data/img/air4.jpg
data/img/air5.jpg
data/img/air6.jpg
data/img/air7.jpg
data/img/bird1.jpg
data/img/bird2.jpg
data/img/bird3.jpg
data/img/bird4.jpg
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echo Example how to start training Darknet Yolo v2 for 2 objects (air, bird)


darknet.exe detector train data/obj.data yolo-obj.cfg darknet19_448.conv.23


pause
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[net]
batch=64
subdivisions=8
height=416
width=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.0001
max_batches = 45000
policy=steps
steps=100,25000,35000
scales=10,.1,.1

[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

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
pad=1
activation=leaky

[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

[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky

[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

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky

[convolutional]
batch_normalize=1
filters=1024
size=3
stride=1
pad=1
activation=leaky


#######

[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=1024
activation=leaky

[route]
layers=-9

[reorg]
stride=2

[route]
layers=-1,-3

[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
size=1
stride=1
pad=1
filters=35
activation=linear

[region]
anchors = 1.08,1.19, 3.42,4.41, 6.63,11.38, 9.42,5.11, 16.62,10.52
bias_match=1
classes=2
coords=4
num=5
softmax=1
jitter=.2
rescore=1

object_scale=5
noobject_scale=1
class_scale=1
coord_scale=1

absolute=1
thresh = .6
random=0
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echo Example how to start marking bouded boxes for training set Yolo v2


yolo_mark.exe data/img data/train.txt data/obj.names


pause
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