reverting merge

Edmond Yoo

2018-09-16 721d5e410e5a2b47ec2de722cc02fd6b9ed6b2fd

 cfg/yolo.cfg

@@ -1,205 +1,258 @@
[net]
batch=64
subdivisions=64
height=448
width=448
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=8
height=416
width=416
channels=3
learning_rate=0.01
momentum=0.9
decay=0.0005
seen = 0

[crop]
crop_width=448
crop_height=448
flip=0
angle=0
saturation = 2
exposure = 2
saturation = 1.5
exposure = 1.5
hue=.1

learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.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=7
stride=2
pad=1
activation=ramp

[convolutional]
filters=192
size=3
stride=2
stride=1
pad=1
activation=ramp
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=ramp
activation=leaky

[convolutional]
batch_normalize=1
filters=256
size=3
stride=1
pad=1
activation=leaky

[maxpool]
size=2
stride=2
pad=1
activation=ramp

[convolutional]
filters=128
size=1
stride=1
pad=1
activation=ramp

[convolutional]
filters=256
size=3
stride=1
pad=1
activation=ramp

[convolutional]
filters=128
size=1
stride=1
pad=1
activation=ramp

[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
pad=1
activation=ramp

[convolutional]
filters=256
size=1
stride=1
pad=1
activation=ramp

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

[convolutional]
filters=256
size=1
stride=1
pad=1
activation=ramp

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

[convolutional]
filters=256
size=1
stride=1
pad=1
activation=ramp

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

[convolutional]
filters=256
size=1
stride=1
pad=1
activation=ramp

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

[convolutional]
filters=256
size=1
stride=1
pad=1
activation=ramp

[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

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

[reorg]
stride=2
pad=1
activation=ramp

[route]
layers=-1,-4

[convolutional]
filters=512
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky

[convolutional]
size=1
stride=1
pad=1
activation=ramp
filters=425
activation=linear

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

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

[convolutional]
size=3
stride=2
pad=1
filters=1024
activation=ramp

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

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

[connected]
output=4096
activation=ramp

[dropout]
probability=.5

[connected]
output=1225
activation=logistic

[detection]
classes=20
[region]
anchors =  0.57273, 0.677385, 1.87446, 2.06253, 3.33843, 5.47434, 7.88282, 3.52778, 9.77052, 9.16828
bias_match=1
classes=80
coords=4
rescore=0
joint=1
objectness = 0
background=0
num=5
softmax=1
jitter=.3
rescore=1

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

absolute=1
thresh = .6
random=1