[net]
# Testing
#batch=1
#subdivisions=1
# Training
batch=64
subdivisions=4
width=544
height=544
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1

mosaic=1


learning_rate=0.0001
burn_in=1000
max_batches = 10000

policy=sgdr
#sgdr_cycle=1000
#sgdr_mult=2
#steps=4000,6000,8000,9000
#scales=1, 1, 0.1, 0.1


[convolutional]
batch_normalize=1
filters=16
size=3
stride=1
pad=1
activation=swish


# remove
[maxpool]
size=2
stride=2

[convolutional]
batch_normalize=1
filters=32
size=3
stride=1
pad=1
activation=swish

[maxpool]
size=2
stride=2


[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=swish

[maxpool]
size=2
stride=2


[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=swish
assisted_excitation=4000

[maxpool]
size=2
stride=2


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

[maxpool]
size=2
stride=2


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

[maxpool]
size=2
stride=1

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

###########

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

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



########### to [yolo-3]


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

[upsample]
stride=2

[route]
layers = -1, 8

###########

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


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

########### to [yolo-2]


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

[upsample]
stride=2

[route]
layers = -1, 6

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

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=swish


########### features of different layers


[route]
layers=2

[maxpool]
size=16
stride=16

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

[route]
layers=4

[maxpool]
size=8
stride=8

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

[route]
layers=4

[maxpool]
size=8
stride=4
stride_x=4
stride_y=8

[convolutional]
batch_normalize=1
filters=64
size=1
stride=2
stride_x=2
stride_y=1
pad=1
activation=swish

[route]
layers=4

[maxpool]
size=8
stride=8
stride_x=8
stride_y=4

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
stride_x=1
stride_y=2
pad=1
activation=swish

[route]
layers=4

[maxpool]
size=8
stride=8

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

[route]
layers=6

[maxpool]
size=4
stride=4

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

[route]
layers=6

[maxpool]
size=4
stride=2
stride_x=2
stride_y=4

[convolutional]
batch_normalize=1
filters=64
size=1
stride=2
stride_x=2
stride_y=1
pad=1
activation=swish

[route]
layers=6

[maxpool]
size=4
stride=4
stride_x=4
stride_y=2

[convolutional]
batch_normalize=1
filters=64
size=1
stride=1
stride_x=1
stride_y=2
pad=1
activation=swish

[route]
layers=8

[maxpool]
size=2
stride=2

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

[route]
layers=10

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

[route]
layers=-1, -3, -6, -9, -12, -15, -18, -21, -24, -27

[maxpool]
maxpool_depth=1
out_channels=64
stride=1
size=1


########### [yolo-1]


[upsample]
stride=4

[route]
layers = -1,24

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=swish


[convolutional]
size=1
stride=1
pad=1
filters=50
activation=linear

[Gaussian_yolo]
yolo_point=center
mask = 0,1,2,3,4
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0


[route]
layers = -4

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=swish


[convolutional]
size=1
stride=1
pad=1
filters=50
activation=linear

[Gaussian_yolo]
yolo_point=left_top
mask = 0,1,2,3,4
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0


[route]
layers = -4

[convolutional]
batch_normalize=1
filters=128
size=3
stride=1
pad=1
activation=swish


[convolutional]
size=1
stride=1
pad=1
filters=50
activation=linear

[Gaussian_yolo]
yolo_point=right_bottom
mask = 0,1,2,3,4
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0


########### [yolo-2]

[route]
layers = -14

[upsample]
stride=2

[route]
layers = -1,19

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

[convolutional]
size=1
stride=1
pad=1
filters=50
activation=linear

[Gaussian_yolo]
yolo_point=center
mask = 4,5,6,7,8
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0


[route]
layers = -4

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

[convolutional]
size=1
stride=1
pad=1
filters=50
activation=linear

[Gaussian_yolo]
yolo_point=left_top
mask = 4,5,6,7,8
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0


[route]
layers = -4

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

[convolutional]
size=1
stride=1
pad=1
filters=50
activation=linear

[Gaussian_yolo]
yolo_point=right_bottom
mask = 4,5,6,7,8
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 64,64,  59,119, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0



########### [yolo-3]

[route]
layers = 55


[route]
layers = -1,14

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

[convolutional]
size=1
stride=1
pad=1
filters=40
activation=linear



[Gaussian_yolo]
yolo_point=center
mask = 8,9,10,11
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 59,119,   80,80, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0


[route]
layers = -4

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

[convolutional]
size=1
stride=1
pad=1
filters=40
activation=linear


[Gaussian_yolo]
yolo_point=left_top
mask = 8,9,10,11
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 59,119,   80,80, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0


[route]
layers = -4

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

[convolutional]
size=1
stride=1
pad=1
filters=40
activation=linear


[Gaussian_yolo]
yolo_point=right_bottom
mask = 8,9,10,11
anchors = 8,8, 10,13, 16,30, 33,23,  32,32, 30,61, 62,45, 59,119,   80,80, 116,90, 156,198, 373,326
classes=1
num=12
jitter=.3
ignore_thresh = .7
truth_thresh = 1
iou_thresh=0.213
iou_normalizer=0.5
uc_normalizer=0.5
cls_normalizer=1.0
iou_loss=mse
scale_x_y = 1.1
random=0