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YOLOv3 WOOOO!!!!!!!

This commit is contained in:
Joseph Redmon 2018-03-25 00:51:26 -07:00
parent 777b098232
commit e84933bfdd
21 changed files with 824 additions and 1780 deletions
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8
Makefile
View File

@ -1,7 +1,7 @@
GPU=1
CUDNN=1
OPENCV=1
OPENMP=1
GPU=0
CUDNN=0
OPENCV=0
OPENMP=0
DEBUG=0
ARCH= -gencode arch=compute_30,code=sm_30 \

244
cfg/yolo-voc.2.0.cfg
View File

@ -1,244 +0,0 @@
[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=125
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=20
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

244
cfg/yolo.2.0.cfg
View File

@ -1,244 +0,0 @@
[net]
batch=1
subdivisions=1
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
saturation = 1.5
exposure = 1.5
hue=.1
learning_rate=0.001
max_batches = 120000
policy=steps
steps=-1,100,80000,100000
scales=.1,10,.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=425
activation=linear
[region]
anchors = 0.738768,0.874946, 2.42204,2.65704, 4.30971,7.04493, 10.246,4.59428, 12.6868,11.8741
bias_match=1
classes=80
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

8
cfg/yolov1/tiny-yolo.cfg → cfg/yolov1-tiny.cfg
View File

@ -1,6 +1,10 @@
[net]
batch=64
subdivisions=2
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=8
height=448
width=448
channels=3

4
cfg/yolov1/yolo.cfg → cfg/yolov1.cfg
View File

@ -1,6 +1,10 @@
[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=8
height=448
width=448
channels=3

125
cfg/yolov1/tiny-coco.cfg
View File

@ -1,125 +0,0 @@
[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

143
cfg/yolov1/xyolo.test.cfg
View File

@ -1,143 +0,0 @@
[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

255
cfg/yolov1/yolo-coco.cfg
View File

@ -1,255 +0,0 @@
[net]
batch=64
subdivisions=4
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=64
size=7
stride=2
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=192
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[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
[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=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
[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
[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
[maxpool]
size=2
stride=2
[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=2
pad=1
filters=1024
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
[local]
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

239
cfg/yolov1/yolo-small.cfg
View File

@ -1,239 +0,0 @@
[net]
batch=64
subdivisions=64
height=448
width=448
channels=3
momentum=0.9
decay=0.0005
learning_rate=0.001
policy=steps
steps=200,400,600,20000,30000
scales=2.5,2,2,.1,.1
max_batches = 40000
[crop]
crop_width=448
crop_height=448
flip=0
angle=0
saturation = 1.5
exposure = 1.5
[convolutional]
filters=64
size=7
stride=2
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
filters=192
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=256
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=512
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=512
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=1024
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=1024
size=3
stride=1
pad=1
activation=leaky
[convolutional]
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
filters=1024
size=3
stride=1
pad=1
activation=leaky
#######
[convolutional]
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=3
stride=2
pad=1
filters=1024
activation=leaky
[convolutional]
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=3
stride=1
pad=1
filters=1024
activation=leaky
[connected]
output=512
activation=leaky
[connected]
output=4096
activation=leaky
[dropout]
probability=.5
[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

257
cfg/yolov1/yolo.train.cfg
View File

@ -1,257 +0,0 @@
[net]
batch=64
subdivisions=4
height=448
width=448
channels=3
momentum=0.9
decay=0.0005
saturation=1.5
exposure=1.5
hue=.1
learning_rate=0.0005
policy=steps
steps=200,400,600,20000,30000
scales=2.5,2,2,.1,.1
max_batches = 40000
[convolutional]
batch_normalize=1
filters=64
size=7
stride=2
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=192
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[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
[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=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
[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
[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
[maxpool]
size=2
stride=2
[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=2
pad=1
filters=1024
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
[local]
size=3
stride=1
pad=1
filters=256
activation=leaky
[dropout]
probability=.5
[connected]
output= 1715
activation=linear
[detection]
classes=20
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

251
cfg/yolov1/yolo2.cfg
View File

@ -1,251 +0,0 @@
[net]
batch=1
subdivisions=1
height=448
width=448
channels=3
momentum=0.9
decay=0.0005
learning_rate=0.0005
policy=steps
steps=200,400,600,20000,30000
scales=2.5,2,2,.1,.1
max_batches = 40000
[convolutional]
batch_normalize=1
filters=64
size=7
stride=2
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[convolutional]
batch_normalize=1
filters=192
size=3
stride=1
pad=1
activation=leaky
[maxpool]
size=2
stride=2
[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
[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=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
[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
[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
[maxpool]
size=2
stride=2
[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=2
pad=1
filters=1024
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
[local]
size=3
stride=1
pad=1
filters=256
activation=leaky
[connected]
output= 1715
activation=linear
[detection]
classes=20
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

8
cfg/tiny-yolo-voc.cfg → cfg/yolov2-tiny-voc.cfg
View File

@ -1,6 +1,10 @@
[net]
batch=64
subdivisions=8
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=2
width=416
height=416
channels=3

6
cfg/tiny-yolo.cfg → cfg/yolov2-tiny.cfg
View File

@ -1,10 +1,10 @@
[net]
# Training
# batch=64
# subdivisions=2
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=2
width=416
height=416
channels=3

0
cfg/yolo-voc.cfg → cfg/yolov2-voc.cfg
View File

14
cfg/yolo.cfg → cfg/yolov2.cfg
View File

@ -1,12 +1,12 @@
[net]
# Testing
# batch=1
# subdivisions=1
batch=1
subdivisions=1
# Training
batch=64
subdivisions=8
width=608
height=608
# batch=64
# subdivisions=8
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
@ -239,7 +239,7 @@ activation=linear
[region]
anchors = 18.3274,21.6763, 59.9827,66.001, 106.83,175.179, 252.25,112.889, 312.657,293.385
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

789
cfg/yolov3.cfg Normal file
View File

@ -0,0 +1,789 @@
[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=16
width=416
height=416
channels=3
momentum=0.9
decay=0.0005
angle=0
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
# Downsample
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=32
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
filters=64
size=3
stride=1
pad=1
activation=leaky
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=128
size=3
stride=2
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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=256
size=3
stride=2
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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=512
size=3
stride=2
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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
# Downsample
[convolutional]
batch_normalize=1
filters=1024
size=3
stride=2
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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
[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
[shortcut]
from=-3
activation=linear
######################
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
batch_normalize=1
filters=512
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=1024
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 6,7,8
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 61
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
batch_normalize=1
filters=256
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=512
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 3,4,5
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1
[route]
layers = -4
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[upsample]
stride=2
[route]
layers = -1, 36
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
batch_normalize=1
filters=128
size=1
stride=1
pad=1
activation=leaky
[convolutional]
batch_normalize=1
size=3
stride=1
pad=1
filters=256
activation=leaky
[convolutional]
size=1
stride=1
pad=1
filters=255
activation=linear
[yolo]
mask = 0,1,2
anchors = 10,13, 16,30, 33,23, 30,61, 62,45, 59,119, 116,90, 156,198, 373,326
classes=80
num=9
jitter=.3
ignore_thresh = .5
truth_thresh = 1
random=1

2
examples/coco.c
View File

@ -320,7 +320,7 @@ void test_coco(char *cfgfile, char *weightfile, char *filename, float thresh)
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, 0, 0, 0, &nboxes);
detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, dets, l.side*l.side*l.n, thresh, coco_classes, alphabet, 80);

2
examples/darknet.c
View File

@ -433,7 +433,7 @@ int main(int argc, char **argv)
} else if (0 == strcmp(argv[1], "detector")){
run_detector(argc, argv);
} else if (0 == strcmp(argv[1], "detect")){
float thresh = find_float_arg(argc, argv, "-thresh", .24);
float thresh = find_float_arg(argc, argv, "-thresh", .5);
char *filename = (argc > 4) ? argv[4]: 0;
char *outfile = find_char_arg(argc, argv, "-out", 0);
int fullscreen = find_arg(argc, argv, "-fullscreen");

2
examples/detector.c
View File

@ -794,7 +794,7 @@ void network_detect(network *net, image im, float thresh, float hier_thresh, flo
void run_detector(int argc, char **argv)
{
char *prefix = find_char_arg(argc, argv, "-prefix", 0);
float thresh = find_float_arg(argc, argv, "-thresh", .24);
float thresh = find_float_arg(argc, argv, "-thresh", .5);
float hier_thresh = find_float_arg(argc, argv, "-hier", .5);
int cam_index = find_int_arg(argc, argv, "-c", 0);
int frame_skip = find_int_arg(argc, argv, "-s", 0);

2
examples/yolo.c
View File

@ -291,7 +291,7 @@ void test_yolo(char *cfgfile, char *weightfile, char *filename, float thresh)
printf("%s: Predicted in %f seconds.\n", input, sec(clock()-time));
int nboxes = 0;
detection *dets = get_network_boxes(net, im.w, im.h, thresh, 0, 0, 0, &nboxes);
detection *dets = get_network_boxes(net, 1, 1, thresh, 0, 0, 0, &nboxes);
if (nms) do_nms_sort(dets, l.side*l.side*l.n, l.classes, nms);
draw_detections(im, dets, l.side*l.side*l.n, thresh, voc_names, alphabet, 20);

1
src/detection_layer.c
View File

@ -263,6 +263,7 @@ void forward_detection_layer_gpu(const detection_layer l, network net)
//float *in_cpu = calloc(l.batch*l.inputs, sizeof(float));
//float *truth_cpu = 0;
cuda_pull_array(net.input_gpu, net.input, l.batch*l.inputs);
forward_detection_layer(l, net);
cuda_push_array(l.output_gpu, l.output, l.batch*l.outputs);
cuda_push_array(l.delta_gpu, l.delta, l.batch*l.inputs);