Added cfg, labels, tree ... files for Yolo9000

AlexeyAB

2017-07-27 1fd398da4f1fe1d4fb438212ea6a0efd8ad6fe37

Added cfg, labels, tree ... files for Yolo9000

 build/darknet/x64/darknet_coco_9000.cmd

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darknet.exe detector test cfg/combine9k.data yolo9000.cfg yolo9000.weights data/dog.jpg


pause

 build/darknet/x64/darknet_coco_9000_demo.cmd

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darknet.exe detector demo cfg/combine9k.data yolo9000.cfg yolo9000.weights street4k.mp4




pause

 build/darknet/x64/data/9k.labels

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 build/darknet/x64/data/9k.names

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 build/darknet/x64/data/9k.tree

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 build/darknet/x64/data/coco9k.map

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 build/darknet/x64/data/combine9k.data

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classes= 9418
#train  = /home/pjreddie/data/coco/trainvalno5k.txt
train  = data/combine9k.train.list
valid  = /home/pjreddie/data/imagenet/det.val.files
labels = data/9k.labels
names  = data/9k.names
backup = backup/
map = data/inet9k.map
eval = imagenet
results = results

 build/darknet/x64/data/inet9k.map

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 build/darknet/x64/yolo9000.cfg

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[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=8
batch=1
subdivisions=1
height=544
width=544
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1

hue=.1
saturation=.75
exposure=.75

[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]
filters=28269
size=1
stride=1
pad=1
activation=linear

[region]
anchors = 0.77871, 1.14074, 3.00525, 4.31277, 9.22725, 9.61974
bias_match=1
classes=9418
coords=4
num=3
softmax=1
jitter=.2
rescore=1

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

thresh = .6
absolute=1
random=1

tree=data/9k.tree
map = data/coco9k.map

 cfg/combine9k.data

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classes= 9418
#train  = /home/pjreddie/data/coco/trainvalno5k.txt
train  = data/combine9k.train.list
valid  = /home/pjreddie/data/imagenet/det.val.files
labels = data/9k.labels
names  = data/9k.names
backup = backup/
map = data/inet9k.map
eval = imagenet
results = results

 cfg/yolo9000.cfg

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[net]
# Testing
batch=1
subdivisions=1
# Training
# batch=64
# subdivisions=8
batch=1
subdivisions=1
height=544
width=544
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.001
burn_in=1000
max_batches = 500200
policy=steps
steps=400000,450000
scales=.1,.1

hue=.1
saturation=.75
exposure=.75

[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]
filters=28269
size=1
stride=1
pad=1
activation=linear

[region]
anchors = 0.77871, 1.14074, 3.00525, 4.31277, 9.22725, 9.61974
bias_match=1
classes=9418
coords=4
num=3
softmax=1
jitter=.2
rescore=1

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

thresh = .6
absolute=1
random=1

tree=data/9k.tree
map = data/coco9k.map

 src/image.c

@@ -543,7 +543,7 @@
            //printf("\n cvCreateVideoWriter, DST output_video = %p  \n", output_video);
        }

        //cvWriteFrame(output_video, disp); // comment this line to improve FPS !!!
        cvWriteFrame(output_video, disp);   // comment this line to improve FPS !!!
        printf("\n cvWriteFrame \n");
    }