Darknet/YOLO or Caffe/R-FCN + Squeezenet #15

Grabber · 2016-07-03T15:01:30Z

I think a very interesting combination with SqueezeNet is RFCN or YOLO for object detection. I'm trying to port SqueezeNet from Caffe to Darknet + YOLO.

Could someone help to review it?

It is a port from v1.1

squeezenet.cfg

[net]
batch=64
subdivisions=1
height=227
width=227
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.001
policy=steps
steps=20,40,60,80,20000,30000
scales=5,5,2,2,.1,.1
max_batches=40000

[crop]
crop_width=227
crop_height=227
flip=0
angle=0
saturation = 1.5
exposure = 1.5

# SqueezeNet: conv1
[convolutional]
filters=64
size=3
stride=2
activation=relu

# SqueezeNet: pool1
[maxpool]
size=3
stride=2

# SqueezeNet: fire2/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet: fire2/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire2/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire2/concat
[route]
layers=-3

# SqueezeNet: fire3/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet:fire3/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire3/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire3/concat
[route]
layers=-3

# SqueezeNet: pool3
[maxpool]
size=3
stride=2

# SqueezeNet: fire4/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire4/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire4/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire4/concat
[route]
layers=-3

# SqueezeNet: fire5/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire5/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire5/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire5/concat
[route]
layers=-3

# SqueezeNet: pool5
[maxpool]
size=3
stride=2

# SqueezeNet: fire6/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire6/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire6/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire6/concat
[route]
layers=-3

# SqueezeNet: fire7/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire7/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire7/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire7/concat
[route]
layers=-3

# SqueezeNet: fire8/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire8/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire8/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire8/concat
[route]
layers=-3

# SqueezeNet: fire9/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire9/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire9/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire9/concat
[route]
layers=-3

# SqueezeNet: drop9
[dropout]
probability=.5

# SqueezeNet: conv10
[convolutional]
filters=1000
size=1
activation=relu

# SqueezeNet: pool10
[avgpool]

# YoLo: output = (5 * 2 + CLASSES) * SIDE^2
[connected]
output=784
activation=linear

# YoLo
[detection]
classes=1
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

I'm no sure how to exactly port these cases:

    weight_filler {
      type: "xavier"
    }

    weight_filler {
      type: "gaussian"
      mean: 0.0
      std: 0.01
    }

Concat layers are strange too, don't know what index use on [route] frame=-?

The text was updated successfully, but these errors were encountered:

forresti · 2016-07-04T17:00:54Z

Great idea! I'm definitely interested in this. Perhaps Mr Darknet (@pjreddie) may also care to weigh in.

The Darknet google group might also be a good place to ask this question: https://groups.google.com/forum/#!forum/darknet

I'm not that experienced with Darknet yet. Do we know if it can handle DAG-shaped CNNs, or only linked lists? I guess this might be the point of the "route" layer, but I haven't looked at darknet/src/route_layer.c in detail.

Grabber · 2016-07-04T19:46:11Z

We may could invite the R-FCN author too, what do you think @daijifeng001?

Grabber · 2016-07-11T05:01:19Z

@forresti I think Darknet implements a DAG style except there is no bi-directional sense of nodes connections. In other works, .cfg processing is procedural, but shortcut and route layers allow us to connect previous nodes/connections to the current one.

Grabber · 2016-07-27T15:30:35Z

I found some errors on my previous Darknet/squeezenet.cfg... still got stuck at 5~6 loss error.

batch=128
subdivisions=2
height=227
width=227
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.001
policy=steps
steps=20,40,60,80,20000,30000
scales=5,5,2,2,.1,.1
max_batches=40000

[crop]
crop_width=227
crop_height=227
flip=0
angle=0
saturation = 1.5
exposure = 1.5

# SqueezeNet: conv1
[convolutional]
filters=64
size=3
stride=2
activation=relu

# SqueezeNet: pool1
[maxpool]
size=3
stride=2

# SqueezeNet: fire2/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet: fire2/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire2/shortcut
[shortcut]
from=-2

# SqueezeNet: fire2/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire2/route
[route]
layers=-3,-1

# SqueezeNet: fire3/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet:fire3/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire3/shortcut
[shortcut]
from=-2

# SqueezeNet: fire3/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire3/route
[route]
layers=-3,-1

# SqueezeNet: pool3
[maxpool]
size=3
stride=2

# SqueezeNet: fire4/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire4/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire4/shortcut
[shortcut]
from=-2

# SqueezeNet: fire4/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire4/route
[route]
layers=-3,-1

# SqueezeNet: fire5/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire5/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire5/shortcut
[shortcut]
from=-2

# SqueezeNet: fire5/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire5/route
[route]
layers=-3,-1

# SqueezeNet: pool5
[maxpool]
size=3
stride=2

# SqueezeNet: fire6/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire6/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire6/shortcut
[shortcut]
from=-2

# SqueezeNet: fire6/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire6/route
[route]
layers=-3,-1

# SqueezeNet: fire7/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire7/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire7/shortcut
[shortcut]
from=-2

# SqueezeNet: fire7/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire7/route
[route]
layers=-3,-1

# SqueezeNet: fire8/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire8/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire8/shortcut
[shortcut]
from=-2

# SqueezeNet: fire8/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire8/route
[route]
layers=-3,-1

# SqueezeNet: fire9/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire9/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire9/shortcut
[shortcut]
from=-2

# SqueezeNet: fire9/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire9/route
[route]
layers=-3,-1

# SqueezeNet: drop9
[dropout]
probability=.5

# SqueezeNet: conv10
[convolutional]
filters=1000
size=1
activation=relu

# SqueezeNet: pool10
[avgpool]

# YoLo: output = (5 * 2 + CLASSES) * SIDE^2
[connected]
output=539
activation=linear

# YoLo
[detection]
classes=1
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```

ghost · 2016-09-06T10:21:23Z

Thank you for sharing. However, your model triggers a seg fault when running the yolo training. Have you changed the source code as well ? thx.

Grabber · 2016-09-08T02:23:50Z

@djebm2, I have not modified the source code to run it. By the way I have a newer version that is now training with loss errors below 1.

[net]
batch=128
subdivisions=2
height=227
width=227
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.001
policy=steps
steps=20,40,60,80,20000,30000
scales=5,5,2,2,.1,.1
max_batches=40000

[crop]
crop_width=227
crop_height=227
flip=0
angle=0
saturation = 1.5
exposure = 1.5

# SqueezeNet: conv1
[convolutional]
filters=64
size=3
stride=2
activation=relu

# SqueezeNet: pool1
[maxpool]
size=3
stride=2

# SqueezeNet: fire2/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet: fire2/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire2/shortcut
[route]
layers=-2

# SqueezeNet: fire2/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire2/route
[route]
layers=-3,-1

# SqueezeNet: fire3/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet:fire3/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire3/shortcut
[route]
layers=-2

# SqueezeNet: fire3/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire3/route
[route]
layers=-3,-1

# SqueezeNet: pool3
[maxpool]
size=3
stride=2

# SqueezeNet: fire4/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire4/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire4/shortcut
[route]
layers=-2

# SqueezeNet: fire4/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire4/route
[route]
layers=-3,-1

# SqueezeNet: fire5/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire5/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire5/shortcut
[route]
layers=-2

# SqueezeNet: fire5/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire5/route
[route]
layers=-3,-1

# SqueezeNet: pool5
[maxpool]
size=3
stride=2

# SqueezeNet: fire6/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire6/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire6/shortcut
[route]
layers=-2

# SqueezeNet: fire6/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire6/route
[route]
layers=-3,-1

# SqueezeNet: fire7/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire7/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire7/shortcut
[route]
layers=-2

# SqueezeNet: fire7/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire7/route
[route]
layers=-3,-1

# SqueezeNet: fire8/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire8/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire8/shortcut
[route]
layers=-2

# SqueezeNet: fire8/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire8/route
[route]
layers=-3,-1

# SqueezeNet: fire9/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire9/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire9/shortcut
[route]
layers=-2

# SqueezeNet: fire9/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire9/route
[route]
layers=-3,-1

# SqueezeNet: drop9
[dropout]
probability=.5

# SqueezeNet: conv10
[convolutional]
filters=1000
size=1
activation=relu

# SqueezeNet: pool10
[avgpool]

# YoLo: output = (5 * 2 + CLASSES) * SIDE^2
[connected]
output=539
activation=linear

# YoLo
[detection]
classes=1
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

pjreddie · 2016-09-08T04:36:12Z

Cfg file looks good, I think the first layer is 7x7 though right? Have you
pre trained in image net? I'll try when I get home

On Sep 7, 2016 7:23 PM, "Luiz Vitor Martinez Cardoso" <
notifications@github.com> wrote:

@djebm2 https://github.com/djebm2, I have not modified the source code
to run it. By the way I have a newer version that is now training with loss
errors below 1.

[net]
batch=128
subdivisions=2
height=227
width=227
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.001
policy=steps
steps=20,40,60,80,20000,30000
scales=5,5,2,2,.1,.1
max_batches=40000

[crop]
crop_width=227
crop_height=227
flip=0
angle=0
saturation = 1.5
exposure = 1.5

SqueezeNet: conv1

[convolutional]
filters=64
size=3
stride=2
activation=relu

SqueezeNet: pool1

[maxpool]
size=3
stride=2

SqueezeNet: fire2/squeeze1x1

[convolutional]
filters=16
size=1
activation=relu

SqueezeNet: fire2/expand1x1

[convolutional]
filters=64
size=1
activation=relu

SqueezeNet: fire2/shortcut

[route]
layers=-2

SqueezeNet: fire2/expand3x3

[convolutional]
filters=64
size=3
pad=1
activation=relu

SqueezeNet: fire2/route

[route]
layers=-3,-1

SqueezeNet: fire3/squeeze1x1

[convolutional]
filters=16
size=1
activation=relu

SqueezeNet:fire3/expand1x1

[convolutional]
filters=64
size=1
activation=relu

SqueezeNet: fire3/shortcut

[route]
layers=-2

SqueezeNet: fire3/expand3x3

[convolutional]
filters=64
size=3
pad=1
activation=relu

SqueezeNet: fire3/route

[route]
layers=-3,-1

SqueezeNet: pool3

[maxpool]
size=3
stride=2

SqueezeNet: fire4/squeeze1x1

[convolutional]
filters=32
size=1
activation=relu

SqueezeNet: fire4/expand1x1

[convolutional]
filters=128
size=1
activation=relu

SqueezeNet: fire4/shortcut

[route]
layers=-2

SqueezeNet: fire4/expand3x3

[convolutional]
filters=128
size=3
pad=1
activation=relu

SqueezeNet: fire4/route

[route]
layers=-3,-1

SqueezeNet: fire5/squeeze1x1

[convolutional]
filters=32
size=1
activation=relu

SqueezeNet: fire5/expand1x1

[convolutional]
filters=128
size=1
activation=relu

SqueezeNet: fire5/shortcut

[route]
layers=-2

SqueezeNet: fire5/expand3x3

[convolutional]
filters=128
size=3
pad=1
activation=relu

SqueezeNet: fire5/route

[route]
layers=-3,-1

SqueezeNet: pool5

[maxpool]
size=3
stride=2

SqueezeNet: fire6/squeeze1x1

[convolutional]
filters=48
size=1
activation=relu

SqueezeNet: fire6/expand1x1

[convolutional]
filters=192
size=1
activation=relu

SqueezeNet: fire6/shortcut

[route]
layers=-2

SqueezeNet: fire6/expand3x3

[convolutional]
filters=192
size=3
pad=1
activation=relu

SqueezeNet: fire6/route

[route]
layers=-3,-1

SqueezeNet: fire7/squeeze1x1

[convolutional]
filters=48
size=1
activation=relu

SqueezeNet: fire7/expand1x1

[convolutional]
filters=192
size=1
activation=relu

SqueezeNet: fire7/shortcut

[route]
layers=-2

SqueezeNet: fire7/expand3x3

[convolutional]
filters=192
size=3
pad=1
activation=relu

SqueezeNet: fire7/route

[route]
layers=-3,-1

SqueezeNet: fire8/squeeze1x1

[convolutional]
filters=64
size=1
activation=relu

SqueezeNet: fire8/expand1x1

[convolutional]
filters=256
size=1
activation=relu

SqueezeNet: fire8/shortcut

[route]
layers=-2

SqueezeNet: fire8/expand3x3

[convolutional]
filters=256
size=3
pad=1
activation=relu

SqueezeNet: fire8/route

[route]
layers=-3,-1

SqueezeNet: fire9/squeeze1x1

[convolutional]
filters=64
size=1
activation=relu

SqueezeNet: fire9/expand1x1

[convolutional]
filters=256
size=1
activation=relu

SqueezeNet: fire9/shortcut

[route]
layers=-2

SqueezeNet: fire9/expand3x3

[convolutional]
filters=256
size=3
pad=1
activation=relu

SqueezeNet: fire9/route

[route]
layers=-3,-1

SqueezeNet: drop9

[dropout]
probability=.5

SqueezeNet: conv10

[convolutional]
filters=1000
size=1
activation=relu

SqueezeNet: pool10

[avgpool]

YoLo: output = (5 * 2 + CLASSES) * SIDE^2

[connected]
output=539
activation=linear

YoLo

[detection]
classes=1
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```

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Grabber · 2016-09-08T04:41:25Z

@pjreddie it is good to hear from you buddy!

Please check my latest versions of SqueezeNet for Darknet with route layers and batchnormalization. Please the check following variants:

[net]
batch=64
subdivisions=2
height=227
width=227
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.001
policy=poly
power=1
max_batches=20000

[crop]
crop_width=227
crop_height=227
flip=0
angle=0
saturation=1.5
exposure=1.5

# SqueezeNet: conv1
[convolutional]
filters=64
size=3
stride=2
activation=relu

# SqueezeNet: pool1
[maxpool]
size=3
stride=2

[batchnorm]

# SqueezeNet: fire2/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet: fire2/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire2/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire2/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire2/route
[route]
layers=-3,-1

[batchnorm]

# SqueezeNet: fire3/squeeze1x1
[convolutional]
filters=16
size=1
activation=relu

# SqueezeNet:fire3/expand1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire3/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire3/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire3/route
[route]
layers=-3,-1

[batchnorm]

# SqueezeNet: pool3
[maxpool]
size=3
stride=2

[batchnorm]

# SqueezeNet: fire4/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire4/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire4/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire4/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire4/route
[route]
layers=-3,-1

[batchnorm]

# SqueezeNet: fire5/squeeze1x1
[convolutional]
filters=32
size=1
activation=relu

# SqueezeNet: fire5/expand1x1
[convolutional]
filters=128
size=1
activation=relu

# SqueezeNet: fire5/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire5/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire5/route
[route]
layers=-3,-1

# SqueezeNet: pool5
[maxpool]
size=3
stride=2

[batchnorm]

# SqueezeNet: fire6/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire6/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire6/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire6/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire6/route
[route]
layers=-3,-1

[batchnorm]

# SqueezeNet: fire7/squeeze1x1
[convolutional]
filters=48
size=1
activation=relu

# SqueezeNet: fire7/expand1x1
[convolutional]
filters=192
size=1
activation=relu

# SqueezeNet: fire7/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire7/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire7/route
[route]
layers=-3,-1

[batchnorm]

# SqueezeNet: fire8/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire8/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire8/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire8/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire8/route
[route]
layers=-3,-1

[batchnorm]

# SqueezeNet: fire9/squeeze1x1
[convolutional]
filters=64
size=1
activation=relu

# SqueezeNet: fire9/expand1x1
[convolutional]
filters=256
size=1
activation=relu

# SqueezeNet: fire9/shortcut
[shortcut]
from=-2
activation=leaky

# SqueezeNet: fire9/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire9/route
[route]
layers=-3,-1

# SqueezeNet: drop9
[dropout]
probability=.5

# SqueezeNet: conv10
[convolutional]
filters=1000
size=1
activation=relu

[batchnorm]

# SqueezeNet: pool10
[avgpool]

# YoLo: output = (5 * 2 + CLASSES) * SIDE^2
[connected]
output=539
activation=linear

# YoLo
[detection]
classes=1
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

OR

[net]
batch=64
subdivisions=2
height=227
width=227
channels=3
momentum=0.9
decay=0.0005

learning_rate=0.04
policy=poly
power=1
max_batches=200000

[crop]
crop_width=227
crop_height=227
flip=0
angle=0
saturation=1.5
exposure=1.5

# SqueezeNet: conv1
[convolutional]
batch_normalize=1
filters=64
size=3
stride=2
activation=relu

# SqueezeNet: pool1
[maxpool]
size=3
stride=2

# SqueezeNet: fire2/squeeze1x1
[convolutional]
batch_normalize=1
filters=16
size=1
activation=relu

# SqueezeNet: fire2/expand1x1
[convolutional]
batch_normalize=1
filters=64
size=1
activation=relu

# SqueezeNet: fire2/route
[route]
layers=-2

# SqueezeNet: fire2/expand3x3
[convolutional]
batch_normalize=1
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire2/route
[route]
layers=-3,-1

# SqueezeNet: fire3/squeeze1x1
[convolutional]
batch_normalize=1
filters=16
size=1
activation=relu

# SqueezeNet:fire3/expand1x1
[convolutional]
batch_normalize=1
filters=64
size=1
activation=relu

# SqueezeNet: fire3/route
[route]
layers=-2

# SqueezeNet: fire3/expand3x3
[convolutional]
batch_normalize=1
filters=64
size=3
pad=1
activation=relu

# SqueezeNet: fire3/route
[route]
layers=-3,-1

# SqueezeNet: pool3
[maxpool]
size=3
stride=2

# SqueezeNet: fire4/squeeze1x1
[convolutional]
batch_normalize=1
filters=32
size=1
activation=relu

# SqueezeNet: fire4/expand1x1
[convolutional]
batch_normalize=1
filters=128
size=1
activation=relu

# SqueezeNet: fire4/route
[route]
layers=-2

# SqueezeNet: fire4/expand3x3
[convolutional]
batch_normalize=1
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire4/route
[route]
layers=-3,-1

# SqueezeNet: fire5/squeeze1x1
[convolutional]
batch_normalize=1
filters=32
size=1
activation=relu

# SqueezeNet: fire5/expand1x1
[convolutional]
batch_normalize=1
filters=128
size=1
activation=relu

# SqueezeNet: fire5/route
[route]
layers=-2

# SqueezeNet: fire5/expand3x3
[convolutional]
batch_normalize=1
filters=128
size=3
pad=1
activation=relu

# SqueezeNet: fire5/route
[route]
layers=-3,-1

# SqueezeNet: pool5
[maxpool]
size=3
stride=2

# SqueezeNet: fire6/squeeze1x1
[convolutional]
batch_normalize=1
filters=48
size=1
activation=relu

# SqueezeNet: fire6/expand1x1
[convolutional]
batch_normalize=1
filters=192
size=1
activation=relu

# SqueezeNet: fire6/route
[route]
layers=-2

# SqueezeNet: fire6/expand3x3
[convolutional]
batch_normalize=1
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire6/route
[route]
layers=-3,-1

# SqueezeNet: fire7/squeeze1x1
[convolutional]
batch_normalize=1
filters=48
size=1
activation=relu

# SqueezeNet: fire7/expand1x1
[convolutional]
batch_normalize=1
filters=192
size=1
activation=relu

# SqueezeNet: fire7/route
[route]
layers=-2

# SqueezeNet: fire7/expand3x3
[convolutional]
batch_normalize=1
filters=192
size=3
pad=1
activation=relu

# SqueezeNet: fire7/route
[route]
layers=-3,-1

# SqueezeNet: fire8/squeeze1x1
[convolutional]
batch_normalize=1
filters=64
size=1
activation=relu

# SqueezeNet: fire8/expand1x1
[convolutional]
batch_normalize=1
filters=256
size=1
activation=relu

# SqueezeNet: fire8/route
[route]
layers=-2

# SqueezeNet: fire8/expand3x3
[convolutional]
batch_normalize=1
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire8/route
[route]
layers=-3,-1

# SqueezeNet: fire9/squeeze1x1
[convolutional]
batch_normalize=1
filters=64
size=1
activation=relu

# SqueezeNet: fire9/expand1x1
[convolutional]
batch_normalize=1
filters=256
size=1
activation=relu

# SqueezeNet: fire9/route
[route]
layers=-2

# SqueezeNet: fire9/expand3x3
[convolutional]
batch_normalize=1
filters=256
size=3
pad=1
activation=relu

# SqueezeNet: fire9/route
[route]
layers=-3,-1

# SqueezeNet: drop9
[dropout]
probability=.5

# SqueezeNet: conv10
[convolutional]
batch_normalize=1
filters=1000
size=1
activation=relu

# SqueezeNet: pool10
[avgpool]

# YoLo: output = (5 * 2 + CLASSES) * SIDE^2
[connected]
output=1331
activation=linear

# YoLo
[detection]
classes=1
coords=4
rescore=1
side=11
num=2
softmax=0
sqrt=1
jitter=.2

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

pjreddie · 2016-09-08T05:13:55Z

the avgpool layer will be really bad for detection since you throw out all
the spatial information. It's good for classification but you should get
rid of it and the last 1000 filter convolutional layer for detection.

Also, just so you know, squeezenet is good in terms of the number of
parameters but it's still uses lots of floating point operations.
squeezenet uses 2.17 billion operations while the standard darknet config:
https://github.com/pjreddie/darknet/blob/master/cfg/darknet.cfg
only uses .81 billion operations so it'll probably be faster.

On Wed, Sep 7, 2016 at 9:41 PM, Luiz Vitor Martinez Cardoso <
notifications@github.com> wrote:

@pjreddie https://github.com/pjreddie it is good to hear from you
buddy!

Please check my latest porting versions of SqueezeNet for Darknet. I had
to change route/shortcut layers and use batchnormalization... now loss
error is going below 1 on my datasets.

https://bitbucket.org/snippets/geeksys/Ap85M

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Grabber · 2016-09-08T05:24:41Z

@pjreddie, yes. What about enabling xnor=1? Is it working?

Grabber · 2016-09-08T05:35:47Z

@pjreddie, by removing

# SqueezeNet: conv10
[convolutional]
batch_normalize=1
filters=1000
size=1
activation=relu

# SqueezeNet: pool10
[avgpool]

Something went totally wrong:

/data/dataset/006_headtop_outdoor/images/out00017358.jpg
Detection Avg IOU: 0.008419, Pos Cat: 0.222350, All Cat: 0.222350, Pos Obj: -285.093384, Any Obj: 94.234314, count: 41
Detection Avg IOU: 0.005466, Pos Cat: 8.992064, All Cat: 8.992064, Pos Obj: -694.999390, Any Obj: 146.219254, count: 35
5: 1363320704.000000, 245519200.000000 avg, 0.039999 rate, 1.584814 seconds, 320 images

Any idea?

pjreddie · 2016-09-08T06:03:09Z

You can also check out the tiny-yolo model, it's based on the darknet
reference network and it's already trained / i know it works.

http://pjreddie.com/darknet/yolo/#tiny

On Wed, Sep 7, 2016 at 10:35 PM, Luiz Vitor Martinez Cardoso <
notifications@github.com> wrote:

@pjreddie https://github.com/pjreddie, by removing

SqueezeNet: conv10

[convolutional]
batch_normalize=1
filters=1000
size=1
activation=relu

SqueezeNet: pool10

[avgpool]

Something goes totally wrong:

/data/dataset/006_headtop_outdoor/images/out00017358.jpg
Detection Avg IOU: 0.008419, Pos Cat: 0.222350, All Cat: 0.222350, Pos Obj: -285.093384, Any Obj: 94.234314, count: 41
Detection Avg IOU: 0.005466, Pos Cat: 8.992064, All Cat: 8.992064, Pos Obj: -694.999390, Any Obj: 146.219254, count: 35
5: 1363320704.000000, 245519200.000000 avg, 0.039999 rate, 1.584814 seconds, 320 images

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Reply to this email directly, view it on GitHub
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Grabber · 2016-09-08T06:07:10Z

@pjreddie, I got a push notification about your commit and already started training it. Thanks!

ghost · 2016-09-08T09:30:23Z

@pjreddie What are the upgrades from yolo-tiny to tiny-yolo ? Don't you think removing dropout might impact generalization ?
@Grabber Squeezing tiny-yolo might be a good idea, specially with FP16.

ghost · 2016-09-08T16:00:16Z

@Grabber I think squeezeNet has specially been designed for mimicking AlexNet which natively address the problem of classification. Therefore, as @pjreddie said it, average polling isn't a good idea for detection. I only see an FC layer that could overcome this, but I am afraid the number of parameters would explode and thereby compromise the pruning process.

tmatas · 2016-09-13T08:41:36Z

Do avgpooling on a cropped area (where the detected object is) might be useful?

Grabber · 2016-09-19T04:13:37Z

@tmatas that is a question that @pjreddie can answer with much more property, I really don't know.

azuryl · 2018-08-23T03:00:06Z

@pjreddie Dear pjreddie
can you deliver a pretrained Squeezenet model and cfg in darknet so I can comapre the difference between Squeezenet and tiny darknet

Grabber changed the title ~~Darknet/YOLO + Squeezenet~~ Darknet/YOLO or Caffe/R-FCN + Squeezenet Jul 4, 2016

forresti closed this as completed Jan 1, 2017

Darknet/YOLO or Caffe/R-FCN + Squeezenet #15

Darknet/YOLO or Caffe/R-FCN + Squeezenet #15

Comments

Grabber commented Jul 3, 2016 • edited Loading

forresti commented Jul 4, 2016

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Grabber commented Jul 11, 2016 • edited Loading

Grabber commented Jul 27, 2016

ghost commented Sep 6, 2016 • edited by ghost Loading

Grabber commented Sep 8, 2016 • edited Loading

pjreddie commented Sep 8, 2016

SqueezeNet: conv1

SqueezeNet: pool1

SqueezeNet: fire2/squeeze1x1

SqueezeNet: fire2/expand1x1

SqueezeNet: fire2/shortcut

SqueezeNet: fire2/expand3x3

SqueezeNet: fire2/route

SqueezeNet: fire3/squeeze1x1

SqueezeNet:fire3/expand1x1

SqueezeNet: fire3/shortcut

SqueezeNet: fire3/expand3x3

SqueezeNet: fire3/route

SqueezeNet: pool3

SqueezeNet: fire4/squeeze1x1

SqueezeNet: fire4/expand1x1

SqueezeNet: fire4/shortcut

SqueezeNet: fire4/expand3x3

SqueezeNet: fire4/route

SqueezeNet: fire5/squeeze1x1

SqueezeNet: fire5/expand1x1

SqueezeNet: fire5/shortcut

SqueezeNet: fire5/expand3x3

SqueezeNet: fire5/route

SqueezeNet: pool5

SqueezeNet: fire6/squeeze1x1

SqueezeNet: fire6/expand1x1

SqueezeNet: fire6/shortcut

SqueezeNet: fire6/expand3x3

SqueezeNet: fire6/route

SqueezeNet: fire7/squeeze1x1

SqueezeNet: fire7/expand1x1

SqueezeNet: fire7/shortcut

SqueezeNet: fire7/expand3x3

SqueezeNet: fire7/route

SqueezeNet: fire8/squeeze1x1

SqueezeNet: fire8/expand1x1

SqueezeNet: fire8/shortcut

SqueezeNet: fire8/expand3x3

SqueezeNet: fire8/route

SqueezeNet: fire9/squeeze1x1

SqueezeNet: fire9/expand1x1

SqueezeNet: fire9/shortcut

SqueezeNet: fire9/expand3x3

SqueezeNet: fire9/route

SqueezeNet: drop9

SqueezeNet: conv10

SqueezeNet: pool10

YoLo: output = (5 * 2 + CLASSES) * SIDE^2

YoLo

Grabber commented Sep 8, 2016 • edited Loading

pjreddie commented Sep 8, 2016

Grabber commented Sep 8, 2016

Grabber commented Sep 8, 2016 • edited Loading

pjreddie commented Sep 8, 2016

SqueezeNet: conv10

SqueezeNet: pool10

Grabber commented Sep 8, 2016 • edited Loading

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ghost commented Sep 8, 2016

tmatas commented Sep 13, 2016

Grabber commented Sep 19, 2016

azuryl commented Aug 23, 2018

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