Comments (18)
Grabber
commented on August 27, 2024
1
@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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forresti
commented on August 27, 2024
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.
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Grabber
commented on August 27, 2024
We may could invite the R-FCN author too, what do you think @daijifeng001?
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Grabber
commented on August 27, 2024
@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.
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Grabber
commented on August 27, 2024
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```
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commented on August 27, 2024
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.
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pjreddie
commented on August 27, 2024
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" <
[email protected]> 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=0.0005learning_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 = 1.5SqueezeNet: conv1
[convolutional]
filters=64
size=3
stride=2
activation=reluSqueezeNet: pool1
[maxpool]
size=3
stride=2SqueezeNet: fire2/squeeze1x1
[convolutional]
filters=16
size=1
activation=reluSqueezeNet: fire2/expand1x1
[convolutional]
filters=64
size=1
activation=reluSqueezeNet: fire2/shortcut
[route]
layers=-2SqueezeNet: fire2/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=reluSqueezeNet: fire2/route
[route]
layers=-3,-1SqueezeNet: fire3/squeeze1x1
[convolutional]
filters=16
size=1
activation=reluSqueezeNet:fire3/expand1x1
[convolutional]
filters=64
size=1
activation=reluSqueezeNet: fire3/shortcut
[route]
layers=-2SqueezeNet: fire3/expand3x3
[convolutional]
filters=64
size=3
pad=1
activation=reluSqueezeNet: fire3/route
[route]
layers=-3,-1SqueezeNet: pool3
[maxpool]
size=3
stride=2SqueezeNet: fire4/squeeze1x1
[convolutional]
filters=32
size=1
activation=reluSqueezeNet: fire4/expand1x1
[convolutional]
filters=128
size=1
activation=reluSqueezeNet: fire4/shortcut
[route]
layers=-2SqueezeNet: fire4/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=reluSqueezeNet: fire4/route
[route]
layers=-3,-1SqueezeNet: fire5/squeeze1x1
[convolutional]
filters=32
size=1
activation=reluSqueezeNet: fire5/expand1x1
[convolutional]
filters=128
size=1
activation=reluSqueezeNet: fire5/shortcut
[route]
layers=-2SqueezeNet: fire5/expand3x3
[convolutional]
filters=128
size=3
pad=1
activation=reluSqueezeNet: fire5/route
[route]
layers=-3,-1SqueezeNet: pool5
[maxpool]
size=3
stride=2SqueezeNet: fire6/squeeze1x1
[convolutional]
filters=48
size=1
activation=reluSqueezeNet: fire6/expand1x1
[convolutional]
filters=192
size=1
activation=reluSqueezeNet: fire6/shortcut
[route]
layers=-2SqueezeNet: fire6/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=reluSqueezeNet: fire6/route
[route]
layers=-3,-1SqueezeNet: fire7/squeeze1x1
[convolutional]
filters=48
size=1
activation=reluSqueezeNet: fire7/expand1x1
[convolutional]
filters=192
size=1
activation=reluSqueezeNet: fire7/shortcut
[route]
layers=-2SqueezeNet: fire7/expand3x3
[convolutional]
filters=192
size=3
pad=1
activation=reluSqueezeNet: fire7/route
[route]
layers=-3,-1SqueezeNet: fire8/squeeze1x1
[convolutional]
filters=64
size=1
activation=reluSqueezeNet: fire8/expand1x1
[convolutional]
filters=256
size=1
activation=reluSqueezeNet: fire8/shortcut
[route]
layers=-2SqueezeNet: fire8/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=reluSqueezeNet: fire8/route
[route]
layers=-3,-1SqueezeNet: fire9/squeeze1x1
[convolutional]
filters=64
size=1
activation=reluSqueezeNet: fire9/expand1x1
[convolutional]
filters=256
size=1
activation=reluSqueezeNet: fire9/shortcut
[route]
layers=-2SqueezeNet: fire9/expand3x3
[convolutional]
filters=256
size=3
pad=1
activation=reluSqueezeNet: fire9/route
[route]
layers=-3,-1SqueezeNet: drop9
[dropout]
probability=.5SqueezeNet: conv10
[convolutional]
filters=1000
size=1
activation=reluSqueezeNet: pool10
[avgpool]
YoLo: output = (5 * 2 + CLASSES) * SIDE^2
[connected]
output=539
activation=linearYoLo
[detection]
classes=1
coords=4
rescore=1
side=7
num=2
softmax=0
sqrt=1
jitter=.2object_scale=1
noobject_scale=.5
class_scale=1
coord_scale=5```—
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Grabber
commented on August 27, 2024
@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
from squeezenet.
pjreddie
commented on August 27, 2024
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 <
[email protected]> 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
commented on August 27, 2024
@pjreddie, yes. What about enabling xnor=1? Is it working?
from squeezenet.
Grabber
commented on August 27, 2024
@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?
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pjreddie
commented on August 27, 2024
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 <
[email protected]> wrote:
@pjreddie https://github.com/pjreddie, by removing
SqueezeNet: conv10
[convolutional]
batch_normalize=1
filters=1000
size=1
activation=reluSqueezeNet: 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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Grabber
commented on August 27, 2024
@pjreddie, I got a push notification about your commit and already started training it. Thanks!
from squeezenet.
commented on August 27, 2024
@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.
from squeezenet.
commented on August 27, 2024
@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.
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tmatas
commented on August 27, 2024
Do avgpooling on a cropped area (where the detected object is) might be useful?
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Grabber
commented on August 27, 2024
@tmatas that is a question that @pjreddie can answer with much more property, I really don't know.
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azuryl
commented on August 27, 2024
@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
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