FCN-8 loss value is constant throughout training

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jas

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May 16, 2016, 9:40:42 AM5/16/16
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Hello,

I'm training the fcn-8 network, but throughout the training loss function is constant. I'm training the network using my dataset, which has around 176 samples and three classes including background. Should I change the solver parameters, if yes which are parameters which I should I change.


Evan Shelhamer

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May 24, 2016, 2:42:58 PM5/24/16
to jas, Caffe Users
Constant loss usually suggests the initial parameters are not initialized properly. See https://github.com/shelhamer/fcn.berkeleyvision.org/blob/master/voc-fcn32s/solve.py for an example of how to solve.

Evan Shelhamer





On Mon, May 16, 2016 at 6:40 AM, jas <m.s...@gmail.com> wrote:
Hello,

I'm training the fcn-8 network, but throughout the training loss function is constant. I'm training the network using my dataset, which has around 176 samples and three classes including background. Should I change the solver parameters, if yes which are parameters which I should I change.


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jas

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May 25, 2016, 2:00:59 AM5/25/16
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Thank you Evan. The link which you have provided, I understood that it is used to train fcn32 using vgg16fc.caffemodel. However, I'm using fcn-8 for training, so for that too can I use vgg16fc.caffemodel as base weights.

Evan Shelhamer

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May 25, 2016, 2:02:46 AM5/25/16
to jas, Caffe Users
See the all-at-once version of FCN-8s for how to train it in one step from VGG-16: https://github.com/shelhamer/fcn.berkeleyvision.org/tree/master/voc-fcn8s-atonce

The FCN-32s, FCN-16s, and FCN-8s are otherwise intended to be trained in stages.

Evan Shelhamer





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