segnet_isprs_vaihingen_irrg.prototxt about deepnetsforeo HOT 4 CLOSED

If you would closely, the examples are designed so that you never train with unclassified values. In the ISPRS dataset, there are 6 classes, 'unclassified' is not one of them.

Now, as far as I understand, you want to perform binary classification with undefined labels. So, what you should have is ignore_label: 2 in your prototxt file (assuming that 0 is 'A', 1 is 'B' and 2 is 'unclassified').

However, the number of outputs in the last convolutional layer has to match the number of labels, even if you ignore the last label. So, if you want to train with A, B and undefined, your final layers should be like those :

layer {
  name: "conv1_1_D"
  type: "Convolution"
  bottom: "conv1_2_D"
  top: "conv1_1_D"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 3 ### because you have 3 labels = A, B, undefined
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "loss"
  type: "SoftmaxWithLoss"
  bottom: "conv1_1_D"
  bottom: "label"
  top: "loss"
  ignore_label: 2 ### Do not compute the loss (and do not train) on the 'undefined' labels
}

I hope this is clear enough.

from deepnetsforeo.

Yes, it's clear. I had tried something similar before, but it fails building the net, because "caffe.LayerParameter has no field named "ignore_label".

So, what I tried now is the following:

layer {
name: "conv1_1_D"
type: "Convolution"
bottom: "conv1_2_D"
top: "conv1_1_D"
param {
lr_mult: 1
decay_mult: 1
}
convolution_param {
num_output: 3 ### because you have 3 labels = A, B, undefined
pad: 1
kernel_size: 3
weight_filler {
type: "msra"
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "conv1_1_D"
bottom: "label"
top: "loss"
accuracy_param {
ignore_label: 2 ### Do not compute the loss (and do not train) on the 'undefined' labels
}
}

Is this correct?

from deepnetsforeo.

After all, i suppose that I also have to modify the net in the inference step.
Is this true?

from deepnetsforeo.

Yes, but it's not mandatory. What's important is that you do not train on unclassified labels. In the inference steps, unclassified labels should not be predicted anyway, although you can add the "ignore_label" parameter in the inference network so that you compute the accuracy and loss without taking those pixels into account.

from deepnetsforeo.