caffe fcn pixel-wise segmentation to regression

Question

hello I am quite new to deep learning and caffe so please do not mind if my question is a little stupid.

I have been looking into pixel-wise classification / segmentation / regression. Therefore I have seen there is a gitlhub repo for image segmentation fcn berkeley and some other posts like question 1, question 2.

What I wanted to do is something similar buf slightly different. I have a dataset of images and their corresponding ground_truth as images. I am not sure if it is better to use pixel-wise classification via SoftmaxLoss or regression via EuclideanLoss. My ground_truth images contain values from 0-255 and only have one channel.

I have been trying to do a regression task and have a fully convolutional network with a few convolutional layers which remain the output size and the last layer looks like this: In the end I want to do a depth prediction task. Therefore I am not sure if it is better to use SoftmaxWithLoss or EuclideanLoss. However this question might be a bit stupid. But is this approach correct? First I have tried to learn the shape of my images, i.e. I have set the values in the ground_truth to 0.5 when my input image has a value greater than 0 at the corresponding location. Could anyone help me please?

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 256
    kernel_size: 53
    stride: 1
    pad: 26
    weight_filler {
      type: "gaussian"
      std: 0.011
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  convolution_param {
    num_output: 128
    kernel_size: 15
    stride: 1
    pad: 7
    weight_filler {
      type: "gaussian"
      std: 0.011
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  convolution_param {
    num_output: 1
    kernel_size: 11
    stride: 1
    pad: 5
    weight_filler {
      type: "gaussian"
      std: 0.011
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}

#layer {
#  name: "loss"
#  type: "SoftmaxWithLoss"
#  bottom: "score"
#  bottom: "label"
#  top: "loss"
#  loss_param {
#    ignore_label: 255
#    normalize: true
#  }
#}
layer {
  name: "loss"
  type: "EuclideanLoss"
  bottom: "conv3"
  bottom: "label"
  top: "loss"
}