caffe fcn pixel-wise segmentation to regression - deep-learning

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"
}