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segnet_simple_train.prototxt
name: "segnet"
layer {
name: "data"
type: "DenseImageData"
top: "data"
top: "label"
dense_image_data_param {
source: "/SegNet/CamVid/train.txt" # Change this to the absolute path to your data file
batch_size: 4 # Change this number to a batch size that will fit on your GPU
shuffle: true
}
}
layer {
name: "norm"
type: "LRN"
bottom: "data"
top: "norm"
lrn_param {
local_size: 5
alpha: 0.0001
beta: 0.75
}
}
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
layer {
name: "conv1"
type: "Convolution"
bottom: "norm"
top: "conv1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv1"
top: "conv1"
name: "conv1_bn"
type: "BN"
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
layer {
name: "relu1"
type: "ReLU"
bottom: "conv1"
top: "conv1"
}
layer {
name: "pool1"
type: "Pooling"
bottom: "conv1"
top: "pool1"
top: "pool1_mask"
pooling_param {
pool: MAX
kernel_size: 2
stride: 2
}
}
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
// After this step doing upsampling
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
layer {
name: "upsample1"
type: "Upsample"
bottom: "pool1"
bottom: "pool1_mask"
top: "upsample1"
upsample_param {
scale: 2
//pad_out_h: true
}
}
layer {
name: "conv_decode1"
type: "Convolution"
bottom: "upsample1"
top: "conv_decode1"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 64
kernel_size: 7
pad: 3
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
bottom: "conv_decode1"
top: "conv_decode1"
name: "conv_decode1_bn"
type: "BN"
bn_param {
scale_filler {
type: "constant"
value: 1
}
shift_filler {
type: "constant"
value: 0.001
}
}
}
//---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
layer {
name: "conv_classifier"
type: "Convolution"
bottom: "conv_decode1"
top: "conv_classifier"
param {
lr_mult: 1
decay_mult: 1
}
param {
lr_mult: 2
decay_mult: 0
}
convolution_param {
num_output: 11
kernel_size: 1
weight_filler {
type: "msra"
}
bias_filler {
type: "constant"
}
}
}
layer {
name: "loss"
type: "SoftmaxWithLoss"
bottom: "conv_classifier"
bottom: "label"
top: "loss"
softmax_param {engine: CAFFE}
loss_param: {
weight_by_label_freqs: true
ignore_label: 11
class_weighting: 0.2595
class_weighting: 0.1826
class_weighting: 4.5640
class_weighting: 0.1417
class_weighting: 0.9051
class_weighting: 0.3826
class_weighting: 9.6446
class_weighting: 1.8418
class_weighting: 0.6823
class_weighting: 6.2478
class_weighting: 7.3614
}
}
layer {
name: "accuracy"
type: "Accuracy"
bottom: "conv_classifier"
bottom: "label"
top: "accuracy"
top: "per_class_accuracy"
}
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