HED-train.prototxt

name: "HED"
layer {
  name: "data"
  type: "ImageLabelmapData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: false
    mean_value: 104.00699
    mean_value: 116.66877
    mean_value: 122.67892
  }
  image_data_param {
    root_folder: "../../data/HED-BSDS/"
    source: "../../data/HED-BSDS/train_pair.lst"
    batch_size: 1
    shuffle: true
    new_height: 0
    new_width: 0
  }
}
layer {
  name: "data"
  type: "ImageLabelmapData"
  top: "data"
  top: "label"
  include {
    phase: TEST
  }
  transform_param {
    mirror: false
    mean_value: 104.00699
    mean_value: 116.66877
    mean_value: 122.67892
  }
  image_data_param {
    root_folder: "../../data/HED-BSDS/"
    source: "../../data/HED-BSDS/train_pair.lst"
    #Just setup the network. No real online testing
    batch_size: 1
    shuffle: true
    new_height: 0
    new_width: 0
  }
}

layer { bottom: 'data' top: 'conv1_1' name: 'conv1_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 64 pad: 35 kernel_size: 3 } }
layer { bottom: 'conv1_1' top: 'conv1_1' name: 'relu1_1' type: "ReLU" }
layer { bottom: 'conv1_1' top: 'conv1_2' name: 'conv1_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 64 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv1_2' top: 'conv1_2' name: 'relu1_2' type: "ReLU" }
layer { name: 'pool1' bottom: 'conv1_2' top: 'pool1' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { name: 'conv2_1' bottom: 'pool1' top: 'conv2_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv2_1' top: 'conv2_1' name: 'relu2_1' type: "ReLU" }
layer { bottom: 'conv2_1' top: 'conv2_2' name: 'conv2_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 128 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv2_2' top: 'conv2_2' name: 'relu2_2' type: "ReLU" }
layer { bottom: 'conv2_2' top: 'pool2' name: 'pool2' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: 'pool2' top: 'conv3_1' name: 'conv3_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_1' top: 'conv3_1' name: 'relu3_1' type: "ReLU" }
layer { bottom: 'conv3_1' top: 'conv3_2' name: 'conv3_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_2' top: 'conv3_2' name: 'relu3_2' type: "ReLU" }
layer { bottom: 'conv3_2' top: 'conv3_3' name: 'conv3_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 256 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv3_3' top: 'conv3_3' name: 'relu3_3' type: "ReLU" }
layer { bottom: 'conv3_3' top: 'pool3' name: 'pool3' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: 'pool3' top: 'conv4_1' name: 'conv4_1' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_1' top: 'conv4_1' name: 'relu4_1' type: "ReLU" }
layer { bottom: 'conv4_1' top: 'conv4_2' name: 'conv4_2' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_2' top: 'conv4_2' name: 'relu4_2' type: "ReLU" }
layer { bottom: 'conv4_2' top: 'conv4_3' name: 'conv4_3' type: "Convolution"
  param { lr_mult: 1 decay_mult: 1 } param { lr_mult: 2 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv4_3' top: 'conv4_3' name: 'relu4_3' type: "ReLU" }
layer { bottom: 'conv4_3' top: 'pool4' name: 'pool4' type: "Pooling"
  pooling_param { pool: MAX kernel_size: 2 stride: 2 } }

layer { bottom: 'pool4' top: 'conv5_1' name: 'conv5_1' type: "Convolution"
  param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_1' top: 'conv5_1' name: 'relu5_1' type: "ReLU" }
layer { bottom: 'conv5_1' top: 'conv5_2' name: 'conv5_2' type: "Convolution"
  param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_2' top: 'conv5_2' name: 'relu5_2' type: "ReLU" }
layer { bottom: 'conv5_2' top: 'conv5_3' name: 'conv5_3' type: "Convolution"
  param { lr_mult: 100 decay_mult: 1 } param { lr_mult: 200 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 512 pad: 1 kernel_size: 3 } }
layer { bottom: 'conv5_3' top: 'conv5_3' name: 'relu5_3' type: "ReLU" }

## DSN conv 1 ###
layer { name: 'score-dsn1' type: "Convolution" bottom: 'conv1_2' top: 'score-dsn1-up'
  param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn1-up' bottom: 'data' top: 'upscore-dsn1' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn1" bottom: "label" top:"dsn1_loss"  loss_weight: 1}

### DSN conv 2 ###
layer { name: 'score-dsn2' type: "Convolution" bottom: 'conv2_2' top: 'score-dsn2'
  param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
layer { type: "Deconvolution" name: 'upsample_2' bottom: 'score-dsn2' top: 'score-dsn2-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 4 stride: 2 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn2-up' bottom: 'data' top: 'upscore-dsn2' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn2" bottom: "label" top:"dsn2_loss"  loss_weight: 1}

### DSN conv 3 ###
layer { name: 'score-dsn3' type: "Convolution" bottom: 'conv3_3' top: 'score-dsn3'
  param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
layer { type: "Deconvolution" name: 'upsample_4' bottom: 'score-dsn3' top: 'score-dsn3-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 8 stride: 4 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn3-up' bottom: 'data' top: 'upscore-dsn3' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn3" bottom: "label" top:"dsn3_loss"  loss_weight: 1}

###DSN conv 4###
layer { name: 'score-dsn4' type: "Convolution" bottom: 'conv4_3' top: 'score-dsn4'
  param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
layer { type: "Deconvolution" name: 'upsample_8' bottom: 'score-dsn4' top: 'score-dsn4-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 16 stride: 8 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn4-up' bottom: 'data' top: 'upscore-dsn4' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn4" bottom: "label" top:"dsn4_loss"  loss_weight: 1}

###DSN conv 5###
layer { name: 'score-dsn5' type: "Convolution" bottom: 'conv5_3' top: 'score-dsn5'
  param { lr_mult: 0.01 decay_mult: 1 } param { lr_mult: 0.02 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 } }
layer { type: "Deconvolution" name: 'upsample_16' bottom: 'score-dsn5' top: 'score-dsn5-up'
  param { lr_mult: 0 decay_mult: 1 } param { lr_mult: 0 decay_mult: 0}
  convolution_param { kernel_size: 32 stride: 16 num_output: 1 } }
layer { type: "Crop" name: 'crop' bottom: 'score-dsn5-up' bottom: 'data' top: 'upscore-dsn5' }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-dsn5" bottom: "label" top:"dsn5_loss"  loss_weight: 1}

### Concat and multiscale weight layer ###
layer { name: "concat" bottom: "upscore-dsn1"  bottom: "upscore-dsn2" bottom: "upscore-dsn3" 
         bottom: "upscore-dsn4" bottom: "upscore-dsn5" top: "concat-upscore" type: "Concat"
  concat_param { concat_dim: 1} }
layer { name: 'new-score-weighting' type: "Convolution" bottom: 'concat-upscore' top: 'upscore-fuse'
  param { lr_mult: 0.001 decay_mult: 1 } param { lr_mult: 0.002 decay_mult: 0}
  convolution_param { engine: CAFFE num_output: 1 kernel_size: 1 weight_filler {type: "constant" value: 0.2} } }
layer { type: "SigmoidCrossEntropyLoss" bottom: "upscore-fuse" bottom: "label" top:"fuse_loss"  loss_weight: 1}