yufacedetectnet-open-v2.train.prototxt

name: "YuFaceDetectNet"

layer {
  name: "data"
  type: "AnnotatedData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  transform_param {
    mirror: true
#    mean_value: 104.0
#    mean_value: 117.0
#    mean_value: 123.0
    resize_param {
      prob: 1.0
      resize_mode: WARP
      height: 240
      width: 320
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: NEAREST
      interp_mode: CUBIC
      interp_mode: LANCZOS4
    }
    emit_constraint {
      emit_type: CENTER
    }
    distort_param {
      brightness_prob: 0.5
      brightness_delta: 32.0
      contrast_prob: 0.5
      contrast_lower: 0.5
      contrast_upper: 1.5
      hue_prob: 0.5
      hue_delta: 18.0
      saturation_prob: 0.5
      saturation_lower: 0.5
      saturation_upper: 1.5
      random_order_prob: 0.0
    }
  }
  data_param {
    source: "../FACE/lmdb/FACE_trainval_lmdb/"
    batch_size: 16
    backend: LMDB
  }
  annotated_data_param {
    batch_sampler {
      sampler {
        min_scale: 1.0
        max_scale: 1.0
        min_aspect_ratio: 1.0
        max_aspect_ratio: 1.0
      }
      sample_constraint {
        min_object_coverage: 1.0
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.300000011921
        max_scale: 1.0
        min_aspect_ratio: 1.0
        max_aspect_ratio: 1.0
      }
      sample_constraint {
        min_object_coverage: 1.0
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.300000011921
        max_scale: 1.0
        min_aspect_ratio: 1.0
        max_aspect_ratio: 1.0
      }
      sample_constraint {
        min_object_coverage: 1.0
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.300000011921
        max_scale: 1.0
        min_aspect_ratio: 1.0
        max_aspect_ratio: 1.0
      }
      sample_constraint {
        min_object_coverage: 1.0
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.300000011921
        max_scale: 1.0
        min_aspect_ratio: 1.0
        max_aspect_ratio: 1.0
      }
      sample_constraint {
        min_object_coverage: 1.0
      }
      max_sample: 1
      max_trials: 50
    }
    label_map_file: "../labelmap_face.prototxt"
  }
}
#CONV1###########################################################

layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    pad: 1
	stride: 2
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}

layer {
  name: "relu1_1"
  type: "ReLU"
  bottom: "conv1_1"
  top: "conv1_1"
}
layer {
  name: "conv1_2"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 16
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}


layer {
  name: "relu1_2"
  type: "ReLU"
  bottom: "conv1_2"
  top: "conv1_2"
}
#CONV2##########################################################
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1_2"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}


layer {
  name: "relu2_1"
  type: "ReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 16
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}

layer {
  name: "relu2_2"
  type: "ReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
#CONV3##########################################################

layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2_2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}

layer {
  name: "relu3_1"
  type: "ReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 32
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}


layer {
  name: "relu3_2"
  type: "ReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "conv3_2"
  top: "conv3_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}

layer {
  name: "relu3_3"
  type: "ReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}


#CONV4##########################################################

layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3_3"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "pool3"
  top: "conv4_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}

layer {
  name: "relu4_1"
  type: "ReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 64
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}

layer {
  name: "relu4_2"
  type: "ReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "conv4_2"
  top: "conv4_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}

layer {
  name: "relu4_3"
  type: "ReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}

#CONV5##########################################################

layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4_3"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv5_1"
  type: "Convolution"
  bottom: "pool4"
  top: "conv5_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu5_1"
  type: "ReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu5_2"
  type: "ReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "conv5_2"
  top: "conv5_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu5_3"
  type: "ReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}

#CONV6##########################################################

layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_3"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer {
  name: "conv6_1"
  type: "Convolution"
  bottom: "pool5"
  top: "conv6_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu6_1"
  type: "ReLU"
  bottom: "conv6_1"
  top: "conv6_1"
}
layer {
  name: "conv6_2"
  type: "Convolution"
  bottom: "conv6_1"
  top: "conv6_2"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu6_2"
  type: "ReLU"
  bottom: "conv6_2"
  top: "conv6_2"
}
layer {
  name: "conv6_3"
  type: "Convolution"
  bottom: "conv6_2"
  top: "conv6_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 128
    pad: 1
    kernel_size: 3
    weight_filler {
      type: "xavier"
    }
    bias_term: false
    dilation: 1
  }
}

layer {
  name: "relu6_3"
  type: "ReLU"
  bottom: "conv6_3"
  top: "conv6_3"
}

#PRIORBOX3##########################################
layer {
  name: "conv3_3_norm"
  type: "Normalize"
  bottom: "conv3_3"
  top: "conv3_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 10.0
    }
    channel_shared: false
  }
}
layer {
  name: "conv3_3_norm_mbox_loc"
  type: "Convolution"
  bottom: "conv3_3_norm"
  top: "conv3_3_norm_mbox_loc"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 12
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv3_3_norm_mbox_loc_perm"
  type: "Permute"
  bottom: "conv3_3_norm_mbox_loc"
  top: "conv3_3_norm_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv3_3_norm_mbox_loc_flat"
  type: "Flatten"
  bottom: "conv3_3_norm_mbox_loc_perm"
  top: "conv3_3_norm_mbox_loc_flat"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv3_3_norm_mbox_conf"
  type: "Convolution"
  bottom: "conv3_3_norm"
  top: "conv3_3_norm_mbox_conf"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 6
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv3_3_norm_mbox_conf_perm"
  type: "Permute"
  bottom: "conv3_3_norm_mbox_conf"
  top: "conv3_3_norm_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv3_3_norm_mbox_conf_flat"
  type: "Flatten"
  bottom: "conv3_3_norm_mbox_conf_perm"
  top: "conv3_3_norm_mbox_conf_flat"
  flatten_param {
    axis: 1
  }
}
layer {

  name: "conv3_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv3_3_norm"
  bottom: "data"
  top: "conv3_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 10.0
    min_size: 16.0
    min_size: 24.0
    clip: false
    variance: 0.10000000149
    variance: 0.10000000149
    variance: 0.20000000298
    variance: 0.20000000298
    step: 8.0
    offset: 0.5
  }
}
#PRIORBOX4##########################################
layer {
  name: "conv4_3_norm"
  type: "Normalize"
  bottom: "conv4_3"
  top: "conv4_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 8.0
    }
    channel_shared: false
  }
}
layer {
  name: "conv4_3_norm_mbox_loc"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_loc"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv4_3_norm_mbox_loc_perm"
  type: "Permute"
  bottom: "conv4_3_norm_mbox_loc"
  top: "conv4_3_norm_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_loc_flat"
  type: "Flatten"
  bottom: "conv4_3_norm_mbox_loc_perm"
  top: "conv4_3_norm_mbox_loc_flat"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_conf"
  type: "Convolution"
  bottom: "conv4_3_norm"
  top: "conv4_3_norm_mbox_conf"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 4
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv4_3_norm_mbox_conf_perm"
  type: "Permute"
  bottom: "conv4_3_norm_mbox_conf"
  top: "conv4_3_norm_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv4_3_norm_mbox_conf_flat"
  type: "Flatten"
  bottom: "conv4_3_norm_mbox_conf_perm"
  top: "conv4_3_norm_mbox_conf_flat"
  flatten_param {
    axis: 1
  }
}
layer {

  name: "conv4_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv4_3_norm"
  bottom: "data"
  top: "conv4_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 32.0
    min_size: 48.0
    clip: false
    variance: 0.10000000149
    variance: 0.10000000149
    variance: 0.20000000298
    variance: 0.20000000298
    step: 16.0
    offset: 0.5
  }
}
#PRIORBOX5##########################################
layer {
  name: "conv5_3_norm"
  type: "Normalize"
  bottom: "conv5_3"
  top: "conv5_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 5.0
    }
    channel_shared: false
  }
}
layer {
  name: "conv5_3_norm_mbox_loc"
  type: "Convolution"
  bottom: "conv5_3_norm"
  top: "conv5_3_norm_mbox_loc"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 8
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv5_3_norm_mbox_loc_perm"
  type: "Permute"
  bottom: "conv5_3_norm_mbox_loc"
  top: "conv5_3_norm_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv5_3_norm_mbox_loc_flat"
  type: "Flatten"
  bottom: "conv5_3_norm_mbox_loc_perm"
  top: "conv5_3_norm_mbox_loc_flat"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv5_3_norm_mbox_conf"
  type: "Convolution"
  bottom: "conv5_3_norm"
  top: "conv5_3_norm_mbox_conf"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 4
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv5_3_norm_mbox_conf_perm"
  type: "Permute"
  bottom: "conv5_3_norm_mbox_conf"
  top: "conv5_3_norm_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv5_3_norm_mbox_conf_flat"
  type: "Flatten"
  bottom: "conv5_3_norm_mbox_conf_perm"
  top: "conv5_3_norm_mbox_conf_flat"
  flatten_param {
    axis: 1
  }
}
layer {

  name: "conv5_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv5_3_norm"
  bottom: "data"
  top: "conv5_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 64.0
    min_size: 96.0
    clip: false
    variance: 0.10000000149
    variance: 0.10000000149
    variance: 0.20000000298
    variance: 0.20000000298
    step: 32.0
    offset: 0.5
  }
}

#PRIORBOX6##########################################
layer {
  name: "conv6_3_norm"
  type: "Normalize"
  bottom: "conv6_3"
  top: "conv6_3_norm"
  norm_param {
    across_spatial: false
    scale_filler {
      type: "constant"
      value: 5.0
    }
    channel_shared: false
  }
}
layer {
  name: "conv6_3_norm_mbox_loc"
  type: "Convolution"
  bottom: "conv6_3_norm"
  top: "conv6_3_norm_mbox_loc"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 12
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv6_3_norm_mbox_loc_perm"
  type: "Permute"
  bottom: "conv6_3_norm_mbox_loc"
  top: "conv6_3_norm_mbox_loc_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv6_3_norm_mbox_loc_flat"
  type: "Flatten"
  bottom: "conv6_3_norm_mbox_loc_perm"
  top: "conv6_3_norm_mbox_loc_flat"
  flatten_param {
    axis: 1
  }
}
layer {
  name: "conv6_3_norm_mbox_conf"
  type: "Convolution"
  bottom: "conv6_3_norm"
  top: "conv6_3_norm_mbox_conf"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  convolution_param {
    num_output: 6
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_term: false
  }
}
layer {
  name: "conv6_3_norm_mbox_conf_perm"
  type: "Permute"
  bottom: "conv6_3_norm_mbox_conf"
  top: "conv6_3_norm_mbox_conf_perm"
  permute_param {
    order: 0
    order: 2
    order: 3
    order: 1
  }
}
layer {
  name: "conv6_3_norm_mbox_conf_flat"
  type: "Flatten"
  bottom: "conv6_3_norm_mbox_conf_perm"
  top: "conv6_3_norm_mbox_conf_flat"
  flatten_param {
    axis: 1
  }
}
layer {

  name: "conv6_3_norm_mbox_priorbox"
  type: "PriorBox"
  bottom: "conv6_3_norm"
  bottom: "data"
  top: "conv6_3_norm_mbox_priorbox"
  prior_box_param {
    min_size: 128.0
    min_size: 192.0
    min_size: 256.0
    clip: false
    variance: 0.10000000149
    variance: 0.10000000149
    variance: 0.20000000298
    variance: 0.20000000298
    step: 64.0
    offset: 0.5
  }
}
########################################################
layer {
  name: "mbox_loc"
  type: "Concat"
  bottom: "conv3_3_norm_mbox_loc_flat"
  bottom: "conv4_3_norm_mbox_loc_flat"
  bottom: "conv5_3_norm_mbox_loc_flat"
  bottom: "conv6_3_norm_mbox_loc_flat"
  top: "mbox_loc"
  concat_param {
    axis: 1
  }
}
layer {
  name: "mbox_conf"
  type: "Concat"
  bottom: "conv3_3_norm_mbox_conf_flat"
  bottom: "conv4_3_norm_mbox_conf_flat"
  bottom: "conv5_3_norm_mbox_conf_flat"
  bottom: "conv6_3_norm_mbox_conf_flat"
  top: "mbox_conf"
  concat_param {
    axis: 1
  }
}
layer {
  name: "mbox_priorbox"
  type: "Concat"
  bottom: "conv3_3_norm_mbox_priorbox"
  bottom: "conv4_3_norm_mbox_priorbox"
  bottom: "conv5_3_norm_mbox_priorbox"
  bottom: "conv6_3_norm_mbox_priorbox"
  top: "mbox_priorbox"
  concat_param {
    axis: 2
  }
}

#####################################################
layer {

  name: "mbox_loss"
  type: "MultiBoxLoss"
  bottom: "mbox_loc"
  bottom: "mbox_conf"
  bottom: "mbox_priorbox"
  bottom: "label"
  top: "mbox_loss"
  include {
    phase: TRAIN
  }
  propagate_down: true
  propagate_down: true
  propagate_down: false
  propagate_down: false
  loss_param {
    normalization: VALID
  }
  multibox_loss_param {
    loc_loss_type: L2
    conf_loss_type: SOFTMAX
    loc_weight: 1.0
    num_classes: 2
    share_location: true
    match_type: PER_PREDICTION
    overlap_threshold: 0.34999999404
    use_prior_for_matching: true
    background_label_id: 0
    use_difficult_gt: true
    neg_pos_ratio: 3.0
    neg_overlap: 0.34999999404
    code_type: CENTER_SIZE
    ignore_cross_boundary_bbox: false
    mining_type: MAX_NEGATIVE
  }
}