name: "YuFaceDetectNet"

input: "data"

input_shape {
  dim: 1
  dim: 3
  dim: 240
  dim: 320
}

#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_conf_reshape"
  type: "Reshape"
  bottom: "mbox_conf"
  top: "mbox_conf_reshape"
  reshape_param {
    shape {
      dim: 0
      dim: -1
      dim: 2
    }
  }
}
layer {
  name: "mbox_conf_softmax"
  type: "Softmax"
  bottom: "mbox_conf_reshape"
  top: "mbox_conf_softmax"
  softmax_param {
    axis: 2
  }
}
layer {
  name: "mbox_conf_flatten"
  type: "Flatten"
  bottom: "mbox_conf_softmax"
  top: "mbox_conf_flatten"
  flatten_param {
    axis: 1
  }
}

layer {
  name: "detection_out"
  type: "DetectionOutput"
  bottom: "mbox_loc"
  bottom: "mbox_conf_flatten"
  bottom: "mbox_priorbox"
#  bottom: "data"
  top: "detection_out"
  include {
    phase: TEST
  }
  transform_param {
    mean_value: 103.94
    mean_value: 116.78
    mean_value: 123.68
  }
  detection_output_param {
    num_classes: 2
    share_location: true
    background_label_id: 0
    nms_param {
      nms_threshold: 0.15
      top_k: 100
    }
    code_type: CENTER_SIZE
    keep_top_k: 50
    confidence_threshold: 0.01
    visualize: false
    visualize_threshold: 0.3
  }
}