text: input: "data"
  input_dim: 1
  input_dim: 4
  input_dim: 368
  input_dim: 368
  layer {
    name: "image"
    type: "Slice"
    bottom: "data"
    top: "image"
    top: "center_map"
    slice_param {
      slice_point: 3
      axis: 1
    }
  }
  layer {
    name: "pool_center_lower"
    type: "Pooling"
    bottom: "center_map"
    top: "pool_center_lower"
    pooling_param {
      pool: AVE
      kernel_size: 9
      stride: 8
    }
  }
  layer {
    name: "conv1_1"
    type: "Convolution"
    bottom: "image"
    top: "conv1_1"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 64
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  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
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 64
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu1_2"
    type: "ReLU"
    bottom: "conv1_2"
    top: "conv1_2"
  }
  layer {
    name: "pool1_stage1"
    type: "Pooling"
    bottom: "conv1_2"
    top: "pool1_stage1"
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layer {
    name: "conv2_1"
    type: "Convolution"
    bottom: "pool1_stage1"
    top: "conv2_1"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  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
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu2_2"
    type: "ReLU"
    bottom: "conv2_2"
    top: "conv2_2"
  }
  layer {
    name: "pool2_stage1"
    type: "Pooling"
    bottom: "conv2_2"
    top: "pool2_stage1"
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layer {
    name: "conv3_1"
    type: "Convolution"
    bottom: "pool2_stage1"
    top: "conv3_1"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  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
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  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
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu3_3"
    type: "ReLU"
    bottom: "conv3_3"
    top: "conv3_3"
  }
  layer {
    name: "conv3_4"
    type: "Convolution"
    bottom: "conv3_3"
    top: "conv3_4"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu3_4"
    type: "ReLU"
    bottom: "conv3_4"
    top: "conv3_4"
  }
  layer {
    name: "pool3_stage1"
    type: "Pooling"
    bottom: "conv3_4"
    top: "pool3_stage1"
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layer {
    name: "conv4_1_CPM"
    type: "Convolution"
    bottom: "pool3_stage1"
    top: "conv4_1_CPM"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu4_1_CPM"
    type: "ReLU"
    bottom: "conv4_1_CPM"
    top: "conv4_1_CPM"
  }
  layer {
    name: "conv4_2_CPM"
    type: "Convolution"
    bottom: "conv4_1_CPM"
    top: "conv4_2_CPM"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu4_2_CPM"
    type: "ReLU"
    bottom: "conv4_2_CPM"
    top: "conv4_2_CPM"
  }
  layer {
    name: "conv4_3_CPM"
    type: "Convolution"
    bottom: "conv4_2_CPM"
    top: "conv4_3_CPM"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu4_3_CPM"
    type: "ReLU"
    bottom: "conv4_3_CPM"
    top: "conv4_3_CPM"
  }
  layer {
    name: "conv4_4_CPM"
    type: "Convolution"
    bottom: "conv4_3_CPM"
    top: "conv4_4_CPM"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu4_4_CPM"
    type: "ReLU"
    bottom: "conv4_4_CPM"
    top: "conv4_4_CPM"
  }
  layer {
    name: "conv4_5_CPM"
    type: "Convolution"
    bottom: "conv4_4_CPM"
    top: "conv4_5_CPM"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 1
      kernel_size: 3
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu4_5_CPM"
    type: "ReLU"
    bottom: "conv4_5_CPM"
    top: "conv4_5_CPM"
  }
  layer {
    name: "conv5_1_CPM"
    type: "Convolution"
    bottom: "conv4_5_CPM"
    top: "conv5_1_CPM"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 512
      pad: 0
      kernel_size: 1
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "relu5_1_CPM"
    type: "ReLU"
    bottom: "conv5_1_CPM"
    top: "conv5_1_CPM"
  }
  layer {
    name: "conv5_2_CPM"
    type: "Convolution"
    bottom: "conv5_1_CPM"
    top: "conv5_2_CPM"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 29
      pad: 0
      kernel_size: 1
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "concat_stage2"
    type: "Concat"
    bottom: "conv5_2_CPM"
    bottom: "conv4_5_CPM"
    bottom: "pool_center_lower"
    top: "concat_stage2"
    concat_param {
      axis: 1
    }
  }
  layer {
    name: "Mconv1_stage2"
    type: "Convolution"
    bottom: "concat_stage2"
    top: "Mconv1_stage2"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 3
      kernel_size: 7
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "Mrelu1_stage2"
    type: "ReLU"
    bottom: "Mconv1_stage2"
    top: "Mconv1_stage2"
  }
  layer {
    name: "Mconv2_stage2"
    type: "Convolution"
    bottom: "Mconv1_stage2"
    top: "Mconv2_stage2"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 3
      kernel_size: 7
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "Mrelu2_stage2"
    type: "ReLU"
    bottom: "Mconv2_stage2"
    top: "Mconv2_stage2"
  }
  layer {
    name: "Mconv3_stage2"
    type: "Convolution"
    bottom: "Mconv2_stage2"
    top: "Mconv3_stage2"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 3
      kernel_size: 7
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "Mrelu3_stage2"
    type: "ReLU"
    bottom: "Mconv3_stage2"
    top: "Mconv3_stage2"
  }
  layer {
    name: "Mconv4_stage2"
    type: "Convolution"
    bottom: "Mconv3_stage2"
    top: "Mconv4_stage2"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 3
      kernel_size: 7
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "Mrelu4_stage2"
    type: "ReLU"
    bottom: "Mconv4_stage2"
    top: "Mconv4_stage2"
  }
  layer {
    name: "Mconv5_stage2"
    type: "Convolution"
    bottom: "Mconv4_stage2"
    top: "Mconv5_stage2"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 3
      kernel_size: 7
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "Mrelu5_stage2"
    type: "ReLU"
    bottom: "Mconv5_stage2"
    top: "Mconv5_stage2"
  }
  layer {
    name: "Mconv6_stage2"
    type: "Convolution"
    bottom: "Mconv5_stage2"
    top: "Mconv6_stage2"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 128
      pad: 0
      kernel_size: 1
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  layer {
    name: "Mrelu6_stage2"
    type: "ReLU"
    bottom: "Mconv6_stage2"
    top: "Mconv6_stage2"
  }
  layer {
    name: "Mconv7_stage2"
    type: "Convolution"
    bottom: "Mconv6_stage2"
    top: "Mconv7_stage2"
    param {
      lr_mult: 1.0
      decay_mult: 1
    }
    param {
      lr_mult: 2.0
      decay_mult: 0
    }
    convolution_param {
      num_output: 29
      pad: 0
      kernel_size: 1
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
      }
    }
  }
  