name: "MOBILENET"
#  transform_param {
#    scale: 0.017
#    mirror: false
#    crop_size: 224
#    mean_value: [103.94,116.78,123.68]
#  }
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv1/bn"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv1/scale"
  type: "Scale"
  bottom: "conv1"
  top: "conv1"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2_1/dw"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2_1/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_1/dw/bn"
  type: "BatchNorm"
  bottom: "conv2_1/dw"
  top: "conv2_1/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv2_1/dw/scale"
  type: "Scale"
  bottom: "conv2_1/dw"
  top: "conv2_1/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu2_1/dw"
  type: "ReLU"
  bottom: "conv2_1/dw"
  top: "conv2_1/dw"
}
layer {
  name: "conv2_1/sep"
  type: "Convolution"
  bottom: "conv2_1/dw"
  top: "conv2_1/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_1/sep/bn"
  type: "BatchNorm"
  bottom: "conv2_1/sep"
  top: "conv2_1/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv2_1/sep/scale"
  type: "Scale"
  bottom: "conv2_1/sep"
  top: "conv2_1/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu2_1/sep"
  type: "ReLU"
  bottom: "conv2_1/sep"
  top: "conv2_1/sep"
}
layer {
  name: "conv2_2/dw"
  type: "Convolution"
  bottom: "conv2_1/sep"
  top: "conv2_2/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 64
    engine: CAFFE
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_2/dw/bn"
  type: "BatchNorm"
  bottom: "conv2_2/dw"
  top: "conv2_2/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv2_2/dw/scale"
  type: "Scale"
  bottom: "conv2_2/dw"
  top: "conv2_2/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu2_2/dw"
  type: "ReLU"
  bottom: "conv2_2/dw"
  top: "conv2_2/dw"
}
layer {
  name: "conv2_2/sep"
  type: "Convolution"
  bottom: "conv2_2/dw"
  top: "conv2_2/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2_2/sep/bn"
  type: "BatchNorm"
  bottom: "conv2_2/sep"
  top: "conv2_2/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv2_2/sep/scale"
  type: "Scale"
  bottom: "conv2_2/sep"
  top: "conv2_2/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu2_2/sep"
  type: "ReLU"
  bottom: "conv2_2/sep"
  top: "conv2_2/sep"
}
layer {
  name: "conv3_1/dw"
  type: "Convolution"
  bottom: "conv2_2/sep"
  top: "conv3_1/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 128
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_1/dw/bn"
  type: "BatchNorm"
  bottom: "conv3_1/dw"
  top: "conv3_1/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv3_1/dw/scale"
  type: "Scale"
  bottom: "conv3_1/dw"
  top: "conv3_1/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu3_1/dw"
  type: "ReLU"
  bottom: "conv3_1/dw"
  top: "conv3_1/dw"
}
layer {
  name: "conv3_1/sep"
  type: "Convolution"
  bottom: "conv3_1/dw"
  top: "conv3_1/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_1/sep/bn"
  type: "BatchNorm"
  bottom: "conv3_1/sep"
  top: "conv3_1/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv3_1/sep/scale"
  type: "Scale"
  bottom: "conv3_1/sep"
  top: "conv3_1/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu3_1/sep"
  type: "ReLU"
  bottom: "conv3_1/sep"
  top: "conv3_1/sep"
}
layer {
  name: "conv3_2/dw"
  type: "Convolution"
  bottom: "conv3_1/sep"
  top: "conv3_2/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 128
    engine: CAFFE
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_2/dw/bn"
  type: "BatchNorm"
  bottom: "conv3_2/dw"
  top: "conv3_2/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv3_2/dw/scale"
  type: "Scale"
  bottom: "conv3_2/dw"
  top: "conv3_2/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu3_2/dw"
  type: "ReLU"
  bottom: "conv3_2/dw"
  top: "conv3_2/dw"
}
layer {
  name: "conv3_2/sep"
  type: "Convolution"
  bottom: "conv3_2/dw"
  top: "conv3_2/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3_2/sep/bn"
  type: "BatchNorm"
  bottom: "conv3_2/sep"
  top: "conv3_2/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv3_2/sep/scale"
  type: "Scale"
  bottom: "conv3_2/sep"
  top: "conv3_2/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu3_2/sep"
  type: "ReLU"
  bottom: "conv3_2/sep"
  top: "conv3_2/sep"
}
layer {
  name: "conv4_1/dw"
  type: "Convolution"
  bottom: "conv3_2/sep"
  top: "conv4_1/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 256
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_1/dw/bn"
  type: "BatchNorm"
  bottom: "conv4_1/dw"
  top: "conv4_1/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv4_1/dw/scale"
  type: "Scale"
  bottom: "conv4_1/dw"
  top: "conv4_1/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu4_1/dw"
  type: "ReLU"
  bottom: "conv4_1/dw"
  top: "conv4_1/dw"
}
layer {
  name: "conv4_1/sep"
  type: "Convolution"
  bottom: "conv4_1/dw"
  top: "conv4_1/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_1/sep/bn"
  type: "BatchNorm"
  bottom: "conv4_1/sep"
  top: "conv4_1/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv4_1/sep/scale"
  type: "Scale"
  bottom: "conv4_1/sep"
  top: "conv4_1/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu4_1/sep"
  type: "ReLU"
  bottom: "conv4_1/sep"
  top: "conv4_1/sep"
}
layer {
  name: "conv4_2/dw"
  type: "Convolution"
  bottom: "conv4_1/sep"
  top: "conv4_2/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 256
    engine: CAFFE
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_2/dw/bn"
  type: "BatchNorm"
  bottom: "conv4_2/dw"
  top: "conv4_2/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv4_2/dw/scale"
  type: "Scale"
  bottom: "conv4_2/dw"
  top: "conv4_2/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu4_2/dw"
  type: "ReLU"
  bottom: "conv4_2/dw"
  top: "conv4_2/dw"
}
layer {
  name: "conv4_2/sep"
  type: "Convolution"
  bottom: "conv4_2/dw"
  top: "conv4_2/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4_2/sep/bn"
  type: "BatchNorm"
  bottom: "conv4_2/sep"
  top: "conv4_2/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv4_2/sep/scale"
  type: "Scale"
  bottom: "conv4_2/sep"
  top: "conv4_2/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu4_2/sep"
  type: "ReLU"
  bottom: "conv4_2/sep"
  top: "conv4_2/sep"
}
layer {
  name: "conv5_1/dw"
  type: "Convolution"
  bottom: "conv4_2/sep"
  top: "conv5_1/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_1/dw/bn"
  type: "BatchNorm"
  bottom: "conv5_1/dw"
  top: "conv5_1/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_1/dw/scale"
  type: "Scale"
  bottom: "conv5_1/dw"
  top: "conv5_1/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_1/dw"
  type: "ReLU"
  bottom: "conv5_1/dw"
  top: "conv5_1/dw"
}
layer {
  name: "conv5_1/sep"
  type: "Convolution"
  bottom: "conv5_1/dw"
  top: "conv5_1/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_1/sep/bn"
  type: "BatchNorm"
  bottom: "conv5_1/sep"
  top: "conv5_1/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_1/sep/scale"
  type: "Scale"
  bottom: "conv5_1/sep"
  top: "conv5_1/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_1/sep"
  type: "ReLU"
  bottom: "conv5_1/sep"
  top: "conv5_1/sep"
}
layer {
  name: "conv5_2/dw"
  type: "Convolution"
  bottom: "conv5_1/sep"
  top: "conv5_2/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_2/dw/bn"
  type: "BatchNorm"
  bottom: "conv5_2/dw"
  top: "conv5_2/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_2/dw/scale"
  type: "Scale"
  bottom: "conv5_2/dw"
  top: "conv5_2/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_2/dw"
  type: "ReLU"
  bottom: "conv5_2/dw"
  top: "conv5_2/dw"
}
layer {
  name: "conv5_2/sep"
  type: "Convolution"
  bottom: "conv5_2/dw"
  top: "conv5_2/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_2/sep/bn"
  type: "BatchNorm"
  bottom: "conv5_2/sep"
  top: "conv5_2/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_2/sep/scale"
  type: "Scale"
  bottom: "conv5_2/sep"
  top: "conv5_2/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_2/sep"
  type: "ReLU"
  bottom: "conv5_2/sep"
  top: "conv5_2/sep"
}
layer {
  name: "conv5_3/dw"
  type: "Convolution"
  bottom: "conv5_2/sep"
  top: "conv5_3/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_3/dw/bn"
  type: "BatchNorm"
  bottom: "conv5_3/dw"
  top: "conv5_3/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_3/dw/scale"
  type: "Scale"
  bottom: "conv5_3/dw"
  top: "conv5_3/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_3/dw"
  type: "ReLU"
  bottom: "conv5_3/dw"
  top: "conv5_3/dw"
}
layer {
  name: "conv5_3/sep"
  type: "Convolution"
  bottom: "conv5_3/dw"
  top: "conv5_3/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_3/sep/bn"
  type: "BatchNorm"
  bottom: "conv5_3/sep"
  top: "conv5_3/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_3/sep/scale"
  type: "Scale"
  bottom: "conv5_3/sep"
  top: "conv5_3/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_3/sep"
  type: "ReLU"
  bottom: "conv5_3/sep"
  top: "conv5_3/sep"
}
layer {
  name: "conv5_4/dw"
  type: "Convolution"
  bottom: "conv5_3/sep"
  top: "conv5_4/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_4/dw/bn"
  type: "BatchNorm"
  bottom: "conv5_4/dw"
  top: "conv5_4/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_4/dw/scale"
  type: "Scale"
  bottom: "conv5_4/dw"
  top: "conv5_4/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_4/dw"
  type: "ReLU"
  bottom: "conv5_4/dw"
  top: "conv5_4/dw"
}
layer {
  name: "conv5_4/sep"
  type: "Convolution"
  bottom: "conv5_4/dw"
  top: "conv5_4/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_4/sep/bn"
  type: "BatchNorm"
  bottom: "conv5_4/sep"
  top: "conv5_4/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_4/sep/scale"
  type: "Scale"
  bottom: "conv5_4/sep"
  top: "conv5_4/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_4/sep"
  type: "ReLU"
  bottom: "conv5_4/sep"
  top: "conv5_4/sep"
}
layer {
  name: "conv5_5/dw"
  type: "Convolution"
  bottom: "conv5_4/sep"
  top: "conv5_5/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_5/dw/bn"
  type: "BatchNorm"
  bottom: "conv5_5/dw"
  top: "conv5_5/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_5/dw/scale"
  type: "Scale"
  bottom: "conv5_5/dw"
  top: "conv5_5/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_5/dw"
  type: "ReLU"
  bottom: "conv5_5/dw"
  top: "conv5_5/dw"
}
layer {
  name: "conv5_5/sep"
  type: "Convolution"
  bottom: "conv5_5/dw"
  top: "conv5_5/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_5/sep/bn"
  type: "BatchNorm"
  bottom: "conv5_5/sep"
  top: "conv5_5/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_5/sep/scale"
  type: "Scale"
  bottom: "conv5_5/sep"
  top: "conv5_5/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_5/sep"
  type: "ReLU"
  bottom: "conv5_5/sep"
  top: "conv5_5/sep"
}
layer {
  name: "conv5_6/dw"
  type: "Convolution"
  bottom: "conv5_5/sep"
  top: "conv5_6/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_6/dw/bn"
  type: "BatchNorm"
  bottom: "conv5_6/dw"
  top: "conv5_6/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_6/dw/scale"
  type: "Scale"
  bottom: "conv5_6/dw"
  top: "conv5_6/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_6/dw"
  type: "ReLU"
  bottom: "conv5_6/dw"
  top: "conv5_6/dw"
}
layer {
  name: "conv5_6/sep"
  type: "Convolution"
  bottom: "conv5_6/dw"
  top: "conv5_6/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5_6/sep/bn"
  type: "BatchNorm"
  bottom: "conv5_6/sep"
  top: "conv5_6/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv5_6/sep/scale"
  type: "Scale"
  bottom: "conv5_6/sep"
  top: "conv5_6/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu5_6/sep"
  type: "ReLU"
  bottom: "conv5_6/sep"
  top: "conv5_6/sep"
}
layer {
  name: "conv6/dw"
  type: "Convolution"
  bottom: "conv5_6/sep"
  top: "conv6/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1024
    engine: CAFFE
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6/dw/bn"
  type: "BatchNorm"
  bottom: "conv6/dw"
  top: "conv6/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv6/dw/scale"
  type: "Scale"
  bottom: "conv6/dw"
  top: "conv6/dw"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu6/dw"
  type: "ReLU"
  bottom: "conv6/dw"
  top: "conv6/dw"
}
layer {
  name: "conv6/sep"
  type: "Convolution"
  bottom: "conv6/dw"
  top: "conv6/sep"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6/sep/bn"
  type: "BatchNorm"
  bottom: "conv6/sep"
  top: "conv6/sep"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  batch_norm_param {
    use_global_stats: true
    eps: 1e-5
  }
}
layer {
  name: "conv6/sep/scale"
  type: "Scale"
  bottom: "conv6/sep"
  top: "conv6/sep"
  param {
    lr_mult: 1
    decay_mult: 0
  }
  param {
    lr_mult: 1
    decay_mult: 0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "relu6/sep"
  type: "ReLU"
  bottom: "conv6/sep"
  top: "conv6/sep"
}
layer {
  name: "pool6"
  type: "Pooling"
  bottom: "conv6/sep"
  top: "pool6"
  pooling_param {
    pool: AVE
    global_pooling: true
  }
}
layer {
  name: "fc7"
  type: "Convolution"
  bottom: "pool6"
  top: "fc7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 1000
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "fc7"
  top: "prob"
}