faceNet-20-light2s4m-bn_deploy.prototxt

name: "facenet_nn1_001"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 96
input_dim: 96
print_net_info: false
layer {
  name: "conv1_1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 48
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu1_1"
  type: "PReLU"
  bottom: "conv1_1"
  top: "conv1_1"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "conv1_3"
  type: "Convolution"
  bottom: "conv1_1"
  top: "conv1_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 48
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu1_3"
  type: "PReLU"
  bottom: "conv1_3"
  top: "conv1_3"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res1_3"
  type: "Eltwise"
  bottom: "conv1_1"
  bottom: "conv1_3"
  top: "res1_3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res1_3_reduce"
  type: "Convolution"
  bottom: "res1_3"
  top: "res1_3_reduce"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 96
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "res1_3_reduce"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "res1_3"
  top: "conv2_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu2_1"
  type: "PReLU"
  bottom: "conv2_1"
  top: "conv2_1"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res1_3_p"
  type: "Eltwise"
  bottom: "pool1"
  bottom: "conv2_1"
  top: "res1_3_p"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv2_3"
  type: "Convolution"
  bottom: "res1_3_p"
  top: "conv2_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu2_3"
  type: "PReLU"
  bottom: "conv2_3"
  top: "conv2_3"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res2_3"
  type: "Eltwise"
  bottom: "res1_3_p"
  bottom: "conv2_3"
  top: "res2_3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv2_5"
  type: "Convolution"
  bottom: "res2_3"
  top: "conv2_5"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 96
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu2_5"
  type: "PReLU"
  bottom: "conv2_5"
  top: "conv2_5"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res2_5"
  type: "Eltwise"
  bottom: "res2_3"
  bottom: "conv2_5"
  top: "res2_5"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res2_5_reduce"
  type: "Convolution"
  bottom: "res2_5"
  top: "res2_5_reduce"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 144
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "res2_5_reduce"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv3_1"
  type: "Convolution"
  bottom: "res2_5"
  top: "conv3_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 144
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu3_1"
  type: "PReLU"
  bottom: "conv3_1"
  top: "conv3_1"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res2_5_p"
  type: "Eltwise"
  bottom: "pool2"
  bottom: "conv3_1"
  top: "res2_5_p"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "res2_5_p"
  top: "conv3_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 144
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu3_3"
  type: "PReLU"
  bottom: "conv3_3"
  top: "conv3_3"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res3_3"
  type: "Eltwise"
  bottom: "res2_5_p"
  bottom: "conv3_3"
  top: "res3_3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv3_5"
  type: "Convolution"
  bottom: "res3_3"
  top: "conv3_5"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 144
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu3_5"
  type: "PReLU"
  bottom: "conv3_5"
  top: "conv3_5"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res3_5"
  type: "Eltwise"
  bottom: "res3_3"
  bottom: "conv3_5"
  top: "res3_5"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "res3_5_reduce"
  type: "Convolution"
  bottom: "res3_5"
  top: "res3_5_reduce"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 288
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "res3_5_reduce"
  top: "pool3"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}
layer {
  name: "conv4_1"
  type: "Convolution"
  bottom: "res3_5"
  top: "conv4_1"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 288
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu4_1"
  type: "PReLU"
  bottom: "conv4_1"
  top: "conv4_1"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res3_5_p"
  type: "Eltwise"
  bottom: "pool3"
  bottom: "conv4_1"
  top: "res3_5_p"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "res3_5_p"
  top: "conv4_3"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 0.0
    decay_mult: 0.0
  }
  convolution_param {
    num_output: 288
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "relu4_3"
  type: "PReLU"
  bottom: "conv4_3"
  top: "conv4_3"
  prelu_param {
    filler {
      type: "gaussian"
      std: 0.03
    }
  }
}
layer {
  name: "res4_3"
  type: "Eltwise"
  bottom: "res3_5_p"
  bottom: "conv4_3"
  top: "res4_3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "fc5"
  type: "InnerProduct"
  bottom: "res4_3"
  top: "fc5"
  param {
    lr_mult: 1.0
    decay_mult: 1.0
  }
  param {
    lr_mult: 2.0
    decay_mult: 0.0
  }
  inner_product_param {
    num_output: 256
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0.0
    }
  }
}
layer {
  name: "fc5_bn"
  type: "BatchNorm"
  bottom: "fc5"
  top: "fc5"
  batch_norm_param {
    use_global_stats: true
  }
}

layer {
  name: "l2"
  type: "L2Norm"
  bottom: "fc5"
  top: "l2"
}
layer {
  name: "silence"
  type: "Silence"
  bottom: "l2"
}