sphereface_model.prototxt

name: "SphereFaceNet"
layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include { 
    phase: TRAIN
  }
  transform_param {
    mean_value: 127.5
    mean_value: 127.5
    mean_value: 127.5
    scale: 0.0078125
    mirror: true
  }
  image_data_param {
    source: "/home/chenweiliang/CASIA-WebFace/CASIA-WebFace-Crop/train.txt"
	root_folder: "/home/chenweiliang/CASIA-WebFace/CASIA-WebFace-Crop/"
    batch_size: 170
    shuffle: true
  }
}

layer {
  name: "data"
  type: "ImageData"
  top: "data"
  top: "label"
  include { 
    phase: TEST
  }
  transform_param {
    mean_value: 127.5
    mean_value: 127.5
    mean_value: 127.5
    scale: 0.0078125
    mirror: true
  }
  image_data_param {
    source: "/home/chenweiliang/CASIA-WebFace/CASIA-WebFace-Crop/val.txt"
	root_folder: "/home/chenweiliang/CASIA-WebFace/CASIA-WebFace-Crop/"
    batch_size: 128
    shuffle: true
  }
}

############## CNN Architecture ###############
layer {
  name: "conv1a"
  type: "Convolution"
  bottom: "data"
  top: "conv1a"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 32
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1a"
  type: "PReLU"
  bottom: "conv1a"
  top: "conv1a"
}
layer {
  name: "conv1b"
  type: "Convolution"
  bottom: "conv1a"
  top: "conv1b"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1b"
  type: "PReLU"
  bottom: "conv1b"
  top: "conv1b"
}
layer {
  name: "pool1b"
  type: "Pooling"
  bottom: "conv1b"
  top: "pool1b"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2 
  }
}
layer {
  name: "conv2_1"
  type: "Convolution"
  bottom: "pool1b"
  top: "conv2_1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2_1"
  type: "PReLU"
  bottom: "conv2_1"
  top: "conv2_1"
}
layer {
  name: "conv2_2"
  type: "Convolution"
  bottom: "conv2_1"
  top: "conv2_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 64
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2_2"
  type: "PReLU"
  bottom: "conv2_2"
  top: "conv2_2"
}
layer {
  name: "res2_2"
  type: "Eltwise"
  bottom: "pool1b"
  bottom: "conv2_2"
  top: "res2_2"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "res2_2"
  top: "conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  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
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3_1"
  type: "PReLU"
  bottom: "conv3_1"
  top: "conv3_1"
}
layer {
  name: "conv3_2"
  type: "Convolution"
  bottom: "conv3_1"
  top: "conv3_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3_2"
  type: "PReLU"
  bottom: "conv3_2"
  top: "conv3_2"
}
layer {
  name: "res3_2"
  type: "Eltwise"
  bottom: "pool2"
  bottom: "conv3_2"
  top: "res3_2"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv3_3"
  type: "Convolution"
  bottom: "res3_2"
  top: "conv3_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3_3"
  type: "PReLU"
  bottom: "conv3_3"
  top: "conv3_3"
}
layer {
  name: "conv3_4"
  type: "Convolution"
  bottom: "conv3_3"
  top: "conv3_4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3_4"
  type: "PReLU"
  bottom: "conv3_4"
  top: "conv3_4"
}
layer {
  name: "res3_4"
  type: "Eltwise"
  bottom: "res3_2"
  bottom: "conv3_4"
  top: "res3_4"
  eltwise_param { 
    operation: 1
  }
}

layer {
  name: "conv3"
  type: "Convolution"
  bottom: "res3_4"
  top: "conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "PReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "pool3"
  type: "Pooling"
  bottom: "conv3"
  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
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_1"
  type: "PReLU"
  bottom: "conv4_1"
  top: "conv4_1"
}
layer {
  name: "conv4_2"
  type: "Convolution"
  bottom: "conv4_1"
  top: "conv4_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_2"
  type: "PReLU"
  bottom: "conv4_2"
  top: "conv4_2"
}
layer {
  name: "res4_2"
  type: "Eltwise"
  bottom: "pool3"
  bottom: "conv4_2"
  top: "res4_2"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv4_3"
  type: "Convolution"
  bottom: "res4_2"
  top: "conv4_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_3"
  type: "PReLU"
  bottom: "conv4_3"
  top: "conv4_3"
}
layer {
  name: "conv4_4"
  type: "Convolution"
  bottom: "conv4_3"
  top: "conv4_4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_4"
  type: "PReLU"
  bottom: "conv4_4"
  top: "conv4_4"
}
layer {
  name: "res4_4"
  type: "Eltwise"
  bottom: "res4_2"
  bottom: "conv4_4"
  top: "res4_4"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv4_5"
  type: "Convolution"
  bottom: "res4_4"
  top: "conv4_5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_5"
  type: "PReLU"
  bottom: "conv4_5"
  top: "conv4_5"
}
layer {
  name: "conv4_6"
  type: "Convolution"
  bottom: "conv4_5"
  top: "conv4_6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_6"
  type: "PReLU"
  bottom: "conv4_6"
  top: "conv4_6"
}
layer {
  name: "res4_6"
  type: "Eltwise"
  bottom: "res4_4"
  bottom: "conv4_6"
  top: "res4_6"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv4_7"
  type: "Convolution"
  bottom: "res4_6"
  top: "conv4_7"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_7"
  type: "PReLU"
  bottom: "conv4_7"
  top: "conv4_7"
}
layer {
  name: "conv4_8"
  type: "Convolution"
  bottom: "conv4_7"
  top: "conv4_8"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_8"
  type: "PReLU"
  bottom: "conv4_8"
  top: "conv4_8"
}
layer {
  name: "res4_8"
  type: "Eltwise"
  bottom: "res4_6"
  bottom: "conv4_8"
  top: "res4_8"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv4_9"
  type: "Convolution"
  bottom: "res4_8"
  top: "conv4_9"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_9"
  type: "PReLU"
  bottom: "conv4_9"
  top: "conv4_9"
}
layer {
  name: "conv4_10"
  type: "Convolution"
  bottom: "conv4_9"
  top: "conv4_10"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4_10"
  type: "PReLU"
  bottom: "conv4_10"
  top: "conv4_10"
}
layer {
  name: "res4_10"
  type: "Eltwise"
  bottom: "res4_8"
  bottom: "conv4_10"
  top: "res4_10"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "res4_10"
  top: "conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4"
  type: "PReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "conv4"
  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
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_1"
  type: "PReLU"
  bottom: "conv5_1"
  top: "conv5_1"
}
layer {
  name: "conv5_2"
  type: "Convolution"
  bottom: "conv5_1"
  top: "conv5_2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_2"
  type: "PReLU"
  bottom: "conv5_2"
  top: "conv5_2"
}
layer {
  name: "res5_2"
  type: "Eltwise"
  bottom: "pool4"
  bottom: "conv5_2"
  top: "res5_2"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv5_3"
  type: "Convolution"
  bottom: "res5_2"
  top: "conv5_3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_3"
  type: "PReLU"
  bottom: "conv5_3"
  top: "conv5_3"
}
layer {
  name: "conv5_4"
  type: "Convolution"
  bottom: "conv5_3"
  top: "conv5_4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_4"
  type: "PReLU"
  bottom: "conv5_4"
  top: "conv5_4"
}
layer {
  name: "res5_4"
  type: "Eltwise"
  bottom: "res5_2"
  bottom: "conv5_4"
  top: "res5_4"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "conv5_5"
  type: "Convolution"
  bottom: "res5_4"
  top: "conv5_5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_5"
  type: "PReLU"
  bottom: "conv5_5"
  top: "conv5_5"
}
layer {
  name: "conv5_6"
  type: "Convolution"
  bottom: "conv5_5"
  top: "conv5_6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  convolution_param {
    num_output: 512
    kernel_size: 3
    stride: 1
    pad: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5_6"
  type: "PReLU"
  bottom: "conv5_6"
  top: "conv5_6"
}
layer {
  name: "res5_6"
  type: "Eltwise"
  bottom: "res5_4"
  bottom: "conv5_6"
  top: "res5_6"
  eltwise_param { 
    operation: 1
  }
}
layer {
  name: "fc5"
  type: "InnerProduct"
  bottom: "res5_6"
  top: "fc5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  inner_product_param {
    num_output: 512
    weight_filler {
      type: "xavier"
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
############### A-Softmax Loss ##############
layer {
  name: "fc6"
  type: "MarginInnerProduct"
  bottom: "fc5"
  bottom: "label"
  top: "fc6"
  top: "lambda"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  margin_inner_product_param {
    num_output: 10575
    type: QUADRUPLE
    weight_filler {
      type: "xavier"
    }
    base: 3.6
    gamma: 0.12
    power: 1
    lambda_min: 3.6
    iteration: 0
  }
}
layer {
  name: "softmax_loss"
  type: "SoftmaxWithLoss"
  bottom: "fc6"
  bottom: "label"
  top: "softmax_loss"
}
layer {
  name: "accuracy"
  type: "Accuracy"
  bottom: "fc6"
  bottom: "label"
  top: "accuracy"
  include {
    phase: TEST
  }
}