model.prototxt

input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 1280
  dim: 1280
}layer {
  name: "Convolution1"
  type: "Convolution"
  bottom: "data"
  top: "Convolution1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 96
    pad: 0
    kernel_size: 11
    stride: 4
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1.0
    }
  }
}
layer {
  name: "conv0"
  type: "ReLU"
  bottom: "Convolution1"
  top: "Convolution1"
}
layer {
  name: "norm0"
  type: "LRN"
  bottom: "Convolution1"
  top: "norm0"
  lrn_param {
    local_size: 5
    alpha: 0.0005
    beta: 0.75
    k: 2
  }
}
layer {
  name: "pool0"
  type: "Pooling"
  bottom: "norm0"
  top: "pool0"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "Convolution2"
  type: "Convolution"
  bottom: "pool0"
  top: "Convolution2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1.0
    }
  }
}
layer {
  name: "conv1"
  type: "ReLU"
  bottom: "Convolution2"
  top: "Convolution2"
}
layer {
  name: "norm1"
  type: "LRN"
  bottom: "Convolution2"
  top: "norm1"
  lrn_param {
    local_size: 5
    alpha: 0.0005
    beta: 0.75
    k: 8
  }
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "norm1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "Convolution3"
  type: "Convolution"
  bottom: "pool1"
  top: "Convolution3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "conv2"
  type: "ReLU"
  bottom: "Convolution3"
  top: "Convolution3"
}
layer {
  name: "Convolution4"
  type: "Convolution"
  bottom: "Convolution3"
  top: "Convolution4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "conv3"
  type: "ReLU"
  bottom: "Convolution4"
  top: "Convolution4"
}
layer {
  name: "Convolution5"
  type: "Convolution"
  bottom: "Convolution4"
  top: "Convolution5"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0.1
    }
  }
}
layer {
  name: "conv4"
  type: "ReLU"
  bottom: "Convolution5"
  top: "Convolution5"
}
layer {
  name: "pool4"
  type: "Pooling"
  bottom: "Convolution5"
  top: "pool4"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "Convolution6"
  type: "Convolution"
  bottom: "pool4"
  top: "Convolution6"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    pad: 3
    kernel_size: 6
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1.0
    }
  }
}
layer {
  name: "drop5"
  type: "ReLU"
  bottom: "Convolution6"
  top: "Convolution6"
}
layer {
  name: "Convolution7"
  type: "Convolution"
  bottom: "Convolution6"
  top: "Convolution7"
  param {
    lr_mult: 5
    decay_mult: 0.1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1.0
    }
  }
}
layer {
  name: "drop6"
  type: "ReLU"
  bottom: "Convolution7"
  top: "Convolution7"
}
layer {
  name: "Convolution8"
  type: "Convolution"
  bottom: "Convolution6"
  top: "Convolution8"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1.0
    }
  }
}
layer {
  name: "output_type"
  type: "Tiling"
  bottom: "Convolution8"
  top: "output_type"
  tiling_param {
    tile_dim: 2
  }
}
layer {
  name: "Convolution9"
  type: "Convolution"
  bottom: "Convolution8"
  top: "Convolution9"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 128
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "Convolution10"
  type: "Convolution"
  bottom: "Convolution6"
  top: "Convolution10"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 4096
    pad: 0
    kernel_size: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 1.0
    }
  }
}
layer {
  name: "ReLU2"
  type: "ReLU"
  bottom: "Convolution10"
  top: "Convolution10"
}
layer {
  name: "output_bb"
  type: "Tiling"
  bottom: "Convolution10"
  top: "output_bb"
  tiling_param {
    tile_dim: 8
  }
}