name: "CaffeNet"
input: "data"
input_shape {
  dim: 10
  dim: 3
  dim: 227
  dim: 227
}
############################## Column s1 ################################################
layer {
  name: "conv1_s1"
  type: "Convolution"
  bottom: "data"
  top: "conv1_s1"
  convolution_param {
    num_output: 96
    kernel_size: 11
    stride: 4
  }
}
layer {
  name: "relu1_s1"
  type: "ReLU"
  bottom: "conv1_s1"
  top: "conv1_s1"
}
layer {
  name: "pool1_s1"
  type: "Pooling"
  bottom: "conv1_s1"
  top: "pool1_s1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm1_s1"
  type: "LRN"
  bottom: "pool1_s1"
  top: "norm1_s1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv2_s1"
  type: "Convolution"
  bottom: "norm1_s1"
  top: "conv2_s1"
  convolution_param {
    num_output: 256
    pad: 2
    kernel_size: 5
    group: 2
  }
}
layer {
  name: "relu2_s1"
  type: "ReLU"
  bottom: "conv2_s1"
  top: "conv2_s1"
}
layer {
  name: "pool2_s1"
  type: "Pooling"
  bottom: "conv2_s1"
  top: "pool2_s1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}
layer {
  name: "norm2_s1"
  type: "LRN"
  bottom: "pool2_s1"
  top: "norm2_s1"
  lrn_param {
    local_size: 5
    alpha: 0.0001
    beta: 0.75
  }
}
layer {
  name: "conv3_s1"
  type: "Convolution"
  bottom: "norm2_s1"
  top: "conv3_s1"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
  }
}
layer {
  name: "relu3_s1"
  type: "ReLU"
  bottom: "conv3_s1"
  top: "conv3_s1"
}
layer {
  name: "conv4_s1"
  type: "Convolution"
  bottom: "conv3_s1"
  top: "conv4_s1"
  convolution_param {
    num_output: 384
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu4_s1"
  type: "ReLU"
  bottom: "conv4_s1"
  top: "conv4_s1"
}
layer {
  name: "conv5_s1"
  type: "Convolution"
  bottom: "conv4_s1"
  top: "conv5_s1"
  convolution_param {
    num_output: 256
    pad: 1
    kernel_size: 3
    group: 2
  }
}
layer {
  name: "relu5_s1"
  type: "ReLU"
  bottom: "conv5_s1"
  top: "conv5_s1"
}
layer {
  name: "pool5"
  type: "Pooling"
  bottom: "conv5_s1"
  top: "pool5"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}