text: name: "ris80_h2_VGG_ILSVRC_19_rgb2imNet"
  input: "data"
  input_dim: 10
  input_dim: 3
  input_dim: 224
  input_dim: 224
  layers {
    bottom: "data"
    top: "conv1_1"
    name: "conv1_1"
    type: CONVOLUTION
    convolution_param {
      num_output: 64
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv1_1"
    top: "conv1_1"
    name: "relu1_1"
    type: RELU
  }
  layers {
    bottom: "conv1_1"
    top: "conv1_2"
    name: "conv1_2"
    type: CONVOLUTION
    convolution_param {
      num_output: 64
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv1_2"
    top: "conv1_2"
    name: "relu1_2"
    type: RELU
  }
  layers {
    bottom: "conv1_2"
    top: "pool1"
    name: "pool1"
    type: POOLING
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layers {
    bottom: "pool1"
    top: "conv2_1"
    name: "conv2_1"
    type: CONVOLUTION
    convolution_param {
      num_output: 128
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv2_1"
    top: "conv2_1"
    name: "relu2_1"
    type: RELU
  }
  layers {
    bottom: "conv2_1"
    top: "conv2_2"
    name: "conv2_2"
    type: CONVOLUTION
    convolution_param {
      num_output: 128
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv2_2"
    top: "conv2_2"
    name: "relu2_2"
    type: RELU
  }
  layers {
    bottom: "conv2_2"
    top: "pool2"
    name: "pool2"
    type: POOLING
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layers {
    bottom: "pool2"
    top: "conv3_1"
    name: "conv3_1"
    type: CONVOLUTION
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv3_1"
    top: "conv3_1"
    name: "relu3_1"
    type: RELU
  }
  layers {
    bottom: "conv3_1"
    top: "conv3_2"
    name: "conv3_2"
    type: CONVOLUTION
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv3_2"
    top: "conv3_2"
    name: "relu3_2"
    type: RELU
  }
  layers {
    bottom: "conv3_2"
    top: "conv3_3"
    name: "conv3_3"
    type: CONVOLUTION
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv3_3"
    top: "conv3_3"
    name: "relu3_3"
    type: RELU
  }
  layers {
    bottom: "conv3_3"
    top: "conv3_4"
    name: "conv3_4"
    type: CONVOLUTION
    convolution_param {
      num_output: 256
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv3_4"
    top: "conv3_4"
    name: "relu3_4"
    type: RELU
  }
  layers {
    bottom: "conv3_4"
    top: "pool3"
    name: "pool3"
    type: POOLING
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layers {
    bottom: "pool3"
    top: "conv4_1"
    name: "conv4_1"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv4_1"
    top: "conv4_1"
    name: "relu4_1"
    type: RELU
  }
  layers {
    bottom: "conv4_1"
    top: "conv4_2"
    name: "conv4_2"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv4_2"
    top: "conv4_2"
    name: "relu4_2"
    type: RELU
  }
  layers {
    bottom: "conv4_2"
    top: "conv4_3"
    name: "conv4_3"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv4_3"
    top: "conv4_3"
    name: "relu4_3"
    type: RELU
  }
  layers {
    bottom: "conv4_3"
    top: "conv4_4"
    name: "conv4_4"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv4_4"
    top: "conv4_4"
    name: "relu4_4"
    type: RELU
  }
  layers {
    bottom: "conv4_4"
    top: "pool4"
    name: "pool4"
    type: POOLING
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layers {
    bottom: "pool4"
    top: "conv5_1"
    name: "conv5_1"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv5_1"
    top: "conv5_1"
    name: "relu5_1"
    type: RELU
  }
  layers {
    bottom: "conv5_1"
    top: "conv5_2"
    name: "conv5_2"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv5_2"
    top: "conv5_2"
    name: "relu5_2"
    type: RELU
  }
  layers {
    bottom: "conv5_2"
    top: "conv5_3"
    name: "conv5_3"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv5_3"
    top: "conv5_3"
    name: "relu5_3"
    type: RELU
  }
  layers {
    bottom: "conv5_3"
    top: "conv5_4"
    name: "conv5_4"
    type: CONVOLUTION
    convolution_param {
      num_output: 512
      pad: 1
      kernel_size: 3
    }
  }
  layers {
    bottom: "conv5_4"
    top: "conv5_4"
    name: "relu5_4"
    type: RELU
  }
  layers {
    bottom: "conv5_4"
    top: "pool5"
    name: "pool5"
    type: POOLING
    pooling_param {
      pool: MAX
      kernel_size: 2
      stride: 2
    }
  }
  layers {
    bottom: "pool5"
    top: "fc6"
    name: "fc6"
    type: INNER_PRODUCT
    inner_product_param {
      num_output: 4096
    }
  }
  layers {
    bottom: "fc6"
    top: "fc6"
    name: "relu6"
    type: RELU
  }
  layers {
    bottom: "fc6"
    top: "fc6"
    name: "drop6"
    type: DROPOUT
    dropout_param {
      dropout_ratio: 0.5
    }
  }
  layers {
    bottom: "fc6"
    top: "fc7"
    name: "fc7"
    type: INNER_PRODUCT
    inner_product_param {
      num_output: 4096
    }
  }
  layers {
    bottom: "fc7"
    top: "fc7"
    name: "relu7"
    type: RELU
  }
  layers {
    bottom: "fc7"
    top: "fc7"
    name: "drop7"
    type: DROPOUT
    dropout_param {
      dropout_ratio: 0.5
    }
  }
  layers {
    name: "fc8_ris80h2ilsvrc12rgb2im"
    type: INNER_PRODUCT
    bottom: "fc7"
    top: "fc8_ris80h2ilsvrc12rgb2im"
    # blobs_lr is set to higher than for other layers, because this layer is starting from random while the others are already trained
    blobs_lr: 10
    blobs_lr: 20
    weight_decay: 1
    weight_decay: 0
    inner_product_param {
      num_output: 385
      weight_filler {
        type: "gaussian"
        std: 0.01
      }
      bias_filler {
        type: "constant"
        value: 0
      }
    }
  }
  layers {
    bottom: "fc8_ris80h2ilsvrc12rgb2im"
    top: "prob"
    name: "prob"
    type: SOFTMAX
  }