name: "VGG_ILSVRC_16_layers"
input: "data"
input_dim: 1
input_dim: 3
input_dim: 224
input_dim: 224
input: "rois"
input_dim: 1 # to be changed on-the-fly to num ROIs
input_dim: 5 # [batch ind, x1, y1, x2, y2] zero-based indexing
input_dim: 1
input_dim: 1
input: "labels"
input_dim: 1 # to be changed on-the-fly to match num ROIs
input_dim: 1
input_dim: 1
input_dim: 1
input: "bbox_targets"
input_dim: 1  # to be changed on-the-fly to match num ROIs
input_dim: 84 # 4 * K (=21) classes
input_dim: 1
input_dim: 1
input: "bbox_loss_weights"
input_dim: 1  # to be changed on-the-fly to match num ROIs
input_dim: 84 # 4 * K (=21) classes
input_dim: 1
input_dim: 1
layers {
  bottom: "data"
  top: "conv1_1"
  name: "conv1_1"
  type: CONVOLUTION
  convolution_param {
    num_output: 64
    pad: 1
    kernel_size: 3
  }
  # Learning parameters
  blobs_lr: 0
  blobs_lr: 0
  weight_decay: 0
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 0
  blobs_lr: 0
  weight_decay: 0
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 0
  blobs_lr: 0
  weight_decay: 0
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 0
  blobs_lr: 0
  weight_decay: 0
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
layers {
  bottom: "conv3_3"
  top: "conv3_3"
  name: "relu3_3"
  type: RELU
}
layers {
  bottom: "conv3_3"
  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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
layers {
  bottom: "conv4_3"
  top: "conv4_3"
  name: "relu4_3"
  type: RELU
}
layers {
  bottom: "conv4_3"
  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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
layers {
  bottom: "conv5_3"
  top: "conv5_3"
  name: "relu5_3"
  type: RELU
}
layers {
  name: "roi_pool5"
  type: ROI_POOLING
  bottom: "conv5_3"
  bottom: "rois"
  top: "pool5"
  roi_pooling_param {
    pooled_w: 7
    pooled_h: 7
  }
}
layers {
  bottom: "pool5"
  top: "fc6"
  name: "fc6"
  type: INNER_PRODUCT
  inner_product_param {
    num_output: 4096
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
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_pascal"
  type: INNER_PRODUCT
  bottom: "fc7"
  top: "fc8_pascal"
  inner_product_param {
    num_output: 21
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
layers {
  name: "fc8_pascal_bbox"
  type: INNER_PRODUCT
  bottom: "fc7"
  top: "fc8_pascal_bbox"
  inner_product_param {
    num_output: 84
    weight_filler {
      type: "gaussian"
      std: 0.001
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
  # Learning parameters
  blobs_lr: 1
  blobs_lr: 2
  weight_decay: 1
  weight_decay: 0
}
layers {
  name: "loss"
  type: SOFTMAX_LOSS
  bottom: "fc8_pascal"
  bottom: "labels"
  top: "loss"
}
layers {
  name: "loss_bbox"
  type: SMOOTH_L1_LOSS
  bottom: "fc8_pascal_bbox"
  bottom: "bbox_targets"
  bottom: "bbox_loss_weights"
  top: "loss_bbox"
  loss_weight: 1
}