PVANet lite from: https://raw.githubusercontent.com/sanghoon/pva-faster-rcnn/master/models/pvanet/lite/original.pt

gistfile1.txt

name: "PVANET-lite"

# https://raw.githubusercontent.com/sanghoon/pva-faster-rcnn/master/models/pvanet/lite/original.pt

################################################################################
## Input
################################################################################

layer {
  name: 'input-data'
  type: 'Python'
  top: 'data'
  top: 'im_info'
  top: 'gt_boxes'
  include { phase: TRAIN }
  python_param {
    module: 'roi_data_layer.layer'
    layer: 'RoIDataLayer'
    param_str: "'num_classes': 21"
  }
}

layer {
  name: "input-data"
  type: "DummyData"
  top: "data"
  top: "im_info"
  include { phase: TEST }
  dummy_data_param {
    shape { dim: 1 dim: 3 dim: 640 dim: 1056 }
    shape { dim: 1 dim: 4 }
  }
}


################################################################################
## Conv 1
################################################################################
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32
    kernel_size: 4  stride: 2  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "conv1/bn"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "conv1/scale"
  type: "Scale"
  bottom: "conv1"
  top: "conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}

################################################################################
## Conv 2
################################################################################
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv1"
  top: "conv2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 48
    kernel_size: 3  stride: 2  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "conv2/bn"
  type: "BatchNorm"
  bottom: "conv2"
  top: "conv2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "conv2/scale"
  type: "Scale"
  bottom: "conv2"
  top: "conv2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}

################################################################################
## Conv 3
################################################################################
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96
    kernel_size: 3  stride: 2  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "conv3/bn"
  type: "BatchNorm"
  bottom: "conv3"
  top: "conv3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "conv3/scale"
  type: "Scale"
  bottom: "conv3"
  top: "conv3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}

################################################################################
## Inception 3a
################################################################################
layer {
  name: "inc3a/pool1"
  type: "Pooling"
  bottom: "conv3"
  top: "inc3a/pool1"
  pooling_param {
    kernel_size: 3  stride: 2  pad: 0
    pool: MAX
  }
}
layer {
  name: "inc3a/conv1"
  type: "Convolution"
  bottom: "inc3a/pool1"
  top: "inc3a/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3a/conv1/bn"
  type: "BatchNorm"
  bottom: "inc3a/conv1"
  top: "inc3a/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3a/conv1/scale"
  type: "Scale"
  bottom: "inc3a/conv1"
  top: "inc3a/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3a/relu1"
  type: "ReLU"
  bottom: "inc3a/conv1"
  top: "inc3a/conv1"
}
layer {
  name: "inc3a/conv3_1"
  type: "Convolution"
  bottom: "conv3"
  top: "inc3a/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3a/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc3a/conv3_1"
  top: "inc3a/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3a/conv3_1/scale"
  type: "Scale"
  bottom: "inc3a/conv3_1"
  top: "inc3a/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3a/relu3_1"
  type: "ReLU"
  bottom: "inc3a/conv3_1"
  top: "inc3a/conv3_1"
}
layer {
  name: "inc3a/conv3_2"
  type: "Convolution"
  bottom: "inc3a/conv3_1"
  top: "inc3a/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 64  kernel_size: 3  stride: 2  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3a/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc3a/conv3_2"
  top: "inc3a/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3a/conv3_2/scale"
  type: "Scale"
  bottom: "inc3a/conv3_2"
  top: "inc3a/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3a/relu3_2"
  type: "ReLU"
  bottom: "inc3a/conv3_2"
  top: "inc3a/conv3_2"
}
layer {
  name: "inc3a/conv5_1"
  type: "Convolution"
  bottom: "conv3"
  top: "inc3a/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3a/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc3a/conv5_1"
  top: "inc3a/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3a/conv5_1/scale"
  type: "Scale"
  bottom: "inc3a/conv5_1"
  top: "inc3a/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3a/relu5_1"
  type: "ReLU"
  bottom: "inc3a/conv5_1"
  top: "inc3a/conv5_1"
}
layer {
  name: "inc3a/conv5_2"
  type: "Convolution"
  bottom: "inc3a/conv5_1"
  top: "inc3a/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3a/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc3a/conv5_2"
  top: "inc3a/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3a/conv5_2/scale"
  type: "Scale"
  bottom: "inc3a/conv5_2"
  top: "inc3a/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3a/relu5_2"
  type: "ReLU"
  bottom: "inc3a/conv5_2"
  top: "inc3a/conv5_2"
}
layer {
  name: "inc3a/conv5_3"
  type: "Convolution"
  bottom: "inc3a/conv5_2"
  top: "inc3a/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  stride: 2  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3a/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc3a/conv5_3"
  top: "inc3a/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3a/conv5_3/scale"
  type: "Scale"
  bottom: "inc3a/conv5_3"
  top: "inc3a/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3a/relu5_3"
  type: "ReLU"
  bottom: "inc3a/conv5_3"
  top: "inc3a/conv5_3"
}
layer {
  name: "inc3a"
  type: "Concat"
  bottom: "inc3a/conv1"
  bottom: "inc3a/conv3_2"
  bottom: "inc3a/conv5_3"
  top: "inc3a"
}

################################################################################
## Inception 3b
################################################################################
layer {
  name: "inc3b/conv1"
  type: "Convolution"
  bottom: "inc3a"
  top: "inc3b/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3b/conv1/bn"
  type: "BatchNorm"
  bottom: "inc3b/conv1"
  top: "inc3b/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3b/conv1/scale"
  type: "Scale"
  bottom: "inc3b/conv1"
  top: "inc3b/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3b/relu1"
  type: "ReLU"
  bottom: "inc3b/conv1"
  top: "inc3b/conv1"
}
layer {
  name: "inc3b/conv3_1"
  type: "Convolution"
  bottom: "inc3a"
  top: "inc3b/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3b/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc3b/conv3_1"
  top: "inc3b/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3b/conv3_1/scale"
  type: "Scale"
  bottom: "inc3b/conv3_1"
  top: "inc3b/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3b/relu3_1"
  type: "ReLU"
  bottom: "inc3b/conv3_1"
  top: "inc3b/conv3_1"
}
layer {
  name: "inc3b/conv3_2"
  type: "Convolution"
  bottom: "inc3b/conv3_1"
  top: "inc3b/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 64  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3b/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc3b/conv3_2"
  top: "inc3b/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3b/conv3_2/scale"
  type: "Scale"
  bottom: "inc3b/conv3_2"
  top: "inc3b/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3b/relu3_2"
  type: "ReLU"
  bottom: "inc3b/conv3_2"
  top: "inc3b/conv3_2"
}
layer {
  name: "inc3b/conv5_1"
  type: "Convolution"
  bottom: "inc3a"
  top: "inc3b/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3b/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc3b/conv5_1"
  top: "inc3b/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3b/conv5_1/scale"
  type: "Scale"
  bottom: "inc3b/conv5_1"
  top: "inc3b/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3b/relu5_1"
  type: "ReLU"
  bottom: "inc3b/conv5_1"
  top: "inc3b/conv5_1"
}
layer {
  name: "inc3b/conv5_2"
  type: "Convolution"
  bottom: "inc3b/conv5_1"
  top: "inc3b/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3b/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc3b/conv5_2"
  top: "inc3b/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3b/conv5_2/scale"
  type: "Scale"
  bottom: "inc3b/conv5_2"
  top: "inc3b/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3b/relu5_2"
  type: "ReLU"
  bottom: "inc3b/conv5_2"
  top: "inc3b/conv5_2"
}
layer {
  name: "inc3b/conv5_3"
  type: "Convolution"
  bottom: "inc3b/conv5_2"
  top: "inc3b/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3b/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc3b/conv5_3"
  top: "inc3b/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3b/conv5_3/scale"
  type: "Scale"
  bottom: "inc3b/conv5_3"
  top: "inc3b/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3b/relu5_3"
  type: "ReLU"
  bottom: "inc3b/conv5_3"
  top: "inc3b/conv5_3"
}
layer {
  name: "inc3b"
  type: "Concat"
  bottom: "inc3b/conv1"
  bottom: "inc3b/conv3_2"
  bottom: "inc3b/conv5_3"
  top: "inc3b"
}

################################################################################
## Inception 3c
################################################################################
layer {
  name: "inc3c/conv1"
  type: "Convolution"
  bottom: "inc3b"
  top: "inc3c/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3c/conv1/bn"
  type: "BatchNorm"
  bottom: "inc3c/conv1"
  top: "inc3c/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3c/conv1/scale"
  type: "Scale"
  bottom: "inc3c/conv1"
  top: "inc3c/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3c/relu1"
  type: "ReLU"
  bottom: "inc3c/conv1"
  top: "inc3c/conv1"
}
layer {
  name: "inc3c/conv3_1"
  type: "Convolution"
  bottom: "inc3b"
  top: "inc3c/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3c/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc3c/conv3_1"
  top: "inc3c/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3c/conv3_1/scale"
  type: "Scale"
  bottom: "inc3c/conv3_1"
  top: "inc3c/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3c/relu3_1"
  type: "ReLU"
  bottom: "inc3c/conv3_1"
  top: "inc3c/conv3_1"
}
layer {
  name: "inc3c/conv3_2"
  type: "Convolution"
  bottom: "inc3c/conv3_1"
  top: "inc3c/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 64  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3c/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc3c/conv3_2"
  top: "inc3c/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3c/conv3_2/scale"
  type: "Scale"
  bottom: "inc3c/conv3_2"
  top: "inc3c/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3c/relu3_2"
  type: "ReLU"
  bottom: "inc3c/conv3_2"
  top: "inc3c/conv3_2"
}
layer {
  name: "inc3c/conv5_1"
  type: "Convolution"
  bottom: "inc3b"
  top: "inc3c/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3c/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc3c/conv5_1"
  top: "inc3c/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3c/conv5_1/scale"
  type: "Scale"
  bottom: "inc3c/conv5_1"
  top: "inc3c/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3c/relu5_1"
  type: "ReLU"
  bottom: "inc3c/conv5_1"
  top: "inc3c/conv5_1"
}
layer {
  name: "inc3c/conv5_2"
  type: "Convolution"
  bottom: "inc3c/conv5_1"
  top: "inc3c/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3c/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc3c/conv5_2"
  top: "inc3c/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3c/conv5_2/scale"
  type: "Scale"
  bottom: "inc3c/conv5_2"
  top: "inc3c/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3c/relu5_2"
  type: "ReLU"
  bottom: "inc3c/conv5_2"
  top: "inc3c/conv5_2"
}
layer {
  name: "inc3c/conv5_3"
  type: "Convolution"
  bottom: "inc3c/conv5_2"
  top: "inc3c/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3c/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc3c/conv5_3"
  top: "inc3c/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3c/conv5_3/scale"
  type: "Scale"
  bottom: "inc3c/conv5_3"
  top: "inc3c/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3c/relu5_3"
  type: "ReLU"
  bottom: "inc3c/conv5_3"
  top: "inc3c/conv5_3"
}
layer {
  name: "inc3c"
  type: "Concat"
  bottom: "inc3c/conv1"
  bottom: "inc3c/conv3_2"
  bottom: "inc3c/conv5_3"
  top: "inc3c"
}

################################################################################
## Inception 3d
################################################################################
layer {
  name: "inc3d/conv1"
  type: "Convolution"
  bottom: "inc3c"
  top: "inc3d/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3d/conv1/bn"
  type: "BatchNorm"
  bottom: "inc3d/conv1"
  top: "inc3d/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3d/conv1/scale"
  type: "Scale"
  bottom: "inc3d/conv1"
  top: "inc3d/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3d/relu1"
  type: "ReLU"
  bottom: "inc3d/conv1"
  top: "inc3d/conv1"
}
layer {
  name: "inc3d/conv3_1"
  type: "Convolution"
  bottom: "inc3c"
  top: "inc3d/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3d/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc3d/conv3_1"
  top: "inc3d/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3d/conv3_1/scale"
  type: "Scale"
  bottom: "inc3d/conv3_1"
  top: "inc3d/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3d/relu3_1"
  type: "ReLU"
  bottom: "inc3d/conv3_1"
  top: "inc3d/conv3_1"
}
layer {
  name: "inc3d/conv3_2"
  type: "Convolution"
  bottom: "inc3d/conv3_1"
  top: "inc3d/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 64  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3d/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc3d/conv3_2"
  top: "inc3d/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3d/conv3_2/scale"
  type: "Scale"
  bottom: "inc3d/conv3_2"
  top: "inc3d/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3d/relu3_2"
  type: "ReLU"
  bottom: "inc3d/conv3_2"
  top: "inc3d/conv3_2"
}
layer {
  name: "inc3d/conv5_1"
  type: "Convolution"
  bottom: "inc3c"
  top: "inc3d/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3d/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc3d/conv5_1"
  top: "inc3d/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3d/conv5_1/scale"
  type: "Scale"
  bottom: "inc3d/conv5_1"
  top: "inc3d/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3d/relu5_1"
  type: "ReLU"
  bottom: "inc3d/conv5_1"
  top: "inc3d/conv5_1"
}
layer {
  name: "inc3d/conv5_2"
  type: "Convolution"
  bottom: "inc3d/conv5_1"
  top: "inc3d/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3d/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc3d/conv5_2"
  top: "inc3d/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3d/conv5_2/scale"
  type: "Scale"
  bottom: "inc3d/conv5_2"
  top: "inc3d/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3d/relu5_2"
  type: "ReLU"
  bottom: "inc3d/conv5_2"
  top: "inc3d/conv5_2"
}
layer {
  name: "inc3d/conv5_3"
  type: "Convolution"
  bottom: "inc3d/conv5_2"
  top: "inc3d/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3d/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc3d/conv5_3"
  top: "inc3d/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3d/conv5_3/scale"
  type: "Scale"
  bottom: "inc3d/conv5_3"
  top: "inc3d/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3d/relu5_3"
  type: "ReLU"
  bottom: "inc3d/conv5_3"
  top: "inc3d/conv5_3"
}
layer {
  name: "inc3d"
  type: "Concat"
  bottom: "inc3d/conv1"
  bottom: "inc3d/conv3_2"
  bottom: "inc3d/conv5_3"
  top: "inc3d"
}

################################################################################
## Inception 3e
################################################################################
layer {
  name: "inc3e/conv1"
  type: "Convolution"
  bottom: "inc3d"
  top: "inc3e/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3e/conv1/bn"
  type: "BatchNorm"
  bottom: "inc3e/conv1"
  top: "inc3e/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3e/conv1/scale"
  type: "Scale"
  bottom: "inc3e/conv1"
  top: "inc3e/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3e/relu1"
  type: "ReLU"
  bottom: "inc3e/conv1"
  top: "inc3e/conv1"
}
layer {
  name: "inc3e/conv3_1"
  type: "Convolution"
  bottom: "inc3d"
  top: "inc3e/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3e/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc3e/conv3_1"
  top: "inc3e/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3e/conv3_1/scale"
  type: "Scale"
  bottom: "inc3e/conv3_1"
  top: "inc3e/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3e/relu3_1"
  type: "ReLU"
  bottom: "inc3e/conv3_1"
  top: "inc3e/conv3_1"
}
layer {
  name: "inc3e/conv3_2"
  type: "Convolution"
  bottom: "inc3e/conv3_1"
  top: "inc3e/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 64  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3e/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc3e/conv3_2"
  top: "inc3e/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3e/conv3_2/scale"
  type: "Scale"
  bottom: "inc3e/conv3_2"
  top: "inc3e/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3e/relu3_2"
  type: "ReLU"
  bottom: "inc3e/conv3_2"
  top: "inc3e/conv3_2"
}
layer {
  name: "inc3e/conv5_1"
  type: "Convolution"
  bottom: "inc3d"
  top: "inc3e/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3e/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc3e/conv5_1"
  top: "inc3e/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3e/conv5_1/scale"
  type: "Scale"
  bottom: "inc3e/conv5_1"
  top: "inc3e/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3e/relu5_1"
  type: "ReLU"
  bottom: "inc3e/conv5_1"
  top: "inc3e/conv5_1"
}
layer {
  name: "inc3e/conv5_2"
  type: "Convolution"
  bottom: "inc3e/conv5_1"
  top: "inc3e/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3e/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc3e/conv5_2"
  top: "inc3e/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3e/conv5_2/scale"
  type: "Scale"
  bottom: "inc3e/conv5_2"
  top: "inc3e/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3e/relu5_2"
  type: "ReLU"
  bottom: "inc3e/conv5_2"
  top: "inc3e/conv5_2"
}
layer {
  name: "inc3e/conv5_3"
  type: "Convolution"
  bottom: "inc3e/conv5_2"
  top: "inc3e/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc3e/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc3e/conv5_3"
  top: "inc3e/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc3e/conv5_3/scale"
  type: "Scale"
  bottom: "inc3e/conv5_3"
  top: "inc3e/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc3e/relu5_3"
  type: "ReLU"
  bottom: "inc3e/conv5_3"
  top: "inc3e/conv5_3"
}
layer {
  name: "inc3e"
  type: "Concat"
  bottom: "inc3e/conv1"
  bottom: "inc3e/conv3_2"
  bottom: "inc3e/conv5_3"
  top: "inc3e"
}

################################################################################
## Inception 4a
################################################################################
layer {
  name: "inc4a/pool1"
  type: "Pooling"
  bottom: "inc3e"
  top: "inc4a/pool1"
  pooling_param {
    kernel_size: 3  stride: 2  pad: 0
    pool: MAX
  }
}
layer {
  name: "inc4a/conv1"
  type: "Convolution"
  bottom: "inc4a/pool1"
  top: "inc4a/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 128  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4a/conv1/bn"
  type: "BatchNorm"
  bottom: "inc4a/conv1"
  top: "inc4a/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4a/conv1/scale"
  type: "Scale"
  bottom: "inc4a/conv1"
  top: "inc4a/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4a/relu1"
  type: "ReLU"
  bottom: "inc4a/conv1"
  top: "inc4a/conv1"
}
layer {
  name: "inc4a/conv3_1"
  type: "Convolution"
  bottom: "inc3e"
  top: "inc4a/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4a/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc4a/conv3_1"
  top: "inc4a/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4a/conv3_1/scale"
  type: "Scale"
  bottom: "inc4a/conv3_1"
  top: "inc4a/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4a/relu3_1"
  type: "ReLU"
  bottom: "inc4a/conv3_1"
  top: "inc4a/conv3_1"
}
layer {
  name: "inc4a/conv3_2"
  type: "Convolution"
  bottom: "inc4a/conv3_1"
  top: "inc4a/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 3  stride: 2  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4a/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc4a/conv3_2"
  top: "inc4a/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4a/conv3_2/scale"
  type: "Scale"
  bottom: "inc4a/conv3_2"
  top: "inc4a/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4a/relu3_2"
  type: "ReLU"
  bottom: "inc4a/conv3_2"
  top: "inc4a/conv3_2"
}
layer {
  name: "inc4a/conv5_1"
  type: "Convolution"
  bottom: "inc3e"
  top: "inc4a/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4a/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc4a/conv5_1"
  top: "inc4a/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4a/conv5_1/scale"
  type: "Scale"
  bottom: "inc4a/conv5_1"
  top: "inc4a/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4a/relu5_1"
  type: "ReLU"
  bottom: "inc4a/conv5_1"
  top: "inc4a/conv5_1"
}
layer {
  name: "inc4a/conv5_2"
  type: "Convolution"
  bottom: "inc4a/conv5_1"
  top: "inc4a/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4a/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc4a/conv5_2"
  top: "inc4a/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4a/conv5_2/scale"
  type: "Scale"
  bottom: "inc4a/conv5_2"
  top: "inc4a/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4a/relu5_2"
  type: "ReLU"
  bottom: "inc4a/conv5_2"
  top: "inc4a/conv5_2"
}
layer {
  name: "inc4a/conv5_3"
  type: "Convolution"
  bottom: "inc4a/conv5_2"
  top: "inc4a/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  stride: 2  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4a/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc4a/conv5_3"
  top: "inc4a/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4a/conv5_3/scale"
  type: "Scale"
  bottom: "inc4a/conv5_3"
  top: "inc4a/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4a/relu5_3"
  type: "ReLU"
  bottom: "inc4a/conv5_3"
  top: "inc4a/conv5_3"
}
layer {
  name: "inc4a"
  type: "Concat"
  bottom: "inc4a/conv1"
  bottom: "inc4a/conv3_2"
  bottom: "inc4a/conv5_3"
  top: "inc4a"
}

################################################################################
## Inception 4b
################################################################################
layer {
  name: "inc4b/conv1"
  type: "Convolution"
  bottom: "inc4a"
  top: "inc4b/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 128  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4b/conv1/bn"
  type: "BatchNorm"
  bottom: "inc4b/conv1"
  top: "inc4b/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4b/conv1/scale"
  type: "Scale"
  bottom: "inc4b/conv1"
  top: "inc4b/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4b/relu1"
  type: "ReLU"
  bottom: "inc4b/conv1"
  top: "inc4b/conv1"
}
layer {
  name: "inc4b/conv3_1"
  type: "Convolution"
  bottom: "inc4a"
  top: "inc4b/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4b/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc4b/conv3_1"
  top: "inc4b/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4b/conv3_1/scale"
  type: "Scale"
  bottom: "inc4b/conv3_1"
  top: "inc4b/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4b/relu3_1"
  type: "ReLU"
  bottom: "inc4b/conv3_1"
  top: "inc4b/conv3_1"
}
layer {
  name: "inc4b/conv3_2"
  type: "Convolution"
  bottom: "inc4b/conv3_1"
  top: "inc4b/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4b/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc4b/conv3_2"
  top: "inc4b/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4b/conv3_2/scale"
  type: "Scale"
  bottom: "inc4b/conv3_2"
  top: "inc4b/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4b/relu3_2"
  type: "ReLU"
  bottom: "inc4b/conv3_2"
  top: "inc4b/conv3_2"
}
layer {
  name: "inc4b/conv5_1"
  type: "Convolution"
  bottom: "inc4a"
  top: "inc4b/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4b/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc4b/conv5_1"
  top: "inc4b/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4b/conv5_1/scale"
  type: "Scale"
  bottom: "inc4b/conv5_1"
  top: "inc4b/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4b/relu5_1"
  type: "ReLU"
  bottom: "inc4b/conv5_1"
  top: "inc4b/conv5_1"
}
layer {
  name: "inc4b/conv5_2"
  type: "Convolution"
  bottom: "inc4b/conv5_1"
  top: "inc4b/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4b/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc4b/conv5_2"
  top: "inc4b/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4b/conv5_2/scale"
  type: "Scale"
  bottom: "inc4b/conv5_2"
  top: "inc4b/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4b/relu5_2"
  type: "ReLU"
  bottom: "inc4b/conv5_2"
  top: "inc4b/conv5_2"
}
layer {
  name: "inc4b/conv5_3"
  type: "Convolution"
  bottom: "inc4b/conv5_2"
  top: "inc4b/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4b/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc4b/conv5_3"
  top: "inc4b/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4b/conv5_3/scale"
  type: "Scale"
  bottom: "inc4b/conv5_3"
  top: "inc4b/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4b/relu5_3"
  type: "ReLU"
  bottom: "inc4b/conv5_3"
  top: "inc4b/conv5_3"
}
layer {
  name: "inc4b"
  type: "Concat"
  bottom: "inc4b/conv1"
  bottom: "inc4b/conv3_2"
  bottom: "inc4b/conv5_3"
  top: "inc4b"
}

################################################################################
## Inception 4c
################################################################################
layer {
  name: "inc4c/conv1"
  type: "Convolution"
  bottom: "inc4b"
  top: "inc4c/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 128  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4c/conv1/bn"
  type: "BatchNorm"
  bottom: "inc4c/conv1"
  top: "inc4c/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4c/conv1/scale"
  type: "Scale"
  bottom: "inc4c/conv1"
  top: "inc4c/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4c/relu1"
  type: "ReLU"
  bottom: "inc4c/conv1"
  top: "inc4c/conv1"
}
layer {
  name: "inc4c/conv3_1"
  type: "Convolution"
  bottom: "inc4b"
  top: "inc4c/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4c/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc4c/conv3_1"
  top: "inc4c/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4c/conv3_1/scale"
  type: "Scale"
  bottom: "inc4c/conv3_1"
  top: "inc4c/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4c/relu3_1"
  type: "ReLU"
  bottom: "inc4c/conv3_1"
  top: "inc4c/conv3_1"
}
layer {
  name: "inc4c/conv3_2"
  type: "Convolution"
  bottom: "inc4c/conv3_1"
  top: "inc4c/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4c/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc4c/conv3_2"
  top: "inc4c/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4c/conv3_2/scale"
  type: "Scale"
  bottom: "inc4c/conv3_2"
  top: "inc4c/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4c/relu3_2"
  type: "ReLU"
  bottom: "inc4c/conv3_2"
  top: "inc4c/conv3_2"
}
layer {
  name: "inc4c/conv5_1"
  type: "Convolution"
  bottom: "inc4b"
  top: "inc4c/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4c/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc4c/conv5_1"
  top: "inc4c/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4c/conv5_1/scale"
  type: "Scale"
  bottom: "inc4c/conv5_1"
  top: "inc4c/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4c/relu5_1"
  type: "ReLU"
  bottom: "inc4c/conv5_1"
  top: "inc4c/conv5_1"
}
layer {
  name: "inc4c/conv5_2"
  type: "Convolution"
  bottom: "inc4c/conv5_1"
  top: "inc4c/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4c/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc4c/conv5_2"
  top: "inc4c/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4c/conv5_2/scale"
  type: "Scale"
  bottom: "inc4c/conv5_2"
  top: "inc4c/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4c/relu5_2"
  type: "ReLU"
  bottom: "inc4c/conv5_2"
  top: "inc4c/conv5_2"
}
layer {
  name: "inc4c/conv5_3"
  type: "Convolution"
  bottom: "inc4c/conv5_2"
  top: "inc4c/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4c/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc4c/conv5_3"
  top: "inc4c/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4c/conv5_3/scale"
  type: "Scale"
  bottom: "inc4c/conv5_3"
  top: "inc4c/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4c/relu5_3"
  type: "ReLU"
  bottom: "inc4c/conv5_3"
  top: "inc4c/conv5_3"
}
layer {
  name: "inc4c"
  type: "Concat"
  bottom: "inc4c/conv1"
  bottom: "inc4c/conv3_2"
  bottom: "inc4c/conv5_3"
  top: "inc4c"
}

################################################################################
## Inception 4d
################################################################################
layer {
  name: "inc4d/conv1"
  type: "Convolution"
  bottom: "inc4c"
  top: "inc4d/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 128  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4d/conv1/bn"
  type: "BatchNorm"
  bottom: "inc4d/conv1"
  top: "inc4d/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4d/conv1/scale"
  type: "Scale"
  bottom: "inc4d/conv1"
  top: "inc4d/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4d/relu1"
  type: "ReLU"
  bottom: "inc4d/conv1"
  top: "inc4d/conv1"
}
layer {
  name: "inc4d/conv3_1"
  type: "Convolution"
  bottom: "inc4c"
  top: "inc4d/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4d/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc4d/conv3_1"
  top: "inc4d/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4d/conv3_1/scale"
  type: "Scale"
  bottom: "inc4d/conv3_1"
  top: "inc4d/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4d/relu3_1"
  type: "ReLU"
  bottom: "inc4d/conv3_1"
  top: "inc4d/conv3_1"
}
layer {
  name: "inc4d/conv3_2"
  type: "Convolution"
  bottom: "inc4d/conv3_1"
  top: "inc4d/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4d/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc4d/conv3_2"
  top: "inc4d/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4d/conv3_2/scale"
  type: "Scale"
  bottom: "inc4d/conv3_2"
  top: "inc4d/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4d/relu3_2"
  type: "ReLU"
  bottom: "inc4d/conv3_2"
  top: "inc4d/conv3_2"
}
layer {
  name: "inc4d/conv5_1"
  type: "Convolution"
  bottom: "inc4c"
  top: "inc4d/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4d/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc4d/conv5_1"
  top: "inc4d/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4d/conv5_1/scale"
  type: "Scale"
  bottom: "inc4d/conv5_1"
  top: "inc4d/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4d/relu5_1"
  type: "ReLU"
  bottom: "inc4d/conv5_1"
  top: "inc4d/conv5_1"
}
layer {
  name: "inc4d/conv5_2"
  type: "Convolution"
  bottom: "inc4d/conv5_1"
  top: "inc4d/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4d/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc4d/conv5_2"
  top: "inc4d/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4d/conv5_2/scale"
  type: "Scale"
  bottom: "inc4d/conv5_2"
  top: "inc4d/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4d/relu5_2"
  type: "ReLU"
  bottom: "inc4d/conv5_2"
  top: "inc4d/conv5_2"
}
layer {
  name: "inc4d/conv5_3"
  type: "Convolution"
  bottom: "inc4d/conv5_2"
  top: "inc4d/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4d/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc4d/conv5_3"
  top: "inc4d/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4d/conv5_3/scale"
  type: "Scale"
  bottom: "inc4d/conv5_3"
  top: "inc4d/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4d/relu5_3"
  type: "ReLU"
  bottom: "inc4d/conv5_3"
  top: "inc4d/conv5_3"
}
layer {
  name: "inc4d"
  type: "Concat"
  bottom: "inc4d/conv1"
  bottom: "inc4d/conv3_2"
  bottom: "inc4d/conv5_3"
  top: "inc4d"
}

################################################################################
## Inception 4e
################################################################################
layer {
  name: "inc4e/conv1"
  type: "Convolution"
  bottom: "inc4d"
  top: "inc4e/conv1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 128  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4e/conv1/bn"
  type: "BatchNorm"
  bottom: "inc4e/conv1"
  top: "inc4e/conv1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4e/conv1/scale"
  type: "Scale"
  bottom: "inc4e/conv1"
  top: "inc4e/conv1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4e/relu1"
  type: "ReLU"
  bottom: "inc4e/conv1"
  top: "inc4e/conv1"
}
layer {
  name: "inc4e/conv3_1"
  type: "Convolution"
  bottom: "inc4d"
  top: "inc4e/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4e/conv3_1/bn"
  type: "BatchNorm"
  bottom: "inc4e/conv3_1"
  top: "inc4e/conv3_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4e/conv3_1/scale"
  type: "Scale"
  bottom: "inc4e/conv3_1"
  top: "inc4e/conv3_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4e/relu3_1"
  type: "ReLU"
  bottom: "inc4e/conv3_1"
  top: "inc4e/conv3_1"
}
layer {
  name: "inc4e/conv3_2"
  type: "Convolution"
  bottom: "inc4e/conv3_1"
  top: "inc4e/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 96  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4e/conv3_2/bn"
  type: "BatchNorm"
  bottom: "inc4e/conv3_2"
  top: "inc4e/conv3_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4e/conv3_2/scale"
  type: "Scale"
  bottom: "inc4e/conv3_2"
  top: "inc4e/conv3_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4e/relu3_2"
  type: "ReLU"
  bottom: "inc4e/conv3_2"
  top: "inc4e/conv3_2"
}
layer {
  name: "inc4e/conv5_1"
  type: "Convolution"
  bottom: "inc4d"
  top: "inc4e/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 16  kernel_size: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4e/conv5_1/bn"
  type: "BatchNorm"
  bottom: "inc4e/conv5_1"
  top: "inc4e/conv5_1"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4e/conv5_1/scale"
  type: "Scale"
  bottom: "inc4e/conv5_1"
  top: "inc4e/conv5_1"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4e/relu5_1"
  type: "ReLU"
  bottom: "inc4e/conv5_1"
  top: "inc4e/conv5_1"
}
layer {
  name: "inc4e/conv5_2"
  type: "Convolution"
  bottom: "inc4e/conv5_1"
  top: "inc4e/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4e/conv5_2/bn"
  type: "BatchNorm"
  bottom: "inc4e/conv5_2"
  top: "inc4e/conv5_2"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4e/conv5_2/scale"
  type: "Scale"
  bottom: "inc4e/conv5_2"
  top: "inc4e/conv5_2"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4e/relu5_2"
  type: "ReLU"
  bottom: "inc4e/conv5_2"
  top: "inc4e/conv5_2"
}
layer {
  name: "inc4e/conv5_3"
  type: "Convolution"
  bottom: "inc4e/conv5_2"
  top: "inc4e/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0.1 }
  param { lr_mult: 0.2  decay_mult: 0 }
  convolution_param {
    num_output: 32  kernel_size: 3  pad: 1
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "inc4e/conv5_3/bn"
  type: "BatchNorm"
  bottom: "inc4e/conv5_3"
  top: "inc4e/conv5_3"
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  param { lr_mult: 0  decay_mult: 0 }
  batch_norm_param { use_global_stats: true }
}
layer {
  name: "inc4e/conv5_3/scale"
  type: "Scale"
  bottom: "inc4e/conv5_3"
  top: "inc4e/conv5_3"
  param { lr_mult: 0.1  decay_mult: 0 }
  param { lr_mult: 0.1  decay_mult: 0 }
  scale_param { bias_term: true }
}
layer {
  name: "inc4e/relu5_3"
  type: "ReLU"
  bottom: "inc4e/conv5_3"
  top: "inc4e/conv5_3"
}
layer {
  name: "inc4e"
  type: "Concat"
  bottom: "inc4e/conv1"
  bottom: "inc4e/conv3_2"
  bottom: "inc4e/conv5_3"
  top: "inc4e"
}


################################################################################
## hyper feature
################################################################################
layer {
  name: "downsample"
  type: "Pooling"
  bottom: "conv3"
  top: "downsample"
  pooling_param {
    kernel_size: 3  stride: 2  pad: 0
    pool: MAX
  }
}
layer {
  name: "upsample"
  type: "Deconvolution"
  bottom: "inc4e"
  top: "upsample"
  param { lr_mult: 0  decay_mult: 0 }
  convolution_param {
    num_output: 256
    kernel_size: 4  stride: 2  pad: 1
    group: 256
    weight_filler: { type: "bilinear" }
    bias_term: false
  }
}
layer {
  name: "concat"
  type: "Concat"
  bottom: "downsample"
  bottom: "inc3e"
  bottom: "upsample"
  top: "concat"
  concat_param { axis: 1 }
}
layer {
  name: "convf"
  type: "Convolution"
  bottom: "concat"
  top: "convf"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  convolution_param {
    num_output: 256
    kernel_size: 1  stride: 1  pad: 0
    weight_filler { type: "xavier"  std: 0.1 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "reluf"
  type: "ReLU"
  bottom: "convf"
  top: "convf"
}


################################################################################
## RPN
################################################################################

### RPN ###
layer {
  name: "rpn_conv1"
  type: "Convolution"
  bottom: "convf"
  top: "rpn_conv1"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  convolution_param {
    num_output: 256
    kernel_size: 1  stride: 1  pad: 0
    weight_filler { type: "gaussian"  std: 0.01 }
    bias_filler { type: "constant"  value: 0 }
  }
}
layer {
  name: "rpn_relu1"
  type: "ReLU"
  bottom: "rpn_conv1"
  top: "rpn_conv1"
}

layer {
  name: "rpn_cls_score"
  type: "Convolution"
  bottom: "rpn_conv1"
  top: "rpn_cls_score"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  convolution_param {
    num_output: 50
    kernel_size: 1  stride: 1  pad: 0
    weight_filler { type: "gaussian"  std: 0.01 }
    bias_filler { type: "constant"  value: 0 }
  }
}
layer {
  name: "rpn_bbox_pred"
  type: "Convolution"
  bottom: "rpn_conv1"
  top: "rpn_bbox_pred"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  convolution_param {
    num_output: 100
    kernel_size: 1  stride: 1  pad: 0
    weight_filler { type: "gaussian"  std: 0.01 }
    bias_filler { type: "constant"  value: 0 }
  }
}

layer {
   bottom: "rpn_cls_score"
   top: "rpn_cls_score_reshape"
   name: "rpn_cls_score_reshape"
   type: "Reshape"
   reshape_param { shape { dim: 0 dim: 2 dim: -1 dim: 0 } }
}
layer {
  name: 'rpn-data'
  type: 'Python'
  bottom: 'rpn_cls_score'
  bottom: 'gt_boxes'
  bottom: 'im_info'
  bottom: 'data'
  top: 'rpn_labels'
  top: 'rpn_bbox_targets'
  top: 'rpn_bbox_inside_weights'
  top: 'rpn_bbox_outside_weights'
  include { phase: TRAIN }
  python_param {
    module: 'rpn.anchor_target_layer'
    layer: 'AnchorTargetLayer'
    param_str: "{'feat_stride': 16, 'ratios': [0.5, 0.667, 1, 1.5, 2], 'scales': [3, 6, 9, 16, 32]}"
  }
}
layer {
  name: "rpn_loss_cls"
  type: "SoftmaxWithLoss"
  bottom: "rpn_cls_score_reshape"
  bottom: "rpn_labels"
  propagate_down: 1
  propagate_down: 0
  top: "rpn_loss_cls"
  include { phase: TRAIN }
  loss_weight: 1
  loss_param { ignore_label: -1  normalize: true }
}
layer {
  name: "rpn_loss_bbox"
  type: "SmoothL1Loss"
  bottom: "rpn_bbox_pred"
  bottom: "rpn_bbox_targets"
  bottom: "rpn_bbox_inside_weights"
  bottom: "rpn_bbox_outside_weights"
  top: "rpn_loss_bbox"
  include { phase: TRAIN }
  loss_weight: 1
  smooth_l1_loss_param { sigma: 3.0 }
}
  
################################################################################
## Proposal
################################################################################
layer {
  name: "rpn_cls_prob"
  type: "Softmax"
  bottom: "rpn_cls_score_reshape"
  top: "rpn_cls_prob"
}
layer {
  name: 'rpn_cls_prob_reshape'
  type: 'Reshape'
  bottom: 'rpn_cls_prob'
  top: 'rpn_cls_prob_reshape'
  reshape_param { shape { dim: 0 dim: 50 dim: -1 dim: 0 } }
}
layer {
  name: 'proposal'
  type: 'Python'
  bottom: 'rpn_cls_prob_reshape'
  bottom: 'rpn_bbox_pred'
  bottom: 'im_info'
  bottom: 'gt_boxes'
  top: 'rois'
  top: 'labels'
  top: 'bbox_targets'
  top: 'bbox_inside_weights'
  top: 'bbox_outside_weights'
  include { phase: TRAIN }
  python_param {
    module: 'rpn.proposal_layer'
    layer: 'ProposalLayer2'
    param_str: "{'feat_stride': 16, 'num_classes': 21, 'ratios': [0.5, 0.667, 1, 1.5, 2], 'scales': [3, 6, 9, 16, 32]}"
  }
}
layer {
  name: 'proposal'
  type: 'Proposal'
  bottom: 'rpn_cls_prob_reshape'
  bottom: 'rpn_bbox_pred'
  bottom: 'im_info'
  top: 'rois'
  top: 'scores'
  include { phase: TEST }
  proposal_param {
    ratio: 0.5 ratio: 0.667 ratio: 1.0 ratio: 1.5 ratio: 2.0
    scale: 3 scale: 6 scale: 9 scale: 16 scale: 32
    base_size: 16
    feat_stride: 16
    pre_nms_topn: 6000
    post_nms_topn: 200
    nms_thresh: 0.7
    min_size: 16
  }
}


################################################################################
## RCNN
################################################################################
layer {
  name: "roi_pool_conv5"
  type: "ROIPooling"
  bottom: "convf"
  bottom: "rois"
  top: "roi_pool_conv5"
  roi_pooling_param {
    pooled_w: 6  pooled_h: 6
    spatial_scale: 0.0625 # 1/16
  }
}
layer {
  name: "fc6_L"
  type: "InnerProduct"
  bottom: "roi_pool_conv5"
  top: "fc6_L"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  inner_product_param {
    num_output: 512
    weight_filler { type: "xavier"  std: 0.005 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "fc6_U"
  type: "InnerProduct"
  bottom: "fc6_L"
  top: "fc6_U"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  inner_product_param {
    num_output: 4096
    weight_filler { type: "xavier"  std: 0.005 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "fc6_U"
  top: "fc6_U"
}

################################################################################
## fc 7
################################################################################
layer {
  name: "fc7_L"
  type: "InnerProduct"
  bottom: "fc6_U"
  top: "fc7_L"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  inner_product_param {
    num_output: 128
    weight_filler { type: "xavier"  std: 0.005 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "fc7_U"
  type: "InnerProduct"
  bottom: "fc7_L"
  top: "fc7_U"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  inner_product_param {
    num_output: 4096
    weight_filler { type: "xavier"  std: 0.005 }
    bias_filler { type: "constant"  value: 0.1 }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "fc7_U"
  top: "fc7_U"
}

################################################################################
## output
################################################################################
layer {
  name: "cls_score"
  type: "InnerProduct"
  bottom: "fc7_U"
  top: "cls_score"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  inner_product_param {
    num_output: 21
    weight_filler { type: "gaussian"  std: 0.01 }
    bias_filler { type: "constant"  value: 0 }
  }
}
layer {
  name: "bbox_pred"
  type: "InnerProduct"
  bottom: "fc7_U"
  top: "bbox_pred"
  param { lr_mult: 1  decay_mult: 1 }
  param { lr_mult: 2  decay_mult: 0 }
  inner_product_param {
    num_output: 84
    weight_filler { type: "gaussian"  std: 0.001 }
    bias_filler { type: "constant"  value: 0 }
  }
}
layer {
  name: "loss_cls"
  type: "SoftmaxWithLoss"
  bottom: "cls_score"
  bottom: "labels"
  propagate_down: 1
  propagate_down: 0
  top: "loss_cls"
  include { phase: TRAIN }
  loss_weight: 1
  loss_param { ignore_label: -1  normalize: true }
}
layer {
  name: "loss_bbox"
  type: "SmoothL1Loss"
  bottom: "bbox_pred"
  bottom: "bbox_targets"
  bottom: "bbox_inside_weights"
  bottom: "bbox_outside_weights"
  top: "loss_bbox"
  include { phase: TRAIN }
  loss_weight: 1
}
layer {
  name: "cls_prob"
  type: "Softmax"
  bottom: "cls_score"
  top: "cls_prob"
  include { phase: TEST }
  loss_param {
    ignore_label: -1
    normalize: true
  }
}