train_pruned.prototxt

name: "MobileNetV2"
layer {
  name: "data"
  type: "AnnotatedData"
  top: "data"
  top: "label"
  include {
    phase: TRAIN
  }
  
  transform_param {
    scale: 0.007843
    mirror: true
    mean_value: 127.5
    mean_value: 127.5
    mean_value: 127.5

    resize_param {
      prob: 0.2
      resize_mode: WARP
      height: 416
      width: 416
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
    resize_param {
      prob: 0.2
      resize_mode: WARP
      height: 384
      width: 384
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
    resize_param {
      prob: 0.2
      resize_mode: WARP
      height: 352
      width: 352
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
    resize_param {
      prob: 0.2
      resize_mode: WARP
      height: 320
      width: 320
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
    resize_param {
      prob: 0.2
      resize_mode: WARP
      height: 288
      width: 288
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }

    emit_constraint {
      emit_type: CENTER
    }
    distort_param {
      brightness_prob: 0.5
      brightness_delta: 32.0
      contrast_prob: 0.5
      contrast_lower: 0.5
      contrast_upper: 1.5
      hue_prob: 0.5
      hue_delta: 18.0
      saturation_prob: 0.5
      saturation_lower: 0.5
      saturation_upper: 1.5
      random_order_prob: 0.0
    }
    expand_param {
      prob: 0.5
      max_expand_ratio: 4.0
    }
  }
  data_param {
    source: "examples/VOC0712/VOC0712_trainval_lmdb"
    batch_size: 8
    backend: LMDB
  }
  annotated_data_param {
    yolo_data_type : 1
    yolo_data_jitter : 0.7
    label_map_file:  "data/VOC0712/labelmap_voc.prototxt"
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1
    stride: 2
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm1"
  type: "BatchNorm"
  bottom: "conv1"
  top: "conv1"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale1"
  type: "Scale"
  bottom: "conv1"
  top: "conv1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2"
  type: "DepthwiseConvolution"
  bottom: "conv1"
  top: "conv2"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm2"
  type: "BatchNorm"
  bottom: "conv2"
  top: "conv2"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale2"
  type: "Scale"
  bottom: "conv2"
  top: "conv2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv2"
  top: "conv3"
  convolution_param {
    num_output: 15
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm3"
  type: "BatchNorm"
  bottom: "conv3"
  top: "conv3"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale3"
  type: "Scale"
  bottom: "conv3"
  top: "conv3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 87
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm4"
  type: "BatchNorm"
  bottom: "conv4"
  top: "conv4"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale4"
  type: "Scale"
  bottom: "conv4"
  top: "conv4"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5"
  type: "DepthwiseConvolution"
  bottom: "conv4"
  top: "conv5"
  convolution_param {
    num_output: 87
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 87
    stride: 2
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm5"
  type: "BatchNorm"
  bottom: "conv5"
  top: "conv5"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale5"
  type: "Scale"
  bottom: "conv5"
  top: "conv5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv5"
  top: "conv6"
  convolution_param {
    num_output: 22
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm6"
  type: "BatchNorm"
  bottom: "conv6"
  top: "conv6"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale6"
  type: "Scale"
  bottom: "conv6"
  top: "conv6"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "conv6"
  top: "conv7"
  convolution_param {
    num_output: 130
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm7"
  type: "BatchNorm"
  bottom: "conv7"
  top: "conv7"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale7"
  type: "Scale"
  bottom: "conv7"
  top: "conv7"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv8"
  type: "DepthwiseConvolution"
  bottom: "conv7"
  top: "conv8"
  convolution_param {
    num_output: 130
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 130
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm8"
  type: "BatchNorm"
  bottom: "conv8"
  top: "conv8"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale8"
  type: "Scale"
  bottom: "conv8"
  top: "conv8"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "conv9"
  type: "Convolution"
  bottom: "conv8"
  top: "conv9"
  convolution_param {
    num_output: 22
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm9"
  type: "BatchNorm"
  bottom: "conv9"
  top: "conv9"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale9"
  type: "Scale"
  bottom: "conv9"
  top: "conv9"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "add1"
  type: "Eltwise"
  bottom: "conv6"
  bottom: "conv9"
  top: "add1"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv10"
  type: "Convolution"
  bottom: "add1"
  top: "conv10"
  convolution_param {
    num_output: 130
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm10"
  type: "BatchNorm"
  bottom: "conv10"
  top: "conv10"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale10"
  type: "Scale"
  bottom: "conv10"
  top: "conv10"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv10"
  top: "conv10"
}
layer {
  name: "conv11"
  type: "DepthwiseConvolution"
  bottom: "conv10"
  top: "conv11"
  convolution_param {
    num_output: 130
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 130
    stride: 2
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm11"
  type: "BatchNorm"
  bottom: "conv11"
  top: "conv11"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale11"
  type: "Scale"
  bottom: "conv11"
  top: "conv11"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv11"
  top: "conv11"
}
layer {
  name: "conv12"
  type: "Convolution"
  bottom: "conv11"
  top: "conv12"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm12"
  type: "BatchNorm"
  bottom: "conv12"
  top: "conv12"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale12"
  type: "Scale"
  bottom: "conv12"
  top: "conv12"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv13"
  type: "Convolution"
  bottom: "conv12"
  top: "conv13"
  convolution_param {
    num_output: 173
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm13"
  type: "BatchNorm"
  bottom: "conv13"
  top: "conv13"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale13"
  type: "Scale"
  bottom: "conv13"
  top: "conv13"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu9"
  type: "ReLU"
  bottom: "conv13"
  top: "conv13"
}
layer {
  name: "conv14"
  type: "DepthwiseConvolution"
  bottom: "conv13"
  top: "conv14"
  convolution_param {
    num_output: 173
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 173
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm14"
  type: "BatchNorm"
  bottom: "conv14"
  top: "conv14"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale14"
  type: "Scale"
  bottom: "conv14"
  top: "conv14"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu10"
  type: "ReLU"
  bottom: "conv14"
  top: "conv14"
}
layer {
  name: "conv15"
  type: "Convolution"
  bottom: "conv14"
  top: "conv15"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm15"
  type: "BatchNorm"
  bottom: "conv15"
  top: "conv15"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale15"
  type: "Scale"
  bottom: "conv15"
  top: "conv15"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "add2"
  type: "Eltwise"
  bottom: "conv12"
  bottom: "conv15"
  top: "add2"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv16"
  type: "Convolution"
  bottom: "add2"
  top: "conv16"
  convolution_param {
    num_output: 173
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm16"
  type: "BatchNorm"
  bottom: "conv16"
  top: "conv16"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale16"
  type: "Scale"
  bottom: "conv16"
  top: "conv16"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu11"
  type: "ReLU"
  bottom: "conv16"
  top: "conv16"
}
layer {
  name: "conv17"
  type: "DepthwiseConvolution"
  bottom: "conv16"
  top: "conv17"
  convolution_param {
    num_output: 173
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 173
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm17"
  type: "BatchNorm"
  bottom: "conv17"
  top: "conv17"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale17"
  type: "Scale"
  bottom: "conv17"
  top: "conv17"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu12"
  type: "ReLU"
  bottom: "conv17"
  top: "conv17"
}
layer {
  name: "conv18"
  type: "Convolution"
  bottom: "conv17"
  top: "conv18"
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm18"
  type: "BatchNorm"
  bottom: "conv18"
  top: "conv18"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale18"
  type: "Scale"
  bottom: "conv18"
  top: "conv18"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "add3"
  type: "Eltwise"
  bottom: "add2"
  bottom: "conv18"
  top: "add3"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv19"
  type: "Convolution"
  bottom: "add3"
  top: "conv19"
  convolution_param {
    num_output: 173
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm19"
  type: "BatchNorm"
  bottom: "conv19"
  top: "conv19"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale19"
  type: "Scale"
  bottom: "conv19"
  top: "conv19"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu13"
  type: "ReLU"
  bottom: "conv19"
  top: "conv19"
}
layer {
  name: "conv20"
  type: "DepthwiseConvolution"
  bottom: "conv19"
  top: "conv20"
  convolution_param {
    num_output: 173
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 173
    stride: 2
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm20"
  type: "BatchNorm"
  bottom: "conv20"
  top: "conv20"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale20"
  type: "Scale"
  bottom: "conv20"
  top: "conv20"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu14"
  type: "ReLU"
  bottom: "conv20"
  top: "conv20"
}
layer {
  name: "conv21"
  type: "Convolution"
  bottom: "conv20"
  top: "conv21"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm21"
  type: "BatchNorm"
  bottom: "conv21"
  top: "conv21"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale21"
  type: "Scale"
  bottom: "conv21"
  top: "conv21"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv22"
  type: "Convolution"
  bottom: "conv21"
  top: "conv22"
  convolution_param {
    num_output: 346
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm22"
  type: "BatchNorm"
  bottom: "conv22"
  top: "conv22"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale22"
  type: "Scale"
  bottom: "conv22"
  top: "conv22"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu15"
  type: "ReLU"
  bottom: "conv22"
  top: "conv22"
}
layer {
  name: "conv23"
  type: "DepthwiseConvolution"
  bottom: "conv22"
  top: "conv23"
  convolution_param {
    num_output: 346
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 346
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm23"
  type: "BatchNorm"
  bottom: "conv23"
  top: "conv23"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale23"
  type: "Scale"
  bottom: "conv23"
  top: "conv23"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu16"
  type: "ReLU"
  bottom: "conv23"
  top: "conv23"
}
layer {
  name: "conv24"
  type: "Convolution"
  bottom: "conv23"
  top: "conv24"
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm24"
  type: "BatchNorm"
  bottom: "conv24"
  top: "conv24"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale24"
  type: "Scale"
  bottom: "conv24"
  top: "conv24"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "add4"
  type: "Eltwise"
  bottom: "conv21"
  bottom: "conv24"
  top: "add4"
  eltwise_param {
    operation: SUM
  }
}
layer {
  name: "conv25"
  type: "Convolution"
  bottom: "add4"
  top: "conv25"
  convolution_param {
    num_output: 327
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
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}
layer {
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  type: "BatchNorm"
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  top: "conv25"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv25"
  scale_param {
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}
layer {
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  type: "ReLU"
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  top: "conv25"
}
layer {
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  type: "DepthwiseConvolution"
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  top: "conv26"
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    pad: 1
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    group: 327
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    weight_filler {
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}
layer {
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  type: "BatchNorm"
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  top: "conv26"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv26"
  top: "conv26"
  scale_param {
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}
layer {
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  type: "ReLU"
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  top: "conv26"
}
layer {
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  type: "Convolution"
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  top: "conv27"
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    weight_filler {
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}
layer {
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  type: "BatchNorm"
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  top: "conv27"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv27"
  top: "conv27"
  scale_param {
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}
layer {
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  type: "Eltwise"
  bottom: "add4"
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  eltwise_param {
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}
layer {
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  type: "Convolution"
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  convolution_param {
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    weight_filler {
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}
layer {
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  type: "BatchNorm"
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  top: "conv28"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv28"
  top: "conv28"
  scale_param {
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}
layer {
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  type: "ReLU"
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  top: "conv28"
}
layer {
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  type: "DepthwiseConvolution"
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    weight_filler {
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}
layer {
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv29"
  scale_param {
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}
layer {
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  top: "conv29"
}
layer {
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}
layer {
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    eps: 9.999999747378752e-06
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}
layer {
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  scale_param {
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}
layer {
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  eltwise_param {
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}
layer {
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}
layer {
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    eps: 9.999999747378752e-06
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}
layer {
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  scale_param {
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}
layer {
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  type: "ReLU"
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}
layer {
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    weight_filler {
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}
layer {
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    eps: 9.999999747378752e-06
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}
layer {
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  scale_param {
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}
layer {
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}
layer {
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    weight_filler {
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}
layer {
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  scale_param {
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}
layer {
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  type: "Convolution"
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}
layer {
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    eps: 9.999999747378752e-06
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}
layer {
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  scale_param {
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}
layer {
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}
layer {
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  type: "DepthwiseConvolution"
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    weight_filler {
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}
layer {
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  scale_param {
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}
layer {
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  top: "conv35"
}
layer {
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  type: "Convolution"
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    weight_filler {
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}
layer {
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv36"
  scale_param {
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}
layer {
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  eltwise_param {
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}
layer {
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  type: "Convolution"
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    weight_filler {
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}
layer {
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  type: "BatchNorm"
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv37"
  scale_param {
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}
layer {
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  top: "conv37"
}
layer {
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  type: "DepthwiseConvolution"
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    weight_filler {
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}
layer {
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv38"
  scale_param {
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}
layer {
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  top: "conv38"
}
layer {
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  type: "Convolution"
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    weight_filler {
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}
layer {
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  type: "BatchNorm"
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  top: "conv39"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv39"
  top: "conv39"
  scale_param {
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}
layer {
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  type: "Eltwise"
  bottom: "add7"
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  eltwise_param {
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}
layer {
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  type: "Convolution"
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    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv40"
  top: "conv40"
  scale_param {
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}
layer {
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  top: "conv40"
}
layer {
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  type: "DepthwiseConvolution"
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    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv41"
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  scale_param {
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}
layer {
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  top: "conv41"
}
layer {
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  type: "Convolution"
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    weight_filler {
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}
layer {
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  scale_param {
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}
layer {
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  type: "Convolution"
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  top: "conv43"
  convolution_param {
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    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  scale_param {
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}
layer {
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  top: "conv43"
}
layer {
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  type: "DepthwiseConvolution"
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  top: "conv44"
  convolution_param {
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    group: 768
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    weight_filler {
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    dilation: 1
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}
layer {
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  type: "BatchNorm"
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv44"
  scale_param {
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}
layer {
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  top: "conv44"
}
layer {
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  type: "Convolution"
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  top: "conv45"
  convolution_param {
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    pad: 0
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    weight_filler {
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    }
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}
layer {
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  type: "BatchNorm"
  bottom: "conv45"
  top: "conv45"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv45"
  top: "conv45"
  scale_param {
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}
layer {
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  bottom: "conv45"
  top: "add9"
  eltwise_param {
    operation: SUM
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}
layer {
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  type: "Convolution"
  bottom: "add9"
  top: "conv46"
  convolution_param {
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    pad: 0
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    group: 1
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    weight_filler {
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    }
    dilation: 1
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}
layer {
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  type: "BatchNorm"
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  top: "conv46"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv46"
  scale_param {
    bias_term: true
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}
layer {
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  top: "conv46"
}
layer {
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  type: "DepthwiseConvolution"
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  top: "conv47"
  convolution_param {
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    pad: 1
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    group: 768
    stride: 1
    weight_filler {
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    }
    dilation: 1
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}
layer {
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  type: "BatchNorm"
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  top: "conv47"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
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  top: "conv47"
  scale_param {
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}
layer {
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  top: "conv47"
}
layer {
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  type: "Convolution"
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  top: "conv48"
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    pad: 0
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    group: 1
    stride: 1
    weight_filler {
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    }
    dilation: 1
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}
layer {
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  type: "BatchNorm"
  bottom: "conv48"
  top: "conv48"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv48"
  top: "conv48"
  scale_param {
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}
layer {
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  type: "Eltwise"
  bottom: "add9"
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  top: "add10"
  eltwise_param {
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}
layer {
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  type: "Convolution"
  bottom: "add10"
  top: "conv49"
  convolution_param {
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    pad: 0
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    group: 1
    stride: 1
    weight_filler {
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    }
    dilation: 1
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}
layer {
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  type: "BatchNorm"
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  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv49"
  top: "conv49"
  scale_param {
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}
layer {
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  top: "conv49"
}
layer {
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  type: "DepthwiseConvolution"
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  top: "conv50"
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    bias_term: false
    pad: 1
    kernel_size: 3
    group: 768
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
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  type: "BatchNorm"
  bottom: "conv50"
  top: "conv50"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
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}
layer {
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  type: "Scale"
  bottom: "conv50"
  top: "conv50"
  scale_param {
    bias_term: true
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}
layer {
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  type: "ReLU"
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  top: "conv50"
}
layer {
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  type: "Convolution"
  bottom: "conv50"
  top: "conv51"
  convolution_param {
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    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
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    }
    dilation: 1
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}
layer {
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  type: "BatchNorm"
  bottom: "conv51"
  top: "conv51"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale51"
  type: "Scale"
  bottom: "conv51"
  top: "conv51"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "conv52"
  type: "Convolution"
  bottom: "conv51"
  top: "conv52"
  convolution_param {
    num_output: 640
    bias_term: false
    pad: 0
    kernel_size: 1
    group: 1
    stride: 1
    weight_filler {
      type: "msra"
    }
    dilation: 1
  }
}
layer {
  name: "batch_norm52"
  type: "BatchNorm"
  bottom: "conv52"
  top: "conv52"
  batch_norm_param {
    
    eps: 9.999999747378752e-06
  }
}
layer {
  name: "bn_scale52"
  type: "Scale"
  bottom: "conv52"
  top: "conv52"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu35"
  type: "ReLU"
  bottom: "conv52"
  top: "conv52"
}

layer {
  name: "yolo/conv1/dw"
  type: "DepthwiseConvolution"
  bottom: "conv52"
  top: "yolo/conv1/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 640
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 640
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "yolo/conv1/dw/bn"
  type: "BatchNorm"
  bottom: "yolo/conv1/dw"
  top: "yolo/conv1/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "yolo/conv1/dw/scale"
  type: "Scale"
  bottom: "yolo/conv1/dw"
  top: "yolo/conv1/dw"
  param {
    lr_mult: 1
    decay_mult: 0.0
  }
  param {
    lr_mult: 2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "yolo/conv1/dw/relu"
  type: "ReLU"
  bottom: "yolo/conv1/dw"
  top: "yolo/conv1/dw"
}
layer {
  name: "yolo/conv1"
  type: "Convolution"
  bottom: "yolo/conv1/dw"
  top: "yolo/conv1"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "yolo/conv1/bn"
  type: "BatchNorm"
  bottom: "yolo/conv1"
  top: "yolo/conv1"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "yolo/conv1/scale"
  type: "Scale"
  bottom: "yolo/conv1"
  top: "yolo/conv1"
  param {
    lr_mult: 1
    decay_mult: 0.0
  }
  param {
    lr_mult: 2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "yolo/conv1/relu"
  type: "ReLU"
  bottom: "yolo/conv1"
  top: "yolo/conv1"
}

layer {
  name: "upsample"
  type: "Deconvolution"
  bottom: "yolo/conv1"
  top: "upsample"
  param { lr_mult: 0  decay_mult: 0 }
  convolution_param {
    num_output: 576
    group: 576
    kernel_size: 1  stride: 2  pad: 0
    weight_filler: { 
      type: "constant"
	  value : 1
    }
    bias_term: false
  }
}

layer {   
    top: "maxpool"
    name: "maxpool"
    bottom: "upsample"
    type: "Pooling"
    pooling_param {
      kernel_size: 2
      stride: 1
      pool: MAX
      pad: 1
    }
}
layer {
  name: "yolo/conv2/dw"
  type: "DepthwiseConvolution"
  bottom: "conv40"
  top: "yolo/conv2/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 576
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "yolo/conv2/dw/bn"
  type: "BatchNorm"
  bottom: "yolo/conv2/dw"
  top: "yolo/conv2/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "yolo/conv2/dw/scale"
  type: "Scale"
  bottom: "yolo/conv2/dw"
  top: "yolo/conv2/dw"
  param {
    lr_mult: 1
    decay_mult: 0.0
  }
  param {
    lr_mult: 2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "yolo/conv2/dw/relu"
  type: "ReLU"
  bottom: "yolo/conv2/dw"
  top: "yolo/conv2/dw"
}
layer {
  name: "yolo/conv2"
  type: "Convolution"
  bottom: "yolo/conv2/dw"
  top: "yolo/conv2"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "yolo/conv2/bn"
  type: "BatchNorm"
  bottom: "yolo/conv2"
  top: "yolo/conv2"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "yolo/conv2/scale"
  type: "Scale"
  bottom: "yolo/conv2"
  top: "yolo/conv2"
  param {
    lr_mult: 1
    decay_mult: 0.0
  }
  param {
    lr_mult: 2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "yolo/conv2/relu"
  type: "ReLU"
  bottom: "yolo/conv2"
  top: "yolo/conv2"
}
layer {
  name: "yolo/conv2/sum"
  type: "Eltwise"
  bottom: "maxpool"
  bottom: "yolo/conv2"
  top: "yolo/conv2/sum"
}

layer {
  name: "yolo/conv3/dw"
  type: "DepthwiseConvolution"
  bottom: "yolo/conv2/sum"
  top: "yolo/conv3/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 576
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 576
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "yolo/conv3/dw/bn"
  type: "BatchNorm"
  bottom: "yolo/conv3/dw"
  top: "yolo/conv3/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "yolo/conv3/dw/scale"
  type: "Scale"
  bottom: "yolo/conv3/dw"
  top: "yolo/conv3/dw"
  param {
    lr_mult: 1
    decay_mult: 0.0
  }
  param {
    lr_mult: 2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "yolo/conv3/dw/relu"
  type: "ReLU"
  bottom: "yolo/conv3/dw"
  top: "yolo/conv3/dw"
}
layer {
  name: "yolo/conv3"
  type: "Convolution"
  bottom: "yolo/conv3/dw"
  top: "yolo/conv3"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 576
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "yolo/conv3/bn"
  type: "BatchNorm"
  bottom: "yolo/conv3"
  top: "yolo/conv3"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "yolo/conv3/scale"
  type: "Scale"
  bottom: "yolo/conv3"
  top: "yolo/conv3"
  param {
    lr_mult: 1
    decay_mult: 0.0
  }
  param {
    lr_mult: 2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "yolo/conv3/relu"
  type: "ReLU"
  bottom: "yolo/conv3"
  top: "yolo/conv3"
}

layer {
  name: "yolo/conv4"
  type: "Convolution"
  bottom: "yolo/conv1"
  top: "yolo/conv4"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 75
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "msra"
    }
	bias_filler {
      value: 0
    }
  }
}
layer {
  name: "yolo/conv5"
  type: "Convolution"
  bottom: "yolo/conv3"
  top: "yolo/conv5"
    param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 75
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "msra"
    }
	bias_filler {
      value: 0
    }
  }
}
layer {
  name: "Yolov3Loss1"
  type: "Yolov3"
  bottom: "yolo/conv4"
  bottom: "label"
  top: "det_loss1"
  loss_weight: 1
  yolov3_param {
    side: 13
    num_class: 20
    num: 3  
    object_scale: 5.0
    noobject_scale: 1.0
    class_scale: 1.0
    coord_scale: 1.0
    thresh: 0.7
    anchors_scale : 32
    use_logic_gradient : false
	#iou_loss : GIOU
    #10,14,  23,27,  37,58,  81,82,  135,169,  344,319
    biases: 20
    biases: 37
    biases: 49
    biases: 94
    biases: 73
    biases: 201
    biases: 143
    biases: 265
    biases: 153
    biases: 121
    biases: 280
    biases: 279
	
    mask:3
    mask:4
    mask:5
  }
}
layer {
  name: "Yolov3Loss2"
  type: "Yolov3"
  bottom: "yolo/conv5"
  bottom: "label"
  top: "det_loss2"
  loss_weight: 1
  yolov3_param {
    side: 26
    num_class: 20
    num: 3  
    object_scale: 5.0
    noobject_scale: 1.0
    class_scale: 1.0
    coord_scale: 1.0
    thresh: 0.6
    anchors_scale : 16
    use_logic_gradient : false
	#iou_loss : GIOU
    #10,14,  23,27,  37,58,  81,82,  135,169,  344,319
    biases: 20
    biases: 37
    biases: 49
    biases: 94
    biases: 73
    biases: 201
    biases: 143
    biases: 265
    biases: 153
    biases: 121
    biases: 280
    biases: 279
	
    mask:0
    mask:1
    mask:2
  }
}