mobilenet_yolov3_lite_train.prototxt

name: "MobileNet-YOLO"
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
	force_color: true
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 608
      width: 608
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
    resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 416
      width: 416
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 320
      width: 320
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 352
      width: 352
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 384
      width: 384
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 448
      width: 448
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 480
      width: 480
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 512
      width: 512
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 544
      width: 544
      interp_mode: LINEAR
      interp_mode: AREA
      interp_mode: LANCZOS4
    }
	resize_param {
      prob: 0.1
      resize_mode: WARP
      height: 576
      width: 576
      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: 2.0
    }
  }
  
  data_param {
    source: "examples/coco/coco_train_lmdb"
    batch_size: 7
    backend: LMDB
  }
  annotated_data_param {
	yolo_data_type : 1
    batch_sampler {
      max_sample: 1
      max_trials: 1
    }
    batch_sampler {
      sampler {
        min_scale: 0.3
        max_scale: 1.0
        min_aspect_ratio: 0.5
        max_aspect_ratio: 2.0
      }
      sample_constraint {
        min_jaccard_overlap: 0.1
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.3
        max_scale: 1.0
        min_aspect_ratio: 0.5
        max_aspect_ratio: 2.0
      }
      sample_constraint {
        min_jaccard_overlap: 0.3
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.3
        max_scale: 1.0
        min_aspect_ratio: 0.5
        max_aspect_ratio: 2.0
      }
      sample_constraint {
        min_jaccard_overlap: 0.5
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.3
        max_scale: 1.0
        min_aspect_ratio: 0.5
        max_aspect_ratio: 2.0
      }
      sample_constraint {
        min_jaccard_overlap: 0.7
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.3
        max_scale: 1.0
        min_aspect_ratio: 0.5
        max_aspect_ratio: 2.0
      }
      sample_constraint {
        min_jaccard_overlap: 0.9
      }
      max_sample: 1
      max_trials: 50
    }
    batch_sampler {
      sampler {
        min_scale: 0.3
        max_scale: 1.0
        min_aspect_ratio: 0.5
        max_aspect_ratio: 2.0
      }
      sample_constraint {
        max_jaccard_overlap: 1.0
      }
      max_sample: 1
      max_trials: 50
    }
    label_map_file:  "data/coco/labelmap_coco.prototxt"
  }
}

layer {
  name: "conv0"
  type: "Convolution"
  bottom: "data"
  top: "conv0"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv0/bn"
  type: "BatchNorm"
  bottom: "conv0"
  top: "conv0"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv0/scale"
  type: "Scale"
  bottom: "conv0"
  top: "conv0"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv0/relu"
  type: "ReLU"
  bottom: "conv0"
  top: "conv0"
}
layer {
  name: "conv1/dw"
  type: "DepthwiseConvolution"
  bottom: "conv0"
  top: "conv1/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 32
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 32
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv1/dw/bn"
  type: "BatchNorm"
  bottom: "conv1/dw"
  top: "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: "conv1/dw/scale"
  type: "Scale"
  bottom: "conv1/dw"
  top: "conv1/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv1/dw/relu"
  type: "ReLU"
  bottom: "conv1/dw"
  top: "conv1/dw"
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "conv1/dw"
  top: "conv1"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 64
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
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
  }
}
layer {
  name: "conv1/scale"
  type: "Scale"
  bottom: "conv1"
  top: "conv1"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv1/relu"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "conv2/dw"
  type: "DepthwiseConvolution"
  bottom: "conv1"
  top: "conv2/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 64
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    group: 64
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv2/dw/bn"
  type: "BatchNorm"
  bottom: "conv2/dw"
  top: "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: "conv2/dw/scale"
  type: "Scale"
  bottom: "conv2/dw"
  top: "conv2/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv2/dw/relu"
  type: "ReLU"
  bottom: "conv2/dw"
  top: "conv2/dw"
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "conv2/dw"
  top: "conv2"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
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
  }
}
layer {
  name: "conv2/scale"
  type: "Scale"
  bottom: "conv2"
  top: "conv2"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv2/relu"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "conv3/dw"
  type: "DepthwiseConvolution"
  bottom: "conv2"
  top: "conv3/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 128
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv3/dw/bn"
  type: "BatchNorm"
  bottom: "conv3/dw"
  top: "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: "conv3/dw/scale"
  type: "Scale"
  bottom: "conv3/dw"
  top: "conv3/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv3/dw/relu"
  type: "ReLU"
  bottom: "conv3/dw"
  top: "conv3/dw"
}
layer {
  name: "conv3"
  type: "Convolution"
  bottom: "conv3/dw"
  top: "conv3"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
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
  }
}
layer {
  name: "conv3/scale"
  type: "Scale"
  bottom: "conv3"
  top: "conv3"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv3/relu"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
}
layer {
  name: "conv4/dw"
  type: "DepthwiseConvolution"
  bottom: "conv3"
  top: "conv4/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 128
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    group: 128
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4/dw/bn"
  type: "BatchNorm"
  bottom: "conv4/dw"
  top: "conv4/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv4/dw/scale"
  type: "Scale"
  bottom: "conv4/dw"
  top: "conv4/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv4/dw/relu"
  type: "ReLU"
  bottom: "conv4/dw"
  top: "conv4/dw"
}
layer {
  name: "conv4"
  type: "Convolution"
  bottom: "conv4/dw"
  top: "conv4"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv4/bn"
  type: "BatchNorm"
  bottom: "conv4"
  top: "conv4"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv4/scale"
  type: "Scale"
  bottom: "conv4"
  top: "conv4"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv4/relu"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
}
layer {
  name: "conv5/dw"
  type: "DepthwiseConvolution"
  bottom: "conv4"
  top: "conv5/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 256
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5/dw/bn"
  type: "BatchNorm"
  bottom: "conv5/dw"
  top: "conv5/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv5/dw/scale"
  type: "Scale"
  bottom: "conv5/dw"
  top: "conv5/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv5/dw/relu"
  type: "ReLU"
  bottom: "conv5/dw"
  top: "conv5/dw"
}
layer {
  name: "conv5"
  type: "Convolution"
  bottom: "conv5/dw"
  top: "conv5"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv5/bn"
  type: "BatchNorm"
  bottom: "conv5"
  top: "conv5"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv5/scale"
  type: "Scale"
  bottom: "conv5"
  top: "conv5"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv5/relu"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
}
layer {
  name: "conv6/dw"
  type: "DepthwiseConvolution"
  bottom: "conv5"
  top: "conv6/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 256
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    group: 256
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6/dw/bn"
  type: "BatchNorm"
  bottom: "conv6/dw"
  top: "conv6/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv6/dw/scale"
  type: "Scale"
  bottom: "conv6/dw"
  top: "conv6/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv6/dw/relu"
  type: "ReLU"
  bottom: "conv6/dw"
  top: "conv6/dw"
}
layer {
  name: "conv6"
  type: "Convolution"
  bottom: "conv6/dw"
  top: "conv6"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv6/bn"
  type: "BatchNorm"
  bottom: "conv6"
  top: "conv6"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv6/scale"
  type: "Scale"
  bottom: "conv6"
  top: "conv6"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv6/relu"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
}
layer {
  name: "conv7/dw"
  type: "DepthwiseConvolution"
  bottom: "conv6"
  top: "conv7/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv7/dw/bn"
  type: "BatchNorm"
  bottom: "conv7/dw"
  top: "conv7/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv7/dw/scale"
  type: "Scale"
  bottom: "conv7/dw"
  top: "conv7/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv7/dw/relu"
  type: "ReLU"
  bottom: "conv7/dw"
  top: "conv7/dw"
}
layer {
  name: "conv7"
  type: "Convolution"
  bottom: "conv7/dw"
  top: "conv7"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv7/bn"
  type: "BatchNorm"
  bottom: "conv7"
  top: "conv7"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv7/scale"
  type: "Scale"
  bottom: "conv7"
  top: "conv7"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv7/relu"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
}
layer {
  name: "conv8/dw"
  type: "DepthwiseConvolution"
  bottom: "conv7"
  top: "conv8/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3

    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv8/dw/bn"
  type: "BatchNorm"
  bottom: "conv8/dw"
  top: "conv8/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv8/dw/scale"
  type: "Scale"
  bottom: "conv8/dw"
  top: "conv8/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv8/dw/relu"
  type: "ReLU"
  bottom: "conv8/dw"
  top: "conv8/dw"
}
layer {
  name: "conv8"
  type: "Convolution"
  bottom: "conv8/dw"
  top: "conv8"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv8/bn"
  type: "BatchNorm"
  bottom: "conv8"
  top: "conv8"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv8/scale"
  type: "Scale"
  bottom: "conv8"
  top: "conv8"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv8/relu"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
}
layer {
  name: "conv9/dw"
  type: "DepthwiseConvolution"
  bottom: "conv8"
  top: "conv9/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv9/dw/bn"
  type: "BatchNorm"
  bottom: "conv9/dw"
  top: "conv9/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv9/dw/scale"
  type: "Scale"
  bottom: "conv9/dw"
  top: "conv9/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv9/dw/relu"
  type: "ReLU"
  bottom: "conv9/dw"
  top: "conv9/dw"
}
layer {
  name: "conv9"
  type: "Convolution"
  bottom: "conv9/dw"
  top: "conv9"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv9/bn"
  type: "BatchNorm"
  bottom: "conv9"
  top: "conv9"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv9/scale"
  type: "Scale"
  bottom: "conv9"
  top: "conv9"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv9/relu"
  type: "ReLU"
  bottom: "conv9"
  top: "conv9"
}
layer {
  name: "conv10/dw"
  type: "DepthwiseConvolution"
  bottom: "conv9"
  top: "conv10/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv10/dw/bn"
  type: "BatchNorm"
  bottom: "conv10/dw"
  top: "conv10/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv10/dw/scale"
  type: "Scale"
  bottom: "conv10/dw"
  top: "conv10/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv10/dw/relu"
  type: "ReLU"
  bottom: "conv10/dw"
  top: "conv10/dw"
}
layer {
  name: "conv10"
  type: "Convolution"
  bottom: "conv10/dw"
  top: "conv10"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv10/bn"
  type: "BatchNorm"
  bottom: "conv10"
  top: "conv10"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv10/scale"
  type: "Scale"
  bottom: "conv10"
  top: "conv10"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv10/relu"
  type: "ReLU"
  bottom: "conv10"
  top: "conv10"
}
layer {
  name: "conv11/dw"
  type: "DepthwiseConvolution"
  bottom: "conv10"
  top: "conv11/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv11/dw/bn"
  type: "BatchNorm"
  bottom: "conv11/dw"
  top: "conv11/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv11/dw/scale"
  type: "Scale"
  bottom: "conv11/dw"
  top: "conv11/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv11/dw/relu"
  type: "ReLU"
  bottom: "conv11/dw"
  top: "conv11/dw"
}
layer {
  name: "conv11"
  type: "Convolution"
  bottom: "conv11/dw"
  top: "conv11"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv11/bn"
  type: "BatchNorm"
  bottom: "conv11"
  top: "conv11"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv11/scale"
  type: "Scale"
  bottom: "conv11"
  top: "conv11"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv11/relu"
  type: "ReLU"
  bottom: "conv11"
  top: "conv11"
}
layer {
  name: "conv12/dw"
  type: "DepthwiseConvolution"
  bottom: "conv11"
  top: "conv12/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    stride: 2
    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv12/dw/bn"
  type: "BatchNorm"
  bottom: "conv12/dw"
  top: "conv12/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv12/dw/scale"
  type: "Scale"
  bottom: "conv12/dw"
  top: "conv12/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv12/dw/relu"
  type: "ReLU"
  bottom: "conv12/dw"
  top: "conv12/dw"
}
layer {
  name: "conv12"
  type: "Convolution"
  bottom: "conv12/dw"
  top: "conv12"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv12/bn"
  type: "BatchNorm"
  bottom: "conv12"
  top: "conv12"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv12/scale"
  type: "Scale"
  bottom: "conv12"
  top: "conv12"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv12/relu"
  type: "ReLU"
  bottom: "conv12"
  top: "conv12"
}
layer {
  name: "conv13/dw"
  type: "DepthwiseConvolution"
  bottom: "conv12"
  top: "conv13/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1024
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv13/dw/bn"
  type: "BatchNorm"
  bottom: "conv13/dw"
  top: "conv13/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv13/dw/scale"
  type: "Scale"
  bottom: "conv13/dw"
  top: "conv13/dw"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv13/dw/relu"
  type: "ReLU"
  bottom: "conv13/dw"
  top: "conv13/dw"
}
layer {
  name: "conv13"
  type: "Convolution"
  bottom: "conv13/dw"
  top: "conv13"
  param {
    lr_mult: 0.1
    decay_mult: 0.1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv13/bn"
  type: "BatchNorm"
  bottom: "conv13"
  top: "conv13"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv13/scale"
  type: "Scale"
  bottom: "conv13"
  top: "conv13"
  param {
    lr_mult: 0.1
    decay_mult: 0.0
  }
  param {
    lr_mult: 0.2
    decay_mult: 0.0
  }
  scale_param {
    filler {
      value: 1
    }
    bias_term: true
    bias_filler {
      value: 0
    }
  }
}
layer {
  name: "conv13/relu"
  type: "ReLU"
  bottom: "conv13"
  top: "conv13"
}

layer {
  name: "conv15/dw"
  type: "DepthwiseConvolution"
  bottom: "conv13"
  top: "conv15/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 1024
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv15/dw/bn"
  type: "BatchNorm"
  bottom: "conv15/dw"
  top: "conv15/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv15/dw/scale"
  type: "Scale"
  bottom: "conv15/dw"
  top: "conv15/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: "conv15/dw/relu"
  type: "ReLU"
  bottom: "conv15/dw"
  top: "conv15/dw"
}
layer {
  name: "conv15"
  type: "Convolution"
  bottom: "conv15/dw"
  top: "conv15"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv15/bn"
  type: "BatchNorm"
  bottom: "conv15"
  top: "conv15"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv15/scale"
  type: "Scale"
  bottom: "conv15"
  top: "conv15"
  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: "conv15/relu"
  type: "ReLU"
  bottom: "conv15"
  top: "conv15"
}
layer {
  name: "upsample"
  type: "Deconvolution"
  bottom: "conv15"
  top: "upsample"
  param { lr_mult: 0  decay_mult: 0 }
  convolution_param {
    num_output: 512
	group: 512
    kernel_size: 1  stride: 2  pad: 0
    weight_filler: { 
      type: "constant"
	  value : 1
    }
    bias_term: false
  }
}
layer {
    name: "maxpool"
    top: "maxpool"
	bottom: "upsample"   
    type: "Pooling"
    pooling_param {
        kernel_size: 2
        stride: 1
        pool: MAX
        pad: 1
    }
}

layer {
  name: "conv17/dw"
  type: "DepthwiseConvolution"
  bottom: "conv11"
  top: "conv17/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv17/dw/bn"
  type: "BatchNorm"
  bottom: "conv17/dw"
  top: "conv17/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv17/dw/scale"
  type: "Scale"
  bottom: "conv17/dw"
  top: "conv17/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: "conv17/dw/relu"
  type: "ReLU"
  bottom: "conv17/dw"
  top: "conv17/dw"
}
layer {
  name: "conv17"
  type: "Convolution"
  bottom: "conv17/dw"
  top: "conv17"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv17/bn"
  type: "BatchNorm"
  bottom: "conv17"
  top: "conv17"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv17/scale"
  type: "Scale"
  bottom: "conv17"
  top: "conv17"
  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: "conv17/relu"
  type: "ReLU"
  bottom: "conv17"
  top: "conv17"
}
layer {
  name: "conv17/sum"
  type: "Eltwise"
  bottom: "maxpool"
  bottom: "conv17"
  top: "conv17/sum"
}

layer {
  name: "conv18/dw"
  type: "DepthwiseConvolution"
  bottom: "conv17/sum"
  top: "conv18/dw"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 512
    bias_term: false
    pad: 1
    kernel_size: 3
    group: 512
    engine: CAFFE
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv18/dw/bn"
  type: "BatchNorm"
  bottom: "conv18/dw"
  top: "conv18/dw"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv18/dw/scale"
  type: "Scale"
  bottom: "conv18/dw"
  top: "conv18/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: "conv18/dw/relu"
  type: "ReLU"
  bottom: "conv18/dw"
  top: "conv18/dw"
}
layer {
  name: "conv18_new"
  type: "Convolution"
  bottom: "conv18/dw"
  top: "conv18_new"
  param {
    lr_mult: 1
    decay_mult: 1
  }
  convolution_param {
    num_output: 1024
    bias_term: false
    kernel_size: 1
    weight_filler {
      type: "msra"
    }
  }
}
layer {
  name: "conv18_new/bn"
  type: "BatchNorm"
  bottom: "conv18_new"
  top: "conv18_new"
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
  param {
    lr_mult: 0
    decay_mult: 0
  }
}
layer {
  name: "conv18_new/scale"
  type: "Scale"
  bottom: "conv18_new"
  top: "conv18_new"
  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: "conv18_new/relu"
  type: "ReLU"
  bottom: "conv18_new"
  top: "conv18_new"
}


layer {
  name: "conv22"
  type: "Convolution"
  bottom: "conv15"
  top: "conv22"
    param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 255
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "xavier"
    }
	bias_filler {
      value: 0
    }
  }
}

layer {
  name: "conv23_new"
  type: "Convolution"
  bottom: "conv18_new"
  top: "conv23_new"
    param {
    lr_mult: 1
    decay_mult: 1
  }
  param {
    lr_mult: 2
    decay_mult: 0
  }
  convolution_param {
    num_output: 255
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "xavier"
    }
	bias_filler {
      value: 0
    }
  }
}


layer {
  name: "Yolov3Loss1"
  type: "Yolov3"
  bottom: "conv22"
  bottom: "label"
  top: "det_loss1"
  loss_weight: 1
  yolov3_param {
    side: 13
    num_class: 80
    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_hard_sigmoid: True
	#10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
	
    biases: 10
    biases: 14
    biases: 23
    biases: 27
    biases: 37
    biases: 58
    biases: 81
    biases: 82
    biases: 135
    biases: 169
	biases: 344
    biases: 319
	
	mask:3
	mask:4
	mask:5
  }
}
layer {
  name: "Yolov3Loss2"
  type: "Yolov3"
  bottom: "conv23_new"
  bottom: "label"
  top: "det_loss2"
  loss_weight: 1
  yolov3_param {
    side: 26
    num_class: 80
    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_hard_sigmoid: True
	#10,13,  16,30,  33,23,  30,61,  62,45,  59,119,  116,90,  156,198,  373,326
	
    biases: 10
    biases: 14
    biases: 23
    biases: 27
    biases: 37
    biases: 58
    biases: 81
    biases: 82
    biases: 135
    biases: 169
	biases: 344
    biases: 319

	
	mask:0
	mask:1
	mask:2
  }
}