YOLOv1

4/13/2018 - 11:26 AM
name: "YOLONet"
input: "data"
input_shape {
  dim: 1
  dim: 3
  dim: 448
  dim: 448
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 64
    kernel_size: 7
    pad: 3
    stride: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "conv1"
  top: "conv1"
  relu_param{
    negative_slope: 0.1
  }		
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer{
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  convolution_param {
    num_output: 192
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "conv2"
  top: "conv2"
  relu_param{
    negative_slope: 0.1
  }		
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer{
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  convolution_param {
    num_output: 128
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu3"
  type: "ReLU"
  bottom: "conv3"
  top: "conv3"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "conv4"
  type: "Convolution"
  bottom: "conv3"
  top: "conv4"
  convolution_param {
    num_output: 256
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu4"
  type: "ReLU"
  bottom: "conv4"
  top: "conv4"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv5"
  type: "Convolution"
  bottom: "conv4"
  top: "conv5"
  convolution_param {
    num_output: 256
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu5"
  type: "ReLU"
  bottom: "conv5"
  top: "conv5"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv6"
  type: "Convolution"
  bottom: "conv5"
  top: "conv6"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu6"
  type: "ReLU"
  bottom: "conv6"
  top: "conv6"
  relu_param{
    negative_slope: 0.1
  }		
}
layer {
  name: "pool6"
  type: "Pooling"
  bottom: "conv6"
  top: "pool6"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}

layer{
  name: "conv7"
  type: "Convolution"
  bottom: "pool6"
  top: "conv7"
  convolution_param {
    num_output: 256
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu7"
  type: "ReLU"
  bottom: "conv7"
  top: "conv7"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv8"
  type: "Convolution"
  bottom: "conv7"
  top: "conv8"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu8"
  type: "ReLU"
  bottom: "conv8"
  top: "conv8"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv9"
  type: "Convolution"
  bottom: "conv8"
  top: "conv9"
  convolution_param {
    num_output: 256
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu9"
  type: "ReLU"
  bottom: "conv9"
  top: "conv9"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv10"
  type: "Convolution"
  bottom: "conv9"
  top: "conv10"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu10"
  type: "ReLU"
  bottom: "conv10"
  top: "conv10"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv11"
  type: "Convolution"
  bottom: "conv10"
  top: "conv11"
  convolution_param {
    num_output: 256
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu11"
  type: "ReLU"
  bottom: "conv11"
  top: "conv11"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "conv12"
  type: "Convolution"
  bottom: "conv11"
  top: "conv12"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu12"
  type: "ReLU"
  bottom: "conv12"
  top: "conv12"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "conv13"
  type: "Convolution"
  bottom: "conv12"
  top: "conv13"
  convolution_param {
    num_output: 256
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu13"
  type: "ReLU"
  bottom: "conv13"
  top: "conv13"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv14"
  type: "Convolution"
  bottom: "conv13"
  top: "conv14"
  convolution_param {
    num_output: 512
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu14"
  type: "ReLU"
  bottom: "conv14"
  top: "conv14"
  relu_param{
    negative_slope: 0.1
  }		
}

layer{
  name: "conv15"
  type: "Convolution"
  bottom: "conv14"
  top: "conv15"
  convolution_param {
    num_output: 512
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu15"
  type: "ReLU"
  bottom: "conv15"
  top: "conv15"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "conv16"
  type: "Convolution"
  bottom: "conv15"
  top: "conv16"
  convolution_param {
    num_output: 1024
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu16"
  type: "ReLU"
  bottom: "conv16"
  top: "conv16"
  relu_param{
    negative_slope: 0.1
  }		
}

layer {
  name: "pool16"
  type: "Pooling"
  bottom: "conv16"
  top: "pool16"
  pooling_param {
    pool: MAX
    kernel_size: 2
    stride: 2
  }
}


layer{
  name: "conv17"
  type: "Convolution"
  bottom: "pool16"
  top: "conv17"
  convolution_param {
    num_output: 512
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu17"
  type: "ReLU"
  bottom: "conv17"
  top: "conv17"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "conv18"
  type: "Convolution"
  bottom: "conv17"
  top: "conv18"
  convolution_param {
    num_output: 1024
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu18"
  type: "ReLU"
  bottom: "conv18"
  top: "conv18"
  relu_param{
    negative_slope: 0.1
  }		
}



layer{
  name: "conv19"
  type: "Convolution"
  bottom: "conv18"
  top: "conv19"
  convolution_param {
    num_output: 512
    kernel_size: 1
    pad: 0
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu19"
  type: "ReLU"
  bottom: "conv19"
  top: "conv19"
  relu_param{
    negative_slope: 0.1
  }		
}



layer{
  name: "conv20"
  type: "Convolution"
  bottom: "conv19"
  top: "conv20"
  convolution_param {
    num_output: 1024
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu20"
  type: "ReLU"
  bottom: "conv20"
  top: "conv20"
  relu_param{
    negative_slope: 0.1
  }		
}



layer{
  name: "conv21"
  type: "Convolution"
  bottom: "conv20"
  top: "conv21"
  convolution_param {
    num_output: 1024
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu21"
  type: "ReLU"
  bottom: "conv21"
  top: "conv21"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "conv22"
  type: "Convolution"
  bottom: "conv21"
  top: "conv22"
  convolution_param {
    num_output: 1024
    kernel_size: 3
    pad: 1
    stride: 2
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu22"
  type: "ReLU"
  bottom: "conv22"
  top: "conv22"
  relu_param{
    negative_slope: 0.1
  }		
}



layer{
  name: "conv23"
  type: "Convolution"
  bottom: "conv22"
  top: "conv23"
  convolution_param {
    num_output: 1024
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu23"
  type: "ReLU"
  bottom: "conv23"
  top: "conv23"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "conv24"
  type: "Convolution"
  bottom: "conv23"
  top: "conv24"
  convolution_param {
    num_output: 1024
    kernel_size: 3
    pad: 1
    stride: 1
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu24"
  type: "ReLU"
  bottom: "conv24"
  top: "conv24"
  relu_param{
    negative_slope: 0.1
  }		
}




layer{
  name: "fc25"
  type: "InnerProduct"
  bottom: "conv24"
  top: "fc25"
  inner_product_param {
    num_output: 4096
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
layer {
  name: "relu25"
  type: "ReLU"
  bottom: "fc25"
  top: "fc25"
  relu_param{
    negative_slope: 0.1
  }		
}


layer{
  name: "fc26"
  type: "InnerProduct"
  bottom: "fc25"
  top: "result"
  inner_product_param {
    num_output: 1470
    weight_filler {
      type: "gaussian"
      std: 0.01
    }
    bias_filler {
      type: "constant"
      value: 0
    }
  }
}
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YOLOv1
