r.prototxt

name: "rNet"
layer {
  name: "data"
  type: "Input"
  top: "data"
  input_param {
    shape: {
      dim: 1
      dim: 3
      dim: 24
      dim: 24
    }
  }
}

layer {
  name: "conv1"
  type: "Convolution"
  bottom: "data"
  top: "conv1"
  convolution_param {
    num_output: 28
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "prelu1"
  type: "PReLU"
  bottom: "conv1"
  top: "conv1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "conv1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  convolution_param {
    num_output: 48
    kernel_size: 3
    stride: 1
  }
}
layer {
  name: "prelu2"
  type: "PReLU"
  bottom: "conv2"
  top: "conv2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "conv2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_size: 3
    stride: 2
  }
}

layer {
  name: "conv3"
  type: "Convolution"
  bottom: "pool2"
  top: "conv3"
  convolution_param {
    num_output: 64
    kernel_size: 2
    stride: 1
  }
}
layer {
  name: "prelu3"
  type: "PReLU"
  bottom: "conv3"
  top: "conv3"
}

layer {
  name: "fc"
  type: "InnerProduct"
  bottom: "conv3"
  top: "fc"
  inner_product_param {
    num_output: 128
  }
}
layer {
  name: "prelu4"
  type: "PReLU"
  bottom: "fc"
  top: "fc"
}

# score
layer {
  name: "score"
  type: "InnerProduct"
  bottom: "fc"
  top: "score"
  inner_product_param {
    num_output: 2
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "score"
  top: "prob"
}
# bbox
layer {
  name: "bbox_pred"
  type: "InnerProduct"
  bottom: "fc"
  top: "bbox_pred"
  inner_product_param {
    num_output: 4
  }
}