modularization_models/detection/license_plate_recognition/lpr/model/SegmentationFree.prototxt

input: "data"
input_dim: 1
input_dim: 3
input_dim: 160
input_dim: 40
layer {
  name: "conv0"
  type: "Convolution"
  bottom: "data"
  top: "conv0"
  convolution_param {
    num_output: 32
    bias_term: true
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "bn0"
  type: "BatchNorm"
  bottom: "conv0"
  top: "bn0"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "bn0_scale"
  type: "Scale"
  bottom: "bn0"
  top: "bn0"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu0"
  type: "ReLU"
  bottom: "bn0"
  top: "bn0"
}
layer {
  name: "pool0"
  type: "Pooling"
  bottom: "bn0"
  top: "pool0"
  pooling_param {
    pool: MAX
    kernel_h: 2
    kernel_w: 2
    stride_h: 2
    stride_w: 2
    pad_h: 0
    pad_w: 0
  }
}
layer {
  name: "conv1"
  type: "Convolution"
  bottom: "pool0"
  top: "conv1"
  convolution_param {
    num_output: 64
    bias_term: true
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "bn1"
  type: "BatchNorm"
  bottom: "conv1"
  top: "bn1"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "bn1_scale"
  type: "Scale"
  bottom: "bn1"
  top: "bn1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu1"
  type: "ReLU"
  bottom: "bn1"
  top: "bn1"
}
layer {
  name: "pool1"
  type: "Pooling"
  bottom: "bn1"
  top: "pool1"
  pooling_param {
    pool: MAX
    kernel_h: 2
    kernel_w: 2
    stride_h: 2
    stride_w: 2
    pad_h: 0
    pad_w: 0
  }
}
layer {
  name: "conv2"
  type: "Convolution"
  bottom: "pool1"
  top: "conv2"
  convolution_param {
    num_output: 128
    bias_term: true
    pad_h: 1
    pad_w: 1
    kernel_h: 3
    kernel_w: 3
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "bn2"
  type: "BatchNorm"
  bottom: "conv2"
  top: "bn2"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "bn2_scale"
  type: "Scale"
  bottom: "bn2"
  top: "bn2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "relu2"
  type: "ReLU"
  bottom: "bn2"
  top: "bn2"
}
layer {
  name: "pool2"
  type: "Pooling"
  bottom: "bn2"
  top: "pool2"
  pooling_param {
    pool: MAX
    kernel_h: 2
    kernel_w: 2
    stride_h: 2
    stride_w: 2
    pad_h: 0
    pad_w: 0
  }
}
layer {
  name: "conv2d_1"
  type: "Convolution"
  bottom: "pool2"
  top: "conv2d_1"
  convolution_param {
    num_output: 256
    bias_term: true
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 5
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "batch_normalization_1"
  type: "BatchNorm"
  bottom: "conv2d_1"
  top: "batch_normalization_1"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "batch_normalization_1_scale"
  type: "Scale"
  bottom: "batch_normalization_1"
  top: "batch_normalization_1"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "activation_1"
  type: "ReLU"
  bottom: "batch_normalization_1"
  top: "batch_normalization_1"
}
layer {
  name: "conv2d_2"
  type: "Convolution"
  bottom: "batch_normalization_1"
  top: "conv2d_2"
  convolution_param {
    num_output: 256
    bias_term: true
    pad_h: 3
    pad_w: 0
    kernel_h: 7
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "conv2d_3"
  type: "Convolution"
  bottom: "batch_normalization_1"
  top: "conv2d_3"
  convolution_param {
    num_output: 256
    bias_term: true
    pad_h: 2
    pad_w: 0
    kernel_h: 5
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "conv2d_4"
  type: "Convolution"
  bottom: "batch_normalization_1"
  top: "conv2d_4"
  convolution_param {
    num_output: 256
    bias_term: true
    pad_h: 1
    pad_w: 0
    kernel_h: 3
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "conv2d_5"
  type: "Convolution"
  bottom: "batch_normalization_1"
  top: "conv2d_5"
  convolution_param {
    num_output: 256
    bias_term: true
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "batch_normalization_2"
  type: "BatchNorm"
  bottom: "conv2d_2"
  top: "batch_normalization_2"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "batch_normalization_2_scale"
  type: "Scale"
  bottom: "batch_normalization_2"
  top: "batch_normalization_2"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "batch_normalization_3"
  type: "BatchNorm"
  bottom: "conv2d_3"
  top: "batch_normalization_3"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "batch_normalization_3_scale"
  type: "Scale"
  bottom: "batch_normalization_3"
  top: "batch_normalization_3"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "batch_normalization_4"
  type: "BatchNorm"
  bottom: "conv2d_4"
  top: "batch_normalization_4"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "batch_normalization_4_scale"
  type: "Scale"
  bottom: "batch_normalization_4"
  top: "batch_normalization_4"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "batch_normalization_5"
  type: "BatchNorm"
  bottom: "conv2d_5"
  top: "batch_normalization_5"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "batch_normalization_5_scale"
  type: "Scale"
  bottom: "batch_normalization_5"
  top: "batch_normalization_5"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "activation_2"
  type: "ReLU"
  bottom: "batch_normalization_2"
  top: "batch_normalization_2"
}
layer {
  name: "activation_3"
  type: "ReLU"
  bottom: "batch_normalization_3"
  top: "batch_normalization_3"
}
layer {
  name: "activation_4"
  type: "ReLU"
  bottom: "batch_normalization_4"
  top: "batch_normalization_4"
}
layer {
  name: "activation_5"
  type: "ReLU"
  bottom: "batch_normalization_5"
  top: "batch_normalization_5"
}
layer {
  name: "concatenate_1"
  type: "Concat"
  bottom: "batch_normalization_2"
  bottom: "batch_normalization_3"
  bottom: "batch_normalization_4"
  bottom: "batch_normalization_5"
  top: "concatenate_1"
  concat_param {
    axis: 1
  }
}
layer {
  name: "conv_1024_11"
  type: "Convolution"
  bottom: "concatenate_1"
  top: "conv_1024_11"
  convolution_param {
    num_output: 1024
    bias_term: true
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "batch_normalization_6"
  type: "BatchNorm"
  bottom: "conv_1024_11"
  top: "batch_normalization_6"
  batch_norm_param {
    moving_average_fraction: 0.99
    eps: 0.001
  }
}
layer {
  name: "batch_normalization_6_scale"
  type: "Scale"
  bottom: "batch_normalization_6"
  top: "batch_normalization_6"
  scale_param {
    bias_term: true
  }
}
layer {
  name: "activation_6"
  type: "ReLU"
  bottom: "batch_normalization_6"
  top: "batch_normalization_6"
}
layer {
  name: "conv_class_11"
  type: "Convolution"
  bottom: "batch_normalization_6"
  top: "conv_class_11"
  convolution_param {
    num_output: 84
    bias_term: true
    pad_h: 0
    pad_w: 0
    kernel_h: 1
    kernel_w: 1
    stride_h: 1
    stride_w: 1
  }
}
layer {
  name: "prob"
  type: "Softmax"
  bottom: "conv_class_11"
  top: "prob"
}