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Prj-Linux/lpr/model/CharacterRecognization.prototxt

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+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 30
+input_dim: 14
+layer {
+  name: "conv2d_1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv2d_1"
+  convolution_param {
+    num_output: 32
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_1"
+  type: "ReLU"
+  bottom: "conv2d_1"
+  top: "activation_1"
+}
+layer {
+  name: "max_pooling2d_1"
+  type: "Pooling"
+  bottom: "activation_1"
+  top: "max_pooling2d_1"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2d_2"
+  type: "Convolution"
+  bottom: "max_pooling2d_1"
+  top: "conv2d_2"
+  convolution_param {
+    num_output: 64
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_2"
+  type: "ReLU"
+  bottom: "conv2d_2"
+  top: "activation_2"
+}
+layer {
+  name: "max_pooling2d_2"
+  type: "Pooling"
+  bottom: "activation_2"
+  top: "max_pooling2d_2"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2d_3"
+  type: "Convolution"
+  bottom: "max_pooling2d_2"
+  top: "conv2d_3"
+  convolution_param {
+    num_output: 128
+    bias_term: true
+    pad: 0
+    kernel_size: 2
+    stride: 1
+  }
+}
+layer {
+  name: "activation_3"
+  type: "ReLU"
+  bottom: "conv2d_3"
+  top: "activation_3"
+}
+layer {
+  name: "flatten_1"
+  type: "Flatten"
+  bottom: "activation_3"
+  top: "flatten_1"
+}
+layer {
+  name: "dense_1"
+  type: "InnerProduct"
+  bottom: "flatten_1"
+  top: "dense_1"
+  inner_product_param {
+    num_output: 256
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "dense_1"
+  top: "relu2"
+}
+layer {
+  name: "dense2"
+  type: "InnerProduct"
+  bottom: "relu2"
+  top: "dense2"
+  inner_product_param {
+    num_output: 65
+  }
+}
+
+layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "dense2"
+  top: "prob"
+}

Prj-Linux/lpr/model/HorizonalFinemapping.prototxt

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+input: "data"
+input_dim: 1
+input_dim: 3
+input_dim: 16
+input_dim: 66
+layer {
+  name: "conv1"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv1"
+  convolution_param {
+    num_output: 10
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu1"
+  type: "ReLU"
+  bottom: "conv1"
+  top: "conv1"
+}
+layer {
+  name: "max_pooling2d_3"
+  type: "Pooling"
+  bottom: "conv1"
+  top: "max_pooling2d_3"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2"
+  type: "Convolution"
+  bottom: "max_pooling2d_3"
+  top: "conv2"
+  convolution_param {
+    num_output: 16
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu2"
+  type: "ReLU"
+  bottom: "conv2"
+  top: "conv2"
+}
+layer {
+  name: "conv3"
+  type: "Convolution"
+  bottom: "conv2"
+  top: "conv3"
+  convolution_param {
+    num_output: 32
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "relu3"
+  type: "ReLU"
+  bottom: "conv3"
+  top: "conv3"
+}
+layer {
+  name: "flatten_2"
+  type: "Flatten"
+  bottom: "conv3"
+  top: "flatten_2"
+}
+layer {
+  name: "dense"
+  type: "InnerProduct"
+  bottom: "flatten_2"
+  top: "dense"
+  inner_product_param {
+    num_output: 2
+  }
+}
+layer {
+  name: "relu4"
+  type: "ReLU"
+  bottom: "dense"
+  top: "dense"
+}

Prj-Linux/lpr/model/SegmentFreeModel.prototxt

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+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: "conv_512_15"
+  type: "Convolution"
+  bottom: "pool2"
+  top: "conv_512_15"
+  convolution_param {
+    num_output: 512
+    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: "conv_512_15"
+  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: "conv_512_51"
+  type: "Convolution"
+  bottom: "batch_normalization_1"
+  top: "conv_512_51"
+  convolution_param {
+    num_output: 512
+    bias_term: true
+    pad_h: 0
+    pad_w: 0
+    kernel_h: 5
+    kernel_w: 1
+    stride_h: 1
+    stride_w: 1
+  }
+}
+layer {
+  name: "batch_normalization_2"
+  type: "BatchNorm"
+  bottom: "conv_512_51"
+  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: "activation_2"
+  type: "ReLU"
+  bottom: "batch_normalization_2"
+  top: "batch_normalization_2"
+}
+layer {
+  name: "conv_1024_11"
+  type: "Convolution"
+  bottom: "batch_normalization_2"
+  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_3"
+  type: "BatchNorm"
+  bottom: "conv_1024_11"
+  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: "activation_3"
+  type: "ReLU"
+  bottom: "batch_normalization_3"
+  top: "batch_normalization_3"
+}
+layer {
+  name: "conv_class_11"
+  type: "Convolution"
+  bottom: "batch_normalization_3"
+  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"
+}

Prj-Linux/lpr/model/Segmentation.prototxt

@@ -0,0 +1,114 @@
+input: "data"
+input_dim: 1
+input_dim: 1
+input_dim: 22
+input_dim: 22
+layer {
+  name: "conv2d_12"
+  type: "Convolution"
+  bottom: "data"
+  top: "conv2d_12"
+  convolution_param {
+    num_output: 16
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_18"
+  type: "ReLU"
+  bottom: "conv2d_12"
+  top: "activation_18"
+}
+layer {
+  name: "max_pooling2d_10"
+  type: "Pooling"
+  bottom: "activation_18"
+  top: "max_pooling2d_10"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "conv2d_13"
+  type: "Convolution"
+  bottom: "max_pooling2d_10"
+  top: "conv2d_13"
+  convolution_param {
+    num_output: 16
+    bias_term: true
+    pad: 0
+    kernel_size: 3
+    stride: 1
+  }
+}
+layer {
+  name: "activation_19"
+  type: "ReLU"
+  bottom: "conv2d_13"
+  top: "activation_19"
+}
+layer {
+  name: "max_pooling2d_11"
+  type: "Pooling"
+  bottom: "activation_19"
+  top: "max_pooling2d_11"
+  pooling_param {
+    pool: MAX
+    kernel_size: 2
+    stride: 2
+    pad: 0
+  }
+}
+layer {
+  name: "flatten_6"
+  type: "Flatten"
+  bottom: "max_pooling2d_11"
+  top: "flatten_6"
+}
+layer {
+  name: "dense_9"
+  type: "InnerProduct"
+  bottom: "flatten_6"
+  top: "dense_9"
+  inner_product_param {
+    num_output: 256
+  }
+}
+layer {
+  name: "dropout_9"
+  type: "Dropout"
+  bottom: "dense_9"
+  top: "dropout_9"
+  dropout_param {
+    dropout_ratio: 0.5
+  }
+}
+layer {
+  name: "activation_20"
+  type: "ReLU"
+  bottom: "dropout_9"
+  top: "activation_20"
+}
+layer {
+  name: "dense_10"
+  type: "InnerProduct"
+  bottom: "activation_20"
+  top: "dense_10"
+  inner_product_param {
+    num_output: 3
+  }
+}
+
+
+  layer {
+  name: "prob"
+  type: "Softmax"
+  bottom: "dense_10"
+  top: "prob"
+}