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HyperLPR/Prj-iOS/lpr/Resource/SegmenationFree-Inception.p...

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  1. input: "data"

  2. input_dim: 1

  3. input_dim: 3

  4. input_dim: 160

  5. input_dim: 40

  6. layer {

  7.   name: "conv0"

  8.   type: "Convolution"

  9.   bottom: "data"

  10.   top: "conv0"

  11.   convolution_param {

  12.     num_output: 32

  13.     bias_term: true

  14.     pad_h: 1

  15.     pad_w: 1

  16.     kernel_h: 3

  17.     kernel_w: 3

  18.     stride_h: 1

  19.     stride_w: 1

  20.   }

  21. }

  22. layer {

  23.   name: "bn0"

  24.   type: "BatchNorm"

  25.   bottom: "conv0"

  26.   top: "bn0"

  27.   batch_norm_param {

  28.     moving_average_fraction: 0.99

  29.     eps: 0.001

  30.   }

  31. }

  32. layer {

  33.   name: "bn0_scale"

  34.   type: "Scale"

  35.   bottom: "bn0"

  36.   top: "bn0"

  37.   scale_param {

  38.     bias_term: true

  39.   }

  40. }

  41. layer {

  42.   name: "relu0"

  43.   type: "ReLU"

  44.   bottom: "bn0"

  45.   top: "bn0"

  46. }

  47. layer {

  48.   name: "pool0"

  49.   type: "Pooling"

  50.   bottom: "bn0"

  51.   top: "pool0"

  52.   pooling_param {

  53.     pool: MAX

  54.     kernel_h: 2

  55.     kernel_w: 2

  56.     stride_h: 2

  57.     stride_w: 2

  58.     pad_h: 0

  59.     pad_w: 0

  60.   }

  61. }

  62. layer {

  63.   name: "conv1"

  64.   type: "Convolution"

  65.   bottom: "pool0"

  66.   top: "conv1"

  67.   convolution_param {

  68.     num_output: 64

  69.     bias_term: true

  70.     pad_h: 1

  71.     pad_w: 1

  72.     kernel_h: 3

  73.     kernel_w: 3

  74.     stride_h: 1

  75.     stride_w: 1

  76.   }

  77. }

  78. layer {

  79.   name: "bn1"

  80.   type: "BatchNorm"

  81.   bottom: "conv1"

  82.   top: "bn1"

  83.   batch_norm_param {

  84.     moving_average_fraction: 0.99

  85.     eps: 0.001

  86.   }

  87. }

  88. layer {

  89.   name: "bn1_scale"

  90.   type: "Scale"

  91.   bottom: "bn1"

  92.   top: "bn1"

  93.   scale_param {

  94.     bias_term: true

  95.   }

  96. }

  97. layer {

  98.   name: "relu1"

  99.   type: "ReLU"

  100.   bottom: "bn1"

  101.   top: "bn1"

  102. }

  103. layer {

  104.   name: "pool1"

  105.   type: "Pooling"

  106.   bottom: "bn1"

  107.   top: "pool1"

  108.   pooling_param {

  109.     pool: MAX

  110.     kernel_h: 2

  111.     kernel_w: 2

  112.     stride_h: 2

  113.     stride_w: 2

  114.     pad_h: 0

  115.     pad_w: 0

  116.   }

  117. }

  118. layer {

  119.   name: "conv2"

  120.   type: "Convolution"

  121.   bottom: "pool1"

  122.   top: "conv2"

  123.   convolution_param {

  124.     num_output: 128

  125.     bias_term: true

  126.     pad_h: 1

  127.     pad_w: 1

  128.     kernel_h: 3

  129.     kernel_w: 3

  130.     stride_h: 1

  131.     stride_w: 1

  132.   }

  133. }

  134. layer {

  135.   name: "bn2"

  136.   type: "BatchNorm"

  137.   bottom: "conv2"

  138.   top: "bn2"

  139.   batch_norm_param {

  140.     moving_average_fraction: 0.99

  141.     eps: 0.001

  142.   }

  143. }

  144. layer {

  145.   name: "bn2_scale"

  146.   type: "Scale"

  147.   bottom: "bn2"

  148.   top: "bn2"

  149.   scale_param {

  150.     bias_term: true

  151.   }

  152. }

  153. layer {

  154.   name: "relu2"

  155.   type: "ReLU"

  156.   bottom: "bn2"

  157.   top: "bn2"

  158. }

  159. layer {

  160.   name: "pool2"

  161.   type: "Pooling"

  162.   bottom: "bn2"

  163.   top: "pool2"

  164.   pooling_param {

  165.     pool: MAX

  166.     kernel_h: 2

  167.     kernel_w: 2

  168.     stride_h: 2

  169.     stride_w: 2

  170.     pad_h: 0

  171.     pad_w: 0

  172.   }

  173. }

  174. layer {

  175.   name: "conv2d_1"

  176.   type: "Convolution"

  177.   bottom: "pool2"

  178.   top: "conv2d_1"

  179.   convolution_param {

  180.     num_output: 256

  181.     bias_term: true

  182.     pad_h: 0

  183.     pad_w: 0

  184.     kernel_h: 1

  185.     kernel_w: 5

  186.     stride_h: 1

  187.     stride_w: 1

  188.   }

  189. }

  190. layer {

  191.   name: "batch_normalization_1"

  192.   type: "BatchNorm"

  193.   bottom: "conv2d_1"

  194.   top: "batch_normalization_1"

  195.   batch_norm_param {

  196.     moving_average_fraction: 0.99

  197.     eps: 0.001

  198.   }

  199. }

  200. layer {

  201.   name: "batch_normalization_1_scale"

  202.   type: "Scale"

  203.   bottom: "batch_normalization_1"

  204.   top: "batch_normalization_1"

  205.   scale_param {

  206.     bias_term: true

  207.   }

  208. }

  209. layer {

  210.   name: "activation_1"

  211.   type: "ReLU"

  212.   bottom: "batch_normalization_1"

  213.   top: "batch_normalization_1"

  214. }

  215. layer {

  216.   name: "conv2d_2"

  217.   type: "Convolution"

  218.   bottom: "batch_normalization_1"

  219.   top: "conv2d_2"

  220.   convolution_param {

  221.     num_output: 256

  222.     bias_term: true

  223.     pad_h: 3

  224.     pad_w: 0

  225.     kernel_h: 7

  226.     kernel_w: 1

  227.     stride_h: 1

  228.     stride_w: 1

  229.   }

  230. }

  231. layer {

  232.   name: "conv2d_3"

  233.   type: "Convolution"

  234.   bottom: "batch_normalization_1"

  235.   top: "conv2d_3"

  236.   convolution_param {

  237.     num_output: 256

  238.     bias_term: true

  239.     pad_h: 2

  240.     pad_w: 0

  241.     kernel_h: 5

  242.     kernel_w: 1

  243.     stride_h: 1

  244.     stride_w: 1

  245.   }

  246. }

  247. layer {

  248.   name: "conv2d_4"

  249.   type: "Convolution"

  250.   bottom: "batch_normalization_1"

  251.   top: "conv2d_4"

  252.   convolution_param {

  253.     num_output: 256

  254.     bias_term: true

  255.     pad_h: 1

  256.     pad_w: 0

  257.     kernel_h: 3

  258.     kernel_w: 1

  259.     stride_h: 1

  260.     stride_w: 1

  261.   }

  262. }

  263. layer {

  264.   name: "conv2d_5"

  265.   type: "Convolution"

  266.   bottom: "batch_normalization_1"

  267.   top: "conv2d_5"

  268.   convolution_param {

  269.     num_output: 256

  270.     bias_term: true

  271.     pad_h: 0

  272.     pad_w: 0

  273.     kernel_h: 1

  274.     kernel_w: 1

  275.     stride_h: 1

  276.     stride_w: 1

  277.   }

  278. }

  279. layer {

  280.   name: "batch_normalization_2"

  281.   type: "BatchNorm"

  282.   bottom: "conv2d_2"

  283.   top: "batch_normalization_2"

  284.   batch_norm_param {

  285.     moving_average_fraction: 0.99

  286.     eps: 0.001

  287.   }

  288. }

  289. layer {

  290.   name: "batch_normalization_2_scale"

  291.   type: "Scale"

  292.   bottom: "batch_normalization_2"

  293.   top: "batch_normalization_2"

  294.   scale_param {

  295.     bias_term: true

  296.   }

  297. }

  298. layer {

  299.   name: "batch_normalization_3"

  300.   type: "BatchNorm"

  301.   bottom: "conv2d_3"

  302.   top: "batch_normalization_3"

  303.   batch_norm_param {

  304.     moving_average_fraction: 0.99

  305.     eps: 0.001

  306.   }

  307. }

  308. layer {

  309.   name: "batch_normalization_3_scale"

  310.   type: "Scale"

  311.   bottom: "batch_normalization_3"

  312.   top: "batch_normalization_3"

  313.   scale_param {

  314.     bias_term: true

  315.   }

  316. }

  317. layer {

  318.   name: "batch_normalization_4"

  319.   type: "BatchNorm"

  320.   bottom: "conv2d_4"

  321.   top: "batch_normalization_4"

  322.   batch_norm_param {

  323.     moving_average_fraction: 0.99

  324.     eps: 0.001

  325.   }

  326. }

  327. layer {

  328.   name: "batch_normalization_4_scale"

  329.   type: "Scale"

  330.   bottom: "batch_normalization_4"

  331.   top: "batch_normalization_4"

  332.   scale_param {

  333.     bias_term: true

  334.   }

  335. }

  336. layer {

  337.   name: "batch_normalization_5"

  338.   type: "BatchNorm"

  339.   bottom: "conv2d_5"

  340.   top: "batch_normalization_5"

  341.   batch_norm_param {

  342.     moving_average_fraction: 0.99

  343.     eps: 0.001

  344.   }

  345. }

  346. layer {

  347.   name: "batch_normalization_5_scale"

  348.   type: "Scale"

  349.   bottom: "batch_normalization_5"

  350.   top: "batch_normalization_5"

  351.   scale_param {

  352.     bias_term: true

  353.   }

  354. }

  355. layer {

  356.   name: "activation_2"

  357.   type: "ReLU"

  358.   bottom: "batch_normalization_2"

  359.   top: "batch_normalization_2"

  360. }

  361. layer {

  362.   name: "activation_3"

  363.   type: "ReLU"

  364.   bottom: "batch_normalization_3"

  365.   top: "batch_normalization_3"

  366. }

  367. layer {

  368.   name: "activation_4"

  369.   type: "ReLU"

  370.   bottom: "batch_normalization_4"

  371.   top: "batch_normalization_4"

  372. }

  373. layer {

  374.   name: "activation_5"

  375.   type: "ReLU"

  376.   bottom: "batch_normalization_5"

  377.   top: "batch_normalization_5"

  378. }

  379. layer {

  380.   name: "concatenate_1"

  381.   type: "Concat"

  382.   bottom: "batch_normalization_2"

  383.   bottom: "batch_normalization_3"

  384.   bottom: "batch_normalization_4"

  385.   bottom: "batch_normalization_5"

  386.   top: "concatenate_1"

  387.   concat_param {

  388.     axis: 1

  389.   }

  390. }

  391. layer {

  392.   name: "conv_1024_11"

  393.   type: "Convolution"

  394.   bottom: "concatenate_1"

  395.   top: "conv_1024_11"

  396.   convolution_param {

  397.     num_output: 1024

  398.     bias_term: true

  399.     pad_h: 0

  400.     pad_w: 0

  401.     kernel_h: 1

  402.     kernel_w: 1

  403.     stride_h: 1

  404.     stride_w: 1

  405.   }

  406. }

  407. layer {

  408.   name: "batch_normalization_6"

  409.   type: "BatchNorm"

  410.   bottom: "conv_1024_11"

  411.   top: "batch_normalization_6"

  412.   batch_norm_param {

  413.     moving_average_fraction: 0.99

  414.     eps: 0.001

  415.   }

  416. }

  417. layer {

  418.   name: "batch_normalization_6_scale"

  419.   type: "Scale"

  420.   bottom: "batch_normalization_6"

  421.   top: "batch_normalization_6"

  422.   scale_param {

  423.     bias_term: true

  424.   }

  425. }

  426. layer {

  427.   name: "activation_6"

  428.   type: "ReLU"

  429.   bottom: "batch_normalization_6"

  430.   top: "batch_normalization_6"

  431. }

  432. layer {

  433.   name: "conv_class_11"

  434.   type: "Convolution"

  435.   bottom: "batch_normalization_6"

  436.   top: "conv_class_11"

  437.   convolution_param {

  438.     num_output: 84

  439.     bias_term: true

  440.     pad_h: 0

  441.     pad_w: 0

  442.     kernel_h: 1

  443.     kernel_w: 1

  444.     stride_h: 1

  445.     stride_w: 1

  446.   }

  447. }

  448. layer {

  449.   name: "prob"

  450.   type: "Softmax"

  451.   bottom: "conv_class_11"

  452.   top: "prob"

  453. }