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HyperLPR/Prj-Linux/lpr/model/mininet_ssd_v1.prototxt

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  1. layer {

  2.   name: "data"

  3.   type: "Input"

  4.   top: "data"

  5.   input_param {

  6.     shape {

  7.       dim: 1

  8.       dim: 3

  9.       dim: 512

  10.       dim: 512

  11.     }

  12.   }

  13. }

  14. layer {

  15.   name: "ssd0_mobilenet0_conv0"

  16.   type: "Convolution"

  17.   bottom: "data"

  18.   top: "ssd0_mobilenet0_conv0"

  19.   convolution_param {

  20.     num_output: 8

  21.     bias_term: true

  22.     pad: 1

  23.     kernel_size: 3

  24.     group: 1

  25.     stride: 2

  26.     dilation: 1

  27.   }

  28. }

  29. layer {

  30.   name: "ssd0_mobilenet0_batchnorm0/relu"

  31.   type: "ReLU"

  32.   bottom: "ssd0_mobilenet0_conv0"

  33.   top: "ssd0_mobilenet0_conv0"

  34.   relu_param {

  35.     negative_slope: 0.0

  36.   }

  37. }

  38. layer {

  39.   name: "ssd0_mobilenet0_conv1"

  40.   type: "Convolution"

  41.   bottom: "ssd0_mobilenet0_conv0"

  42.   top: "ssd0_mobilenet0_conv1"

  43.   convolution_param {

  44.     num_output: 8

  45.     bias_term: true

  46.     pad: 1

  47.     kernel_size: 3

  48.     group: 8

  49.     stride: 1

  50.     dilation: 1

  51.   }

  52. }

  53. layer {

  54.   name: "ssd0_mobilenet0_batchnorm1/relu"

  55.   type: "ReLU"

  56.   bottom: "ssd0_mobilenet0_conv1"

  57.   top: "ssd0_mobilenet0_conv1"

  58.   relu_param {

  59.     negative_slope: 0.0

  60.   }

  61. }

  62. layer {

  63.   name: "ssd0_mobilenet0_conv2"

  64.   type: "Convolution"

  65.   bottom: "ssd0_mobilenet0_conv1"

  66.   top: "ssd0_mobilenet0_conv2"

  67.   convolution_param {

  68.     num_output: 16

  69.     bias_term: true

  70.     pad: 0

  71.     kernel_size: 1

  72.     group: 1

  73.     stride: 1

  74.     dilation: 1

  75.   }

  76. }

  77. layer {

  78.   name: "ssd0_mobilenet0_batchnorm2/relu"

  79.   type: "ReLU"

  80.   bottom: "ssd0_mobilenet0_conv2"

  81.   top: "ssd0_mobilenet0_conv2"

  82.   relu_param {

  83.     negative_slope: 0.0

  84.   }

  85. }

  86. layer {

  87.   name: "ssd0_mobilenet0_conv3"

  88.   type: "Convolution"

  89.   bottom: "ssd0_mobilenet0_conv2"

  90.   top: "ssd0_mobilenet0_conv3"

  91.   convolution_param {

  92.     num_output: 16

  93.     bias_term: true

  94.     pad: 1

  95.     kernel_size: 3

  96.     group: 16

  97.     stride: 2

  98.     dilation: 1

  99.   }

  100. }

  101. layer {

  102.   name: "ssd0_mobilenet0_batchnorm3/relu"

  103.   type: "ReLU"

  104.   bottom: "ssd0_mobilenet0_conv3"

  105.   top: "ssd0_mobilenet0_conv3"

  106.   relu_param {

  107.     negative_slope: 0.0

  108.   }

  109. }

  110. layer {

  111.   name: "ssd0_mobilenet0_conv4"

  112.   type: "Convolution"

  113.   bottom: "ssd0_mobilenet0_conv3"

  114.   top: "ssd0_mobilenet0_conv4"

  115.   convolution_param {

  116.     num_output: 32

  117.     bias_term: true

  118.     pad: 0

  119.     kernel_size: 1

  120.     group: 1

  121.     stride: 1

  122.     dilation: 1

  123.   }

  124. }

  125. layer {

  126.   name: "ssd0_mobilenet0_batchnorm4/relu"

  127.   type: "ReLU"

  128.   bottom: "ssd0_mobilenet0_conv4"

  129.   top: "ssd0_mobilenet0_conv4"

  130.   relu_param {

  131.     negative_slope: 0.0

  132.   }

  133. }

  134. layer {

  135.   name: "ssd0_mobilenet0_conv5"

  136.   type: "Convolution"

  137.   bottom: "ssd0_mobilenet0_conv4"

  138.   top: "ssd0_mobilenet0_conv5"

  139.   convolution_param {

  140.     num_output: 32

  141.     bias_term: true

  142.     pad: 1

  143.     kernel_size: 3

  144.     group: 32

  145.     stride: 1

  146.     dilation: 1

  147.   }

  148. }

  149. layer {

  150.   name: "ssd0_mobilenet0_batchnorm5/relu"

  151.   type: "ReLU"

  152.   bottom: "ssd0_mobilenet0_conv5"

  153.   top: "ssd0_mobilenet0_conv5"

  154.   relu_param {

  155.     negative_slope: 0.0

  156.   }

  157. }

  158. layer {

  159.   name: "ssd0_mobilenet0_conv6"

  160.   type: "Convolution"

  161.   bottom: "ssd0_mobilenet0_conv5"

  162.   top: "ssd0_mobilenet0_conv6"

  163.   convolution_param {

  164.     num_output: 32

  165.     bias_term: true

  166.     pad: 0

  167.     kernel_size: 1

  168.     group: 1

  169.     stride: 1

  170.     dilation: 1

  171.   }

  172. }

  173. layer {

  174.   name: "ssd0_mobilenet0_batchnorm6/relu"

  175.   type: "ReLU"

  176.   bottom: "ssd0_mobilenet0_conv6"

  177.   top: "ssd0_mobilenet0_conv6"

  178.   relu_param {

  179.     negative_slope: 0.0

  180.   }

  181. }

  182. layer {

  183.   name: "ssd0_mobilenet0_conv7"

  184.   type: "Convolution"

  185.   bottom: "ssd0_mobilenet0_conv6"

  186.   top: "ssd0_mobilenet0_conv7"

  187.   convolution_param {

  188.     num_output: 32

  189.     bias_term: true

  190.     pad: 1

  191.     kernel_size: 3

  192.     group: 32

  193.     stride: 2

  194.     dilation: 1

  195.   }

  196. }

  197. layer {

  198.   name: "ssd0_mobilenet0_batchnorm7/relu"

  199.   type: "ReLU"

  200.   bottom: "ssd0_mobilenet0_conv7"

  201.   top: "ssd0_mobilenet0_conv7"

  202.   relu_param {

  203.     negative_slope: 0.0

  204.   }

  205. }

  206. layer {

  207.   name: "ssd0_mobilenet0_conv8"

  208.   type: "Convolution"

  209.   bottom: "ssd0_mobilenet0_conv7"

  210.   top: "ssd0_mobilenet0_conv8"

  211.   convolution_param {

  212.     num_output: 64

  213.     bias_term: true

  214.     pad: 0

  215.     kernel_size: 1

  216.     group: 1

  217.     stride: 1

  218.     dilation: 1

  219.   }

  220. }

  221. layer {

  222.   name: "ssd0_mobilenet0_batchnorm8/relu"

  223.   type: "ReLU"

  224.   bottom: "ssd0_mobilenet0_conv8"

  225.   top: "ssd0_mobilenet0_conv8"

  226.   relu_param {

  227.     negative_slope: 0.0

  228.   }

  229. }

  230. layer {

  231.   name: "ssd0_mobilenet0_conv9"

  232.   type: "Convolution"

  233.   bottom: "ssd0_mobilenet0_conv8"

  234.   top: "ssd0_mobilenet0_conv9"

  235.   convolution_param {

  236.     num_output: 64

  237.     bias_term: true

  238.     pad: 1

  239.     kernel_size: 3

  240.     group: 64

  241.     stride: 1

  242.     dilation: 1

  243.   }

  244. }

  245. layer {

  246.   name: "ssd0_mobilenet0_batchnorm9/relu"

  247.   type: "ReLU"

  248.   bottom: "ssd0_mobilenet0_conv9"

  249.   top: "ssd0_mobilenet0_conv9"

  250.   relu_param {

  251.     negative_slope: 0.0

  252.   }

  253. }

  254. layer {

  255.   name: "ssd0_mobilenet0_conv10"

  256.   type: "Convolution"

  257.   bottom: "ssd0_mobilenet0_conv9"

  258.   top: "ssd0_mobilenet0_conv10"

  259.   convolution_param {

  260.     num_output: 64

  261.     bias_term: true

  262.     pad: 0

  263.     kernel_size: 1

  264.     group: 1

  265.     stride: 1

  266.     dilation: 1

  267.   }

  268. }

  269. layer {

  270.   name: "ssd0_mobilenet0_batchnorm10/relu"

  271.   type: "ReLU"

  272.   bottom: "ssd0_mobilenet0_conv10"

  273.   top: "ssd0_mobilenet0_conv10"

  274.   relu_param {

  275.     negative_slope: 0.0

  276.   }

  277. }

  278. layer {

  279.   name: "ssd0_mobilenet0_conv11"

  280.   type: "Convolution"

  281.   bottom: "ssd0_mobilenet0_conv10"

  282.   top: "ssd0_mobilenet0_conv11"

  283.   convolution_param {

  284.     num_output: 64

  285.     bias_term: true

  286.     pad: 1

  287.     kernel_size: 3

  288.     group: 64

  289.     stride: 2

  290.     dilation: 1

  291.   }

  292. }

  293. layer {

  294.   name: "ssd0_mobilenet0_batchnorm11/relu"

  295.   type: "ReLU"

  296.   bottom: "ssd0_mobilenet0_conv11"

  297.   top: "ssd0_mobilenet0_conv11"

  298.   relu_param {

  299.     negative_slope: 0.0

  300.   }

  301. }

  302. layer {

  303.   name: "ssd0_mobilenet0_conv12"

  304.   type: "Convolution"

  305.   bottom: "ssd0_mobilenet0_conv11"

  306.   top: "ssd0_mobilenet0_conv12"

  307.   convolution_param {

  308.     num_output: 128

  309.     bias_term: true

  310.     pad: 0

  311.     kernel_size: 1

  312.     group: 1

  313.     stride: 1

  314.     dilation: 1

  315.   }

  316. }

  317. layer {

  318.   name: "ssd0_mobilenet0_batchnorm12/relu"

  319.   type: "ReLU"

  320.   bottom: "ssd0_mobilenet0_conv12"

  321.   top: "ssd0_mobilenet0_conv12"

  322.   relu_param {

  323.     negative_slope: 0.0

  324.   }

  325. }

  326. layer {

  327.   name: "ssd0_mobilenet0_conv13"

  328.   type: "Convolution"

  329.   bottom: "ssd0_mobilenet0_conv12"

  330.   top: "ssd0_mobilenet0_conv13"

  331.   convolution_param {

  332.     num_output: 128

  333.     bias_term: true

  334.     pad: 1

  335.     kernel_size: 3

  336.     group: 128

  337.     stride: 1

  338.     dilation: 1

  339.   }

  340. }

  341. layer {

  342.   name: "ssd0_mobilenet0_batchnorm13/relu"

  343.   type: "ReLU"

  344.   bottom: "ssd0_mobilenet0_conv13"

  345.   top: "ssd0_mobilenet0_conv13"

  346.   relu_param {

  347.     negative_slope: 0.0

  348.   }

  349. }

  350. layer {

  351.   name: "ssd0_mobilenet0_conv14"

  352.   type: "Convolution"

  353.   bottom: "ssd0_mobilenet0_conv13"

  354.   top: "ssd0_mobilenet0_conv14"

  355.   convolution_param {

  356.     num_output: 128

  357.     bias_term: true

  358.     pad: 0

  359.     kernel_size: 1

  360.     group: 1

  361.     stride: 1

  362.     dilation: 1

  363.   }

  364. }

  365. layer {

  366.   name: "ssd0_mobilenet0_batchnorm14/relu"

  367.   type: "ReLU"

  368.   bottom: "ssd0_mobilenet0_conv14"

  369.   top: "ssd0_mobilenet0_conv14"

  370.   relu_param {

  371.     negative_slope: 0.0

  372.   }

  373. }

  374. layer {

  375.   name: "ssd0_mobilenet0_conv15"

  376.   type: "Convolution"

  377.   bottom: "ssd0_mobilenet0_conv14"

  378.   top: "ssd0_mobilenet0_conv15"

  379.   convolution_param {

  380.     num_output: 128

  381.     bias_term: true

  382.     pad: 1

  383.     kernel_size: 3

  384.     group: 128

  385.     stride: 1

  386.     dilation: 1

  387.   }

  388. }

  389. layer {

  390.   name: "ssd0_mobilenet0_batchnorm15/relu"

  391.   type: "ReLU"

  392.   bottom: "ssd0_mobilenet0_conv15"

  393.   top: "ssd0_mobilenet0_conv15"

  394.   relu_param {

  395.     negative_slope: 0.0

  396.   }

  397. }

  398. layer {

  399.   name: "ssd0_mobilenet0_conv16"

  400.   type: "Convolution"

  401.   bottom: "ssd0_mobilenet0_conv15"

  402.   top: "ssd0_mobilenet0_conv16"

  403.   convolution_param {

  404.     num_output: 128

  405.     bias_term: true

  406.     pad: 0

  407.     kernel_size: 1

  408.     group: 1

  409.     stride: 1

  410.     dilation: 1

  411.   }

  412. }

  413. layer {

  414.   name: "ssd0_mobilenet0_batchnorm16/relu"

  415.   type: "ReLU"

  416.   bottom: "ssd0_mobilenet0_conv16"

  417.   top: "ssd0_mobilenet0_conv16"

  418.   relu_param {

  419.     negative_slope: 0.0

  420.   }

  421. }

  422. layer {

  423.   name: "ssd0_mobilenet0_conv17"

  424.   type: "Convolution"

  425.   bottom: "ssd0_mobilenet0_conv16"

  426.   top: "ssd0_mobilenet0_conv17"

  427.   convolution_param {

  428.     num_output: 128

  429.     bias_term: true

  430.     pad: 1

  431.     kernel_size: 3

  432.     group: 128

  433.     stride: 1

  434.     dilation: 1

  435.   }

  436. }

  437. layer {

  438.   name: "ssd0_mobilenet0_batchnorm17/relu"

  439.   type: "ReLU"

  440.   bottom: "ssd0_mobilenet0_conv17"

  441.   top: "ssd0_mobilenet0_conv17"

  442.   relu_param {

  443.     negative_slope: 0.0

  444.   }

  445. }

  446. layer {

  447.   name: "ssd0_mobilenet0_conv18"

  448.   type: "Convolution"

  449.   bottom: "ssd0_mobilenet0_conv17"

  450.   top: "ssd0_mobilenet0_conv18"

  451.   convolution_param {

  452.     num_output: 128

  453.     bias_term: true

  454.     pad: 0

  455.     kernel_size: 1

  456.     group: 1

  457.     stride: 1

  458.     dilation: 1

  459.   }

  460. }

  461. layer {

  462.   name: "ssd0_mobilenet0_batchnorm18/relu"

  463.   type: "ReLU"

  464.   bottom: "ssd0_mobilenet0_conv18"

  465.   top: "ssd0_mobilenet0_conv18"

  466.   relu_param {

  467.     negative_slope: 0.0

  468.   }

  469. }

  470. layer {

  471.   name: "ssd0_mobilenet0_conv19"

  472.   type: "Convolution"

  473.   bottom: "ssd0_mobilenet0_conv18"

  474.   top: "ssd0_mobilenet0_conv19"

  475.   convolution_param {

  476.     num_output: 128

  477.     bias_term: true

  478.     pad: 1

  479.     kernel_size: 3

  480.     group: 128

  481.     stride: 1

  482.     dilation: 1

  483.   }

  484. }

  485. layer {

  486.   name: "ssd0_mobilenet0_batchnorm19/relu"

  487.   type: "ReLU"

  488.   bottom: "ssd0_mobilenet0_conv19"

  489.   top: "ssd0_mobilenet0_conv19"

  490.   relu_param {

  491.     negative_slope: 0.0

  492.   }

  493. }

  494. layer {

  495.   name: "ssd0_mobilenet0_conv20"

  496.   type: "Convolution"

  497.   bottom: "ssd0_mobilenet0_conv19"

  498.   top: "ssd0_mobilenet0_conv20"

  499.   convolution_param {

  500.     num_output: 128

  501.     bias_term: true

  502.     pad: 0

  503.     kernel_size: 1

  504.     group: 1

  505.     stride: 1

  506.     dilation: 1

  507.   }

  508. }

  509. layer {

  510.   name: "ssd0_mobilenet0_batchnorm20/relu"

  511.   type: "ReLU"

  512.   bottom: "ssd0_mobilenet0_conv20"

  513.   top: "ssd0_mobilenet0_conv20"

  514.   relu_param {

  515.     negative_slope: 0.0

  516.   }

  517. }

  518. layer {

  519.   name: "ssd0_mobilenet0_conv21"

  520.   type: "Convolution"

  521.   bottom: "ssd0_mobilenet0_conv20"

  522.   top: "ssd0_mobilenet0_conv21"

  523.   convolution_param {

  524.     num_output: 128

  525.     bias_term: true

  526.     pad: 1

  527.     kernel_size: 3

  528.     group: 128

  529.     stride: 1

  530.     dilation: 1

  531.   }

  532. }

  533. layer {

  534.   name: "ssd0_mobilenet0_batchnorm21/relu"

  535.   type: "ReLU"

  536.   bottom: "ssd0_mobilenet0_conv21"

  537.   top: "ssd0_mobilenet0_conv21"

  538.   relu_param {

  539.     negative_slope: 0.0

  540.   }

  541. }

  542. layer {

  543.   name: "ssd0_mobilenet0_conv22"

  544.   type: "Convolution"

  545.   bottom: "ssd0_mobilenet0_conv21"

  546.   top: "ssd0_mobilenet0_conv22"

  547.   convolution_param {

  548.     num_output: 128

  549.     bias_term: true

  550.     pad: 0

  551.     kernel_size: 1

  552.     group: 1

  553.     stride: 1

  554.     dilation: 1

  555.   }

  556. }

  557. layer {

  558.   name: "ssd0_mobilenet0_batchnorm22/relu"

  559.   type: "ReLU"

  560.   bottom: "ssd0_mobilenet0_conv22"

  561.   top: "ssd0_mobilenet0_conv22"

  562.   relu_param {

  563.     negative_slope: 0.0

  564.   }

  565. }

  566. layer {

  567.   name: "ssd0_convpredictor1_conv0"

  568.   type: "Convolution"

  569.   bottom: "ssd0_mobilenet0_conv22"

  570.   top: "ssd0_convpredictor1_conv0"

  571.   convolution_param {

  572.     num_output: 12

  573.     bias_term: true

  574.     pad: 1

  575.     kernel_size: 3

  576.     group: 1

  577.     stride: 1

  578.     dilation: 1

  579.   }

  580. }

  581. layer {

  582.   name: "ssd0_transpose6"

  583.   type: "Permute"

  584.   bottom: "ssd0_convpredictor1_conv0"

  585.   top: "ssd0_transpose6"

  586.   permute_param {

  587.     order: 0

  588.     order: 2

  589.     order: 3

  590.     order: 1

  591.   }

  592. }

  593. layer {

  594.   name: "ssd0_flatten6"

  595.   type: "Flatten"

  596.   bottom: "ssd0_transpose6"

  597.   top: "ssd0_flatten6"

  598. }

  599. layer {

  600.   name: "ssd0_mobilenet0_conv23"

  601.   type: "Convolution"

  602.   bottom: "ssd0_mobilenet0_conv22"

  603.   top: "ssd0_mobilenet0_conv23"

  604.   convolution_param {

  605.     num_output: 128

  606.     bias_term: true

  607.     pad: 1

  608.     kernel_size: 3

  609.     group: 128

  610.     stride: 2

  611.     dilation: 1

  612.   }

  613. }

  614. layer {

  615.   name: "ssd0_mobilenet0_batchnorm23/relu"

  616.   type: "ReLU"

  617.   bottom: "ssd0_mobilenet0_conv23"

  618.   top: "ssd0_mobilenet0_conv23"

  619.   relu_param {

  620.     negative_slope: 0.0

  621.   }

  622. }

  623. layer {

  624.   name: "ssd0_mobilenet0_conv24"

  625.   type: "Convolution"

  626.   bottom: "ssd0_mobilenet0_conv23"

  627.   top: "ssd0_mobilenet0_conv24"

  628.   convolution_param {

  629.     num_output: 256

  630.     bias_term: true

  631.     pad: 0

  632.     kernel_size: 1

  633.     group: 1

  634.     stride: 1

  635.     dilation: 1

  636.   }

  637. }

  638. layer {

  639.   name: "ssd0_mobilenet0_batchnorm24/relu"

  640.   type: "ReLU"

  641.   bottom: "ssd0_mobilenet0_conv24"

  642.   top: "ssd0_mobilenet0_conv24"

  643.   relu_param {

  644.     negative_slope: 0.0

  645.   }

  646. }

  647. layer {

  648.   name: "ssd0_mobilenet0_conv25"

  649.   type: "Convolution"

  650.   bottom: "ssd0_mobilenet0_conv24"

  651.   top: "ssd0_mobilenet0_conv25"

  652.   convolution_param {

  653.     num_output: 256

  654.     bias_term: true

  655.     pad: 1

  656.     kernel_size: 3

  657.     group: 256

  658.     stride: 1

  659.     dilation: 1

  660.   }

  661. }

  662. layer {

  663.   name: "ssd0_mobilenet0_batchnorm25/relu"

  664.   type: "ReLU"

  665.   bottom: "ssd0_mobilenet0_conv25"

  666.   top: "ssd0_mobilenet0_conv25"

  667.   relu_param {

  668.     negative_slope: 0.0

  669.   }

  670. }

  671. layer {

  672.   name: "ssd0_mobilenet0_conv26"

  673.   type: "Convolution"

  674.   bottom: "ssd0_mobilenet0_conv25"

  675.   top: "ssd0_mobilenet0_conv26"

  676.   convolution_param {

  677.     num_output: 256

  678.     bias_term: true

  679.     pad: 0

  680.     kernel_size: 1

  681.     group: 1

  682.     stride: 1

  683.     dilation: 1

  684.   }

  685. }

  686. layer {

  687.   name: "ssd0_mobilenet0_batchnorm26/relu"

  688.   type: "ReLU"

  689.   bottom: "ssd0_mobilenet0_conv26"

  690.   top: "ssd0_mobilenet0_conv26"

  691.   relu_param {

  692.     negative_slope: 0.0

  693.   }

  694. }

  695. layer {

  696.   name: "ssd0_convpredictor3_conv0"

  697.   type: "Convolution"

  698.   bottom: "ssd0_mobilenet0_conv26"

  699.   top: "ssd0_convpredictor3_conv0"

  700.   convolution_param {

  701.     num_output: 16

  702.     bias_term: true

  703.     pad: 1

  704.     kernel_size: 3

  705.     group: 1

  706.     stride: 1

  707.     dilation: 1

  708.   }

  709. }

  710. layer {

  711.   name: "ssd0_transpose7"

  712.   type: "Permute"

  713.   bottom: "ssd0_convpredictor3_conv0"

  714.   top: "ssd0_transpose7"

  715.   permute_param {

  716.     order: 0

  717.     order: 2

  718.     order: 3

  719.     order: 1

  720.   }

  721. }

  722. layer {

  723.   name: "ssd0_flatten7"

  724.   type: "Flatten"

  725.   bottom: "ssd0_transpose7"

  726.   top: "ssd0_flatten7"

  727. }

  728. layer {

  729.   name: "ssd0_expand_trans_conv0"

  730.   type: "Convolution"

  731.   bottom: "ssd0_mobilenet0_conv26"

  732.   top: "ssd0_expand_trans_conv0"

  733.   convolution_param {

  734.     num_output: 128

  735.     bias_term: true

  736.     pad: 1

  737.     kernel_size: 1

  738.     group: 1

  739.     stride: 1

  740.     dilation: 1

  741.   }

  742. }

  743. layer {

  744.   name: "ssd0_expand_trans_bn0/relu"

  745.   type: "ReLU"

  746.   bottom: "ssd0_expand_trans_conv0"

  747.   top: "ssd0_expand_trans_conv0"

  748.   relu_param {

  749.     negative_slope: 0.0

  750.   }

  751. }

  752. layer {

  753.   name: "ssd0_expand_conv0"

  754.   type: "Convolution"

  755.   bottom: "ssd0_expand_trans_conv0"

  756.   top: "ssd0_expand_conv0"

  757.   convolution_param {

  758.     num_output: 128

  759.     bias_term: true

  760.     pad: 1

  761.     kernel_size: 3

  762.     group: 1

  763.     stride: 2

  764.     dilation: 1

  765.   }

  766. }

  767. layer {

  768.   name: "ssd0_expand_bn0/relu"

  769.   type: "ReLU"

  770.   bottom: "ssd0_expand_conv0"

  771.   top: "ssd0_expand_conv0"

  772.   relu_param {

  773.     negative_slope: 0.0

  774.   }

  775. }

  776. layer {

  777.   name: "ssd0_convpredictor5_conv0"

  778.   type: "Convolution"

  779.   bottom: "ssd0_expand_conv0"

  780.   top: "ssd0_convpredictor5_conv0"

  781.   convolution_param {

  782.     num_output: 16

  783.     bias_term: true

  784.     pad: 1

  785.     kernel_size: 3

  786.     group: 1

  787.     stride: 1

  788.     dilation: 1

  789.   }

  790. }

  791. layer {

  792.   name: "ssd0_transpose8"

  793.   type: "Permute"

  794.   bottom: "ssd0_convpredictor5_conv0"

  795.   top: "ssd0_transpose8"

  796.   permute_param {

  797.     order: 0

  798.     order: 2

  799.     order: 3

  800.     order: 1

  801.   }

  802. }

  803. layer {

  804.   name: "ssd0_flatten8"

  805.   type: "Flatten"

  806.   bottom: "ssd0_transpose8"

  807.   top: "ssd0_flatten8"

  808. }

  809. layer {

  810.   name: "ssd0_expand_trans_conv1"

  811.   type: "Convolution"

  812.   bottom: "ssd0_expand_conv0"

  813.   top: "ssd0_expand_trans_conv1"

  814.   convolution_param {

  815.     num_output: 128

  816.     bias_term: true

  817.     pad: 1

  818.     kernel_size: 1

  819.     group: 1

  820.     stride: 1

  821.     dilation: 1

  822.   }

  823. }

  824. layer {

  825.   name: "ssd0_expand_trans_bn1/relu"

  826.   type: "ReLU"

  827.   bottom: "ssd0_expand_trans_conv1"

  828.   top: "ssd0_expand_trans_conv1"

  829.   relu_param {

  830.     negative_slope: 0.0

  831.   }

  832. }

  833. layer {

  834.   name: "ssd0_expand_conv1"

  835.   type: "Convolution"

  836.   bottom: "ssd0_expand_trans_conv1"

  837.   top: "ssd0_expand_conv1"

  838.   convolution_param {

  839.     num_output: 128

  840.     bias_term: true

  841.     pad: 1

  842.     kernel_size: 3

  843.     group: 1

  844.     stride: 2

  845.     dilation: 1

  846.   }

  847. }

  848. layer {

  849.   name: "ssd0_expand_bn1/relu"

  850.   type: "ReLU"

  851.   bottom: "ssd0_expand_conv1"

  852.   top: "ssd0_expand_conv1"

  853.   relu_param {

  854.     negative_slope: 0.0

  855.   }

  856. }

  857. layer {

  858.   name: "ssd0_convpredictor7_conv0"

  859.   type: "Convolution"

  860.   bottom: "ssd0_expand_conv1"

  861.   top: "ssd0_convpredictor7_conv0"

  862.   convolution_param {

  863.     num_output: 16

  864.     bias_term: true

  865.     pad: 1

  866.     kernel_size: 3

  867.     group: 1

  868.     stride: 1

  869.     dilation: 1

  870.   }

  871. }

  872. layer {

  873.   name: "ssd0_transpose9"

  874.   type: "Permute"

  875.   bottom: "ssd0_convpredictor7_conv0"

  876.   top: "ssd0_transpose9"

  877.   permute_param {

  878.     order: 0

  879.     order: 2

  880.     order: 3

  881.     order: 1

  882.   }

  883. }

  884. layer {

  885.   name: "ssd0_flatten9"

  886.   type: "Flatten"

  887.   bottom: "ssd0_transpose9"

  888.   top: "ssd0_flatten9"

  889. }

  890. layer {

  891.   name: "ssd0_expand_trans_conv2"

  892.   type: "Convolution"

  893.   bottom: "ssd0_expand_conv1"

  894.   top: "ssd0_expand_trans_conv2"

  895.   convolution_param {

  896.     num_output: 128

  897.     bias_term: true

  898.     pad: 1

  899.     kernel_size: 1

  900.     group: 1

  901.     stride: 1

  902.     dilation: 1

  903.   }

  904. }

  905. layer {

  906.   name: "ssd0_expand_trans_bn2/relu"

  907.   type: "ReLU"

  908.   bottom: "ssd0_expand_trans_conv2"

  909.   top: "ssd0_expand_trans_conv2"

  910.   relu_param {

  911.     negative_slope: 0.0

  912.   }

  913. }

  914. layer {

  915.   name: "ssd0_expand_conv2"

  916.   type: "Convolution"

  917.   bottom: "ssd0_expand_trans_conv2"

  918.   top: "ssd0_expand_conv2"

  919.   convolution_param {

  920.     num_output: 64

  921.     bias_term: true

  922.     pad: 1

  923.     kernel_size: 3

  924.     group: 1

  925.     stride: 2

  926.     dilation: 1

  927.   }

  928. }

  929. layer {

  930.   name: "ssd0_expand_bn2/relu"

  931.   type: "ReLU"

  932.   bottom: "ssd0_expand_conv2"

  933.   top: "ssd0_expand_conv2"

  934.   relu_param {

  935.     negative_slope: 0.0

  936.   }

  937. }

  938. layer {

  939.   name: "ssd0_convpredictor9_conv0"

  940.   type: "Convolution"

  941.   bottom: "ssd0_expand_conv2"

  942.   top: "ssd0_convpredictor9_conv0"

  943.   convolution_param {

  944.     num_output: 12

  945.     bias_term: true

  946.     pad: 1

  947.     kernel_size: 3

  948.     group: 1

  949.     stride: 1

  950.     dilation: 1

  951.   }

  952. }

  953. layer {

  954.   name: "ssd0_transpose10"

  955.   type: "Permute"

  956.   bottom: "ssd0_convpredictor9_conv0"

  957.   top: "ssd0_transpose10"

  958.   permute_param {

  959.     order: 0

  960.     order: 2

  961.     order: 3

  962.     order: 1

  963.   }

  964. }

  965. layer {

  966.   name: "ssd0_flatten10"

  967.   type: "Flatten"

  968.   bottom: "ssd0_transpose10"

  969.   top: "ssd0_flatten10"

  970. }

  971. layer {

  972.   name: "ssd0_expand_trans_conv3"

  973.   type: "Convolution"

  974.   bottom: "ssd0_expand_conv2"

  975.   top: "ssd0_expand_trans_conv3"

  976.   convolution_param {

  977.     num_output: 128

  978.     bias_term: true

  979.     pad: 1

  980.     kernel_size: 1

  981.     group: 1

  982.     stride: 1

  983.     dilation: 1

  984.   }

  985. }

  986. layer {

  987.   name: "ssd0_expand_trans_bn3/relu"

  988.   type: "ReLU"

  989.   bottom: "ssd0_expand_trans_conv3"

  990.   top: "ssd0_expand_trans_conv3"

  991.   relu_param {

  992.     negative_slope: 0.0

  993.   }

  994. }

  995. layer {

  996.   name: "ssd0_expand_conv3"

  997.   type: "Convolution"

  998.   bottom: "ssd0_expand_trans_conv3"

  999.   top: "ssd0_expand_conv3"

  1000.   convolution_param {

  1001.     num_output: 64

  1002.     bias_term: true

  1003.     pad: 1

  1004.     kernel_size: 3

  1005.     group: 1

  1006.     stride: 2

  1007.     dilation: 1

  1008.   }

  1009. }

  1010. layer {

  1011.   name: "ssd0_expand_bn3/relu"

  1012.   type: "ReLU"

  1013.   bottom: "ssd0_expand_conv3"

  1014.   top: "ssd0_expand_conv3"

  1015.   relu_param {

  1016.     negative_slope: 0.0

  1017.   }

  1018. }

  1019. layer {

  1020.   name: "ssd0_convpredictor11_conv0"

  1021.   type: "Convolution"

  1022.   bottom: "ssd0_expand_conv3"

  1023.   top: "ssd0_convpredictor11_conv0"

  1024.   convolution_param {

  1025.     num_output: 12

  1026.     bias_term: true

  1027.     pad: 1

  1028.     kernel_size: 3

  1029.     group: 1

  1030.     stride: 1

  1031.     dilation: 1

  1032.   }

  1033. }

  1034. layer {

  1035.   name: "ssd0_transpose11"

  1036.   type: "Permute"

  1037.   bottom: "ssd0_convpredictor11_conv0"

  1038.   top: "ssd0_transpose11"

  1039.   permute_param {

  1040.     order: 0

  1041.     order: 2

  1042.     order: 3

  1043.     order: 1

  1044.   }

  1045. }

  1046. layer {

  1047.   name: "ssd0_flatten11"

  1048.   type: "Flatten"

  1049.   bottom: "ssd0_transpose11"

  1050.   top: "ssd0_flatten11"

  1051. }

  1052. layer {

  1053.   name: "ssd0_concat1"

  1054.   type: "Concat"

  1055.   bottom: "ssd0_flatten6"

  1056.   bottom: "ssd0_flatten7"

  1057.   bottom: "ssd0_flatten8"

  1058.   bottom: "ssd0_flatten9"

  1059.   bottom: "ssd0_flatten10"

  1060.   bottom: "ssd0_flatten11"

  1061.   top: "ssd0_concat1"

  1062.   concat_param {

  1063.     axis: 1

  1064.   }

  1065. }

  1066. layer {

  1067.   name: "ssd0_convpredictor0_conv0"

  1068.   type: "Convolution"

  1069.   bottom: "ssd0_mobilenet0_conv22"

  1070.   top: "ssd0_convpredictor0_conv0"

  1071.   convolution_param {

  1072.     num_output: 6

  1073.     bias_term: true

  1074.     pad: 1

  1075.     kernel_size: 3

  1076.     group: 1

  1077.     stride: 1

  1078.     dilation: 1

  1079.   }

  1080. }

  1081. layer {

  1082.   name: "ssd0_transpose0"

  1083.   type: "Permute"

  1084.   bottom: "ssd0_convpredictor0_conv0"

  1085.   top: "ssd0_transpose0"

  1086.   permute_param {

  1087.     order: 0

  1088.     order: 2

  1089.     order: 3

  1090.     order: 1

  1091.   }

  1092. }

  1093. layer {

  1094.   name: "ssd0_flatten0"

  1095.   type: "Flatten"

  1096.   bottom: "ssd0_transpose0"

  1097.   top: "ssd0_flatten0"

  1098. }

  1099. layer {

  1100.   name: "ssd0_convpredictor2_conv0"

  1101.   type: "Convolution"

  1102.   bottom: "ssd0_mobilenet0_conv26"

  1103.   top: "ssd0_convpredictor2_conv0"

  1104.   convolution_param {

  1105.     num_output: 8

  1106.     bias_term: true

  1107.     pad: 1

  1108.     kernel_size: 3

  1109.     group: 1

  1110.     stride: 1

  1111.     dilation: 1

  1112.   }

  1113. }

  1114. layer {

  1115.   name: "ssd0_transpose1"

  1116.   type: "Permute"

  1117.   bottom: "ssd0_convpredictor2_conv0"

  1118.   top: "ssd0_transpose1"

  1119.   permute_param {

  1120.     order: 0

  1121.     order: 2

  1122.     order: 3

  1123.     order: 1

  1124.   }

  1125. }

  1126. layer {

  1127.   name: "ssd0_flatten1"

  1128.   type: "Flatten"

  1129.   bottom: "ssd0_transpose1"

  1130.   top: "ssd0_flatten1"

  1131. }

  1132. layer {

  1133.   name: "ssd0_convpredictor4_conv0"

  1134.   type: "Convolution"

  1135.   bottom: "ssd0_expand_conv0"

  1136.   top: "ssd0_convpredictor4_conv0"

  1137.   convolution_param {

  1138.     num_output: 8

  1139.     bias_term: true

  1140.     pad: 1

  1141.     kernel_size: 3

  1142.     group: 1

  1143.     stride: 1

  1144.     dilation: 1

  1145.   }

  1146. }

  1147. layer {

  1148.   name: "ssd0_transpose2"

  1149.   type: "Permute"

  1150.   bottom: "ssd0_convpredictor4_conv0"

  1151.   top: "ssd0_transpose2"

  1152.   permute_param {

  1153.     order: 0

  1154.     order: 2

  1155.     order: 3

  1156.     order: 1

  1157.   }

  1158. }

  1159. layer {

  1160.   name: "ssd0_flatten2"

  1161.   type: "Flatten"

  1162.   bottom: "ssd0_transpose2"

  1163.   top: "ssd0_flatten2"

  1164. }

  1165. layer {

  1166.   name: "ssd0_convpredictor6_conv0"

  1167.   type: "Convolution"

  1168.   bottom: "ssd0_expand_conv1"

  1169.   top: "ssd0_convpredictor6_conv0"

  1170.   convolution_param {

  1171.     num_output: 8

  1172.     bias_term: true

  1173.     pad: 1

  1174.     kernel_size: 3

  1175.     group: 1

  1176.     stride: 1

  1177.     dilation: 1

  1178.   }

  1179. }

  1180. layer {

  1181.   name: "ssd0_transpose3"

  1182.   type: "Permute"

  1183.   bottom: "ssd0_convpredictor6_conv0"

  1184.   top: "ssd0_transpose3"

  1185.   permute_param {

  1186.     order: 0

  1187.     order: 2

  1188.     order: 3

  1189.     order: 1

  1190.   }

  1191. }

  1192. layer {

  1193.   name: "ssd0_flatten3"

  1194.   type: "Flatten"

  1195.   bottom: "ssd0_transpose3"

  1196.   top: "ssd0_flatten3"

  1197. }

  1198. layer {

  1199.   name: "ssd0_convpredictor8_conv0"

  1200.   type: "Convolution"

  1201.   bottom: "ssd0_expand_conv2"

  1202.   top: "ssd0_convpredictor8_conv0"

  1203.   convolution_param {

  1204.     num_output: 6

  1205.     bias_term: true

  1206.     pad: 1

  1207.     kernel_size: 3

  1208.     group: 1

  1209.     stride: 1

  1210.     dilation: 1

  1211.   }

  1212. }

  1213. layer {

  1214.   name: "ssd0_transpose4"

  1215.   type: "Permute"

  1216.   bottom: "ssd0_convpredictor8_conv0"

  1217.   top: "ssd0_transpose4"

  1218.   permute_param {

  1219.     order: 0

  1220.     order: 2

  1221.     order: 3

  1222.     order: 1

  1223.   }

  1224. }

  1225. layer {

  1226.   name: "ssd0_flatten4"

  1227.   type: "Flatten"

  1228.   bottom: "ssd0_transpose4"

  1229.   top: "ssd0_flatten4"

  1230. }

  1231. layer {

  1232.   name: "ssd0_convpredictor10_conv0"

  1233.   type: "Convolution"

  1234.   bottom: "ssd0_expand_conv3"

  1235.   top: "ssd0_convpredictor10_conv0"

  1236.   convolution_param {

  1237.     num_output: 6

  1238.     bias_term: true

  1239.     pad: 1

  1240.     kernel_size: 3

  1241.     group: 1

  1242.     stride: 1

  1243.     dilation: 1

  1244.   }

  1245. }

  1246. layer {

  1247.   name: "ssd0_transpose5"

  1248.   type: "Permute"

  1249.   bottom: "ssd0_convpredictor10_conv0"

  1250.   top: "ssd0_transpose5"

  1251.   permute_param {

  1252.     order: 0

  1253.     order: 2

  1254.     order: 3

  1255.     order: 1

  1256.   }

  1257. }

  1258. layer {

  1259.   name: "ssd0_flatten5"

  1260.   type: "Flatten"

  1261.   bottom: "ssd0_transpose5"

  1262.   top: "ssd0_flatten5"

  1263. }

  1264. layer {

  1265.   name: "ssd0_concat0"

  1266.   type: "Concat"

  1267.   bottom: "ssd0_flatten0"

  1268.   bottom: "ssd0_flatten1"

  1269.   bottom: "ssd0_flatten2"

  1270.   bottom: "ssd0_flatten3"

  1271.   bottom: "ssd0_flatten4"

  1272.   bottom: "ssd0_flatten5"

  1273.   top: "ssd0_concat0"

  1274.   concat_param {

  1275.     axis: 1

  1276.   }

  1277. }

  1278. layer {

  1279.   name: "ssd0_reshape6"

  1280.   type: "Reshape"

  1281.   bottom: "ssd0_concat0"

  1282.   top: "ssd0_reshape6"

  1283.   reshape_param {

  1284.     shape {

  1285.       dim: 0

  1286.       dim: -1

  1287.       dim: 2

  1288.     }

  1289.   }

  1290. }

  1291. layer {

  1292.   name: "softmax0"

  1293.   type: "Softmax"

  1294.   bottom: "ssd0_reshape6"

  1295.   top: "softmax0"

  1296.   softmax_param {

  1297.     axis: 2

  1298.   }

  1299. }

  1300. layer {

  1301.   name: "flatten0"

  1302.   type: "Flatten"

  1303.   bottom: "softmax0"

  1304.   top: "flatten0"

  1305. }

  1306. layer {

  1307.   name: "ssd0_mobilenet0_relu22_fwd_priorbox"

  1308.   type: "PriorBox"

  1309.   bottom: "ssd0_mobilenet0_conv22"

  1310.   bottom: "data"

  1311.   top: "ssd0_mobilenet0_relu22_fwd_priorbox"

  1312.   prior_box_param {

  1313.     min_size: 51.20000076293945

  1314.     max_size: 102.4000015258789

  1315.     aspect_ratio: 1.0

  1316.     aspect_ratio: 2.0

  1317.     flip: false

  1318.     clip: false

  1319.     variance: 0.10000000149011612

  1320.     variance: 0.10000000149011612

  1321.     variance: 0.20000000298023224

  1322.     variance: 0.20000000298023224

  1323.     offset: 0.5

  1324.   }

  1325. }

  1326. layer {

  1327.   name: "ssd0_mobilenet0_relu26_fwd_priorbox"

  1328.   type: "PriorBox"

  1329.   bottom: "ssd0_mobilenet0_conv26"

  1330.   bottom: "data"

  1331.   top: "ssd0_mobilenet0_relu26_fwd_priorbox"

  1332.   prior_box_param {

  1333.     min_size: 102.4000015258789

  1334.     max_size: 189.39999389648438

  1335.     aspect_ratio: 1.0

  1336.     aspect_ratio: 2.0

  1337.     aspect_ratio: 3.0

  1338.     flip: false

  1339.     clip: false

  1340.     variance: 0.10000000149011612

  1341.     variance: 0.10000000149011612

  1342.     variance: 0.20000000298023224

  1343.     variance: 0.20000000298023224

  1344.     offset: 0.5

  1345.   }

  1346. }

  1347. layer {

  1348.   name: "ssd0_expand_reu0_priorbox"

  1349.   type: "PriorBox"

  1350.   bottom: "ssd0_expand_conv0"

  1351.   bottom: "data"

  1352.   top: "ssd0_expand_reu0_priorbox"

  1353.   prior_box_param {

  1354.     min_size: 189.39999389648438

  1355.     max_size: 276.3999938964844

  1356.     aspect_ratio: 1.0

  1357.     aspect_ratio: 2.0

  1358.     aspect_ratio: 3.0

  1359.     flip: false

  1360.     clip: false

  1361.     variance: 0.10000000149011612

  1362.     variance: 0.10000000149011612

  1363.     variance: 0.20000000298023224

  1364.     variance: 0.20000000298023224

  1365.     offset: 0.5

  1366.   }

  1367. }

  1368. layer {

  1369.   name: "ssd0_expand_reu1_priorbox"

  1370.   type: "PriorBox"

  1371.   bottom: "ssd0_expand_conv1"

  1372.   bottom: "data"

  1373.   top: "ssd0_expand_reu1_priorbox"

  1374.   prior_box_param {

  1375.     min_size: 276.3999938964844

  1376.     max_size: 363.5199890136719

  1377.     aspect_ratio: 1.0

  1378.     aspect_ratio: 2.0

  1379.     aspect_ratio: 3.0

  1380.     flip: false

  1381.     clip: false

  1382.     variance: 0.10000000149011612

  1383.     variance: 0.10000000149011612

  1384.     variance: 0.20000000298023224

  1385.     variance: 0.20000000298023224

  1386.     offset: 0.5

  1387.   }

  1388. }

  1389. layer {

  1390.   name: "ssd0_expand_reu2_priorbox"

  1391.   type: "PriorBox"

  1392.   bottom: "ssd0_expand_conv2"

  1393.   bottom: "data"

  1394.   top: "ssd0_expand_reu2_priorbox"

  1395.   prior_box_param {

  1396.     min_size: 363.5199890136719

  1397.     max_size: 450.6000061035156

  1398.     aspect_ratio: 1.0

  1399.     aspect_ratio: 2.0

  1400.     flip: false

  1401.     clip: false

  1402.     variance: 0.10000000149011612

  1403.     variance: 0.10000000149011612

  1404.     variance: 0.20000000298023224

  1405.     variance: 0.20000000298023224

  1406.     offset: 0.5

  1407.   }

  1408. }

  1409. layer {

  1410.   name: "ssd0_expand_reu3_priorbox"

  1411.   type: "PriorBox"

  1412.   bottom: "ssd0_expand_conv3"

  1413.   bottom: "data"

  1414.   top: "ssd0_expand_reu3_priorbox"

  1415.   prior_box_param {

  1416.     min_size: 450.6000061035156

  1417.     max_size: 492.0

  1418.     aspect_ratio: 1.0

  1419.     aspect_ratio: 2.0

  1420.     flip: false

  1421.     clip: false

  1422.     variance: 0.10000000149011612

  1423.     variance: 0.10000000149011612

  1424.     variance: 0.20000000298023224

  1425.     variance: 0.20000000298023224

  1426.     offset: 0.5

  1427.   }

  1428. }

  1429. layer {

  1430.   name: "concat0"

  1431.   type: "Concat"

  1432.   bottom: "ssd0_mobilenet0_relu22_fwd_priorbox"

  1433.   bottom: "ssd0_mobilenet0_relu26_fwd_priorbox"

  1434.   bottom: "ssd0_expand_reu0_priorbox"

  1435.   bottom: "ssd0_expand_reu1_priorbox"

  1436.   bottom: "ssd0_expand_reu2_priorbox"

  1437.   bottom: "ssd0_expand_reu3_priorbox"

  1438.   top: "concat0"

  1439.   concat_param {

  1440.     axis: 2

  1441.   }

  1442. }

  1443. layer {

  1444.   name: "detection_out"

  1445.   type: "DetectionOutput"

  1446.   bottom: "ssd0_concat1"

  1447.   bottom: "flatten0"

  1448.   bottom: "concat0"

  1449.   top: "detection_out"

  1450.   detection_output_param {

  1451.     num_classes: 2

  1452.     share_location: true

  1453.     background_label_id: 0

  1454.     nms_param {

  1455.       nms_threshold: 0.44999998807907104

  1456.       top_k: 400

  1457.     }

  1458.     code_type: CENTER_SIZE

  1459.     keep_top_k: 100

  1460.     confidence_threshold: 0.009999999776482582

  1461.   }

  1462. }