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wangsheng1991
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AgeAndGenderEstimation
mirror of https://github.com/wangsheng1991/AgeAndGenderEstimation.git

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  1. # Fully convolutional network version of CaffeNet.
    2. 

  2. name: "CaffeNetConv"
    3. 

  3. input: "data"
    4. 

  4. input_dim: 1
    5. 

  5. input_dim: 3
    6. 

  6. input_dim: 227
    7. 

  7. input_dim: 227
    8. 

  8. layer {
    9. 

  9.   name: "conv1"
    10. 

  10.   type: "Convolution"
    11. 

  11.   bottom: "data"
    12. 

  12.   top: "conv1"
    13. 

  13.   convolution_param {
    14. 

  14.     num_output: 96
    15. 

  15.     kernel_size: 11
    16. 

  16.     stride: 4
    17. 

  17.   }
    18. 

  18. }
    19. 

  19. layer {
    20. 

  20.   name: "relu1"
    21. 

  21.   type: "ReLU"
    22. 

  22.   bottom: "conv1"
    23. 

  23.   top: "conv1"
    24. 

  24. }
    25. 

  25. layer {
    26. 

  26.   name: "pool1"
    27. 

  27.   type: "Pooling"
    28. 

  28.   bottom: "conv1"
    29. 

  29.   top: "pool1"
    30. 

  30.   pooling_param {
    31. 

  31.     pool: MAX
    32. 

  32.     kernel_size: 3
    33. 

  33.     stride: 2
    34. 

  34.   }
    35. 

  35. }
    36. 

  36. layer {
    37. 

  37.   name: "norm1"
    38. 

  38.   type: "LRN"
    39. 

  39.   bottom: "pool1"
    40. 

  40.   top: "norm1"
    41. 

  41.   lrn_param {
    42. 

  42.     local_size: 5
    43. 

  43.     alpha: 0.0001
    44. 

  44.     beta: 0.75
    45. 

  45.   }
    46. 

  46. }
    47. 

  47. layer {
    48. 

  48.   name: "conv2"
    49. 

  49.   type: "Convolution"
    50. 

  50.   bottom: "norm1"
    51. 

  51.   top: "conv2"
    52. 

  52.   convolution_param {
    53. 

  53.     num_output: 256
    54. 

  54.     pad: 2
    55. 

  55.     kernel_size: 5
    56. 

  56.     group: 2
    57. 

  57.   }
    58. 

  58. }
    59. 

  59. layer {
    60. 

  60.   name: "relu2"
    61. 

  61.   type: "ReLU"
    62. 

  62.   bottom: "conv2"
    63. 

  63.   top: "conv2"
    64. 

  64. }
    65. 

  65. layer {
    66. 

  66.   name: "pool2"
    67. 

  67.   type: "Pooling"
    68. 

  68.   bottom: "conv2"
    69. 

  69.   top: "pool2"
    70. 

  70.   pooling_param {
    71. 

  71.     pool: MAX
    72. 

  72.     kernel_size: 3
    73. 

  73.     stride: 2
    74. 

  74.   }
    75. 

  75. }
    76. 

  76. layer {
    77. 

  77.   name: "norm2"
    78. 

  78.   type: "LRN"
    79. 

  79.   bottom: "pool2"
    80. 

  80.   top: "norm2"
    81. 

  81.   lrn_param {
    82. 

  82.     local_size: 5
    83. 

  83.     alpha: 0.0001
    84. 

  84.     beta: 0.75
    85. 

  85.   }
    86. 

  86. }
    87. 

  87. layer {
    88. 

  88.   name: "conv3"
    89. 

  89.   type: "Convolution"
    90. 

  90.   bottom: "norm2"
    91. 

  91.   top: "conv3"
    92. 

  92.   convolution_param {
    93. 

  93.     num_output: 384
    94. 

  94.     pad: 1
    95. 

  95.     kernel_size: 3
    96. 

  96.   }
    97. 

  97. }
    98. 

  98. layer {
    99. 

  99.   name: "relu3"
    100. 

  100.   type: "ReLU"
    101. 

  101.   bottom: "conv3"
    102. 

  102.   top: "conv3"
    103. 

  103. }
    104. 

  104. layer {
    105. 

  105.   name: "conv4"
    106. 

  106.   type: "Convolution"
    107. 

  107.   bottom: "conv3"
    108. 

  108.   top: "conv4"
    109. 

  109.   convolution_param {
    110. 

  110.     num_output: 384
    111. 

  111.     pad: 1
    112. 

  112.     kernel_size: 3
    113. 

  113.     group: 2
    114. 

  114.   }
    115. 

  115. }
    116. 

  116. layer {
    117. 

  117.   name: "relu4"
    118. 

  118.   type: "ReLU"
    119. 

  119.   bottom: "conv4"
    120. 

  120.   top: "conv4"
    121. 

  121. }
    122. 

  122. layer {
    123. 

  123.   name: "conv5"
    124. 

  124.   type: "Convolution"
    125. 

  125.   bottom: "conv4"
    126. 

  126.   top: "conv5"
    127. 

  127.   convolution_param {
    128. 

  128.     num_output: 256
    129. 

  129.     pad: 1
    130. 

  130.     kernel_size: 3
    131. 

  131.     group: 2
    132. 

  132.   }
    133. 

  133. }
    134. 

  134. layer {
    135. 

  135.   name: "relu5"
    136. 

  136.   type: "ReLU"
    137. 

  137.   bottom: "conv5"
    138. 

  138.   top: "conv5"
    139. 

  139. }
    140. 

  140. layer {
    141. 

  141.   name: "pool5"
    142. 

  142.   type: "Pooling"
    143. 

  143.   bottom: "conv5"
    144. 

  144.   top: "pool5"
    145. 

  145.   pooling_param {
    146. 

  146.     pool: MAX
    147. 

  147.     kernel_size: 3
    148. 

  148.     stride: 2
    149. 

  149.   }
    150. 

  150. }
    151. 

  151. layer {
    152. 

  152.   name: "fc6-conv"
    153. 

  153.   type: "Convolution"
    154. 

  154.   bottom: "pool5"
    155. 

  155.   top: "fc6-conv"
    156. 

  156.   convolution_param {
    157. 

  157.     num_output: 4096
    158. 

  158.     kernel_size: 6
    159. 

  159.   }
    160. 

  160. }
    161. 

  161. layer {
    162. 

  162.   name: "relu6"
    163. 

  163.   type: "ReLU"
    164. 

  164.   bottom: "fc6-conv"
    165. 

  165.   top: "fc6-conv"
    166. 

  166. }
    167. 

  167. layer {
    168. 

  168.   name: "drop6"
    169. 

  169.   type: "Dropout"
    170. 

  170.   bottom: "fc6-conv"
    171. 

  171.   top: "fc6-conv"
    172. 

  172.   dropout_param {
    173. 

  173.     dropout_ratio: 0.5
    174. 

  174.   }
    175. 

  175. }
    176. 

  176. layer {
    177. 

  177.   name: "fc7-conv"
    178. 

  178.   type: "Convolution"
    179. 

  179.   bottom: "fc6-conv"
    180. 

  180.   top: "fc7-conv"
    181. 

  181.   convolution_param {
    182. 

  182.     num_output: 4096
    183. 

  183.     kernel_size: 1
    184. 

  184.   }
    185. 

  185. }
    186. 

  186. layer {
    187. 

  187.   name: "relu7"
    188. 

  188.   type: "ReLU"
    189. 

  189.   bottom: "fc7-conv"
    190. 

  190.   top: "fc7-conv"
    191. 

  191. }
    192. 

  192. layer {
    193. 

  193.   name: "drop7"
    194. 

  194.   type: "Dropout"
    195. 

  195.   bottom: "fc7-conv"
    196. 

  196.   top: "fc7-conv"
    197. 

  197.   dropout_param {
    198. 

  198.     dropout_ratio: 0.5
    199. 

  199.   }
    200. 

  200. }
    201. 

  201. layer {
    202. 

  202.   name: "fc8-conv"
    203. 

  203.   type: "Convolution"
    204. 

  204.   bottom: "fc7-conv"
    205. 

  205.   top: "fc8-conv"
    206. 

  206.   convolution_param {
    207. 

  207.     num_output: 2
    208. 

  208.     kernel_size: 1
    209. 

  209.   }
    210. 

  210. }
    211. 

  211. layer {
    212. 

  212.   name: "prob"
    213. 

  213.   type: "Softmax"
    214. 

  214.   bottom: "fc8-conv"
    215. 

  215.   top: "prob"
    216. 

  216. }
    217.
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