yolov3-tiny_3l.cfg 2.4 KB

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  1. [net]
  2. # Testing
  3. # batch=1
  4. # subdivisions=1
  5. # Training
  6. batch=64
  7. subdivisions=16
  8. width=608
  9. height=608
  10. channels=3
  11. momentum=0.9
  12. decay=0.0005
  13. angle=0
  14. saturation = 1.5
  15. exposure = 1.5
  16. hue=.1
  17. learning_rate=0.001
  18. burn_in=1000
  19. max_batches = 200000
  20. policy=steps
  21. steps=180000,190000
  22. scales=.1,.1
  23. [convolutional]
  24. batch_normalize=1
  25. filters=16
  26. size=3
  27. stride=1
  28. pad=1
  29. activation=leaky
  30. [maxpool]
  31. size=2
  32. stride=2
  33. [convolutional]
  34. batch_normalize=1
  35. filters=32
  36. size=3
  37. stride=1
  38. pad=1
  39. activation=leaky
  40. [maxpool]
  41. size=2
  42. stride=2
  43. [convolutional]
  44. batch_normalize=1
  45. filters=64
  46. size=3
  47. stride=1
  48. pad=1
  49. activation=leaky
  50. [maxpool]
  51. size=2
  52. stride=2
  53. [convolutional]
  54. batch_normalize=1
  55. filters=128
  56. size=3
  57. stride=1
  58. pad=1
  59. activation=leaky
  60. [maxpool]
  61. size=2
  62. stride=2
  63. [convolutional]
  64. batch_normalize=1
  65. filters=256
  66. size=3
  67. stride=1
  68. pad=1
  69. activation=leaky
  70. [maxpool]
  71. size=2
  72. stride=2
  73. [convolutional]
  74. batch_normalize=1
  75. filters=512
  76. size=3
  77. stride=1
  78. pad=1
  79. activation=leaky
  80. [maxpool]
  81. size=2
  82. stride=1
  83. [convolutional]
  84. batch_normalize=1
  85. filters=1024
  86. size=3
  87. stride=1
  88. pad=1
  89. activation=leaky
  90. ###########
  91. [convolutional]
  92. batch_normalize=1
  93. filters=256
  94. size=1
  95. stride=1
  96. pad=1
  97. activation=leaky
  98. [convolutional]
  99. batch_normalize=1
  100. filters=512
  101. size=3
  102. stride=1
  103. pad=1
  104. activation=leaky
  105. [convolutional]
  106. size=1
  107. stride=1
  108. pad=1
  109. filters=21
  110. activation=linear
  111. [yolo]
  112. mask = 6,7,8
  113. anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332
  114. classes=2
  115. num=9
  116. jitter=.3
  117. ignore_thresh = .7
  118. truth_thresh = 1
  119. random=1
  120. [route]
  121. layers = -4
  122. [convolutional]
  123. batch_normalize=1
  124. filters=128
  125. size=1
  126. stride=1
  127. pad=1
  128. activation=leaky
  129. [upsample]
  130. stride=2
  131. [route]
  132. layers = -1, 8
  133. [convolutional]
  134. batch_normalize=1
  135. filters=256
  136. size=3
  137. stride=1
  138. pad=1
  139. activation=leaky
  140. [convolutional]
  141. size=1
  142. stride=1
  143. pad=1
  144. filters=21
  145. activation=linear
  146. [yolo]
  147. mask = 3,4,5
  148. anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332
  149. classes=2
  150. num=9
  151. jitter=.3
  152. ignore_thresh = .7
  153. truth_thresh = 1
  154. random=1
  155. [route]
  156. layers = -3
  157. [convolutional]
  158. batch_normalize=1
  159. filters=128
  160. size=1
  161. stride=1
  162. pad=1
  163. activation=leaky
  164. [upsample]
  165. stride=2
  166. [route]
  167. layers = -1, 6
  168. [convolutional]
  169. batch_normalize=1
  170. filters=128
  171. size=3
  172. stride=1
  173. pad=1
  174. activation=leaky
  175. [convolutional]
  176. size=1
  177. stride=1
  178. pad=1
  179. filters=21
  180. activation=linear
  181. [yolo]
  182. mask = 0,1,2
  183. anchors = 4,7, 7,15, 13,25, 25,42, 41,67, 75,94, 91,162, 158,205, 250,332
  184. classes=2
  185. num=9
  186. jitter=.3
  187. ignore_thresh = .7
  188. truth_thresh = 1
  189. random=1