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Bananaware

classificação qualidade fine-tuning

Bananaware
Jan 24th, 2020 (edited)
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  1. tl;dr:
  2. - resultado novo com fine-tuning: accuracy 85.44% 3387/3964 (antigo 78.13% 3097/3964)
  3. - mesmas condições da execução anterior:
  4. - input: 3964/12453 detecções de narinas da YOLO-Lucas nas Bases_Purunã (2088 ruins, 1876 boas)
  5. - 5-fold cross-validation
  6.  
  7. tuning - congelados primeiros 2 blocos de convolução:
  8. sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
  9. model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
  10.  
  11.  
  12. modelo:
  13.  
  14. bois_finetune-v4
  15. Model: "sequential_1"
  16. _________________________________________________________________
  17. Layer (type) Output Shape Param #
  18. =================================================================
  19. ============
  20. frozen
  21. ============
  22. zero_padding2d_1 (ZeroPaddin (None, 226, 226, 3) 0
  23. _________________________________________________________________
  24. conv2d_1 (Conv2D) (None, 224, 224, 64) 1792
  25. _________________________________________________________________
  26. zero_padding2d_2 (ZeroPaddin (None, 226, 226, 64) 0
  27. _________________________________________________________________
  28. conv2d_2 (Conv2D) (None, 224, 224, 64) 36928
  29. _________________________________________________________________
  30. max_pooling2d_1 (MaxPooling2 (None, 112, 112, 64) 0
  31. _________________________________________________________________
  32. zero_padding2d_3 (ZeroPaddin (None, 114, 114, 64) 0
  33. _________________________________________________________________
  34. conv2d_3 (Conv2D) (None, 112, 112, 128) 73856
  35. _________________________________________________________________
  36. zero_padding2d_4 (ZeroPaddin (None, 114, 114, 128) 0
  37. _________________________________________________________________
  38. conv2d_4 (Conv2D) (None, 112, 112, 128) 147584
  39. _________________________________________________________________
  40. max_pooling2d_2 (MaxPooling2 (None, 56, 56, 128) 0
  41. _________________________________________________________________
  42. zero_padding2d_5 (ZeroPaddin (None, 58, 58, 128) 0
  43. _________________________________________________________________
  44. ============
  45. tuning a partir daqui
  46. ============
  47. conv2d_5 (Conv2D) (None, 56, 56, 256) 295168
  48. _________________________________________________________________
  49. zero_padding2d_6 (ZeroPaddin (None, 58, 58, 256) 0
  50. _________________________________________________________________
  51. conv2d_6 (Conv2D) (None, 56, 56, 256) 590080
  52. _________________________________________________________________
  53. zero_padding2d_7 (ZeroPaddin (None, 58, 58, 256) 0
  54. _________________________________________________________________
  55. conv2d_7 (Conv2D) (None, 56, 56, 256) 590080
  56. _________________________________________________________________
  57. max_pooling2d_3 (MaxPooling2 (None, 28, 28, 256) 0
  58. _________________________________________________________________
  59. zero_padding2d_8 (ZeroPaddin (None, 30, 30, 256) 0
  60. _________________________________________________________________
  61. conv2d_8 (Conv2D) (None, 28, 28, 512) 1180160
  62. _________________________________________________________________
  63. zero_padding2d_9 (ZeroPaddin (None, 30, 30, 512) 0
  64. _________________________________________________________________
  65. conv2d_9 (Conv2D) (None, 28, 28, 512) 2359808
  66. _________________________________________________________________
  67. zero_padding2d_10 (ZeroPaddi (None, 30, 30, 512) 0
  68. _________________________________________________________________
  69. conv2d_10 (Conv2D) (None, 28, 28, 512) 2359808
  70. _________________________________________________________________
  71. max_pooling2d_4 (MaxPooling2 (None, 14, 14, 512) 0
  72. _________________________________________________________________
  73. zero_padding2d_11 (ZeroPaddi (None, 16, 16, 512) 0
  74. _________________________________________________________________
  75. conv2d_11 (Conv2D) (None, 14, 14, 512) 2359808
  76. _________________________________________________________________
  77. zero_padding2d_12 (ZeroPaddi (None, 16, 16, 512) 0
  78. _________________________________________________________________
  79. conv2d_12 (Conv2D) (None, 14, 14, 512) 2359808
  80. _________________________________________________________________
  81. zero_padding2d_13 (ZeroPaddi (None, 16, 16, 512) 0
  82. _________________________________________________________________
  83. conv2d_13 (Conv2D) (None, 14, 14, 512) 2359808
  84. _________________________________________________________________
  85. max_pooling2d_5 (MaxPooling2 (None, 7, 7, 512) 0
  86. _________________________________________________________________
  87. ============
  88. classifier
  89. ============
  90. flatten_1 (Flatten) (None, 25088) 0
  91. _________________________________________________________________
  92. dense_1 (Dense) (None, 4096) 102764544
  93. _________________________________________________________________
  94. dropout_1 (Dropout) (None, 4096) 0
  95. _________________________________________________________________
  96. dense_2 (Dense) (None, 4096) 16781312
  97. _________________________________________________________________
  98. dropout_2 (Dropout) (None, 4096) 0
  99. _________________________________________________________________
  100. dense_3 (Dense) (None, 1000) 4097000
  101. _________________________________________________________________
  102. dense_4 (Dense) (None, 2) 2002
  103. =================================================================
  104.  
  105.  
  106.  
  107.  
  108. random logs:
  109.  
  110.  
  111. Epoch 13/14
  112. 16/3567 [..............................] - ETA: 18:25 - loss: 0.4921 - accuracy: 0.8 32/3567 [..............................] - ETA: 18:18 - loss: 0.3536 - accuracy: 0.8 48/3567 [..............................] - ETA: 18:12 - loss: 0.3305 - accuracy: 0.8 64/3567 [..............................] - ETA: 18:08 - loss: 0.2993 - accuracy: 0.9 80/3567 [..............................] - ETA: 18:03 - loss: 0.3334 - accuracy: 0.8 96/3567 [..............................] - ETA: 17:58 - loss: 0.3063 - accuracy: 0.9 112/3567 [..............................] - ETA: 17:54 - loss: 0.3383 - accuracy: 0.8 128/3567 [>.............................] - ETA: 17:49 - loss: 0.3560 - accuracy: 0.8 144/3567 [>.............................] - ETA: 17:43 - loss: 0.3557 - accuracy: 0.8 160/3567 [>.............................] - ETA: 17:38 - loss: 0.3555 - accuracy: 0.8 176/3567 [>.............................] - ETA: 17:34 - loss: 0.3390 - accuracy: 0.8 192/3567 [>.............................] - ETA: 17:29 - 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  113. Epoch 14/14
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  115. Train Feature Extraction...
  116. Train feature extraction finished in 389.860070 seconds.
  117. Test feature extraction finished in 97.165978 seconds.
  118. precision recall f1-score support
  119.  
  120. 0 0.86 0.86 0.86 418
  121. 1 0.84 0.85 0.84 376
  122.  
  123. accuracy 0.85 794
  124. macro avg 0.85 0.85 0.85 794
  125. weighted avg 0.85 0.85 0.85 794
  126.  
  127. Acc: 0.8526448362720404 794 677.0
  128. Train Feature Extraction...
  129. Train feature extraction finished in 387.882236 seconds.
  130. Test feature extraction finished in 96.936096 seconds.
  131. precision recall f1-score support
  132.  
  133. 0 0.85 0.85 0.85 418
  134. 1 0.83 0.83 0.83 375
  135.  
  136. accuracy 0.84 793
  137. macro avg 0.84 0.84 0.84 793
  138. weighted avg 0.84 0.84 0.84 793
  139.  
  140. Acc: 0.8411097099621689 793 667.0
  141. Train Feature Extraction...
  142. Train feature extraction finished in 387.630016 seconds.
  143. Test feature extraction finished in 96.930455 seconds.
  144. precision recall f1-score support
  145.  
  146. 0 0.89 0.86 0.87 418
  147. 1 0.85 0.88 0.86 375
  148.  
  149. accuracy 0.87 793
  150. macro avg 0.87 0.87 0.87 793
  151. weighted avg 0.87 0.87 0.87 793
  152.  
  153. Acc: 0.8688524590163934 793 689.0
  154. Train Feature Extraction...
  155. Train feature extraction finished in 387.783374 seconds.
  156. Test feature extraction finished in 96.856200 seconds.
  157. precision recall f1-score support
  158.  
  159. 0 0.87 0.86 0.87 417
  160. 1 0.85 0.86 0.85 375
  161.  
  162. accuracy 0.86 792
  163. macro avg 0.86 0.86 0.86 792
  164. weighted avg 0.86 0.86 0.86 792
  165.  
  166. Acc: 0.8611111111111112 792 682.0
  167. Train Feature Extraction...
  168. Train feature extraction finished in 387.778629 seconds.
  169. Test feature extraction finished in 96.868554 seconds.
  170. precision recall f1-score support
  171.  
  172. 0 0.85 0.87 0.86 417
  173. 1 0.85 0.83 0.84 375
  174.  
  175. accuracy 0.85 792
  176. macro avg 0.85 0.85 0.85 792
  177. weighted avg 0.85 0.85 0.85 792
  178.  
  179. Acc: 0.8484848484848485 792 672.0
  180. End: 3387 3964 0.8544399596367306
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