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ivan-zykov95

64 model evaluation

ivan-zykov95
Mar 17th, 2019
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  1. ## Old model with old image
  2.  
  3. ========================Evaluation Metrics========================
  4. # of classes: 24
  5. Accuracy: 0.9471
  6. Precision: 0.9488
  7. Recall: 0.9423
  8. F1 Score: 0.9449
  9. Precision, recall & F1: macro-averaged (equally weighted avg. of 24 classes)
  10.  
  11.  
  12. =========================Confusion Matrix=========================
  13. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
  14. -------------------------------------------------------------------------------------------------
  15. 204 0 0 0 0 0 5 0 0 0 0 0 0 0 0 0 0 1 4 0 0 0 0 0 | 0 = 0
  16. 0 215 0 0 0 3 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 1 = 1
  17. 3 0 231 0 0 0 0 0 0 0 0 0 0 3 0 1 0 0 0 0 0 0 0 0 | 2 = 2
  18. 0 0 0 206 1 0 0 0 0 0 1 0 0 1 0 0 0 0 0 1 4 0 0 0 | 3 = 3
  19. 0 0 0 0 214 0 0 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 | 4 = 4
  20. 0 0 0 0 1 207 0 0 0 0 0 0 0 0 0 0 0 1 0 0 0 0 0 0 | 5 = 5
  21. 0 0 0 0 1 0 199 10 0 0 1 0 0 0 1 0 0 0 2 0 0 1 0 0 | 6 = 6
  22. 0 0 0 0 0 0 12 260 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 | 7 = 7
  23. 0 0 0 0 1 0 0 0 209 0 0 0 0 0 0 0 0 0 0 0 0 0 0 1 | 8 = 8
  24. 0 3 0 0 0 0 3 0 0 115 2 0 0 0 1 0 0 0 0 3 0 3 0 0 | 9 = 9
  25. 0 0 0 0 1 0 0 0 0 0 222 0 0 0 0 0 0 0 0 0 0 0 0 1 | 10 = 10
  26. 0 0 0 0 0 0 0 0 1 0 0 208 0 0 0 0 0 5 4 0 0 1 0 0 | 11 = 11
  27. 0 0 0 0 2 2 0 0 0 0 0 5 200 2 0 0 0 1 5 0 0 0 0 0 | 12 = 12
  28. 0 1 0 0 5 0 0 0 0 0 0 0 1 198 0 3 0 3 2 0 0 0 0 0 | 13 = 13
  29. 0 0 0 0 0 0 0 0 0 0 1 1 0 0 218 5 0 0 0 0 0 0 2 1 | 14 = 14
  30. 0 0 1 0 0 0 0 1 1 0 0 0 0 1 2 207 0 0 0 0 0 0 1 1 | 15 = 15
  31. 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 64 0 0 8 4 0 2 0 | 16 = 16
  32. 3 1 0 0 7 0 0 0 1 0 0 6 0 4 0 0 0 199 4 0 0 0 1 0 | 17 = 17
  33. 2 0 0 0 0 0 0 1 0 0 0 2 7 0 0 0 0 6 192 0 0 0 0 0 | 18 = 18
  34. 0 4 0 0 0 1 0 0 0 0 1 0 0 0 0 0 2 1 0 190 7 7 0 0 | 19 = 19
  35. 0 3 0 0 0 3 0 0 0 1 1 0 0 0 0 2 0 0 0 3 197 11 0 1 | 20 = 20
  36. 0 1 0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 257 1 0 | 21 = 21
  37. 0 0 0 0 0 4 1 0 0 0 0 1 0 0 0 0 0 0 1 1 1 1 211 0 | 22 = 22
  38. 0 0 0 0 0 0 1 1 2 0 0 0 0 1 2 0 0 0 0 0 0 0 1 207 | 23 = 23
  39.  
  40. Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
  41. ==================================================================
  42.  
  43. ## New model with old images
  44.  
  45. ========================Evaluation Metrics========================
  46. # of classes: 24
  47. Accuracy: 0.6618
  48. Precision: 0.7333
  49. Recall: 0.6590
  50. F1 Score: 0.6466
  51. Precision, recall & F1: macro-averaged (equally weighted avg. of 24 classes)
  52.  
  53.  
  54. =========================Confusion Matrix=========================
  55. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
  56. -------------------------------------------------------------------------------------------------
  57. 200 0 6 0 0 0 2 0 0 0 1 0 0 0 3 1 0 1 0 0 0 0 0 0 | 0 = 0
  58. 7 62 27 0 0 7 0 0 0 17 6 0 4 7 1 0 0 1 0 76 2 2 1 0 | 1 = 1
  59. 3 0 229 1 0 0 0 0 0 0 0 0 1 1 1 2 0 0 0 0 0 0 0 0 | 2 = 2
  60. 0 0 10 185 2 0 0 0 0 2 2 0 0 0 3 0 0 0 0 3 0 0 7 0 | 3 = 3
  61. 14 0 45 7 93 0 0 0 1 5 7 1 18 1 0 0 0 13 9 0 0 0 1 0 | 4 = 4
  62. 1 2 4 5 1 106 0 0 4 34 10 0 1 0 0 2 0 5 0 18 2 0 9 5 | 5 = 5
  63. 13 0 4 0 0 0 128 9 0 3 32 0 3 0 13 4 0 1 0 0 0 0 2 3 | 6 = 6
  64. 43 0 1 3 0 0 46 117 0 0 6 0 0 0 23 33 0 0 0 0 0 0 0 1 | 7 = 7
  65. 1 0 1 22 0 0 1 1 113 18 12 0 1 1 1 1 0 0 2 2 1 0 31 2 | 8 = 8
  66. 0 0 1 5 0 0 0 0 1 99 6 0 0 0 0 0 0 0 0 6 6 0 6 0 | 9 = 9
  67. 2 0 1 3 0 0 0 0 0 3 214 0 0 0 0 1 0 0 0 0 0 0 0 0 | 10 = 10
  68. 8 0 17 2 1 0 0 0 0 0 0 104 26 1 3 0 0 23 26 0 0 0 1 7 | 11 = 11
  69. 7 0 3 4 1 0 0 0 0 2 0 1 171 2 3 0 0 3 6 1 0 0 9 4 | 12 = 12
  70. 3 0 29 1 0 0 0 0 0 0 1 4 5 142 1 5 0 14 7 0 0 0 0 1 | 13 = 13
  71. 0 0 4 2 0 0 0 1 0 4 10 0 0 1 181 23 0 0 0 0 0 0 1 1 | 14 = 14
  72. 2 0 1 0 0 0 0 1 0 0 8 0 0 0 6 195 0 0 0 0 0 0 0 2 | 15 = 15
  73. 0 0 0 11 0 0 0 0 1 3 1 0 0 0 0 0 28 0 0 31 0 0 3 0 | 16 = 16
  74. 15 0 3 1 0 0 0 0 0 0 0 5 10 13 1 1 0 156 19 0 0 0 1 1 | 17 = 17
  75. 7 0 7 1 0 0 0 0 0 2 0 1 45 8 4 0 0 20 98 0 0 0 15 2 | 18 = 18
  76. 0 0 0 1 0 0 0 0 0 9 1 0 0 0 0 0 4 1 0 196 0 0 1 0 | 19 = 19
  77. 2 0 2 14 0 1 0 0 1 4 4 0 0 0 2 2 0 1 0 90 94 0 4 1 | 20 = 20
  78. 0 0 5 2 1 5 0 0 0 18 4 0 0 0 0 0 0 3 0 94 31 84 13 0 | 21 = 21
  79. 0 0 0 5 0 0 0 0 0 5 6 0 1 1 11 0 0 0 0 0 2 0 190 0 | 22 = 22
  80. 1 0 1 0 0 0 0 1 1 1 6 0 1 1 6 2 0 3 0 0 0 0 1 190 | 23 = 23
  81.  
  82. Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
  83. ==================================================================
  84.  
  85. ## New model with new images
  86.  
  87. ========================Evaluation Metrics========================
  88. # of classes: 24
  89. Accuracy: 0.6875
  90. Precision: 0.7456
  91. Recall: 0.6700
  92. F1 Score: 0.6642
  93. Precision, recall & F1: macro-averaged (equally weighted avg. of 24 classes)
  94.  
  95.  
  96. =========================Confusion Matrix=========================
  97. 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23
  98. -------------------------------------------------------------------------------------------------
  99. 323 0 9 0 0 0 2 0 0 0 1 0 0 0 3 1 0 1 0 0 0 0 0 0 | 0 = 0
  100. 13 160 29 0 0 7 0 0 0 17 12 0 4 7 1 0 0 1 0 76 2 2 1 0 | 1 = 1
  101. 3 0 355 1 0 0 0 0 0 0 0 0 1 1 1 2 0 0 0 0 0 0 0 0 | 2 = 2
  102. 4 0 11 307 2 0 0 0 0 2 2 0 0 0 3 0 0 0 0 3 0 0 7 0 | 3 = 3
  103. 14 0 45 7 93 0 0 0 1 5 7 1 18 1 0 0 0 13 9 0 0 0 1 0 | 4 = 4
  104. 1 2 4 5 1 106 0 0 4 34 10 0 1 0 0 2 0 5 0 18 2 0 9 5 | 5 = 5
  105. 13 0 4 0 0 0 128 9 0 3 32 0 3 0 13 4 0 1 0 0 0 0 2 3 | 6 = 6
  106. 43 0 1 3 0 0 46 117 0 0 6 0 0 0 23 33 0 0 0 0 0 0 0 1 | 7 = 7
  107. 1 0 1 22 0 0 1 1 113 18 12 0 1 1 1 1 0 0 2 2 1 0 31 2 | 8 = 8
  108. 0 0 1 5 0 0 0 0 1 99 6 0 0 0 0 0 0 0 0 6 6 0 6 0 | 9 = 9
  109. 2 0 1 3 0 0 0 0 0 3 214 0 0 0 0 1 0 0 0 0 0 0 0 0 | 10 = 10
  110. 8 0 17 2 1 0 0 0 0 0 0 104 26 1 3 0 0 23 26 0 0 0 1 7 | 11 = 11
  111. 7 0 3 4 1 0 0 0 0 2 0 1 171 2 3 0 0 3 6 1 0 0 9 4 | 12 = 12
  112. 3 0 29 1 0 0 0 0 0 0 1 4 5 142 1 5 0 14 7 0 0 0 0 1 | 13 = 13
  113. 0 0 4 2 0 0 0 1 0 4 10 0 0 1 181 23 0 0 0 0 0 0 1 1 | 14 = 14
  114. 2 0 1 0 0 0 0 1 0 0 8 0 0 0 6 195 0 0 0 0 0 0 0 2 | 15 = 15
  115. 0 0 0 11 0 0 0 0 1 3 1 0 0 0 0 0 28 0 0 31 0 0 3 0 | 16 = 16
  116. 15 0 3 1 0 0 0 0 0 0 0 5 10 13 1 1 0 156 19 0 0 0 1 1 | 17 = 17
  117. 7 0 7 1 0 0 0 0 0 2 0 1 45 8 4 0 0 20 98 0 0 0 15 2 | 18 = 18
  118. 0 0 0 1 0 0 0 0 0 9 1 0 0 0 0 0 4 1 0 196 0 0 1 0 | 19 = 19
  119. 2 0 2 14 0 1 0 0 1 4 4 0 0 0 2 2 0 1 0 90 94 0 4 1 | 20 = 20
  120. 0 0 5 2 1 5 0 0 0 18 4 0 0 0 0 0 0 3 0 94 31 84 13 0 | 21 = 21
  121. 0 0 0 5 0 0 0 0 0 5 6 0 1 1 11 0 0 0 0 0 2 0 190 0 | 22 = 22
  122. 1 0 1 0 0 0 0 1 1 1 6 0 1 1 6 2 0 3 0 0 0 0 1 190 | 23 = 23
  123.  
  124. Confusion matrix format: Actual (rowClass) predicted as (columnClass) N times
  125. ==================================================================
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