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metrics.json 2.2 KB

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  1. {{
  2. "Best_Threshold": 0.49,
  3. "Confusion_Matrix": [[246, 308], [166, 123]],
  4. "Classification_Report MobileNet": {
  5. "No_Fire": {
  6. "precision": 0.60,
  7. "recall": 0.44,
  8. "f1-score": 0.51,
  9. "support": 554
  10. },
  11. "Fire": {
  12. "precision": 0.29,
  13. "recall": 0.43,
  14. "f1-score": 0.34,
  15. "support": 289
  16. },
  17. "accuracy": 0.44,
  18. "macro_avg": {
  19. "precision": 0.44,
  20. "recall": 0.43,
  21. "f1-score": 0.43,
  22. "support": 843
  23. },
  24. "weighted_avg": {
  25. "precision": 0.49,
  26. "recall": 0.44,
  27. "f1-score": 0.45,
  28. "support": 843
  29. }
  30. }
  31. },
  32. {
  33. "Best_Threshold": 0.93,
  34. "Confusion_Matrix": [[310, 244], [112, 177]],
  35. "Classification_Report EfficientNet": {
  36. "No_Fire": {
  37. "precision": 0.73,
  38. "recall": 0.56,
  39. "f1-score": 0.64,
  40. "support": 554
  41. },
  42. "Fire": {
  43. "precision": 0.42,
  44. "recall": 0.61,
  45. "f1-score": 0.50,
  46. "support": 289
  47. },
  48. "accuracy": 0.58,
  49. "macro_avg": {
  50. "precision": 0.58,
  51. "recall": 0.59,
  52. "f1-score": 0.57,
  53. "support": 843
  54. },
  55. "weighted_avg": {
  56. "precision": 0.63,
  57. "recall": 0.58,
  58. "f1-score": 0.59,
  59. "support": 843
  60. }
  61. }
  62. },
  63. {
  64. "Best_Threshold": 0.98,
  65. "Confusion_Matrix": [[266, 288], [142, 147]],
  66. "Classification_Report Custom CNN": {
  67. "No_Fire": {
  68. "precision": 0.65,
  69. "recall": 0.48,
  70. "f1-score": 0.55,
  71. "support": 554
  72. },
  73. "Fire": {
  74. "precision": 0.34,
  75. "recall": 0.51,
  76. "f1-score": 0.41,
  77. "support": 289
  78. },
  79. "accuracy": 0.49,
  80. "macro_avg": {
  81. "precision": 0.49,
  82. "recall": 0.49,
  83. "f1-score": 0.48,
  84. "support": 843
  85. },
  86. "weighted_avg": {
  87. "precision": 0.54,
  88. "recall": 0.49,
  89. "f1-score": 0.50,
  90. "support": 843
  91. }
  92. }
  93. }
  94. }
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