model.yml 1.2 KB

12345678910111213141516171819202122232425262728293031323334353637383940414243444546474849505152535455565758596061626364656667686970717273747576777879808182838485
  1. Model: MobileNetV3
  2. Transforms:
  3. - Resize:
  4. interp: LINEAR
  5. keep_ratio: false
  6. target_size:
  7. - 256
  8. - 256
  9. - Normalize:
  10. apply_to_tar: true
  11. max_val:
  12. - 255.0
  13. - 255.0
  14. - 255.0
  15. mean:
  16. - 0.5
  17. - 0.5
  18. - 0.5
  19. min_val:
  20. - 0
  21. - 0
  22. - 0
  23. std:
  24. - 0.5
  25. - 0.5
  26. - 0.5
  27. _Attributes:
  28. best_accuracy: !!python/object/apply:numpy.core.multiarray.scalar
  29. - !!python/object/apply:numpy.dtype
  30. args:
  31. - f4
  32. - false
  33. - true
  34. state: !!python/tuple
  35. - 3
  36. - <
  37. - null
  38. - null
  39. - null
  40. - -1
  41. - -1
  42. - 0
  43. - !!binary |
  44. mH95Pw==
  45. best_model_epoch: 2
  46. eval_metrics:
  47. top1: 0.9746031761169434
  48. fixed_input_shape:
  49. - 1
  50. - 3
  51. - 256
  52. - 256
  53. in_channels: 3
  54. labels:
  55. - agricultural
  56. - airplane
  57. - baseballdiamond
  58. - beach
  59. - buildings
  60. - chaparral
  61. - denseresidential
  62. - forest
  63. - freeway
  64. - golfcourse
  65. - harbor
  66. - intersection
  67. - mediumresidential
  68. - mobilehomepark
  69. - overpass
  70. - parkinglot
  71. - river
  72. - runway
  73. - sparseresidential
  74. - storagetanks
  75. - tenniscourt
  76. model_type: classifier
  77. num_classes: 21
  78. _init_params:
  79. in_channels: 3
  80. losses: null
  81. num_classes: 21
  82. use_mixed_loss: false
  83. completed_epochs: 0
  84. status: Infer
  85. version: 2.2.0