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cameronraysmith
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magnetic-tiles-defect
connected to https://github.com/cameronraysmith/magnetic-tiles-defect.git

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  1. schema: '2.0'
    2. 

  2. stages:
    3. 

  3.   data_load:
    4. 

  4.     cmd: python src/stages/data_load.py --config=params.yaml
    5. 

  5.     deps:
    6. 

  6.     - path: src/stages/data_load.py
    7. 

  7.       md5: 0e7d94ebf056d3219f49ac51a93145c2
    8. 

  8.       size: 917
    9. 

  9.     - path: src/utils/data_utils.py
    10. 

  10.       md5: 0db1c9fb62a1a788678e637c0eb09af4
    11. 

  11.       size: 3390
    12. 

  12.     params:
    13. 

  13.       params.yaml:
    14. 

  14.         base:
    15. 

  15.           project: defect_detection
    16. 

  16.           pkg_list_fname: pkg_list.txt
    17. 

  17.           random_state: 0
    18. 

  18.         data_load:
    19. 

  19.           dataset_url: https://github.com/abin24/Magnetic-tile-defect-datasets./archive/refs/heads/master.zip
    20. 

  20.           data_dir: data
    21. 

  21.           orig_dirname: Magnetic-tile-defect-datasets.-master
    22. 

  22.           new_dirname: MAGNETIC_TILE_SURFACE_DEFECTS
    23. 

  23.     outs:
    24. 

  24.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/images
    25. 

  25.       md5: b7178caeac712acf11359c9f183f2cdc.dir
    26. 

  26.       size: 14666517
    27. 

  27.       nfiles: 392
    28. 

  28.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/masks
    29. 

  29.       md5: f92c29262e93249baf066776487c8535.dir
    30. 

  30.       size: 199417
    31. 

  31.       nfiles: 392
    32. 

  32.   data_split:
    33. 

  33.     cmd: python src/stages/data_split.py --config=params.yaml
    34. 

  34.     deps:
    35. 

  35.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/images
    36. 

  36.       md5: b7178caeac712acf11359c9f183f2cdc.dir
    37. 

  37.       size: 14666517
    38. 

  38.       nfiles: 392
    39. 

  39.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/masks
    40. 

  40.       md5: f92c29262e93249baf066776487c8535.dir
    41. 

  41.       size: 199417
    42. 

  42.       nfiles: 392
    43. 

  43.     - path: src/stages/data_split.py
    44. 

  44.       md5: 5e4fd64bdf78e73c628ad1452c556584
    45. 

  45.       size: 1415
    46. 

  46.     - path: src/utils/data_utils.py
    47. 

  47.       md5: 0db1c9fb62a1a788678e637c0eb09af4
    48. 

  48.       size: 3390
    49. 

  49.     params:
    50. 

  50.       params.yaml:
    51. 

  51.         base:
    52. 

  52.           project: defect_detection
    53. 

  53.           pkg_list_fname: pkg_list.txt
    54. 

  54.           random_state: 0
    55. 

  55.         data_load:
    56. 

  56.           dataset_url: https://github.com/abin24/Magnetic-tile-defect-datasets./archive/refs/heads/master.zip
    57. 

  57.           data_dir: data
    58. 

  58.           orig_dirname: Magnetic-tile-defect-datasets.-master
    59. 

  59.           new_dirname: MAGNETIC_TILE_SURFACE_DEFECTS
    60. 

  60.         data_split:
    61. 

  61.           test_pct: 0.2
    62. 

  62.           train_img_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_images
    63. 

  63.           train_mask_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_masks
    64. 

  64.           test_img_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_images
    65. 

  65.           test_mask_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_masks
    66. 

  66.     outs:
    67. 

  67.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_images
    68. 

  68.       md5: 2052af64848d11ce62c4bf778eb73028.dir
    69. 

  69.       size: 2922595
    70. 

  70.       nfiles: 78
    71. 

  71.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_masks
    72. 

  72.       md5: 36c4f7325edf349f9b2a416acf00aa32.dir
    73. 

  73.       size: 37791
    74. 

  74.       nfiles: 78
    75. 

  75.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_images
    76. 

  76.       md5: 6fec0ace9aca431036dc85d2d2cc4438.dir
    77. 

  77.       size: 11743922
    78. 

  78.       nfiles: 314
    79. 

  79.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_masks
    80. 

  80.       md5: fde9372f0cb2c91210a8afe82b852025.dir
    81. 

  81.       size: 161626
    82. 

  82.       nfiles: 314
    83. 

  83.   train:
    84. 

  84.     cmd: python src/stages/train.py --config=params.yaml
    85. 

  85.     deps:
    86. 

  86.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_images
    87. 

  87.       md5: 6fec0ace9aca431036dc85d2d2cc4438.dir
    88. 

  88.       size: 11743922
    89. 

  89.       nfiles: 314
    90. 

  90.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_masks
    91. 

  91.       md5: fde9372f0cb2c91210a8afe82b852025.dir
    92. 

  92.       size: 161626
    93. 

  93.       nfiles: 314
    94. 

  94.     - path: src/stages/train.py
    95. 

  95.       md5: 2a154a0b2d10608e988210bbc6bf590f
    96. 

  96.       size: 1550
    97. 

  97.     - path: src/utils/train_utils.py
    98. 

  98.       md5: 8a667de3ce15bba115f721999c31ab83
    99. 

  99.       size: 1709
    100. 

  100.     params:
    101. 

  101.       params.yaml:
    102. 

  102.         base:
    103. 

  103.           project: defect_detection
    104. 

  104.           pkg_list_fname: pkg_list.txt
    105. 

  105.           random_state: 0
    106. 

  106.         data_split:
    107. 

  107.           test_pct: 0.2
    108. 

  108.           train_img_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_images
    109. 

  109.           train_mask_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_masks
    110. 

  110.           test_img_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_images
    111. 

  111.           test_mask_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_masks
    112. 

  112.         train:
    113. 

  113.           img_size: 224
    114. 

  114.           valid_pct: 0.2
    115. 

  115.           learning_rate: 0.02
    116. 

  116.           batch_size: 16
    117. 

  117.           n_epochs: 30
    118. 

  118.           use_cpu: false
    119. 

  119.           model_pickle_fpath: models/model_pickle_fastai.pkl
    120. 

  120.           augmentations:
    121. 

  121.             do_flip: false
    122. 

  122.             flip_vert: false
    123. 

  123.             max_rotate: 10.0
    124. 

  124.             min_zoom: 1.0
    125. 

  125.             max_zoom: 1.0
    126. 

  126.     outs:
    127. 

  127.     - path: models/model_pickle_fastai.pkl
    128. 

  128.       md5: d9072110d859c44603bcd49c1d9a7079
    129. 

  129.       size: 229790017
    130. 

  130.     - path: training/metrics.json
    131. 

  131.       md5: 042e4d51c9ff28f0d2e5b91022e84d6e
    132. 

  132.       size: 225
    133. 

  133.     - path: training/plots
    134. 

  134.       md5: 75abb93a9c073bcf2e152e907135bbaa.dir
    135. 

  135.       size: 2957
    136. 

  136.       nfiles: 5
    137. 

  137.   evaluate:
    138. 

  138.     cmd: python src/stages/eval.py --config=params.yaml
    139. 

  139.     deps:
    140. 

  140.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_images
    141. 

  141.       md5: 2052af64848d11ce62c4bf778eb73028.dir
    142. 

  142.       size: 2922595
    143. 

  143.       nfiles: 78
    144. 

  144.     - path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_masks
    145. 

  145.       md5: 36c4f7325edf349f9b2a416acf00aa32.dir
    146. 

  146.       size: 37791
    147. 

  147.       nfiles: 78
    148. 

  148.     - path: models/model_pickle_fastai.pkl
    149. 

  149.       md5: d9072110d859c44603bcd49c1d9a7079
    150. 

  150.       size: 229790017
    151. 

  151.     - path: src/stages/eval.py
    152. 

  152.       md5: 3f4de3de51fe509971183b706c935866
    153. 

  153.       size: 1563
    154. 

  154.     - path: src/utils/eval_utils.py
    155. 

  155.       md5: 8fa6e37eec07910cbc10b49fb69404bb
    156. 

  156.       size: 2427
    157. 

  157.     params:
    158. 

  158.       params.yaml:
    159. 

  159.         base:
    160. 

  160.           project: defect_detection
    161. 

  161.           pkg_list_fname: pkg_list.txt
    162. 

  162.           random_state: 0
    163. 

  163.         data_split:
    164. 

  164.           test_pct: 0.2
    165. 

  165.           train_img_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_images
    166. 

  166.           train_mask_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/train_masks
    167. 

  167.           test_img_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_images
    168. 

  168.           test_mask_dir_path: data/MAGNETIC_TILE_SURFACE_DEFECTS/test_masks
    169. 

  169.         evaluate:
    170. 

  170.           save_test_preds: true
    171. 

  171.           metrics_file: evaluation/metrics.json
    172. 

  172.         train:
    173. 

  173.           img_size: 224
    174. 

  174.           valid_pct: 0.2
    175. 

  175.           learning_rate: 0.02
    176. 

  176.           batch_size: 16
    177. 

  177.           n_epochs: 30
    178. 

  178.           use_cpu: false
    179. 

  179.           model_pickle_fpath: models/model_pickle_fastai.pkl
    180. 

  180.           augmentations:
    181. 

  181.             do_flip: false
    182. 

  182.             flip_vert: false
    183. 

  183.             max_rotate: 10.0
    184. 

  184.             min_zoom: 1.0
    185. 

  185.             max_zoom: 1.0
    186. 

  186.     outs:
    187. 

  187.     - path: evaluation/metrics.json
    188. 

  188.       md5: dd58b06cb165f225d91446342356ab35
    189. 

  189.       size: 111
    190.
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