libs/solaris/nets/configs/selimsef_densenet121unet_spacenet4.yml [27:131]:
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training_data_csv: '/path/to/training_df.csv'
validation_data_csv:
inference_data_csv: '/path/to/test_df.csv'

training_augmentation:
  augmentations:
    RandomScale:
      scale_limit:
        - 0.5
        - 1.5
      interpolation: nearest
    Rotate:
      limit:
        - 5
        - 6
      border_mode: constant
      p: 0.3
    RandomCrop:
      height: 416
      width: 416
      always_apply: true
      p: 1.0
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0
  shuffle: true

validation_augmentation:
  augmentations:
    CenterCrop:
      height: 384
      width: 384
      p: 1.0
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0

inference_augmentation:
  augmentations:
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0
training:
  epochs:  70
  steps_per_epoch:
  optimizer: AdamW
  lr: 2e-4
  opt_args:
    weight_decay: 0.0001
  loss:
    focal:
    dice:
  loss_weights:
    focal: 1
    dice: 1
  metrics:
    training:
    validation:
  checkpoint_frequency: 10
  callbacks:
    lr_schedule:
      schedule_type: 'arbitrary'
      schedule_dict:
        milestones:
          - 1
          - 5
          - 15
          - 30
          - 50
          - 60
        gamma: 0.5
    model_checkpoint:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



libs/solaris/nets/configs/selimsef_resnet34unet_spacenet4.yml [27:131]:
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training_data_csv: '/path/to/training_df.csv'
validation_data_csv:
inference_data_csv: '/path/to/test_df.csv'

training_augmentation:
  augmentations:
    RandomScale:
      scale_limit:
        - 0.5
        - 1.5
      interpolation: nearest
    Rotate:
      limit:
        - 5
        - 6
      border_mode: constant
      p: 0.3
    RandomCrop:
      height: 416
      width: 416
      always_apply: true
      p: 1.0
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0
  shuffle: true

validation_augmentation:
  augmentations:
    CenterCrop:
      height: 384
      width: 384
      p: 1.0
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0

inference_augmentation:
  augmentations:
    Normalize:
      mean:
        - 0.006479
        - 0.009328
        - 0.01123
        - 0.02082
      std:
        - 0.004986
        - 0.004964
        - 0.004950
        - 0.004878
      max_pixel_value: 65535.0
      p: 1.0
  p: 1.0
training:
  epochs:  70
  steps_per_epoch:
  optimizer: AdamW
  lr: 2e-4
  opt_args:
    weight_decay: 0.0001
  loss:
    focal:
    dice:
  loss_weights:
    focal: 1
    dice: 1
  metrics:
    training:
    validation:
  checkpoint_frequency: 10
  callbacks:
    lr_schedule:
      schedule_type: 'arbitrary'
      schedule_dict:
        milestones:
          - 1
          - 5
          - 15
          - 30
          - 50
          - 60
        gamma: 0.5
    model_checkpoint:
- - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - -



