IVPP/config.yml at main · bvanberl/IVPP · GitHub

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paths:
  images: "data/bmodes"
  splits: "splits"
  model_weights: "results/models"

data:
  height: 224
  width: 224
  min_pixel_value: 0.0
  max_pixel_value: 255.0
  num_channels: 3
  us_mode: 'mmode'

pretrain:
  seed: 11
  epochs: 500
  batch_size: 384
  optimizer: 'lars'
  init_lr: 0.2
  warmup_epochs: 2 #10 #30
  weight_decay: 0.000001
  extractor: 'mobilenetv3'
  proj_nodes: [768, 768]
  use_bias: true
  imagenet_weights: true
  use_unlabelled: true
  method: "ncus_vicreg"
  augment_pipeline: "ncus"
  n_cutoff_layers: 0
  hparams:
    simclr:
      tau: 0.1
    barlow_twins:
      lambda_: 0.005
    vicreg:
      lambda_: 25.0
      mu: 25.0
      nu: 1.0
    ncus_vicreg:
      lambda_: 25.0
      mu: 25.0
      nu: 1.0
      max_time_delta: 0.5
      max_x_delta: 10
      sample_weights: true
    ncus_barlow_twins:
      lambda_: 0.005
      max_time_delta: 3
      max_x_delta: 15
      sample_weights: false
    ncus_simclr:
      tau: 0.1
      max_time_delta: 1.5
      max_x_delta: 10
      sample_weights: true

train:
  experiment: 'fine-tune'
  folds: 5
  seed: 1
  extractor_type: 'mobilenetv3'
  label: 'ab_lines_label'
  prop_train: 1.0
  extractor_weights: 'final_model.pth' #'imagenet'
  batch_size: 384
  n_cutoff_layers: 0
  epochs: 100
  lr_extractor: 0.00001
  lr_head: 0.0001
  momentum: 0.9
  weight_decay: 0.0001
  mlp_hidden_layers: [32]
  freeze_prefix: []
  optimizer: "adam"
  augment_pipeline: "ncus"
  use_class_weights: false

augment:
  ncus:
    min_crop_area: 0.4
    max_crop_area: 1.0
    min_crop_ratio: 0.4
    max_crop_ratio: 0.6
    brightness_prob: 0.5
    max_brightness: 0.25
    contrast_prob: 0.5
    max_contrast: 0.25
    blur_prob: 0.25
    min_blur_sigma: 0.1
    max_blur_sigma: 2.0
  uscl:
    min_crop_area: 0.8
    max_crop_area: 1.0
    min_crop_ratio: 0.8
    max_crop_ratio: 1.25