experiment:
  seed: 88
  save_dir: ../experiments/


data:
  annotations: ../data/train_vertebra_chunks_kfold.csv
  data_dir: ../data/train-numpy-vertebra-chunks
  input: filename
  target: fracture
  outer_fold: 0
  dataset:
    name: NumpyChunkDataset
    params:
      flip: true
      invert: false
      channels: grayscale
      z_lt: resample_resample
      z_gt: resample_resample
      num_images: 64


transform:
  resize:
    name: resize_ignore_3d
    params:
      imsize: [64, 288, 288]
  augment:
    null
  crop:
    null
  preprocess:
    name: Preprocessor
    params:
      image_range: [0, 255]
      input_range: [0, 1]
      mean: [0.5]
      sdev: [0.5]


task:
  name: ClassificationTask
  params:


model:
  name: Net3D
  params:
    backbone: x3d_l
    backbone_params:
      z_strides: [1, 1, 1, 1, 1]
    pretrained: true
    num_classes: 1
    dropout: 0.2
    pool: avg
    in_channels: 1
    multisample_dropout: true


loss:
  name: BCEWithLogitsLoss
  params:


optimizer:
  name: AdamW
  params:
    lr: 3.0e-4
    weight_decay: 5.0e-4


scheduler:
  name: CosineAnnealingLR
  params:
    final_lr: 0.0


train:
  batch_size: 4
  num_epochs: 10


evaluate:
  metrics: [AUROC]
  monitor: auc_mean
  mode: max