general.yaml

TRAIN:
  ENABLE: True
  DATASET: AISC
  BATCH_SIZE: 32
  EVAL_PERIOD: 2
  CHECKPOINT_PERIOD: 2
  AUTO_RESUME: True
DATA:
  PATH_TO_DATA: [data/processed/additional-dermoscopic-images,
                 data/processed/main-dermoscopic-images]
  PATH_TO_LABEL: data/processed/labels.csv
  PATH_TO_DIFFICULTIES: data/processed/difficulties.pkl
  PATH_TO_SPLIT: data/processed/splits.pkl
NETWORK:
  PATH_TO_SAVED: None
  BACKBONE:
    NAME: 'efficientnet-b5'
    OUTPUT_DIM: 128
    ALREADY_TRAINED: False
    FREEZE_BATCHNORM: True
  HEAD:
    STRUCTURE: [128, 64, 16, 1]
    ACTIVATION: sigmoid
    BATCH_NORM_STRUCTURE: [False, False, False, False]
TRAINING:
  BACKBONE:
    MAX_EPOCH: 100
    LOSS: Contrastive
    EARLY_STOP_PATIENCE: 3
  HEAD:
    MAX_EPOCH: 20
    LOSS: LeastSquares
    EARLY_STOP_PATIENCE: 2
  COMBINED:
    MAX_EPOCH: 10
    ALPHA: 0.5
    EARLY_STOP_PATIENCE: 1
SOLVER:
  BASE_LR: 0.1
  MOMENTUM: 0.9
  WEIGHT_DECAY: 1e-4
  WARMUP_START_LR: 0.01
  OPTIMIZER: ADAM
  ALPHA: 0.5
AUGMENTATION:
  NAME: ngessert
  CONFIG: config_augmentations/standard_augmenter.yaml
TEST:
  ENABLE: True
  BATCH_SIZE: 64
DATA_LOADER:
  NUM_WORKERS: 0
  PIN_MEMORY: True
EVAL_METRICS:
  BACKBONE: knn
  HEAD: MSE
NUM_GPUS: 1 # Not set up to handle more currently
NUM_SHARDS: 1
RNG_SEED: 0
OUTPUT_DIR: r"data/output"