# ----------------------------------------------------------
# Dataset
# ----------------------------------------------------------
dataset:
  name: ddsm
  eval_splits: [val, test]
  kwargs:
    image_size: 224

# ----------------------------------------------------------
# Model
# ----------------------------------------------------------
model:
  name: densenet121
  pretrained_remote: true   # load ImageNet weights

# ----------------------------------------------------------
# Evaluation metrics
# ----------------------------------------------------------
metrics:
  - AUROC

# ----------------------------------------------------------
# Training
# ----------------------------------------------------------
training:
  # --- Experiment tracking ---
  project_name: libauc
  experiment_name: densenet121_AUCMLoss_ddsm

  # --- Core hyperparameters ---
  epochs: 60
  batch_size: 32
  eval_batch_size: 64
  sampling_rate: 0.2
  num_workers: 2
  SEED: 2023

  # --- Loss function ---
  loss: AUCMLoss
  loss_kwargs:
    margin: 1.0

  # --- Optimizer ---
  optimizer: PESG
  optimizer_kwargs:
    lr: 0.1
    epoch_decay: 3.0e-3
    weight_decay: 1.0e-5
    momentum: 0.9

  # --- LR decay ---
  decay_epochs: [0.5, 0.75]

  # --- Checkpointing ---
  output_path: ./output
  resume_from_checkpoint: false
  save_checkpoint_every: 10
  verbose: 1