dataset:
  name: mvtec #options: [mvtec, btech, folder]
  format: mvtec
  path: ./datasets/MVTec
  category: bottle
  task: classification
  image_size: 256
  train_batch_size: 32
  test_batch_size: 32
  num_workers: 36
  transform_config:
    train: null
    val: null
  create_validation_set: false

model:
  name: dfm
  backbone: resnet18
  layer: layer3
  pooling_kernel_size: 4
  pca_level: 0.97
  score_type: fre # nll: for Gaussian modeling, fre: pca feature reconstruction error
  project_path: ./results
  normalization_method: min_max # options: [null, min_max, cdf]
  threshold:
    image_default: 0
    adaptive: true

metrics:
  image:
    - F1Score
    - AUROC
  pixel:
    - F1Score
    - AUROC

project:
  seed: 42
  path: ./results
  log_images_to: []
  logger: false # options: [tensorboard, wandb, csv] or combinations.

# PL Trainer Args. Don't add extra parameter here.
trainer:
  accelerator: auto # <"cpu", "gpu", "tpu", "ipu", "hpu", "auto">
  accumulate_grad_batches: 1
  amp_backend: native
  auto_lr_find: false
  auto_scale_batch_size: false
  auto_select_gpus: false
  benchmark: false
  check_val_every_n_epoch: 1 # Don't validate before extracting features.
  default_root_dir: null
  detect_anomaly: false
  deterministic: false
  enable_checkpointing: true
  enable_model_summary: true
  enable_progress_bar: true
  fast_dev_run: false
  gpus: null # Set automatically
  gradient_clip_val: 0
  ipus: null
  limit_predict_batches: 1.0
  limit_test_batches: 1.0
  limit_train_batches: 1.0
  limit_val_batches: 1.0
  log_every_n_steps: 50
  log_gpu_memory: null
  max_epochs: 1
  max_steps: -1
  max_time: null
  min_epochs: null
  min_steps: null
  move_metrics_to_cpu: false
  multiple_trainloader_mode: max_size_cycle
  num_nodes: 1
  num_processes: 1
  num_sanity_val_steps: 0
  overfit_batches: 0.0
  plugins: null
  precision: 32
  profiler: null
  reload_dataloaders_every_n_epochs: 0
  replace_sampler_ddp: true
  strategy: null
  sync_batchnorm: false
  tpu_cores: null
  track_grad_norm: -1
  val_check_interval: 1.0 # Don't validate before extracting features.