model:
  class_path: anomalib.models.Patchcore
  init_args:
    backbone: "wide_resnet50_2"
    layers: ["layer2", "layer3"]
    coreset_sampling_ratio: 0.1
    num_neighbors: 9

data:
  # You can also use: class_path: anomalib.data.Folder (re-export)
  class_path: anomalib.data.datamodules.image.folder.Folder
  init_args:
    name: "transformers"
    root: "./dataset"
    normal_dir: "train/normal"       # ONLY normal images here
    abnormal_dir: "test/faulty"      # faulty images for eval
    normal_test_dir: "test/normal"   # a few normals for eval
    train_batch_size: 4
    eval_batch_size: 4
    num_workers: 4
    # (optional) add augmentations later via train_augmentations/val_augmentations/test_augmentations

trainer:
  max_epochs: 1
  accelerator: "auto"
  devices: 1
  enable_checkpointing: true