How to do CLASSIFICATION task with config file in 2.0.0

[panp4n]

How to do classification task with config file in 2.0.0b3?
Is there a parameter in the config file of 2.0.0b3 like task: CLASSIFICATION in 1.2.0?
I update anomalib to 2.0.0b3 from 1.2.0, there are too much difference in usage. I think detailed usage documentation is what we need more.

My custom config yaml file:

seed_everything: true
trainer:
  accelerator: auto
  strategy: auto
  devices: auto
  num_nodes: 1
  precision: null
  logger: null
  callbacks: null
  fast_dev_run: false
  max_epochs: null
  min_epochs: null
  max_steps: -1
  min_steps: null
  max_time: null
  limit_train_batches: null
  limit_val_batches: null
  limit_test_batches: null
  limit_predict_batches: null
  overfit_batches: 0.0
  val_check_interval: null
  check_val_every_n_epoch: 1
  num_sanity_val_steps: null
  log_every_n_steps: null
  enable_checkpointing: null
  enable_progress_bar: null
  enable_model_summary: null
  accumulate_grad_batches: 1
  gradient_clip_val: null
  gradient_clip_algorithm: null
  deterministic: null
  benchmark: null
  inference_mode: true
  use_distributed_sampler: true
  profiler: null
  detect_anomaly: false
  barebones: false
  plugins: null
  sync_batchnorm: false
  reload_dataloaders_every_n_epochs: 0

model:
  class_path: anomalib.models.Patchcore
  init_args:
    backbone: wide_resnet50_2
    layers:
    - layer2
    - layer3
    pre_trained: true
    coreset_sampling_ratio: 0.1
    num_neighbors: 9
    pre_processor: true
    post_processor: true
    evaluator: true
    visualizer: true
data:
  class_path: anomalib.data.Folder
  init_args:
    name: hazelnut
    normal_dir: train/good
    root: datasets/MVTec/hazelnut
    abnormal_dir: ['test/crack', 'test/cut', 'test/hole', 'test/print']
    normal_test_dir: test/good
    mask_dir: null
    normal_split_ratio: 0.0
    extensions:
    - .png
    train_batch_size: 16
    eval_batch_size: 16
    num_workers: 8
    train_augmentations: null
    val_augmentations: null
    test_augmentations: null
    augmentations: null
    test_split_mode: from_dir
    test_split_ratio: 0.2
    val_split_mode: same_as_test
    val_split_ratio: 0.5
    seed: null
    
logging:
  log_graph: false
default_root_dir: results
ckpt_path: null

[haimat]

Hello, I have the same issue, don't know how to start with 2.0 -
have you had any luck in that regard so far?

[syshin0116]

Hi, same issue here, I would like to train a classification model without masks. any updates?

[djdameln]

Hi, you can just omit the task parameter. Anomalib will automatically recognize that it should ignore pixel-level evaluation due to absence of the mask_dir parameter.