How to train all classes of MVTec using PatchCore

[RayL0221]

Describe the bug

I am attempting to reproduce the experimental results of the PatchCore, but I don't know how to train all class together.
When I set the "category" in config.yaml to "all", It starts downloading the whole dataset of MVTec.
So, could you please provide me with some suggestions about the problem?

Dataset

MVTec

Model

PatchCore

Steps to reproduce the behavior

No question

OS information

OS information:

• OS: Windows 10
• Python version: 3.8.16
• Anomalib version: main
• PyTorch version: 1.12.1
• CUDA/cuDNN version: 11.3
• GPU models and configuration: GeForce RTX 4080
• Any other relevant information: I'm using the MVTec AD dataset

Expected behavior

Try to train all classes of MVTec using PatchCore

Screenshots

No response

Pip/GitHub

pip

What version/branch did you use?

No response

Configuration YAML

dataset:
  name: mvtec
  format: mvtec
  path: ./datasets/MVTec
  task: segmentation
  category: all
  train_batch_size: 32
  test_batch_size: 32
  num_workers: 8
  image_size: 256 # dimensions to which images are resized (mandatory)
  center_crop: 224 # dimensions to which images are center-cropped after resizing (optional)
  normalization: imagenet # data distribution to which the images will be normalized: [none, imagenet]
  transform_config:
    train: null
    eval: null
  test_split_mode: from_dir # options: [from_dir, synthetic]
  test_split_ratio: 0.2 # fraction of train images held out testing (usage depends on test_split_mode)
  val_split_mode: same_as_test # options: [same_as_test, from_test, synthetic]
  val_split_ratio: 0.5 # fraction of train/test images held out for validation (usage depends on val_split_mode)
  tiling:
    apply: false
    tile_size: null
    stride: null
    remove_border_count: 0
    use_random_tiling: False
    random_tile_count: 16

model:
  name: patchcore
  backbone: wide_resnet50_2
  pre_trained: true
  layers:
    - layer2
    - layer3
  coreset_sampling_ratio: 0.1
  num_neighbors: 9
  normalization_method: min_max # options: [null, min_max, cdf]

metrics:
  image:
    - F1Score
    - AUROC
  pixel:
    - F1Score
    - AUROC
  threshold:
    method: adaptive #options: [adaptive, manual]
    manual_image: null
    manual_pixel: null

visualization:
  show_images: False # show images on the screen
  save_images: True # save images to the file system
  log_images: True # log images to the available loggers (if any)
  image_save_path: null # path to which images will be saved
  mode: full # options: ["full", "simple"]

project:
  seed: 0
  path: ./results

logging:
  logger: [] # options: [comet, tensorboard, wandb, csv] or combinations.
  log_graph: false # Logs the model graph to respective logger.

optimization:
  export_mode: null # options: onnx, openvino

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

Logs

2023-04-10 15:54:55,310 - anomalib.data - INFO - Loading the datamodule
2023-04-10 15:54:55,311 - anomalib.data.utils.transform - INFO - No config file has been provided. Using default transforms.
2023-04-10 15:54:55,311 - anomalib.data.utils.transform - INFO - No config file has been provided. Using default transforms.
2023-04-10 15:54:55,311 - anomalib.models - INFO - Loading the model.
2023-04-10 15:54:55,311 - anomalib.models.components.base.anomaly_module - INFO - Initializing PatchcoreLightning model.
2023-04-10 15:54:56,171 - pytorch_lightning.utilities.rank_zero - INFO - GPU available: True (cuda), used: True
2023-04-10 15:54:56,171 - pytorch_lightning.utilities.rank_zero - INFO - TPU available: False, using: 0 TPU cores
2023-04-10 15:54:56,171 - pytorch_lightning.utilities.rank_zero - INFO - IPU available: False, using: 0 IPUs
2023-04-10 15:54:56,171 - pytorch_lightning.utilities.rank_zero - INFO - HPU available: False, using: 0 HPUs
2023-04-10 15:54:56,172 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_train_batches=1.0)` was configured so 100% of the batches 
per epoch will be used..
2023-04-10 15:54:56,172 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_val_batches=1.0)` was configured so 100% of the batches wi
ll be used..
2023-04-10 15:54:56,172 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_test_batches=1.0)` was configured so 100% of the batches w
ill be used..
2023-04-10 15:54:56,172 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(limit_predict_batches=1.0)` was configured so 100% of the batche
s will be used..
2023-04-10 15:54:56,172 - pytorch_lightning.utilities.rank_zero - INFO - `Trainer(val_check_interval=1.0)` was configured so validation will run a
t the end of the training epoch..
2023-04-10 15:54:56,172 - anomalib - INFO - Training the model.
2023-04-10 15:54:56,176 - anomalib.data.utils.download - INFO - Downloading the mvtec dataset.

Code of Conduct

• I agree to follow this project's Code of Conduct

[samet-akcay]

Hi @RayL0221, you could use benchmarking script for this purpose.
https://github.com/openvinotoolkit/anomalib#benchmarking