🚀 Add Real-IAD dataset

examples/configs/data/realiad.yaml

@@ -0,0 +1,13 @@
+data:
+  class_path: anomalib.data.RealIAD
+  init_args:
+    root: ./datasets/Real-IAD
+    category: audiojack
+    resolution: 256
+    train_batch_size: 32
+    eval_batch_size: 32
+    num_workers: 8
+    train_augmentations: null
+    val_augmentations: null
+    test_augmentations: null
+    augmentations: null

src/anomalib/data/__init__.py

@@ -56,7 +56,7 @@
 # Datamodules
 from .datamodules.base import AnomalibDataModule
 from .datamodules.depth import DepthDataFormat, Folder3D, MVTec3D
-from .datamodules.image import BTech, Datumaro, Folder, ImageDataFormat, Kolektor, MVTec, Visa
+from .datamodules.image import BTech, Datumaro, Folder, ImageDataFormat, Kolektor, MVTec, RealIAD, Visa
 from .datamodules.video import Avenue, ShanghaiTech, UCSDped, VideoDataFormat
 
 # Datasets
@@ -168,6 +168,7 @@ def get_datamodule(config: DictConfig | ListConfig | dict) -> AnomalibDataModule
     "Kolektor",
     "MVTec",
     "MVTec3D",
+    "RealIAD",
     "Avenue",
     "UCSDped",
     "ShanghaiTech",

src/anomalib/data/datamodules/image/__init__.py

@@ -20,7 +20,7 @@
         ... )
 """
 
-# Copyright (C) 2024 Intel Corporation
+# Copyright (C) 2024-2025 Intel Corporation
 # SPDX-License-Identifier: Apache-2.0
 
 from enum import Enum
@@ -30,6 +30,7 @@
 from .folder import Folder
 from .kolektor import Kolektor
 from .mvtec import MVTec
+from .realiad import RealIAD
 from .visa import Visa
 
 
@@ -45,6 +46,7 @@ class ImageDataFormat(str, Enum):
     - ``KOLEKTOR``: Kolektor Surface-Defect Dataset
     - ``MVTEC``: MVTec AD Dataset
     - ``MVTEC_3D``: MVTec 3D AD Dataset
+    - ``REALIAD``: Real-IAD Dataset
     - ``VISA``: Visual Inspection for Steel Anomaly Dataset
     """
 
@@ -55,7 +57,8 @@ class ImageDataFormat(str, Enum):
     KOLEKTOR = "kolektor"
     MVTEC = "mvtec"
     MVTEC_3D = "mvtec_3d"
+    REAL_IAD = "real_iad"
     VISA = "visa"
 
 
-__all__ = ["BTech", "Datumaro", "Folder", "Kolektor", "MVTec", "Visa"]
+__all__ = ["BTech", "Datumaro", "Folder", "Kolektor", "MVTec", "RealIAD", "Visa"]

src/anomalib/data/datamodules/image/realiad.py

@@ -0,0 +1,320 @@
+"""Real-IAD Data Module.
+
+This module provides a PyTorch Lightning DataModule for the Real-IAD dataset.
+
+The Real-IAD dataset is a large-scale industrial anomaly detection dataset containing
+30 categories of industrial objects with both normal and anomalous samples. Each object
+is captured from 5 different camera viewpoints (C1-C5).
+
+Dataset Structure:
+    The dataset follows this directory structure:
+        Real-IAD/
+        ├── realiad_256/      # 256x256 resolution images
+        ├── realiad_512/      # 512x512 resolution images
+        ├── realiad_1024/     # 1024x1024 resolution images
+        └── realiad_jsons/    # JSON metadata files
+            ├── realiad_jsons/       # Base metadata
+            ├── realiad_jsons_sv/    # Single-view metadata
+            └── realiad_jsons_fuiad/ # FUIAD metadata versions
+
+Example:
+    Create a Real-IAD datamodule::
+
+        >>> from anomalib.data import RealIAD
+        >>> datamodule = RealIAD(
+        ...     root="./datasets/Real-IAD",
+        ...     category="audiojack",
+        ...     resolution="1024"
+        ... )
+
+Notes:
+    The dataset should be downloaded manually from Hugging Face and placed in the
+    appropriate directory. See ``DOWNLOAD_INSTRUCTIONS`` for detailed steps.
+
+License:
+    Real-IAD dataset is released under the Creative Commons
+    Attribution-NonCommercial-ShareAlike 4.0 International License
+    (CC BY-NC-SA 4.0).
+    https://creativecommons.org/licenses/by-nc-sa/4.0/
+"""
+
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+from pathlib import Path
+from textwrap import dedent
+
+from torchvision.transforms.v2 import Transform
+
+from anomalib.data.datamodules.base.image import AnomalibDataModule
+from anomalib.data.datasets.image.realiad import CATEGORIES, RESOLUTIONS, RealIADDataset
+from anomalib.data.utils import Split, TestSplitMode, ValSplitMode
+
+
+class RealIAD(AnomalibDataModule):
+    """Real-IAD Datamodule.
+
+    Args:
+        root (Path | str): Path to root directory containing the dataset.
+            Defaults to ``"./datasets/Real-IAD"``.
+        category (str): Category of the Real-IAD dataset (e.g. ``"audiojack"`` or
+            ``"button_battery"``). Defaults to ``"audiojack"``.
+        resolution (str | int): Image resolution to use (e.g. ``"256"``, ``"512"``,
+            ``"1024"``, ``"raw"`` or their integer equivalents).
+            For example, both "256" and 256 are valid. Defaults to ``256``.
+        json_path (str | Path): Path to JSON metadata file, relative to root directory.
+            Can use {category} placeholder which will be replaced with the category name.
+            Common paths are:
+            - "realiad_jsons/realiad_jsons/{category}.json" - Base metadata (multi-view)
+            - "realiad_jsons/realiad_jsons_sv/{category}.json" - Single-view metadata
+            - "realiad_jsons/realiad_jsons_fuiad_0.4/{category}.json" - FUIAD v0.4 metadata
+        train_batch_size (int, optional): Training batch size.
+            Defaults to ``32``.
+        eval_batch_size (int, optional): Test batch size.
+            Defaults to ``32``.
+        num_workers (int, optional): Number of workers.
+            Defaults to ``8``.
+        train_augmentations (Transform | None): Augmentations to apply to the training images
+            Defaults to ``None``.
+        val_augmentations (Transform | None): Augmentations to apply to the validation images.
+            Defaults to ``None``.
+        test_augmentations (Transform | None): Augmentations to apply to the test images.
+            Defaults to ``None``.
+        augmentations (Transform | None): General augmentations to apply if stage-specific
+            augmentations are not provided.
+        test_split_mode (TestSplitMode): Method to create test set.
+            Defaults to ``TestSplitMode.FROM_DIR``.
+        test_split_ratio (float): Fraction of data to use for testing.
+            Defaults to ``0.2``.
+        val_split_mode (ValSplitMode): Method to create validation set.
+            Defaults to ``ValSplitMode.SAME_AS_TEST``.
+        val_split_ratio (float): Fraction of data to use for validation.
+            Defaults to ``0.5``.
+        seed (int | None, optional): Seed for reproducibility.
+            Defaults to ``None``.
+
+    Example:
+        Create Real-IAD datamodule with default settings::
+
+            >>> datamodule = RealIAD()
+            >>> datamodule.setup()
+            >>> i, data = next(enumerate(datamodule.train_dataloader()))
+            >>> data.keys()
+            dict_keys(['image_path', 'label', 'image', 'mask_path', 'mask'])
+
+            >>> data["image"].shape
+            torch.Size([32, 3, 256, 256])
+
+        Change the category and resolution::
+
+            >>> # Using string resolution
+            >>> datamodule = RealIAD(
+            ...     category="button_battery",
+            ...     resolution="512"
+            ... )
+
+            >>> # Using integer resolution
+            >>> datamodule = RealIAD(
+            ...     category="button_battery",
+            ...     resolution=1024
+            ... )
+
+        Use different JSON metadata files::
+
+            >>> # Base metadata (multi-view)
+            >>> datamodule = RealIAD(
+            ...     json_path="realiad_jsons/realiad_jsons/{category}.json"
+            ... )
+
+            >>> # Single-view metadata
+            >>> datamodule = RealIAD(
+            ...     json_path="realiad_jsons/realiad_jsons_sv/{category}.json"
+            ... )
+
+            >>> # FUIAD v0.4 metadata (filtered subset)
+            >>> datamodule = RealIAD(
+            ...     json_path="realiad_jsons/realiad_jsons_fuiad_0.4/{category}.json"
+            ... )
+
+            >>> # Custom metadata
+            >>> datamodule = RealIAD(
+            ...     json_path="path/to/custom/metadata.json"
+            ... )
+
+        Create validation set from test data::
+
+            >>> datamodule = RealIAD(
+            ...     val_split_mode=ValSplitMode.FROM_TEST,
+            ...     val_split_ratio=0.1
+            ... )
+
+    Notes:
+        - The dataset contains both normal (OK) and anomalous (NG) samples
+        - Each object is captured from 5 different camera viewpoints (C1-C5)
+        - Images are available in multiple resolutions (256x256, 512x512, 1024x1024)
+        - JSON metadata files provide additional information and different dataset splits
+        - Segmentation masks are provided for anomalous samples
+    """
+
+    def __init__(
+        self,
+        root: Path | str = "./datasets/Real-IAD",
+        category: str = "audiojack",
+        resolution: str | int = 256,
+        json_path: str | Path = "realiad_jsons/realiad_jsons/{category}.json",
+        train_batch_size: int = 32,
+        eval_batch_size: int = 32,
+        num_workers: int = 8,
+        train_augmentations: Transform | None = None,
+        val_augmentations: Transform | None = None,
+        test_augmentations: Transform | None = None,
+        augmentations: Transform | None = None,
+        test_split_mode: TestSplitMode | str = TestSplitMode.FROM_DIR,
+        test_split_ratio: float = 0.2,
+        val_split_mode: ValSplitMode | str = ValSplitMode.SAME_AS_TEST,
+        val_split_ratio: float = 0.5,
+        seed: int | None = None,
+    ) -> None:
+        super().__init__(
+            train_batch_size=train_batch_size,
+            eval_batch_size=eval_batch_size,
+            num_workers=num_workers,
+            train_augmentations=train_augmentations,
+            val_augmentations=val_augmentations,
+            test_augmentations=test_augmentations,
+            augmentations=augmentations,
+            test_split_mode=test_split_mode,
+            test_split_ratio=test_split_ratio,
+            val_split_mode=val_split_mode,
+            val_split_ratio=val_split_ratio,
+            seed=seed,
+        )
+
+        self.root = Path(root)
+        self.category = category
+
+        # Convert resolution to string if it's an integer
+        if isinstance(resolution, int):
+            resolution = str(resolution)
+
+        self.resolution = resolution
+        self.json_path = json_path
+
+        # Validate inputs
+        if category not in CATEGORIES:
+            msg = f"Category {category} not found in Real-IAD dataset. Available categories: {CATEGORIES}"
+            raise ValueError(msg)
+
+        if resolution not in RESOLUTIONS:
+            msg = f"Resolution {resolution} not found in Real-IAD dataset. Available resolutions: {RESOLUTIONS}"
+            raise ValueError(msg)
+
+    def prepare_data(self) -> None:
+        """Verify that the dataset is available and provide download instructions.
+
+        This method checks if the dataset exists in the root directory. If not, it provides
+        instructions for requesting access and downloading from Hugging Face.
+
+        The Real-IAD dataset is available at:
+        https://huggingface.co/datasets/REAL-IAD/Real-IAD
+
+        Note:
+            The dataset requires approval from the authors. You need to:
+            1. Create a Hugging Face account
+            2. Request access to the dataset
+            3. Wait for approval
+            4. Download and extract to the root directory
+        """
+        root_path = Path(self.root)
+        required_dirs = [root_path / f"realiad_{res}" for res in RESOLUTIONS] + [
+            root_path / "realiad_jsons",
+            root_path / "realiad_jsons_sv",
+            root_path / "realiad_jsons_fuiad_0.0",
+            root_path / "realiad_jsons_fuiad_0.1",
+            root_path / "realiad_jsons_fuiad_0.2",
+            root_path / "realiad_jsons_fuiad_0.4",
+        ]
+
+        if not any(d.exists() for d in required_dirs):
+            raise RuntimeError(get_download_instructions(root_path))
+
+    def _setup(self, _stage: str | None = None) -> None:
+        """Set up the datasets and perform dynamic subset splitting."""
+        self.train_data = RealIADDataset(
+            split=Split.TRAIN,
+            root=self.root,
+            category=self.category,
+            resolution=self.resolution,
+            json_path=self.json_path,
+        )
+        self.test_data = RealIADDataset(
+            split=Split.TEST,
+            root=self.root,
+            category=self.category,
+            resolution=self.resolution,
+            json_path=self.json_path,
+        )
+
+
+def get_download_instructions(root_path: Path) -> str:
+    """Get download instructions for the Real-IAD dataset.
+
+    Args:
+        root_path: Path where the dataset should be downloaded.
+
+    Returns:
+        str: Formatted download instructions.
+    """
+    return dedent(f"""
+        Real-IAD dataset not found in {root_path}
+
+        The Real-IAD dataset requires approval from the authors. To get access:
+
+        1. Create a Hugging Face account at https://huggingface.co
+        2. Visit https://huggingface.co/datasets/REAL-IAD/Real-IAD
+        3. Click "Access Repository" and fill out the form
+        4. Wait for approval from the dataset authors
+        5. Once approved, you have two options to download the dataset:
+
+        Option 1: Using Hugging Face CLI (Recommended)
+        --------------------------------------------
+        a. Install the Hugging Face CLI:
+           pip install huggingface_hub
+
+        b. Login to Hugging Face:
+           huggingface-cli login
+
+        c. Download the dataset:
+           huggingface-cli download \
+               --repo-type dataset \
+               --local-dir {root_path} REAL-IAD/Real-IAD \
+               --include="*" \
+               --token YOUR_HF_TOKEN
+
+        Option 2: Manual Download
+        -----------------------
+        a. Visit https://huggingface.co/datasets/REAL-IAD/Real-IAD
+        b. Download all files manually
+        c. Extract the contents to: {root_path}
+
+        Expected directory structure:
+        {root_path}/
+        ├── realiad_256/      # 256x256 resolution images
+        ├── realiad_512/      # 512x512 resolution images
+        ├── realiad_1024/     # 1024x1024 resolution images
+        ├── realiad_raw/      # Original resolution images
+        └── realiad_jsons/          # Base JSON metadata
+            ├── realiad_jsons_sv/       # Single-view JSON metadata
+            ├── realiad_jsons_fuiad_0.0/  # FUIAD v0.0 metadata
+            ├── realiad_jsons_fuiad_0.1/  # FUIAD v0.1 metadata
+            ├── realiad_jsons_fuiad_0.2/  # FUIAD v0.2 metadata
+            └── realiad_jsons_fuiad_0.4/  # FUIAD v0.4 metadata
+
+        Note: Replace YOUR_HF_TOKEN with your Hugging Face access token.
+              To get your token, visit: https://huggingface.co/settings/tokens
+
+        For more information about the dataset, see:
+        - Paper: https://arxiv.org/abs/2401.02749
+        - Code: https://github.com/REAL-IAD/REAL-IAD
+        - Dataset: https://huggingface.co/datasets/REAL-IAD/Real-IAD
+    """)