🚀 Add MVTecAD2 dataset

examples/api/02_data/mvtecad2.py

@@ -0,0 +1,147 @@
+"""Example showing how to use the MVTec AD 2 dataset with Anomalib.
+
+This example demonstrates how to:
+1. Load and visualize the MVTec AD 2 dataset
+2. Create a datamodule and use it for training
+3. Access different test sets (public, private, mixed)
+4. Work with custom transforms and visualization
+"""
+
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+import matplotlib.pyplot as plt
+import torch
+from torch.utils.data import DataLoader
+from torchvision.transforms.v2 import Compose, Resize, ToDtype, ToImage
+
+from anomalib.data import MVTecAD2
+from anomalib.data.datasets.base.image import ImageItem
+from anomalib.data.datasets.image.mvtecad2 import MVTecAD2Dataset, TestType
+from anomalib.data.utils import Split
+
+# 1. Basic Usage
+print("1. Basic Usage")
+datamodule = MVTecAD2(
+    root="./datasets/MVTec_AD_2",
+    category="sheet_metal",
+    train_batch_size=32,
+    eval_batch_size=32,
+    num_workers=8,
+)
+datamodule.setup()  # This will prepare the dataset
+
+# Print some information about the splits
+print(f"Number of training samples: {len(datamodule.train_data)}")
+print(f"Number of validation samples: {len(datamodule.val_data)}")
+print(f"Number of test samples (public): {len(datamodule.test_public_data)}")
+print(f"Number of test samples (private): {len(datamodule.test_private_data)}")
+print(f"Number of test samples (private mixed): {len(datamodule.test_private_mixed_data)}")
+
+# 2. Custom Transforms
+print("\n2. Custom Transforms")
+transform = Compose([
+    ToImage(),
+    Resize((256, 256)),
+    ToDtype(torch.float32, scale=True),
+])
+
+# Create dataset with custom transform
+datamodule = MVTecAD2(
+    root="./datasets/MVTec_AD_2",
+    category="sheet_metal",
+    train_augmentations=transform,
+    val_augmentations=transform,
+    test_augmentations=transform,
+)
+datamodule.setup()
+
+# 3. Different Test Sets
+print("\n3. Accessing Different Test Sets")
+
+# Get loaders for each test set
+public_loader = datamodule.test_dataloader(test_type=TestType.PUBLIC)
+private_loader = datamodule.test_dataloader(test_type=TestType.PRIVATE)
+mixed_loader = datamodule.test_dataloader(test_type=TestType.PRIVATE_MIXED)
+
+# Get sample batches
+public_batch = next(iter(public_loader))
+private_batch = next(iter(private_loader))
+mixed_batch = next(iter(mixed_loader))
+
+print("Public test batch shape:", public_batch.image.shape)
+print("Private test batch shape:", private_batch.image.shape)
+print("Private mixed test batch shape:", mixed_batch.image.shape)
+
+# 4. Advanced Usage - Direct Dataset Access
+print("\n4. Advanced Usage")
+
+# Create datasets for each split
+train_dataset = MVTecAD2Dataset(
+    root="./datasets/MVTec_AD_2",
+    category="sheet_metal",
+    split=Split.TRAIN,
+    augmentations=transform,
+)
+
+test_dataset = MVTecAD2Dataset(
+    root="./datasets/MVTec_AD_2",
+    category="sheet_metal",
+    split=Split.TEST,
+    test_type=TestType.PUBLIC,  # Use public test set
+    augmentations=transform,
+)
+
+# Create dataloaders
+train_loader = DataLoader(train_dataset, batch_size=4, shuffle=True, collate_fn=train_dataset.collate_fn)
+test_loader = DataLoader(test_dataset, batch_size=4, shuffle=False, collate_fn=test_dataset.collate_fn)
+
+# Get some sample images
+train_samples = next(iter(train_loader))
+test_samples = next(iter(test_loader))
+
+print("Train Dataset:")
+print(f"- Number of samples: {len(train_dataset)}")
+print(f"- Image shape: {train_samples.image.shape}")
+print(f"- Labels: {train_samples.gt_label}")
+
+print("\nTest Dataset:")
+print(f"- Number of samples: {len(test_dataset)}")
+print(f"- Image shape: {test_samples.image.shape}")
+print(f"- Labels: {test_samples.gt_label}")
+if hasattr(test_samples, "gt_mask") and test_samples.gt_mask is not None:
+    print(f"- Mask shape: {test_samples.gt_mask.shape}")
+
+
+# 5. Visualize some samples
+def show_samples(samples: ImageItem, title: str) -> None:
+    """Helper function to display samples."""
+    if samples.image is None or samples.gt_label is None:
+        msg = "Samples must have image and label data"
+        raise ValueError(msg)
+
+    fig, axes = plt.subplots(1, 4, figsize=(15, 4))
+    fig.suptitle(title)
+
+    for i in range(4):
+        img = samples.image[i].permute(1, 2, 0).numpy()
+        axes[i].imshow(img)
+        axes[i].axis("off")
+        if hasattr(samples, "gt_mask") and samples.gt_mask is not None:
+            mask = samples.gt_mask[i].squeeze().numpy()
+            axes[i].imshow(mask, alpha=0.3, cmap="Reds")
+        label = "Normal" if samples.gt_label[i] == 0 else "Anomaly"
+        axes[i].set_title(label)
+
+    plt.tight_layout()
+    plt.show()
+
+
+# Show training samples (normal only)
+show_samples(train_samples, "Training Samples (Normal)")
+
+# Show test samples (mix of normal and anomalous)
+show_samples(test_samples, "Test Samples (Normal + Anomalous)")
+
+if __name__ == "__main__":
+    print("\nMVTec AD 2 Dataset example completed successfully!")

examples/configs/data/mvtecad2.yaml

@@ -0,0 +1,9 @@
+class_path: anomalib.data.MVTecAD2
+init_args:
+  root: "./datasets/MVTec_AD_2"
+  category: "sheet_metal"
+  train_batch_size: 32
+  eval_batch_size: 32
+  num_workers: 8
+  test_type: "public"
+  seed: null

src/anomalib/data/__init__.py

@@ -49,7 +49,7 @@
 # Datamodules
 from .datamodules.base import AnomalibDataModule
 from .datamodules.depth import DepthDataFormat, Folder3D, MVTec3D
-from .datamodules.image import BTech, Datumaro, Folder, ImageDataFormat, Kolektor, MVTecAD, Visa
+from .datamodules.image import BTech, Datumaro, Folder, ImageDataFormat, Kolektor, MVTec, MVTecAD, MVTecAD2, Visa
 from .datamodules.video import Avenue, ShanghaiTech, UCSDped, VideoDataFormat
 
 # Datasets
@@ -160,8 +160,10 @@ def get_datamodule(config: DictConfig | ListConfig | dict) -> AnomalibDataModule
     "ImageDataFormat",
     "Kolektor",
     "KolektorDataset",
+    "MVTec",  # Deprecated, use MVTecAD instead
     "MVTecAD",
     "MVTecADDataset",
+    "MVTecAD2",
     "Visa",
     "VisaDataset",
     # Video

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

@@ -317,6 +317,9 @@ def _create_val_split(self) -> None:
         This handles sampling from train/test sets and optionally creating
         synthetic anomalies.
         """
+        if self.val_split_mode == ValSplitMode.FROM_DIR:
+            # If the validation split mode is FROM_DIR, we don't need to create a validation set
+            return
         if self.val_split_mode == ValSplitMode.FROM_TRAIN:
             # randomly sample from train set
             self.train_data, self.val_data = random_split(

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

@@ -8,6 +8,7 @@
 - ``Folder``: Custom folder structure with normal/abnormal images
 - ``Kolektor``: Kolektor Surface-Defect Dataset
 - ``MVTecAD``: MVTec Anomaly Detection Dataset
+- ``MVTecAD2``: MVTec Anomaly Detection Dataset 2
 - ``Visa``: Visual Inspection for Steel Anomaly Dataset
 
 Example:
@@ -30,6 +31,7 @@
 from .folder import Folder
 from .kolektor import Kolektor
 from .mvtecad import MVTec, MVTecAD
+from .mvtecad2 import MVTecAD2
 from .visa import Visa
 
 
@@ -44,6 +46,7 @@ class ImageDataFormat(str, Enum):
     - ``FOLDER_3D``: Custom folder structure for 3D images
     - ``KOLEKTOR``: Kolektor Surface-Defect Dataset
     - ``MVTEC_AD``: MVTec AD Dataset
+    - ``MVTEC_AD_2``: MVTec AD 2 Dataset
     - ``MVTEC_3D``: MVTec 3D AD Dataset
     - ``VISA``: Visual Inspection for Steel Anomaly Dataset
     """
@@ -54,6 +57,7 @@ class ImageDataFormat(str, Enum):
     FOLDER_3D = "folder_3d"
     KOLEKTOR = "kolektor"
     MVTEC_AD = "mvtecad"
+    MVTEC_AD_2 = "mvtecad2"
     MVTEC_3D = "mvtec_3d"
     VISA = "visa"
 
@@ -63,7 +67,8 @@ class ImageDataFormat(str, Enum):
     "Datumaro",
     "Folder",
     "Kolektor",
+    "MVTec",  # Include MVTec for backward compatibility
     "MVTecAD",
-    "MVTec",  # Include both for backward compatibility
+    "MVTecAD2",
     "Visa",
 ]