feat(model): add DINOv2 official implementation and AnomalyDINO

docs/source/markdown/guides/reference/models/image/anomaly_dino.md

@@ -0,0 +1,13 @@
+# AnomalyDINO
+
+```{eval-rst}
+.. automodule:: anomalib.models.image.anomaly_dino.lightning_model
+   :members: AnomalyDINO
+   :show-inheritance:
+```
+
+```{eval-rst}
+.. automodule:: anomalib.models.image.anomaly_dino.torch_model
+   :members: AnomalyDINOModel
+   :show-inheritance:
+```

docs/source/markdown/guides/reference/models/image/index.md

@@ -4,6 +4,13 @@
 :margin: 1 1 0 0
 :gutter: 1
 
+:::{grid-item-card} {material-regular}`model_training;1.5em` AnomalyDINO
+:link: ./anomaly_dino
+:link-type: doc
+
+Boosting Patch-based Few-shot Anomaly Detection with DINOv2
+:::
+
 :::{grid-item-card} {material-regular}`model_training;1.5em` CFA
 :link: ./cfa
 :link-type: doc
@@ -142,6 +149,7 @@ WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
 :caption: Data
 :hidden:
 
+./anomaly_dino
 ./cfa
 ./cflow
 ./csflow

examples/configs/README.md

@@ -21,6 +21,7 @@ configs/
 │   └── visa.yaml
 └── model
     ├── ai_vad.yaml
+    ├── anomaly_dino.yaml
     ├── cfa.yaml
     ├── cflow.yaml
     ├── csflow.yaml

examples/configs/model/anomaly_dino.yaml

@@ -0,0 +1,7 @@
+model:
+  class_path: anomalib.models.AnomalyDINO
+  init_args:
+    num_neighbours: 1
+    encoder_name: dinov2_vit_small_14
+    masking: False
+    coreset_subsampling: False

src/anomalib/models/__init__.py

@@ -55,6 +55,7 @@
 from anomalib.utils.path import convert_snake_to_pascal_case, convert_to_snake_case, convert_to_title_case
 
 from .image import (
+    AnomalyDINO,
     Cfa,
     Cflow,
     Csflow,
@@ -93,6 +94,8 @@ class UnknownModelError(ModuleNotFoundError):
 
 
 __all__ = [
+    "AiVad",
+    "AnomalyDINO",
     "Cfa",
     "Cflow",
     "Csflow",
@@ -104,6 +107,7 @@ class UnknownModelError(ModuleNotFoundError):
     "EfficientAd",
     "Fastflow",
     "Fre",
+    "Fuvas",
     "Ganomaly",
     "Padim",
     "Patchcore",
@@ -114,8 +118,6 @@ class UnknownModelError(ModuleNotFoundError):
     "UniNet",
     "VlmAd",
     "WinClip",
-    "AiVad",
-    "Fuvas",
 ]
 
 logger = logging.getLogger(__name__)

src/anomalib/models/components/dinov2/__init__.py

@@ -0,0 +1,35 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+
+"""Anomalib's Vision Transformer implementation.
+
+References:
+https://github.com/facebookresearch/dinov2/blob/main/dinov2/
+
+Classes:
+    DinoVisionTransformer: DINOv2 implementation.
+    DinoV2Loader: Loader class to support downloading and loading weights.
+"""
+
+# vision transformer
+# loader
+from .dinov2_loader import DinoV2Loader
+from .vision_transformer import (
+    DinoVisionTransformer,
+    vit_base,
+    vit_giant2,
+    vit_large,
+    vit_small,
+)
+
+__all__ = [
+    # vision transformer
+    "DinoVisionTransformer",
+    "vit_base",
+    "vit_giant2",
+    "vit_large",
+    "vit_small",
+    # loader
+    "DinoV2Loader",
+]

src/anomalib/models/components/dinov2/dinov2_loader.py

@@ -0,0 +1,243 @@
+# Copyright (C) 2025 Intel Corporation
+# SPDX-License-Identifier: Apache-2.0
+
+"""Loading pre-trained DINOv2 Vision Transformer models.
+
+This module provides the :class:`DinoV2Loader` class for constructing and loading
+pre-trained DINOv2 Vision Transformer models used in the Dinomaly anomaly detection
+framework. It supports both standard DINOv2 models and register-token variants, and
+allows custom Vision Transformer factories to be supplied.
+
+Example:
+    >>> from anomalib.models.components.dinov2 import DinoV2Loader
+    >>> loader = DinoV2Loader()
+    >>> model = loader.load("dinov2_vit_base_14")
+    >>> model = loader.load("vit_base_14")
+    >>> custom_loader = DinoV2Loader(vit_factory=my_custom_vit_module)
+    >>> model = custom_loader.load("dinov2reg_vit_base_14")
+
+The DINOv2 loader handles:
+
+- Parsing model names and validating architecture types
+- Constructing the appropriate Vision Transformer model
+- Locating or downloading the corresponding pre-trained weights
+- Supporting custom ViT implementations via a pluggable factory
+
+This enables a simple, unified interface for accessing DINOv2-based backbones in
+downstream anomaly detection tasks.
+"""
+
+import logging
+from pathlib import Path
+from typing import ClassVar
+from urllib.request import urlretrieve
+
+import torch
+
+from anomalib.data.utils import DownloadInfo
+from anomalib.data.utils.download import DownloadProgressBar
+from anomalib.models.components.dinov2 import vision_transformer as dinov2_models
+
+logger = logging.getLogger(__name__)
+
+MODEL_FACTORIES: dict[str, object] = {
+    "dinov2": dinov2_models,
+    "dinov2_reg": dinov2_models,
+}
+
+
+class DinoV2Loader:
+    """Simple loader for DINOv2 Vision Transformer models.
+
+    Supports loading dinov2, dinov2_reg, and dinomaly model variants across small, base,
+    and large architectures.
+    """
+
+    DINOV2_BASE_URL: ClassVar[str] = "https://dl.fbaipublicfiles.com/dinov2"
+
+    MODEL_CONFIGS: ClassVar[dict[str, dict[str, int]]] = {
+        "small": {"embed_dim": 384, "num_heads": 6},
+        "base": {"embed_dim": 768, "num_heads": 12},
+        "large": {"embed_dim": 1024, "num_heads": 16},
+    }
+
+    def __init__(
+        self,
+        cache_dir: str | Path = "./pre_trained/",
+        vit_factory: object | None = None,
+    ) -> None:
+        self.cache_dir = Path(cache_dir)
+        self.vit_factory = vit_factory
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+
+    def load(self, model_name: str) -> torch.nn.Module:
+        """Load a DINOv2 model by name.
+
+        Args:
+            model_name: Model identifier such as "dinov2_vit_base_14".
+
+        Returns:
+            A fully constructed and weight-loaded PyTorch module.
+
+        Raises:
+            ValueError: If the requested model name is malformed or unsupported.
+        """
+        model_type, architecture, patch_size = self._parse_name(model_name)
+        model = self.create_model(model_type, architecture, patch_size)
+        self._load_weights(model, model_type, architecture, patch_size)
+
+        logger.info(f"Loaded model: {model_name}")
+        return model
+
+    @classmethod
+    def from_name(
+        cls,
+        model_name: str,
+        cache_dir: str | Path = "./pre_trained/",
+    ) -> torch.nn.Module:
+        """Instantiate a loader and return the requested model."""
+        loader = cls(cache_dir)
+        return loader.load(model_name)
+
+    def _parse_name(self, name: str) -> tuple[str, str, int]:
+        """Parse a model name string into components.
+
+        Args:
+            name: Full model name string.
+
+        Returns:
+            Tuple of (model_type, architecture_name, patch_size).
+
+        Raises:
+            ValueError: If the prefix or architecture is unknown.
+        """
+        parts = name.split("_")
+        prefix = parts[0]
+        architecture = parts[-2]
+        patch_size = int(parts[-1])
+
+        if prefix == "dinov2reg":
+            model_type = "dinov2_reg"
+        elif prefix == "dinov2":
+            model_type = "dinov2"
+        elif prefix == "dinomaly":
+            model_type = "dinomaly"
+        else:
+            msg = f"Unknown model type prefix '{prefix}'."
+            raise ValueError(msg)
+
+        if architecture not in self.MODEL_CONFIGS:
+            msg = f"Invalid architecture '{architecture}'. Expected one of: {list(self.MODEL_CONFIGS)}"
+            raise ValueError(
+                msg,
+            )
+
+        return model_type, architecture, patch_size
+
+    def create_model(self, model_type: str, architecture: str, patch_size: int) -> torch.nn.Module:
+        """Create a Vision Transformer model.
+
+        Args:
+            model_type: Normalized model family name (e.g., "dinov2", "dinov2_reg").
+            architecture: Architecture size (e.g., "small", "base", "large").
+            patch_size: ViT patch size.
+
+        Returns:
+            Instantiated Vision Transformer model.
+
+        Raises:
+            ValueError: If no matching constructor exists.
+        """
+        model_kwargs = {
+            "patch_size": patch_size,
+            "img_size": 518,
+            "block_chunks": 0,
+            "init_values": 1e-8,
+            "interpolate_antialias": False,
+            "interpolate_offset": 0.1,
+        }
+
+        if model_type == "dinov2_reg":
+            model_kwargs["num_register_tokens"] = 4
+
+        # If user supplied a custom ViT module, use it
+        module = self.vit_factory if self.vit_factory is not None else MODEL_FACTORIES[model_type]
+
+        ctor = getattr(module, f"vit_{architecture}", None)
+        if ctor is None:
+            msg = f"No constructor vit_{architecture} in module {module}"
+            raise ValueError(msg)
+
+        return ctor(**model_kwargs)
+
+    def _load_weights(
+        self,
+        model: torch.nn.Module,
+        model_type: str,
+        architecture: str,
+        patch_size: int,
+    ) -> None:
+        """Load pre-trained weights from disk, downloading them if necessary."""
+        weight_path = self._get_weight_path(model_type, architecture, patch_size)
+
+        if not weight_path.exists():
+            self._download_weights(model_type, architecture, patch_size)
+
+        # Using weights_only=True for safety mitigation (see Anomalib PR #2729)
+        state_dict = torch.load(weight_path, map_location="cpu", weights_only=True)  # nosec B614
+        model.load_state_dict(state_dict, strict=False)
+
+    def _get_weight_path(
+        self,
+        model_type: str,
+        architecture: str,
+        patch_size: int,
+    ) -> Path:
+        """Return the expected local path for downloaded weights."""
+        arch_code = architecture[0]
+
+        if model_type == "dinov2_reg":
+            filename = f"dinov2_vit{arch_code}{patch_size}_reg4_pretrain.pth"
+        else:
+            filename = f"dinov2_vit{arch_code}{patch_size}_pretrain.pth"
+
+        return self.cache_dir / filename
+
+    def _download_weights(
+        self,
+        model_type: str,
+        architecture: str,
+        patch_size: int,
+    ) -> None:
+        """Download DINOv2 weight files using Anomalib's standardized utilities."""
+        weight_path = self._get_weight_path(model_type, architecture, patch_size)
+        arch_code = architecture[0]
+
+        model_dir = f"dinov2_vit{arch_code}{patch_size}"
+        url = f"{self.DINOV2_BASE_URL}/{model_dir}/{weight_path.name}"
+
+        download_info = DownloadInfo(
+            name=f"DINOv2 {model_type} {architecture} weights",
+            url=url,
+            hashsum="",  # DINOv2 publishes no official hash
+            filename=weight_path.name,
+        )
+
+        logger.info(
+            f"Downloading DINOv2 weights: {weight_path.name} to {self.cache_dir}",
+        )
+
+        self.cache_dir.mkdir(parents=True, exist_ok=True)
+
+        with DownloadProgressBar(
+            unit="B",
+            unit_scale=True,
+            miniters=1,
+            desc=download_info.name,
+        ) as progress_bar:
+            # nosemgrep: python.lang.security.audit.dynamic-urllib-use-detected.dynamic-urllib-use-detected  # noqa: ERA001, E501
+            urlretrieve(  # noqa: S310  # nosec B310
+                url=url,
+                filename=weight_path,
+                reporthook=progress_bar.update_to,
+            )