fix(doc): Update documentation with last added models and datasets

docs/source/markdown/guides/reference/data/datamodules/depth/adam_3d.md

@@ -0,0 +1,8 @@
+# ADAM3D
+
+```{eval-rst}
+.. automodule:: anomalib.data.datamodules.depth.adam_3d
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```

docs/source/markdown/guides/reference/data/datamodules/depth/index.md

@@ -7,6 +7,13 @@ Anomalib provides datamodules for handling depth-based anomaly detection dataset
 ```{grid} 2
 :gutter: 2
 
+:::{grid-item-card} ADAM 3D
+:link: adam_3d
+:link-type: doc
+
+3D-ADAM (3D Anomaly Detection in Additive Manufacturing) Dataset for surface defects
+:::
+
 :::{grid-item-card} MVTec 3D
 :link: mvtec_3d
 :link-type: doc
@@ -26,6 +33,7 @@ Custom folder-based 3D datamodule for organizing your own depth-based anomaly de
 :hidden:
 :maxdepth: 1
 
+adam_3d
 mvtec_3d
 folder_3d
 ```

docs/source/markdown/guides/reference/data/datamodules/image.md

@@ -42,6 +42,13 @@ Custom folder-based dataset organization.
 Surface defect detection in electrical commutators.
 :::
 
+:::{grid-item-card} MPDD
+:link: anomalib.data.datamodules.image.MPDD
+:link-type: doc
+
+Metal parts defect detection dataset for visual defect detection in manufacturing
+:::
+
 :::{grid-item-card} MVTecAD
 :link: anomalib.data.datamodules.image.MVTecAD
 :link-type: doc
@@ -96,7 +103,7 @@ Visual Anomaly dataset.
 
 ```{eval-rst}
 .. automodule:: anomalib.data
-   :members: BMAD, BTech, Datumaro, Folder, Kolektor, MVTecAD, MVTecAD2, MVTecLOCO, RealIAD, Tabular, VAD, Visa
+   :members: BMAD, BTech, Datumaro, Folder, Kolektor, MPDD, MVTecAD, MVTecAD2, MVTecLOCO, RealIAD, Tabular, VAD, Visa
    :undoc-members:
    :show-inheritance:
 ```

docs/source/markdown/guides/reference/data/datamodules/image/mpdd.md

@@ -0,0 +1,8 @@
+# MPDD
+
+```{eval-rst}
+.. automodule:: anomalib.data.datamodules.image.mpdd
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```

docs/source/markdown/guides/reference/metrics/index.md

@@ -1,7 +1,168 @@
 # Metrics
 
+Anomalib provides a comprehensive set of metrics for evaluating anomaly detection model performance. All metrics extend TorchMetrics' functionality with Anomalib-specific features.
+
+## Available Metrics
+
+### Area Under Curve Metrics
+
+::::{grid} 2
+:gutter: 2
+
+:::{grid-item-card} AUROC
+:link: anomalib.metrics.AUROC
+:link-type: doc
+
+Area Under the Receiver Operating Characteristic curve. Measures the model's ability to distinguish between normal and anomalous samples.
+:::
+
+:::{grid-item-card} AUPR
+:link: anomalib.metrics.AUPR
+:link-type: doc
+
+Area Under the Precision-Recall curve. Particularly useful for imbalanced datasets.
+:::
+
+:::{grid-item-card} AUPRO
+:link: anomalib.metrics.AUPRO
+:link-type: doc
+
+Area Under the Per-Region Overlap curve. Evaluates pixel-level anomaly localization performance.
+:::
+
+:::{grid-item-card} AUPIMO
+:link: anomalib.metrics.AUPIMO
+:link-type: doc
+
+Area Under the Per-Image Missed Overlap curve. Advanced metric for evaluating localization quality.
+:::
+
+::::
+
+### F1 Score Metrics
+
+::::{grid} 2
+:gutter: 2
+
+:::{grid-item-card} F1Score
+:link: anomalib.metrics.F1Score
+:link-type: doc
+
+Standard F1 score for binary classification. Harmonic mean of precision and recall.
+:::
+
+:::{grid-item-card} F1Max
+:link: anomalib.metrics.F1Max
+:link-type: doc
+
+Maximum F1 score across all possible thresholds. Useful for finding optimal operating points.
+:::
+
+::::
+
+### Threshold Metrics
+
+::::{grid} 2
+:gutter: 2
+
+:::{grid-item-card} F1AdaptiveThreshold
+:link: anomalib.metrics.F1AdaptiveThreshold
+:link-type: doc
+
+Automatically determines the optimal threshold by maximizing F1 score.
+:::
+
+:::{grid-item-card} ManualThreshold
+:link: anomalib.metrics.ManualThreshold
+:link-type: doc
+
+Uses a manually specified threshold for classification.
+:::
+
+::::
+
+### Other Metrics
+
+::::{grid} 2
+:gutter: 2
+
+:::{grid-item-card} PRO
+:link: anomalib.metrics.PRO
+:link-type: doc
+
+Per-Region Overlap score for evaluating pixel-level localization.
+:::
+
+:::{grid-item-card} PIMO
+:link: anomalib.metrics.PIMO
+:link-type: doc
+
+Per-Image Missed Overlap for assessing localization errors.
+:::
+
+:::{grid-item-card} PGn
+:link: anomalib.metrics.PGn
+:link-type: doc
+
+Presorted Good with n% bad samples missed. Measures false negative rate at specific operating points.
+:::
+
+:::{grid-item-card} PBn
+:link: anomalib.metrics.PBn
+:link-type: doc
+
+Presorted Bad with n% good samples misclassified. Measures false positive rate at specific operating points.
+:::
+
+:::{grid-item-card} MinMax
+:link: anomalib.metrics.MinMax
+:link-type: doc
+
+Normalizes anomaly scores to [0, 1] range using min-max scaling.
+:::
+
+:::{grid-item-card} AnomalyScoreDistribution
+:link: anomalib.metrics.AnomalyScoreDistribution
+:link-type: doc
+
+Analyzes and tracks the distribution of anomaly scores for model diagnostics.
+:::
+
+::::
+
+### Utility Classes
+
+::::{grid} 2
+:gutter: 2
+
+:::{grid-item-card} AnomalibMetric
+:link: anomalib.metrics.AnomalibMetric
+:link-type: doc
+
+Base class for all Anomalib metrics. Extends TorchMetrics with field-based updates.
+:::
+
+:::{grid-item-card} Evaluator
+:link: anomalib.metrics.Evaluator
+:link-type: doc
+
+Orchestrates multiple metrics for comprehensive model evaluation.
+:::
+
+:::{grid-item-card} BinaryPrecisionRecallCurve
+:link: anomalib.metrics.BinaryPrecisionRecallCurve
+:link-type: doc
+
+Computes precision-recall curves for binary classification tasks.
+:::
+
+::::
+
+## API Reference
+
 ```{eval-rst}
 .. automodule:: anomalib.metrics
-   :members:
+   :members: AUROC, AUPR, AUPRO, AUPIMO, F1Score, F1Max, F1AdaptiveThreshold, ManualThreshold, PRO, PIMO, PGn, PBn, MinMax, AnomalyScoreDistribution, AnomalibMetric, Evaluator, BinaryPrecisionRecallCurve, create_anomalib_metric
+   :undoc-members:
    :show-inheritance:
 ```

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

@@ -39,6 +39,14 @@ Deep Feature Kernel Density Estimation
 Probabilistic Modeling of Deep Features for Out-of-Distribution and Adversarial Detection
 :::
 
+:::{grid-item-card} {material-regular}`model_training;1.5em` Dinomaly
+:link: ./dinomaly
+:link-type: doc
+
+Dinomaly: The Less Is More Philosophy in Unsupervised Anomaly Detection
+
+:::
+
 :::{grid-item-card} {material-regular}`model_training;1.5em` DRAEM
 :link: ./draem
 :link-type: doc
@@ -123,6 +131,13 @@ SuperSimpleNet: A Unified Surface Defect Detection Model for all Supervision Reg
 U-Flow: A U-shaped Normalizing Flow for Anomaly Detection with Unsupervised Threshold
 :::
 
+:::{grid-item-card} {material-regular}`model_training;1.5em` UniNet
+:link: ./uninet
+:link-type: doc
+
+UniNet: A Contrastive Learning-guided Unified Framework with Feature Selection for Anomaly Detection
+:::
+
 :::{grid-item-card} {material-regular}`model_training;1.5em` VLM-AD
 :link: ./vlm_ad
 :link-type: doc
@@ -147,6 +162,7 @@ WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
 ./csflow
 ./dfkde
 ./dfm
+./dinomaly
 ./draem
 ./dsr
 ./efficient_ad
@@ -157,6 +173,9 @@ WinCLIP: Zero-/Few-Shot Anomaly Classification and Segmentation
 ./patchcore
 ./reverse_distillation
 ./stfpm
+./supersimplenet
 ./uflow
+./uninet
+./vlm_ad
 ./winclip
 ```

docs/source/markdown/guides/reference/models/video/fuvas.md

@@ -0,0 +1,8 @@
+# FUVAS
+
+```{eval-rst}
+.. automodule:: anomalib.models.video.fuvas
+   :members:
+   :undoc-members:
+   :show-inheritance:
+```

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

@@ -1,10 +1,21 @@
 # Video Models
 
-::::{grid}
+::::{grid} 2
+:margin: 1 1 0 0
+:gutter: 1
 
 :::{grid-item-card} {material-regular}`model_training;1.5em` AI VAD
 :link: ./ai_vad
 :link-type: doc
+
+Attribute-based Representations for Accurate and Interpretable Video Anomaly Detection
+:::
+
+:::{grid-item-card} {material-regular}`model_training;1.5em` FUVAS
+:link: ./fuvas
+:link-type: doc
+
+Few-shot Unsupervised Video Anomaly Segmentation via Low-Rank Factorization of Spatio-Temporal Features
 :::
 
 ::::
@@ -14,4 +25,5 @@
 :hidden:
 
 ./ai_vad
+./fuvas
 ```

src/anomalib/models/__init__.py

@@ -26,6 +26,7 @@
     - CSFlow (:class:`anomalib.models.image.Csflow`)
     - DFKDE (:class:`anomalib.models.image.Dfkde`)
     - DFM (:class:`anomalib.models.image.Dfm`)
+    - Dinomaly (:class:`anomalib.models.image.Dinomaly`)
     - DRAEM (:class:`anomalib.models.image.Draem`)
     - DSR (:class:`anomalib.models.image.Dsr`)
     - EfficientAd (:class:`anomalib.models.image.EfficientAd`)
@@ -38,11 +39,13 @@
     - STFPM (:class:`anomalib.models.image.Stfpm`)
     - SuperSimpleNet (:class:`anomalib.models.image.Supersimplenet`)
     - UFlow (:class:`anomalib.models.image.Uflow`)
+    - UniNet (:class:`anomalib.models.image.UniNet`)
     - VLM-AD (:class:`anomalib.models.image.VlmAd`)
     - WinCLIP (:class:`anomalib.models.image.WinClip`)
 
 Video Models:
     - AI-VAD (:class:`anomalib.models.video.AiVad`)
+    - FUVAS (:class:`anomalib.models.video.Fuvas`)
 """
 
 import logging

src/anomalib/models/image/__init__.py

@@ -28,6 +28,7 @@
     - :class:`Csflow`: Conditional Split Flow
     - :class:`Dfkde`: Deep Feature Kernel Density Estimation
     - :class:`Dfm`: Deep Feature Modeling
+    - :class:`Dinomaly`: DinoV2-based Reconstruction Error Model
     - :class:`Draem`: Dual Reconstruction by Adversarial Masking
     - :class:`Dsr`: Deep Spatial Reconstruction
     - :class:`EfficientAd`: Efficient Anomaly Detection
@@ -40,6 +41,7 @@
     - :class:`Stfpm`: Student-Teacher Feature Pyramid Matching
     - :class:`SuperSimpleNet`: SuperSimpleNet
     - :class:`Uflow`: Unsupervised Flow
+    - :class:`UniNet`: Student-Teacher Contrastive Learning Model
     - :class:`VlmAd`: Vision Language Model Anomaly Detection
     - :class:`WinClip`: Zero-/Few-Shot CLIP-based Detection
 """

src/anomalib/models/video/__init__.py

@@ -28,6 +28,7 @@
 
 Available Models:
     - :class:`AiVad`: AI-based Video Anomaly Detection
+    - :class:`Fuvas`: Few-shot Unsupervised Video Anomaly Segmentation
 """
 
 from .ai_vad import AiVad