diff --git a/.github/workflows/pre_merge.yaml b/.github/workflows/pre_merge.yaml
index ceae7f0fc2..97a5dc3b16 100644
--- a/.github/workflows/pre_merge.yaml
+++ b/.github/workflows/pre_merge.yaml
@@ -94,6 +94,7 @@ jobs:
           - task: "instance_segmentation"
           - task: "semantic_segmentation"
           - task: "visual_prompting"
+          - task: "anomaly"
     name: Integration-Test-${{ matrix.task }}-py310
     # This is what will cancel the job concurrency
     concurrency:
diff --git a/pyproject.toml b/pyproject.toml
index 09f521cbc5..29ec1343a8 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -88,7 +88,18 @@ mmlab = [
     "oss2==2.17.0",
 ]
 anomaly = [
-    "anomalib==1.0.0",
+    # [FIXME] @ashwinvaidya17: Install using a temporary hot-fix commit due to a torchmetrics version conflict.
+    "anomalib @ git+https://github.com/openvinotoolkit/anomalib.git@e78091883a620229c277a79674a904d9f785f8d5",
+    # This is a dependency to avoid conflicts with installing the anomalib[core] option.
+    "av>=10.0.0",
+    "einops>=0.3.2",
+    "freia>=0.2",
+    "imgaug==0.4.0",
+    "kornia>=0.6.6,<0.6.10",
+    "matplotlib>=3.4.3",
+    "opencv-python>=4.5.3.56",
+    "pandas>=1.1.0",
+    "open-clip-torch>=2.23.0",
 ]
 
 [project.scripts]
diff --git a/src/otx/algo/anomaly/padim.py b/src/otx/algo/anomaly/padim.py
index de9bbb290b..4d70e02542 100644
--- a/src/otx/algo/anomaly/padim.py
+++ b/src/otx/algo/anomaly/padim.py
@@ -15,26 +15,26 @@ class Padim(OTXAnomaly, OTXModel, AnomalibPadim):
     """OTX Padim model.
 
     Args:
-        input_size (tuple[int, int], optional): Input size. Defaults to (256, 256).
         backbone (str, optional): Feature extractor backbone. Defaults to "resnet18".
         layers (list[str], optional): Feature extractor layers. Defaults to ["layer1", "layer2", "layer3"].
         pre_trained (bool, optional): Pretrained backbone. Defaults to True.
         n_features (int | None, optional): Number of features. Defaults to None.
+        num_classes (int, optional): Anoamly don't use num_classes ,
+            but OTXModel always receives num_classes, so need this.
     """
 
     def __init__(
         self,
-        input_size: tuple[int, int] = (256, 256),
         backbone: str = "resnet18",
         layers: list[str] = ["layer1", "layer2", "layer3"],  # noqa: B006
         pre_trained: bool = True,
         n_features: int | None = None,
+        num_classes: int = 2,
     ) -> None:
         OTXAnomaly.__init__(self)
-        OTXModel.__init__(self, num_classes=2)
+        OTXModel.__init__(self, num_classes=num_classes)
         AnomalibPadim.__init__(
             self,
-            input_size=input_size,
             backbone=backbone,
             layers=layers,
             pre_trained=pre_trained,
diff --git a/src/otx/algo/anomaly/stfpm.py b/src/otx/algo/anomaly/stfpm.py
index 889ea33ab8..f77b70c4dd 100644
--- a/src/otx/algo/anomaly/stfpm.py
+++ b/src/otx/algo/anomaly/stfpm.py
@@ -21,21 +21,21 @@ class Stfpm(OTXAnomaly, OTXModel, AnomalibStfpm):
 
     Args:
         layers (Sequence[str]): Feature extractor layers.
-        input_size (tuple[int, int]): Input size.
         backbone (str, optional): Feature extractor backbone. Defaults to "resnet18".
+        num_classes (int, optional): Anoamly don't use num_classes ,
+            but OTXModel always receives num_classes, so need this.
     """
 
     def __init__(
         self,
         layers: Sequence[str] = ["layer1", "layer2", "layer3"],
-        input_size: tuple[int, int] = (256, 256),
         backbone: str = "resnet18",
+        num_classes: int = 2,
     ) -> None:
         OTXAnomaly.__init__(self)
-        OTXModel.__init__(self, num_classes=2)
+        OTXModel.__init__(self, num_classes=num_classes)
         AnomalibStfpm.__init__(
             self,
-            input_size=input_size,
             backbone=backbone,
             layers=layers,
         )
diff --git a/src/otx/cli/cli.py b/src/otx/cli/cli.py
index 69d386e2bd..8aa0591936 100644
--- a/src/otx/cli/cli.py
+++ b/src/otx/cli/cli.py
@@ -413,7 +413,7 @@ def instantiate_model(self, model_config: Namespace) -> tuple:
         # Update num_classes
         if not self.get_config_value(self.config_init, "disable_infer_num_classes", False):
             num_classes = self.datamodule.label_info.num_classes
-            if num_classes != model_config.init_args.num_classes:
+            if hasattr(model_config.init_args, "num_classes") and num_classes != model_config.init_args.num_classes:
                 warning_msg = (
                     f"The `num_classes` in dataset is {num_classes} "
                     f"but, the `num_classes` of model is {model_config.init_args.num_classes}. "
diff --git a/src/otx/core/data/entity/anomaly/classification.py b/src/otx/core/data/entity/anomaly/classification.py
index 8cce41e722..cacdeb792c 100644
--- a/src/otx/core/data/entity/anomaly/classification.py
+++ b/src/otx/core/data/entity/anomaly/classification.py
@@ -53,7 +53,7 @@ def collate_fn(
     ) -> AnomalyClassificationDataBatch:
         """Collection function to collect `OTXDataEntity` into `OTXBatchDataEntity` in data loader."""
         batch = super().collate_fn(entities)
-        images = tv_tensors.Image(data=torch.stack(batch.images, dim=0)) if stack_images else batch.images
+        images = tv_tensors.Image(data=torch.stack(tuple(batch.images), dim=0)) if stack_images else batch.images
         return AnomalyClassificationDataBatch(
             batch_size=batch.batch_size,
             images=images,
diff --git a/src/otx/core/data/entity/anomaly/detection.py b/src/otx/core/data/entity/anomaly/detection.py
index ad15d936d0..08a5e63805 100644
--- a/src/otx/core/data/entity/anomaly/detection.py
+++ b/src/otx/core/data/entity/anomaly/detection.py
@@ -56,7 +56,7 @@ def collate_fn(
     ) -> AnomalyDetectionDataBatch:
         """Collection function to collect `OTXDataEntity` into `OTXBatchDataEntity` in data loader."""
         batch = super().collate_fn(entities)
-        images = tv_tensors.Image(data=torch.stack(batch.images, dim=0)) if stack_images else batch.images
+        images = tv_tensors.Image(data=torch.stack(tuple(batch.images), dim=0)) if stack_images else batch.images
         return AnomalyDetectionDataBatch(
             batch_size=batch.batch_size,
             images=images,
diff --git a/src/otx/core/data/entity/anomaly/segmentation.py b/src/otx/core/data/entity/anomaly/segmentation.py
index 1266c7529c..0ec64845cd 100644
--- a/src/otx/core/data/entity/anomaly/segmentation.py
+++ b/src/otx/core/data/entity/anomaly/segmentation.py
@@ -54,7 +54,7 @@ def collate_fn(
     ) -> AnomalySegmentationDataBatch:
         """Collection function to collect `OTXDataEntity` into `OTXBatchDataEntity` in data loader."""
         batch = super().collate_fn(entities)
-        images = tv_tensors.Image(data=torch.stack(batch.images, dim=0)) if stack_images else batch.images
+        images = tv_tensors.Image(data=torch.stack(tuple(batch.images), dim=0)) if stack_images else batch.images
         return AnomalySegmentationDataBatch(
             batch_size=batch.batch_size,
             images=images,
diff --git a/src/otx/core/model/module/anomaly/anomaly_lightning.py b/src/otx/core/model/module/anomaly/anomaly_lightning.py
index f831cfd647..01fca867d9 100644
--- a/src/otx/core/model/module/anomaly/anomaly_lightning.py
+++ b/src/otx/core/model/module/anomaly/anomaly_lightning.py
@@ -134,7 +134,7 @@ class OTXAnomaly:
     def __init__(self) -> None:
         self.optimizer: list[OptimizerCallable] | OptimizerCallable = None
         self.scheduler: list[LRSchedulerCallable] | LRSchedulerCallable = None
-        self.input_size: list[int] = [256, 256]
+        self._input_size: tuple[int, int] = (256, 256)
         self.mean_values: tuple[float, float, float] = (0.0, 0.0, 0.0)
         self.scale_values: tuple[float, float, float] = (1.0, 1.0, 1.0)
         self.trainer: Trainer
@@ -147,6 +147,19 @@ def __init__(self) -> None:
         self.image_metrics: AnomalibMetricCollection
         self.pixel_metrics: AnomalibMetricCollection
 
+    @property
+    def input_size(self) -> tuple[int, int]:
+        """Returns the input size of the model.
+
+        Returns:
+            tuple[int, int]: The input size of the model as a tuple of (height, width).
+        """
+        return self._input_size
+
+    @input_size.setter
+    def input_size(self, value: tuple[int, int]) -> None:
+        self._input_size = value
+
     @property
     def task(self) -> AnomalibTaskType:
         """Return the task type of the model."""
@@ -342,13 +355,13 @@ def state_dict(self) -> dict[str, Any]:
         """
         state_dict = super().state_dict()  # type: ignore[misc]
         # This is defined in OTXModel
-        state_dict["meta_info"] = self.meta_info  # type: ignore[attr-defined]
+        state_dict["label_info"] = self.label_info  # type: ignore[attr-defined]
         return state_dict
 
     def load_state_dict(self, ckpt: OrderedDict[str, Any], *args, **kwargs) -> None:
         """Pass the checkpoint to the anomaly model."""
         ckpt = ckpt.get("state_dict", ckpt)
-        ckpt.pop("meta_info", None)  # [TODO](ashwinvaidya17): Revisit this method when OTXModel is the lightning model
+        ckpt.pop("label_info", None)  # [TODO](ashwinvaidya17): Revisit this method when OTXModel is the lightning model
         return super().load_state_dict(ckpt, *args, **kwargs)  # type: ignore[misc]
 
     def forward(
@@ -441,8 +454,9 @@ def export(
         """
         min_val = self.normalization_metrics.state_dict()["min"].cpu().numpy().tolist()
         max_val = self.normalization_metrics.state_dict()["max"].cpu().numpy().tolist()
+        image_shape = (256, 256) if self.input_size is None else self.input_size
         exporter = _AnomalyModelExporter(
-            image_shape=(self.input_size[0], self.input_size[1]),
+            image_shape=image_shape,
             image_threshold=self.image_threshold.value.cpu().numpy().tolist(),
             pixel_threshold=self.pixel_threshold.value.cpu().numpy().tolist(),
             task=self.task,
diff --git a/src/otx/engine/utils/auto_configurator.py b/src/otx/engine/utils/auto_configurator.py
index 73ae76dcb6..90ce62ae6f 100644
--- a/src/otx/engine/utils/auto_configurator.py
+++ b/src/otx/engine/utils/auto_configurator.py
@@ -42,9 +42,9 @@
     OTXTaskType.INSTANCE_SEGMENTATION: RECIPE_PATH / "instance_segmentation" / "maskrcnn_r50.yaml",
     OTXTaskType.ACTION_CLASSIFICATION: RECIPE_PATH / "action" / "action_classification" / "x3d.yaml",
     OTXTaskType.ACTION_DETECTION: RECIPE_PATH / "action" / "action_detection" / "x3d_fastrcnn.yaml",
-    OTXTaskType.ANOMALY_CLASSIFICATION: RECIPE_PATH / "anomaly" / "anomaly_classification" / "padim.yaml",
-    OTXTaskType.ANOMALY_SEGMENTATION: RECIPE_PATH / "anomaly" / "anomaly_segmentation" / "padim.yaml",
-    OTXTaskType.ANOMALY_DETECTION: RECIPE_PATH / "anomaly" / "anomaly_detection" / "padim.yaml",
+    OTXTaskType.ANOMALY_CLASSIFICATION: RECIPE_PATH / "anomaly_classification" / "padim.yaml",
+    OTXTaskType.ANOMALY_SEGMENTATION: RECIPE_PATH / "anomaly_segmentation" / "padim.yaml",
+    OTXTaskType.ANOMALY_DETECTION: RECIPE_PATH / "anomaly_detection" / "padim.yaml",
     OTXTaskType.VISUAL_PROMPTING: RECIPE_PATH / "visual_prompting" / "sam_tiny_vit.yaml",
     OTXTaskType.ZERO_SHOT_VISUAL_PROMPTING: RECIPE_PATH / "zero_shot_visual_prompting" / "sam_tiny_vit.yaml",
 }
@@ -67,6 +67,7 @@
     "common_semantic_segmentation_with_subset_dirs": [OTXTaskType.SEMANTIC_SEGMENTATION],
     "kinetics": [OTXTaskType.ACTION_CLASSIFICATION],
     "ava": [OTXTaskType.ACTION_DETECTION],
+    "mvtec": [OTXTaskType.ANOMALY_CLASSIFICATION, OTXTaskType.ANOMALY_DETECTION, OTXTaskType.ANOMALY_SEGMENTATION],
 }
 
 OVMODEL_PER_TASK = {
diff --git a/src/otx/recipe/anomaly_classification/padim.yaml b/src/otx/recipe/anomaly_classification/padim.yaml
index a759123a8e..75e037f551 100644
--- a/src/otx/recipe/anomaly_classification/padim.yaml
+++ b/src/otx/recipe/anomaly_classification/padim.yaml
@@ -1,9 +1,6 @@
 model:
   class_path: otx.algo.anomaly.padim.Padim
   init_args:
-    input_size:
-      - 256
-      - 256
     layers: ["layer1", "layer2", "layer3"]
     backbone: "resnet18"
     pre_trained: True
@@ -15,10 +12,15 @@ engine:
 
 callback_monitor: step # this has no effect as Padim does not need to be trained
 
-data: ../../_base_/data/torchvision_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
+  precision: 32
   max_epochs: 1
   limit_val_batches: 0 # this is set to 0 as the default dataloader does not have validation set. But this also means that the model will not give correct performance numbers
+  callbacks:
+    - class_path: otx.algo.callbacks.adaptive_train_scheduling.AdaptiveTrainScheduling
+      init_args:
+        max_interval: 1
   data:
     task: ANOMALY_CLASSIFICATION
     config:
diff --git a/src/otx/recipe/anomaly_classification/stfpm.yaml b/src/otx/recipe/anomaly_classification/stfpm.yaml
index 9905505dd9..f518883423 100644
--- a/src/otx/recipe/anomaly_classification/stfpm.yaml
+++ b/src/otx/recipe/anomaly_classification/stfpm.yaml
@@ -1,9 +1,6 @@
 model:
   class_path: otx.algo.anomaly.stfpm.Stfpm
   init_args:
-    input_size:
-      - 256
-      - 256
     layers: ["layer1", "layer2", "layer3"]
     backbone: "resnet18"
 
@@ -21,7 +18,7 @@ engine:
 
 callback_monitor: train_loss_epoch # val loss is not available as there is no validation set from default dataloader
 
-data: ../../_base_/data/torchvision_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
   max_epochs: 100
   limit_val_batches: 0 # this is set to 0 as the default dataloader does not have validation set. But this also means that the model will not give correct performance numbers
@@ -29,6 +26,9 @@ overrides:
     - class_path: lightning.pytorch.callbacks.EarlyStopping
       init_args:
         patience: 5
+    - class_path: otx.algo.callbacks.adaptive_train_scheduling.AdaptiveTrainScheduling
+      init_args:
+        max_interval: 1
   data:
     task: ANOMALY_CLASSIFICATION
     config:
diff --git a/src/otx/recipe/anomaly_detection/padim.yaml b/src/otx/recipe/anomaly_detection/padim.yaml
index 0b8a73fbe5..fc62ee39d1 100644
--- a/src/otx/recipe/anomaly_detection/padim.yaml
+++ b/src/otx/recipe/anomaly_detection/padim.yaml
@@ -1,9 +1,6 @@
 model:
   class_path: otx.algo.anomaly.padim.Padim
   init_args:
-    input_size:
-      - 256
-      - 256
     layers: ["layer1", "layer2", "layer3"]
     backbone: "resnet18"
     pre_trained: True
@@ -15,10 +12,15 @@ engine:
 
 callback_monitor: step # this has no effect as Padim does not need to be trained
 
-data: ../../_base_/data/torchvision_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
+  precision: 32
   max_epochs: 1
   limit_val_batches: 0 # this is set to 0 as the default dataloader does not have validation set. But this also means that the model will not give correct performance numbers
+  callbacks:
+    - class_path: otx.algo.callbacks.adaptive_train_scheduling.AdaptiveTrainScheduling
+      init_args:
+        max_interval: 1
   data:
     task: ANOMALY_DETECTION
     config:
diff --git a/src/otx/recipe/anomaly_detection/stfpm.yaml b/src/otx/recipe/anomaly_detection/stfpm.yaml
index 7cefa80329..221865aab9 100644
--- a/src/otx/recipe/anomaly_detection/stfpm.yaml
+++ b/src/otx/recipe/anomaly_detection/stfpm.yaml
@@ -1,9 +1,6 @@
 model:
   class_path: otx.algo.anomaly.stfpm.Stfpm
   init_args:
-    input_size:
-      - 256
-      - 256
     layers: ["layer1", "layer2", "layer3"]
     backbone: "resnet18"
 
@@ -21,7 +18,7 @@ engine:
 
 callback_monitor: train_loss_epoch # val loss is not available as there is no validation set from default dataloader
 
-data: ../../_base_/data/torchvision_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
   max_epochs: 100
   limit_val_batches: 0 # this is set to 0 as the default dataloader does not have validation set. But this also means that the model will not give correct performance numbers
@@ -29,6 +26,9 @@ overrides:
     - class_path: lightning.pytorch.callbacks.EarlyStopping
       init_args:
         patience: 5
+    - class_path: otx.algo.callbacks.adaptive_train_scheduling.AdaptiveTrainScheduling
+      init_args:
+        max_interval: 1
   data:
     task: ANOMALY_DETECTION
     config:
diff --git a/src/otx/recipe/anomaly_segmentation/padim.yaml b/src/otx/recipe/anomaly_segmentation/padim.yaml
index a2035f8001..3ed2934eb8 100644
--- a/src/otx/recipe/anomaly_segmentation/padim.yaml
+++ b/src/otx/recipe/anomaly_segmentation/padim.yaml
@@ -1,9 +1,6 @@
 model:
   class_path: otx.algo.anomaly.padim.Padim
   init_args:
-    input_size:
-      - 256
-      - 256
     layers: ["layer1", "layer2", "layer3"]
     backbone: "resnet18"
     pre_trained: True
@@ -15,10 +12,15 @@ engine:
 
 callback_monitor: step # this has no effect as Padim does not need to be trained
 
-data: ../../_base_/data/torchvision_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
+  precision: 32
   max_epochs: 1
   limit_val_batches: 0 # this is set to 0 as the default dataloader does not have validation set. But this also means that the model will not give correct performance numbers
+  callbacks:
+    - class_path: otx.algo.callbacks.adaptive_train_scheduling.AdaptiveTrainScheduling
+      init_args:
+        max_interval: 1
   data:
     task: ANOMALY_SEGMENTATION
     config:
diff --git a/src/otx/recipe/anomaly_segmentation/stfpm.yaml b/src/otx/recipe/anomaly_segmentation/stfpm.yaml
index 9cb153e24a..a045fdd348 100644
--- a/src/otx/recipe/anomaly_segmentation/stfpm.yaml
+++ b/src/otx/recipe/anomaly_segmentation/stfpm.yaml
@@ -1,9 +1,6 @@
 model:
   class_path: otx.algo.anomaly.stfpm.Stfpm
   init_args:
-    input_size:
-      - 256
-      - 256
     layers: ["layer1", "layer2", "layer3"]
     backbone: "resnet18"
 
@@ -21,7 +18,7 @@ engine:
 
 callback_monitor: train_loss_epoch # val loss is not available as there is no validation set from default dataloader
 
-data: ../../_base_/data/torchvision_base.yaml
+data: ../_base_/data/torchvision_base.yaml
 overrides:
   max_epochs: 100
   limit_val_batches: 0 # this is set to 0 as the default dataloader does not have validation set. But this also means that the model will not give correct performance numbers
@@ -29,6 +26,9 @@ overrides:
     - class_path: lightning.pytorch.callbacks.EarlyStopping
       init_args:
         patience: 5
+    - class_path: otx.algo.callbacks.adaptive_train_scheduling.AdaptiveTrainScheduling
+      init_args:
+        max_interval: 1
   data:
     task: ANOMALY_SEGMENTATION
     config:
diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/00.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/00.png
new file mode 100644
index 0000000000..0650051dc1
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diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/01.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/01.png
new file mode 100644
index 0000000000..3193a7fcab
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diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/02.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/02.png
new file mode 100644
index 0000000000..7edbb42a0f
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diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/03.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/03.png
new file mode 100644
index 0000000000..78d290ed8d
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diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/04.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/04.png
new file mode 100644
index 0000000000..ae404f0494
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diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/05.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/05.png
new file mode 100644
index 0000000000..c60c760b1e
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diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/06.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/06.png
new file mode 100644
index 0000000000..dffa48d8ef
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diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/07.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/07.png
new file mode 100644
index 0000000000..2412619f14
Binary files /dev/null and b/tests/assets/anomaly_hazelnut/ground_truth/colour/07.png differ
diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/08.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/08.png
new file mode 100644
index 0000000000..361ff79b7c
Binary files /dev/null and b/tests/assets/anomaly_hazelnut/ground_truth/colour/08.png differ
diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/09.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/09.png
new file mode 100644
index 0000000000..ef51fd2c26
Binary files /dev/null and b/tests/assets/anomaly_hazelnut/ground_truth/colour/09.png differ
diff --git a/tests/assets/anomaly_hazelnut/ground_truth/colour/10.png b/tests/assets/anomaly_hazelnut/ground_truth/colour/10.png
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diff --git a/tests/integration/api/test_auto_configuration.py b/tests/integration/api/test_auto_configuration.py
index 1b45bdf423..217a1e3868 100644
--- a/tests/integration/api/test_auto_configuration.py
+++ b/tests/integration/api/test_auto_configuration.py
@@ -32,6 +32,8 @@ def test_auto_configuration(
         pytest.skip(
             reason="H-labels require num_multiclass_head, num_multilabel_classes, which skip until we have the ability to automate this.",
         )
+    if task.lower().startswith("anomaly"):
+        pytest.skip(reason="This will be added in a future pipeline behavior.")
 
     tmp_path_train = tmp_path / f"auto_train_{task}"
     data_root = fxt_target_dataset_per_task[task.lower()]
diff --git a/tests/integration/api/test_engine_api.py b/tests/integration/api/test_engine_api.py
index 13d3b6888e..6ee5ea67c7 100644
--- a/tests/integration/api/test_engine_api.py
+++ b/tests/integration/api/test_engine_api.py
@@ -37,8 +37,6 @@ def test_engine_from_config(
         pytest.skip(
             reason="H-labels require num_multiclass_head, num_multilabel_classes, which skip until we have the ability to automate this.",
         )
-    if "anomaly" in task.lower():
-        pytest.skip(reason="There's no dataset for anomaly tasks.")
 
     tmp_path_train = tmp_path / task
     engine = Engine.from_config(
@@ -70,6 +68,9 @@ def test_engine_from_config(
         OTXTaskType.ACTION_DETECTION,
         OTXTaskType.H_LABEL_CLS,
         OTXTaskType.ROTATED_DETECTION,
+        OTXTaskType.ANOMALY_CLASSIFICATION,
+        OTXTaskType.ANOMALY_DETECTION,
+        OTXTaskType.ANOMALY_SEGMENTATION,
     ]:
         return
 
diff --git a/tests/integration/cli/test_cli.py b/tests/integration/cli/test_cli.py
index 295043fac0..0339c6378d 100644
--- a/tests/integration/cli/test_cli.py
+++ b/tests/integration/cli/test_cli.py
@@ -129,6 +129,9 @@ def test_otx_e2e(
             "dino_v2",
             "instance_segmentation",
             "action",
+            "anomaly_classification",
+            "anomaly_detection",
+            "anomaly_segmentation",
         ]
     ):
         return
@@ -372,6 +375,9 @@ def test_otx_ov_test(
         "h_label_cls",
         "visual_prompting",
         "zero_shot_visual_prompting",
+        "anomaly_classification",
+        "anomaly_detection",
+        "anomaly_segmentation",
     ]:
         # OMZ doesn't have proper model for Pytorch MaskRCNN interface
         # TODO(Kirill):  Need to change this test when export enabled #noqa: TD003
@@ -469,4 +475,8 @@ def test_otx_hpo_e2e(
     )
     hpo_work_dor = latest_dir / "hpo"
     assert hpo_work_dor.exists()
+    # Anomaly doesn't do validation. Check just there is no error.
+    if task.startswith("anomaly"):
+        return
+
     assert len([val for val in hpo_work_dor.rglob("*.json") if str(val.stem).isdigit()]) == 2
diff --git a/tests/integration/conftest.py b/tests/integration/conftest.py
index 3c9d706c30..22a75105e2 100644
--- a/tests/integration/conftest.py
+++ b/tests/integration/conftest.py
@@ -66,6 +66,8 @@ def get_task_list(task: str) -> list[OTXTaskType]:
         return [OTXTaskType.ACTION_CLASSIFICATION, OTXTaskType.ACTION_DETECTION]
     if task == "visual_prompting":
         return [OTXTaskType.VISUAL_PROMPTING, OTXTaskType.ZERO_SHOT_VISUAL_PROMPTING]
+    if task == "anomaly":
+        return [OTXTaskType.ANOMALY_CLASSIFICATION, OTXTaskType.ANOMALY_DETECTION, OTXTaskType.ANOMALY_SEGMENTATION]
     return [OTXTaskType(task.upper())]
 
 
@@ -132,6 +134,9 @@ def fxt_target_dataset_per_task() -> dict:
         "action_detection": "tests/assets/action_detection_dataset/",
         "visual_prompting": "tests/assets/car_tree_bug",
         "zero_shot_visual_prompting": "tests/assets/car_tree_bug_zero_shot",
+        "anomaly_classification": "tests/assets/anomaly_hazelnut",
+        "anomaly_detection": "tests/assets/anomaly_hazelnut",
+        "anomaly_segmentation": "tests/assets/anomaly_hazelnut",
     }
 
 
@@ -157,4 +162,7 @@ def fxt_cli_override_command_per_task() -> dict:
         ],
         "visual_prompting": [],
         "zero_shot_visual_prompting": [],
+        "anomaly_classification": ["--limit_val_batches", "0"],
+        "anomaly_detection": ["--limit_val_batches", "0"],
+        "anomaly_segmentation": ["--limit_val_batches", "0"],
     }
diff --git a/tox.ini b/tox.ini
index 1f73249b96..c1c7ab0e9f 100644
--- a/tox.ini
+++ b/tox.ini
@@ -17,6 +17,7 @@ task =
     instance_segmentation: "instance_segmentation"
     semantic_segmentation: "semantic_segmentation"
     visual_prompting: "visual_prompting"
+    anomaly: "anomaly"
 passenv =
     ftp_proxy
     HTTP_PROXY
@@ -38,8 +39,6 @@ deps =
 commands_pre =
     ; [TODO]: Needs to be fixed so that this is not duplicated for each test run
     otx install -v
-    ; temporary as Anomalib v1 is not available on PyPI
-    pip install git+https://github.com/openvinotoolkit/anomalib.git@cbb623e33876e446b7788375cc355e3a3dd44cef
 commands =
     ; Run Unit-Test with coverage report.
     pytest tests/unit \
@@ -50,7 +49,7 @@ commands =
         {posargs}
 
 
-[testenv:integration-test-{all, action, classification, detection, rotated_detection, instance_segmentation, semantic_segmentation, visual_prompting}]
+[testenv:integration-test-{all, action, classification, detection, rotated_detection, instance_segmentation, semantic_segmentation, visual_prompting, anomaly}]
 setenv =
     CUBLAS_WORKSPACE_CONFIG=:4096:8
 deps =
@@ -58,8 +57,6 @@ deps =
 commands_pre =
     ; [TODO]: Needs to be fixed so that this is not duplicated for each test run
     otx install -v
-    ; temporary as Anomalib v1 is not available on PyPI
-    pip install git+https://github.com/openvinotoolkit/anomalib.git@cbb623e33876e446b7788375cc355e3a3dd44cef
 commands =
     python -m pytest tests/integration -ra --showlocals --csv={toxworkdir}/{envname}.csv --task {[testenv]task} --open-subprocess {posargs}
 
@@ -69,8 +66,6 @@ deps =
 commands_pre =
     ; [TODO]: Needs to be fixed so that this is not duplicated for each test run
     otx install -v
-    ; temporary as Anomalib v1 is not available on PyPI
-    pip install git+https://github.com/openvinotoolkit/anomalib.git@cbb623e33876e446b7788375cc355e3a3dd44cef
 commands =
     pytest -ra --showlocals --csv={toxworkdir}/{envname}.csv {posargs:tests/perf}