diff --git a/docs/detection/detection.md b/docs/detection/detection.md
new file mode 100644
index 000000000..deb90456c
--- /dev/null
+++ b/docs/detection/detection.md
@@ -0,0 +1,48 @@
+### `transform`
+
+Transform detections to match the dataset's class names and IDs.
+
+This method performs the following steps:
+
+1. **Remaps class names** using the provided `class_mapping` dictionary.
+2. **Filters out predictions** that are not present in the dataset's classes.
+3. **Remaps class IDs** to match the dataset's class IDs.
+
+#### Parameters
+
+- **dataset**: The dataset object containing class names and IDs.
+- **class_mapping** (`Optional[Dict[str, str]]`): A dictionary to map model class names to dataset class names. If `None`, no remapping is performed.
+
+#### Returns
+
+- **Detections**: A new `Detections` object with transformed class names and IDs.
+
+#### Raises
+
+- **ValueError**: If the dataset does not contain the required class names.
+
+#### Example
+
+```python
+# Example dataset with class names
+class DatasetMock:
+    def __init__(self):
+        self.classes = ["animal", "bird"]
+
+# Example detections
+detections = Detections(
+    xyxy=np.array([[10, 10, 50, 50], [60, 60, 100, 100]]),
+    confidence=np.array([0.9, 0.8]),
+    class_id=np.array([0, 1]),
+    data={"class_name": ["dog", "eagle"]}
+)
+
+# Class mapping
+class_mapping = {"dog": "animal", "eagle": "bird"}
+
+# Transform detections
+transformed_detections = detections.transform(DatasetMock(), class_mapping)
+
+print(transformed_detections.class_id)  # Output: [0, 1]
+print(transformed_detections.data["class_name"])  # Output: ["animal", "bird"]
+```
diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index ca4ded1ff..01987abc3 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -1530,3 +1530,65 @@ def validate_fields_both_defined_or_none(
                 f"Field '{attribute}' should be consistently None or not None in both "
                 "Detections."
             )
+
+
+def transform(
+    self, dataset, class_mapping: Optional[Dict[str, str]] = None
+) -> Detections:
+    """
+    Transform detections to match the dataset's class names and IDs.
+
+    Args:
+        dataset: The dataset object containing class names and IDs.
+        class_mapping (Optional[Dict[str, str]]): A dictionary to map model class names
+            to dataset class names. If None, no remapping is performed.
+
+    Returns:
+        Detections: A new Detections object with transformed class names and IDs.
+
+    Raises:
+        ValueError: If the dataset does not contain the required class names.
+    """
+    if self.is_empty():
+        return self
+
+    if class_mapping is not None:
+        if self.class_id is None or self.data.get(CLASS_NAME_DATA_FIELD) is None:
+            raise ValueError(
+                "Class names must be available in the data field for remapping."
+            )
+
+        current_class_names = self.data[CLASS_NAME_DATA_FIELD]
+
+        remapped_class_names = np.array(
+            [class_mapping.get(name, name) for name in current_class_names]
+        )
+
+        if not all(name in dataset.classes for name in np.unique(remapped_class_names)):
+            raise ValueError("All mapped class names must be in the dataset's classes.")
+
+        self.data[CLASS_NAME_DATA_FIELD] = remapped_class_names
+
+    if self.class_id is not None and self.data.get(CLASS_NAME_DATA_FIELD) is not None:
+        class_names = self.data[CLASS_NAME_DATA_FIELD]
+
+        mask = np.isin(class_names, dataset.classes)
+
+        self.xyxy = self.xyxy[mask]
+        self.mask = self.mask[mask] if self.mask is not None else None
+        self.confidence = self.confidence[mask] if self.confidence is not None else None
+        self.class_id = self.class_id[mask] if self.class_id is not None else None
+        self.tracker_id = self.tracker_id[mask] if self.tracker_id is not None else None
+
+        for key, value in self.data.items():
+            if isinstance(value, np.ndarray):
+                self.data[key] = value[mask]
+            elif isinstance(value, list):
+                self.data[key] = [value[i] for i in np.where(mask)[0]]
+
+    if self.class_id is not None and self.data.get(CLASS_NAME_DATA_FIELD) is not None:
+        class_names = self.data[CLASS_NAME_DATA_FIELD]
+
+        self.class_id = np.array([dataset.classes.index(name) for name in class_names])
+
+    return self
diff --git a/test/detection/test_detection.py b/test/detection/test_detection.py
new file mode 100644
index 000000000..311be33a8
--- /dev/null
+++ b/test/detection/test_detection.py
@@ -0,0 +1,37 @@
+import unittest
+
+import numpy as np
+
+from supervision.detections.core import Detections
+
+
+class TestDetectionsTransform(unittest.TestCase):
+    def test_transform(self):
+        # Mock dataset
+        class DatasetMock:
+            def __init__(self):
+                self.classes = ["animal", "bird"]
+
+        # Example detections
+        detections = Detections(
+            xyxy=np.array([[10, 10, 50, 50], [60, 60, 100, 100]]),
+            confidence=np.array([0.9, 0.8]),
+            class_id=np.array([0, 1]),
+            data={"class_name": ["dog", "eagle"]},
+        )
+
+        # Class mapping
+        class_mapping = {"dog": "animal", "eagle": "bird"}
+
+        # Transform detections
+        transformed_detections = detections.transform(DatasetMock(), class_mapping)
+
+        # Verify results
+        self.assertEqual(transformed_detections.class_id.tolist(), [0, 1])
+        self.assertEqual(
+            transformed_detections.data["class_name"].tolist(), ["animal", "bird"]
+        )
+
+
+if __name__ == "__main__":
+    unittest.main()