diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 427c645c..208982bc 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -1199,130 +1199,3 @@ class HeatMapAnnotator:
         ]
         return scene
 
-
-class ClassificationAnnotator:
-    """
-    Annotate classification results on an image.
-    """
-
-    def __init__(
-        self,
-        color: Union[Color, ColorPalette] = ColorPalette.default(),
-        text_color: Color = Color.black(),
-        text_scale: float = 0.5,
-        text_thickness: int = 1,
-        text_padding: int = 10,
-        text_position: Position = Position.TOP_LEFT,
-        color_lookup: ColorLookup = ColorLookup.CLASS,
-    ):
-        """
-        Args:
-            color (Union[Color, ColorPalette]): The color or color palette to use for
-                annotating the text background.
-            text_color (Color): The color to use for the text.
-            text_scale (float): Font scale for the text.
-            text_thickness (int): Thickness of the text characters.
-            text_position (Position): Position of the text relative to the image.
-                Possible values are defined in the `Position` enum.
-            color_lookup (str): Strategy for mapping colors to annotations.
-                Options are `INDEX`, `CLASS`, `TRACE`.
-        """
-        self.color: Union[Color, ColorPalette] = color
-        self.text_color: Color = text_color
-        self.text_scale: float = text_scale
-        self.text_thickness: int = text_thickness
-        self.text_position: Position = text_position
-        self.color_lookup: ColorLookup = color_lookup
-        self.text_padding: int = text_padding
-
-    def annotate(
-        self,
-        scene: np.ndarray,
-        classifications: Classifications,
-        labels: List[str] = None,
-        custom_color_lookup: Optional[np.ndarray] = None,
-    ) -> np.ndarray:
-        """
-        Annotates the given scene with labels based on the provided detections.
-
-        Args:
-            scene (np.ndarray): The image where labels will be drawn.
-            detections (Detections): Object detections to annotate.
-            labels (List[str]): Optional. Custom labels for each detection.
-            custom_color_lookup (Optional[np.ndarray]): Custom color lookup array.
-                Allows to override the default color mapping strategy.
-
-        Returns:
-            np.ndarray: The annotated image.
-
-        Example:
-            ```python
-            >>> import supervision as sv
-
-            >>> image = ...
-            >>> detections = sv.Detections(...)
-
-            >>> classification_annotator = sv.ClassificationAnnotator(
-            ...     text_position=sv.Position.CENTER
-            ... )
-            >>> annotated_frame = classification_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
-            ```
-        """
-        font = cv2.FONT_HERSHEY_SIMPLEX
-
-        classification = classifications.get_top_k(k=1)
-        classification_idx = classification[0][0]
-
-        color = resolve_color(
-            color=self.color,
-            detections=classifications,
-            detection_idx=classification_idx,
-            color_lookup=self.color_lookup
-            if custom_color_lookup is None
-            else custom_color_lookup,
-        )
-
-        self.text_color = color
-
-        percent_confidence = classifications.confidence[classification_idx] * 100
-
-        text = f"{labels[classification_idx]} ({percent_confidence:.2f}%)"
-
-        text_wh = cv2.getTextSize(
-            text=text,
-            fontFace=font,
-            fontScale=self.text_scale,
-            thickness=self.text_thickness,
-        )[0]
-
-        if self.text_position == Position.BOTTOM_LEFT:
-            text_x = self.text_padding
-            text_y = scene.shape[0] - self.text_padding
-        elif self.text_position == Position.BOTTOM_RIGHT:
-            text_x = scene.shape[1] - text_wh[0] - self.text_padding
-            text_y = scene.shape[0] - self.text_padding
-        elif self.text_position == Position.TOP_LEFT:
-            text_x = self.text_padding
-            text_y = text_wh[1] + self.text_padding
-        elif self.text_position == Position.TOP_RIGHT:
-            text_x = scene.shape[1] - text_wh[0] - self.text_padding
-            text_y = text_wh[1] + self.text_padding
-        else:
-            raise ValueError(
-                f"Invalid position {self.text_position} for classification annotator."
-            )
-
-        cv2.putText(
-            img=scene,
-            text=text,
-            org=(text_x, text_y),
-            fontFace=font,
-            fontScale=self.text_scale,
-            color=self.text_color.as_rgb(),
-            thickness=self.text_thickness,
-            lineType=cv2.LINE_AA,
-        )
-        return scene