diff --git a/docs/detection/annotators.md b/docs/detection/annotators.md
index e76ffb27..0e93d6f9 100644
--- a/docs/detection/annotators.md
+++ b/docs/detection/annotators.md
@@ -411,7 +411,7 @@ status: new
     trace_annotator = sv.TraceAnnotator()
 
     video_info = sv.VideoInfo.from_video_path(video_path='...')
-    frames_generator = get_video_frames_generator(source_path='...')
+    frames_generator = sv.get_video_frames_generator(source_path='...')
     tracker = sv.ByteTrack()
 
     with sv.VideoSink(target_path='...', video_info=video_info) as sink:
@@ -442,7 +442,7 @@ status: new
     heat_map_annotator = sv.HeatMapAnnotator()
 
     video_info = sv.VideoInfo.from_video_path(video_path='...')
-    frames_generator = get_video_frames_generator(source_path='...')
+    frames_generator = sv.get_video_frames_generator(source_path='...')
 
     with sv.VideoSink(target_path='...', video_info=video_info) as sink:
         for frame in frames_generator:
diff --git a/examples/count_people_in_zone/inference_example.py b/examples/count_people_in_zone/inference_example.py
index 96e3e83c..8f42ff43 100644
--- a/examples/count_people_in_zone/inference_example.py
+++ b/examples/count_people_in_zone/inference_example.py
@@ -75,7 +75,7 @@ def detect(
         frame (np.ndarray): The frame to process, expected to be a NumPy array.
         model (RoboflowInferenceModel): The Inference model used for processing the
             frame.
-        confidence_threshold (float, optional): The confidence threshold for filtering
+        confidence_threshold (float): The confidence threshold for filtering
             detections. Default is 0.5.
 
     Returns:
diff --git a/examples/count_people_in_zone/ultralytics_example.py b/examples/count_people_in_zone/ultralytics_example.py
index 87badadb..2fd07782 100644
--- a/examples/count_people_in_zone/ultralytics_example.py
+++ b/examples/count_people_in_zone/ultralytics_example.py
@@ -72,7 +72,7 @@ def detect(
     Args:
         frame (np.ndarray): The frame to process, expected to be a NumPy array.
         model (YOLO): The YOLO model used for processing the frame.
-        confidence_threshold (float, optional): The confidence threshold for filtering
+        confidence_threshold (float): The confidence threshold for filtering
             detections. Default is 0.5.
 
     Returns:
diff --git a/examples/time_in_zone/utils/timers.py b/examples/time_in_zone/utils/timers.py
index cb5b471f..7afa9af3 100644
--- a/examples/time_in_zone/utils/timers.py
+++ b/examples/time_in_zone/utils/timers.py
@@ -22,7 +22,7 @@ class FPSBasedTimer:
         """Initializes the FPSBasedTimer with the specified frames per second rate.
 
         Args:
-            fps (int, optional): The frame rate of the video stream. Defaults to 30.
+            fps (int): The frame rate of the video stream. Defaults to 30.
         """
         self.fps = fps
         self.frame_id = 0
diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 21bd9ce5..29fe3158 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -4,7 +4,8 @@ from typing import List, Optional, Tuple, Union
 
 import cv2
 import numpy as np
-from PIL import ImageDraw, ImageFont
+import numpy.typing as npt
+from PIL import Image, ImageDraw, ImageFont
 
 from supervision.annotators.base import BaseAnnotator, ImageType
 from supervision.annotators.utils import (
@@ -93,6 +94,7 @@ class BoxAnnotator(BaseAnnotator):
         ![bounding-box-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/bounding-box-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
             color = resolve_color(
@@ -178,6 +180,7 @@ class BoundingBoxAnnotator(BaseAnnotator):
         ![bounding-box-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/bounding-box-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
             color = resolve_color(
@@ -262,12 +265,13 @@ class OrientedBoxAnnotator(BaseAnnotator):
             )
             ```
         """  # noqa E501 // docs
-
+        assert isinstance(scene, np.ndarray)
         if detections.data is None or ORIENTED_BOX_COORDINATES not in detections.data:
             return scene
+        obb_boxes = np.array(detections.data[ORIENTED_BOX_COORDINATES]).astype(int)
 
         for detection_idx in range(len(detections)):
-            bbox = np.intp(detections.data.get(ORIENTED_BOX_COORDINATES)[detection_idx])
+            obb = obb_boxes[detection_idx]
             color = resolve_color(
                 color=self.color,
                 detections=detections,
@@ -277,7 +281,7 @@ class OrientedBoxAnnotator(BaseAnnotator):
                 else custom_color_lookup,
             )
 
-            cv2.drawContours(scene, [bbox], 0, color.as_bgr(), self.thickness)
+            cv2.drawContours(scene, [obb], 0, color.as_bgr(), self.thickness)
 
         return scene
 
@@ -348,6 +352,7 @@ class MaskAnnotator(BaseAnnotator):
         ![mask-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/mask-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         if detections.mask is None:
             return scene
 
@@ -437,6 +442,7 @@ class PolygonAnnotator(BaseAnnotator):
         ![polygon-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/polygon-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         if detections.mask is None:
             return scene
 
@@ -523,6 +529,7 @@ class ColorAnnotator(BaseAnnotator):
         ![box-mask-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/box-mask-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         scene_with_boxes = scene.copy()
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
@@ -618,6 +625,7 @@ class HaloAnnotator(BaseAnnotator):
         ![halo-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/halo-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         if detections.mask is None:
             return scene
         colored_mask = np.zeros_like(scene, dtype=np.uint8)
@@ -717,6 +725,7 @@ class EllipseAnnotator(BaseAnnotator):
         ![ellipse-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/ellipse-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
             color = resolve_color(
@@ -808,6 +817,7 @@ class BoxCornerAnnotator(BaseAnnotator):
         ![box-corner-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/box-corner-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
             color = resolve_color(
@@ -897,6 +907,7 @@ class CircleAnnotator(BaseAnnotator):
         ![circle-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/circle-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
             center = ((x1 + x2) // 2, (y1 + y2) // 2)
@@ -994,6 +1005,7 @@ class DotAnnotator(BaseAnnotator):
         ![dot-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/dot-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         xy = detections.get_anchors_coordinates(anchor=self.position)
         for detection_idx in range(len(detections)):
             color = resolve_color(
@@ -1115,6 +1127,7 @@ class LabelAnnotator(BaseAnnotator):
         ![label-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/label-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         font = cv2.FONT_HERSHEY_SIMPLEX
         anchors_coordinates = detections.get_anchors_coordinates(
             anchor=self.text_anchor
@@ -1151,8 +1164,8 @@ class LabelAnnotator(BaseAnnotator):
 
             if labels is not None:
                 text = labels[detection_idx]
-            elif detections[CLASS_NAME_DATA_FIELD] is not None:
-                text = detections[CLASS_NAME_DATA_FIELD][detection_idx]
+            elif CLASS_NAME_DATA_FIELD in detections.data:
+                text = detections.data[CLASS_NAME_DATA_FIELD][detection_idx]
             elif detections.class_id is not None:
                 text = str(detections.class_id[detection_idx])
             else:
@@ -1331,6 +1344,7 @@ class RichLabelAnnotator(BaseAnnotator):
             ```
 
         """
+        assert isinstance(scene, Image.Image)
         draw = ImageDraw.Draw(scene)
         anchors_coordinates = detections.get_anchors_coordinates(
             anchor=self.text_anchor
@@ -1366,8 +1380,8 @@ class RichLabelAnnotator(BaseAnnotator):
 
             if labels is not None:
                 text = labels[detection_idx]
-            elif detections[CLASS_NAME_DATA_FIELD] is not None:
-                text = detections[CLASS_NAME_DATA_FIELD][detection_idx]
+            elif CLASS_NAME_DATA_FIELD in detections.data:
+                text = detections.data[CLASS_NAME_DATA_FIELD][detection_idx]
             elif detections.class_id is not None:
                 text = str(detections.class_id[detection_idx])
             else:
@@ -1563,6 +1577,7 @@ class BlurAnnotator(BaseAnnotator):
         ![blur-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/blur-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         image_height, image_width = scene.shape[:2]
         clipped_xyxy = clip_boxes(
             xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
@@ -1661,8 +1676,14 @@ class TraceAnnotator(BaseAnnotator):
         ![trace-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/trace-annotator-example-purple.png)
         """
-        self.trace.put(detections)
+        assert isinstance(scene, np.ndarray)
+        if detections.tracker_id is None:
+            raise ValueError(
+                "The `tracker_id` field is missing in the provided detections."
+                " See more: https://supervision.roboflow.com/latest/how_to/track_objects"
+            )
 
+        self.trace.put(detections)
         for detection_idx in range(len(detections)):
             tracker_id = int(detections.tracker_id[detection_idx])
             color = resolve_color(
@@ -1715,9 +1736,9 @@ class HeatMapAnnotator(BaseAnnotator):
         self.opacity = opacity
         self.radius = radius
         self.kernel_size = kernel_size
-        self.heat_mask = None
         self.top_hue = top_hue
         self.low_hue = low_hue
+        self.heat_mask: Optional[npt.NDArray[np.float32]] = None
 
     @ensure_cv2_image_for_annotation
     def annotate(self, scene: ImageType, detections: Detections) -> ImageType:
@@ -1744,7 +1765,7 @@ class HeatMapAnnotator(BaseAnnotator):
             heat_map_annotator = sv.HeatMapAnnotator()
 
             video_info = sv.VideoInfo.from_video_path(video_path='...')
-            frames_generator = get_video_frames_generator(source_path='...')
+            frames_generator = sv.get_video_frames_generator(source_path='...')
 
             with sv.VideoSink(target_path='...', video_info=video_info) as sink:
                for frame in frames_generator:
@@ -1759,12 +1780,20 @@ class HeatMapAnnotator(BaseAnnotator):
         ![heatmap-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/heat-map-annotator-example-purple.png)
         """
-
+        assert isinstance(scene, np.ndarray)
         if self.heat_mask is None:
-            self.heat_mask = np.zeros(scene.shape[:2])
+            self.heat_mask = np.zeros(scene.shape[:2], dtype=np.float32)
+
         mask = np.zeros(scene.shape[:2])
         for xy in detections.get_anchors_coordinates(self.position):
-            cv2.circle(mask, (int(xy[0]), int(xy[1])), self.radius, 1, -1)
+            x, y = int(xy[0]), int(xy[1])
+            cv2.circle(
+                img=mask,
+                center=(x, y),
+                radius=self.radius,
+                color=(1,),
+                thickness=-1,  # fill
+            )
         self.heat_mask = mask + self.heat_mask
         temp = self.heat_mask.copy()
         temp = self.low_hue - temp / temp.max() * (self.low_hue - self.top_hue)
@@ -1832,6 +1861,7 @@ class PixelateAnnotator(BaseAnnotator):
         ![pixelate-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/pixelate-annotator-example-10.png)
         """
+        assert isinstance(scene, np.ndarray)
         image_height, image_width = scene.shape[:2]
         clipped_xyxy = clip_boxes(
             xyxy=detections.xyxy, resolution_wh=(image_width, image_height)
@@ -1930,6 +1960,7 @@ class TriangleAnnotator(BaseAnnotator):
         ![triangle-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/triangle-annotator-example.png)
         """
+        assert isinstance(scene, np.ndarray)
         xy = detections.get_anchors_coordinates(anchor=self.position)
         for detection_idx in range(len(detections)):
             color = resolve_color(
@@ -2042,7 +2073,7 @@ class RoundBoxAnnotator(BaseAnnotator):
         ![round-box-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/round-box-annotator-example-purple.png)
         """
-
+        assert isinstance(scene, np.ndarray)
         for detection_idx in range(len(detections)):
             x1, y1, x2, y2 = detections.xyxy[detection_idx].astype(int)
             color = resolve_color(
@@ -2115,7 +2146,7 @@ class PercentageBarAnnotator(BaseAnnotator):
         border_color: Color = Color.BLACK,
         position: Position = Position.TOP_CENTER,
         color_lookup: ColorLookup = ColorLookup.CLASS,
-        border_thickness: int = None,
+        border_thickness: Optional[int] = None,
     ):
         """
         Args:
@@ -2127,7 +2158,7 @@ class PercentageBarAnnotator(BaseAnnotator):
             position (Position): The anchor position of drawing the percentage bar.
             color_lookup (ColorLookup): Strategy for mapping colors to annotations.
                 Options are `INDEX`, `CLASS`, `TRACK`.
-            border_thickness (int): The thickness of the border lines.
+            border_thickness (Optional[int]): The thickness of the border lines.
         """
         self.height: int = height
         self.width: int = width
@@ -2186,9 +2217,9 @@ class PercentageBarAnnotator(BaseAnnotator):
         ![percentage-bar-example](https://media.roboflow.com/
         supervision-annotator-examples/percentage-bar-annotator-example-purple.png)
         """
-        self.validate_custom_values(
-            custom_values=custom_values, detections_count=len(detections)
-        )
+        assert isinstance(scene, np.ndarray)
+        self.validate_custom_values(custom_values=custom_values, detections=detections)
+
         anchors = detections.get_anchors_coordinates(anchor=self.position)
         for detection_idx in range(len(detections)):
             anchor = anchors[detection_idx]
@@ -2199,11 +2230,11 @@ class PercentageBarAnnotator(BaseAnnotator):
             )
             border_width = border_coordinates[1][0] - border_coordinates[0][0]
 
-            value = (
-                custom_values[detection_idx]
-                if custom_values is not None
-                else detections.confidence[detection_idx]
-            )
+            if custom_values is not None:
+                value = custom_values[detection_idx]
+            else:
+                assert detections.confidence is not None  # MyPy type hint
+                value = detections.confidence[detection_idx]
 
             color = resolve_color(
                 color=self.color,
@@ -2263,15 +2294,23 @@ class PercentageBarAnnotator(BaseAnnotator):
 
     @staticmethod
     def validate_custom_values(
-        custom_values: Optional[Union[np.ndarray, List[float]]], detections_count: int
+        custom_values: Optional[Union[np.ndarray, List[float]]], detections: Detections
     ) -> None:
-        if custom_values is not None:
+        if custom_values is None:
+            if detections.confidence is None:
+                raise ValueError(
+                    "The provided detections do not contain confidence values. "
+                    "Please provide `custom_values` or ensure that the detections "
+                    "contain confidence values (e.g. by using a different model)."
+                )
+
+        else:
             if not isinstance(custom_values, (np.ndarray, list)):
                 raise TypeError(
                     "custom_values must be either a numpy array or a list of floats."
                 )
 
-            if len(custom_values) != detections_count:
+            if len(custom_values) != len(detections):
                 raise ValueError(
                     "The length of custom_values must match the number of detections."
                 )
@@ -2351,6 +2390,7 @@ class CropAnnotator(BaseAnnotator):
             )
             ```
         """
+        assert isinstance(scene, np.ndarray)
         crops = [
             crop_image(image=scene, xyxy=xyxy) for xyxy in detections.xyxy.astype(int)
         ]
@@ -2489,6 +2529,7 @@ class BackgroundOverlayAnnotator(BaseAnnotator):
         ![background-overlay-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/background-color-annotator-example-purple.png)
         """
+        assert isinstance(scene, np.ndarray)
         colored_mask = np.full_like(scene, self.color.as_bgr(), dtype=np.uint8)
 
         cv2.addWeighted(
diff --git a/supervision/dataset/core.py b/supervision/dataset/core.py
index 7d320bbc..f2cf7bce 100644
--- a/supervision/dataset/core.py
+++ b/supervision/dataset/core.py
@@ -43,7 +43,10 @@ class BaseDataset(ABC):
 
     @abstractmethod
     def split(
-        self, split_ratio=0.8, random_state=None, shuffle: bool = True
+        self,
+        split_ratio: float = 0.8,
+        random_state: Optional[int] = None,
+        shuffle: bool = True,
     ) -> Tuple[BaseDataset, BaseDataset]:
         pass
 
@@ -174,18 +177,21 @@ class DetectionDataset(BaseDataset):
         return True
 
     def split(
-        self, split_ratio=0.8, random_state=None, shuffle: bool = True
+        self,
+        split_ratio: float = 0.8,
+        random_state: Optional[int] = None,
+        shuffle: bool = True,
     ) -> Tuple[DetectionDataset, DetectionDataset]:
         """
         Splits the dataset into two parts (training and testing)
             using the provided split_ratio.
 
         Args:
-            split_ratio (float, optional): The ratio of the training
+            split_ratio (float): The ratio of the training
                 set to the entire dataset.
-            random_state (int, optional): The seed for the random number generator.
+            random_state (Optional[int]): The seed for the random number generator.
                 This is used for reproducibility.
-            shuffle (bool, optional): Whether to shuffle the data before splitting.
+            shuffle (bool): Whether to shuffle the data before splitting.
 
         Returns:
             Tuple[DetectionDataset, DetectionDataset]: A tuple containing
@@ -396,7 +402,7 @@ class DetectionDataset(BaseDataset):
             images_directory_path (str): Path to the directory containing the images.
             annotations_directory_path (str): Path to the directory
                 containing the PASCAL VOC XML annotations.
-            force_masks (bool, optional): If True, forces masks to
+            force_masks (bool): If True, forces masks to
                 be loaded for all annotations, regardless of whether they are present.
 
         Returns:
@@ -455,10 +461,10 @@ class DetectionDataset(BaseDataset):
                 containing the YOLO annotation files.
             data_yaml_path (str): The path to the data
                 YAML file containing class information.
-            force_masks (bool, optional): If True, forces
+            force_masks (bool): If True, forces
                 masks to be loaded for all annotations,
                 regardless of whether they are present.
-            is_obb (bool, optional): If True, loads the annotations in OBB format.
+            is_obb (bool): If True, loads the annotations in OBB format.
                 OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
                 where pairs of [x, y] are box corners.
 
@@ -565,7 +571,7 @@ class DetectionDataset(BaseDataset):
             images_directory_path (str): The path to the
                 directory containing the images.
             annotations_path (str): The path to the json annotation files.
-            force_masks (bool, optional): If True,
+            force_masks (bool): If True,
                 forces masks to be loaded for all annotations,
                 regardless of whether they are present.
 
@@ -777,18 +783,21 @@ class ClassificationDataset(BaseDataset):
         return True
 
     def split(
-        self, split_ratio=0.8, random_state=None, shuffle: bool = True
+        self,
+        split_ratio: float = 0.8,
+        random_state: Optional[int] = None,
+        shuffle: bool = True,
     ) -> Tuple[ClassificationDataset, ClassificationDataset]:
         """
         Splits the dataset into two parts (training and testing)
             using the provided split_ratio.
 
         Args:
-            split_ratio (float, optional): The ratio of the training
+            split_ratio (float): The ratio of the training
                 set to the entire dataset.
-            random_state (int, optional): The seed for the
+            random_state (Optional[int]): The seed for the
                 random number generator. This is used for reproducibility.
-            shuffle (bool, optional): Whether to shuffle the data before splitting.
+            shuffle (bool): Whether to shuffle the data before splitting.
 
         Returns:
             Tuple[ClassificationDataset, ClassificationDataset]: A tuple containing
diff --git a/supervision/dataset/formats/pascal_voc.py b/supervision/dataset/formats/pascal_voc.py
index b9724564..49c59a39 100644
--- a/supervision/dataset/formats/pascal_voc.py
+++ b/supervision/dataset/formats/pascal_voc.py
@@ -147,7 +147,7 @@ def load_pascal_voc_annotations(
         images_directory_path (str): The path to the directory containing the images.
         annotations_directory_path (str): The path to the directory containing the
             PASCAL VOC annotation files.
-        force_masks (bool, optional): If True, forces masks to be loaded for all
+        force_masks (bool): If True, forces masks to be loaded for all
             annotations, regardless of whether they are present.
 
     Returns:
diff --git a/supervision/dataset/formats/yolo.py b/supervision/dataset/formats/yolo.py
index 0ecbac4b..1f9033fc 100644
--- a/supervision/dataset/formats/yolo.py
+++ b/supervision/dataset/formats/yolo.py
@@ -138,9 +138,9 @@ def load_yolo_annotations(
             containing the YOLO annotation files.
         data_yaml_path (str): The path to the data
             YAML file containing class information.
-        force_masks (bool, optional): If True, forces masks to be loaded
+        force_masks (bool): If True, forces masks to be loaded
             for all annotations, regardless of whether they are present.
-        is_obb (bool, optional): If True, loads the annotations in OBB format.
+        is_obb (bool): If True, loads the annotations in OBB format.
             OBB annotations are defined as `[class_id, x, y, x, y, x, y, x, y]`,
             where pairs of [x, y] are box corners.
 
diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 7216b612..06995f90 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -1158,10 +1158,10 @@ class Detections:
         from a segmentation model, the IoU mask is applied. Otherwise, box IoU is used.
 
         Args:
-            threshold (float, optional): The intersection-over-union threshold
+            threshold (float): The intersection-over-union threshold
                 to use for non-maximum suppression. I'm the lower the value the more
                 restrictive the NMS becomes. Defaults to 0.5.
-            class_agnostic (bool, optional): Whether to perform class-agnostic
+            class_agnostic (bool): Whether to perform class-agnostic
                 non-maximum suppression. If True, the class_id of each detection
                 will be ignored. Defaults to False.
 
@@ -1213,9 +1213,9 @@ class Detections:
         Perform non-maximum merging on the current set of object detections.
 
         Args:
-            threshold (float, optional): The intersection-over-union threshold
+            threshold (float): The intersection-over-union threshold
                 to use for non-maximum merging. Defaults to 0.5.
-            class_agnostic (bool, optional): Whether to perform class-agnostic
+            class_agnostic (bool): Whether to perform class-agnostic
                 non-maximum merging. If True, the class_id of each detection
                 will be ignored. Defaults to False.
 
diff --git a/supervision/detection/overlap_filter.py b/supervision/detection/overlap_filter.py
index f51f1dce..461e413c 100644
--- a/supervision/detection/overlap_filter.py
+++ b/supervision/detection/overlap_filter.py
@@ -55,9 +55,9 @@ def mask_non_max_suppression(
         masks (np.ndarray): A 3D array of binary masks corresponding to the predictions.
             Shape: `(N, H, W)`, where N is the number of predictions, and H, W are the
             dimensions of each mask.
-        iou_threshold (float, optional): The intersection-over-union threshold
+        iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression.
-        mask_dimension (int, optional): The dimension to which the masks should be
+        mask_dimension (int): The dimension to which the masks should be
             resized before computing IOU values. Defaults to 640.
 
     Returns:
@@ -103,7 +103,7 @@ def box_non_max_suppression(
         predictions (np.ndarray): An array of object detection predictions in
             the format of `(x_min, y_min, x_max, y_max, score)`
             or `(x_min, y_min, x_max, y_max, score, class)`.
-        iou_threshold (float, optional): The intersection-over-union threshold
+        iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression.
 
     Returns:
@@ -158,7 +158,7 @@ def group_overlapping_boxes(
         predictions (npt.NDArray[np.float64]): An array of shape `(n, 5)` containing
             the bounding boxes coordinates in format `[x1, y1, x2, y2]`
             and the confidence scores.
-        iou_threshold (float, optional): The intersection-over-union threshold
+        iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
 
     Returns:
@@ -202,7 +202,7 @@ def box_non_max_merge(
             containing the bounding boxes coordinates in format `[x1, y1, x2, y2]`,
             the confidence scores and class_ids. Omit class_id column to allow
             detections of different classes to be merged.
-        iou_threshold (float, optional): The intersection-over-union threshold
+        iou_threshold (float): The intersection-over-union threshold
             to use for non-maximum suppression. Defaults to 0.5.
 
     Returns:
diff --git a/supervision/detection/tools/polygon_zone.py b/supervision/detection/tools/polygon_zone.py
index f1c48f94..c1e030f9 100644
--- a/supervision/detection/tools/polygon_zone.py
+++ b/supervision/detection/tools/polygon_zone.py
@@ -147,7 +147,7 @@ class PolygonZoneAnnotator:
 
         Parameters:
             scene (np.ndarray): The image on which the polygon zone will be annotated
-            label (Optional[str]): An optional label for the count of detected objects
+            label (Optional[str]): A label for the count of detected objects
                 within the polygon zone (default: None)
 
         Returns:
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index a02a6a1f..1a336ca7 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -106,7 +106,7 @@ def mask_iou_batch(
     Args:
         masks_true (np.ndarray): 3D `np.ndarray` representing ground-truth masks.
         masks_detection (np.ndarray): 3D `np.ndarray` representing detection masks.
-        memory_limit (int, optional): memory limit in MB, default is 1024 * 5 MB (5GB).
+        memory_limit (int): memory limit in MB, default is 1024 * 5 MB (5GB).
 
     Returns:
         np.ndarray: Pairwise IoU of masks from `masks_true` and `masks_detection`.
diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 884f6d18..36651811 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -142,7 +142,7 @@ def draw_polygon(
         scene (np.ndarray): The scene to draw the polygon on.
         polygon (np.ndarray): The polygon to be drawn, given as a list of vertices.
         color (Color): The color of the polygon.
-        thickness (int, optional): The thickness of the polygon lines, by default 2.
+        thickness (int): The thickness of the polygon lines, by default 2.
 
     Returns:
         np.ndarray: The scene with the polygon drawn on it.
@@ -172,14 +172,14 @@ def draw_text(
         text (str): The text to be drawn.
         text_anchor (Point): The anchor point for the text, represented as a
             Point object with x and y attributes.
-        text_color (Color, optional): The color of the text. Defaults to black.
-        text_scale (float, optional): The scale of the text. Defaults to 0.5.
-        text_thickness (int, optional): The thickness of the text. Defaults to 1.
-        text_padding (int, optional): The amount of padding to add around the text
+        text_color (Color): The color of the text. Defaults to black.
+        text_scale (float): The scale of the text. Defaults to 0.5.
+        text_thickness (int): The thickness of the text. Defaults to 1.
+        text_padding (int): The amount of padding to add around the text
             when drawing a rectangle in the background. Defaults to 10.
-        text_font (int, optional): The font to use for the text.
+        text_font (int): The font to use for the text.
             Defaults to cv2.FONT_HERSHEY_SIMPLEX.
-        background_color (Color, optional): The color of the background rectangle,
+        background_color (Optional[Color]): The color of the background rectangle,
             if one is to be drawn. Defaults to None.
 
     Returns:
diff --git a/supervision/keypoint/annotators.py b/supervision/keypoint/annotators.py
index 38dea23c..559bfa92 100644
--- a/supervision/keypoint/annotators.py
+++ b/supervision/keypoint/annotators.py
@@ -34,8 +34,8 @@ class VertexAnnotator(BaseKeyPointAnnotator):
     ) -> None:
         """
         Args:
-            color (Color, optional): The color to use for annotating key points.
-            radius (int, optional): The radius of the circles used to represent the key
+            color (Color): The color to use for annotating key points.
+            radius (int): The radius of the circles used to represent the key
                 points.
         """
         self.color = color
@@ -78,6 +78,7 @@ class VertexAnnotator(BaseKeyPointAnnotator):
         ![vertex-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/vertex-annotator-example.png)
         """
+        assert isinstance(scene, np.ndarray)
         if len(key_points) == 0:
             return scene
 
@@ -108,8 +109,8 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
     ) -> None:
         """
         Args:
-            color (Color, optional): The color to use for the edges.
-            thickness (int, optional): The thickness of the edges.
+            color (Color): The color to use for the edges.
+            thickness (int): The thickness of the edges.
             edges (Optional[List[Tuple[int, int]]]): The edges to draw.
                 If set to `None`, will attempt to select automatically.
         """
@@ -155,6 +156,7 @@ class EdgeAnnotator(BaseKeyPointAnnotator):
         ![edge-annotator-example](https://media.roboflow.com/
         supervision-annotator-examples/edge-annotator-example.png)
         """
+        assert isinstance(scene, np.ndarray)
         if len(key_points) == 0:
             return scene
 
@@ -202,16 +204,16 @@ class VertexLabelAnnotator:
     ):
         """
         Args:
-            color (Union[Color, List[Color]], optional): The color to use for each
+            color (Union[Color, List[Color]]): The color to use for each
                 keypoint label. If a list is provided, the colors will be used in order
                 for each keypoint.
-            text_color (Union[Color, List[Color]], optional): The color to use
+            text_color (Union[Color, List[Color]]): The color to use
                 for the labels. If a list is provided, the colors will be used in order
                 for each keypoint.
-            text_scale (float, optional): The scale of the text.
-            text_thickness (int, optional): The thickness of the text.
-            text_padding (int, optional): The padding around the text.
-            border_radius (int, optional): The radius of the rounded corners of the
+            text_scale (float): The scale of the text.
+            text_thickness (int): The thickness of the text.
+            text_padding (int): The padding around the text.
+            border_radius (int): The radius of the rounded corners of the
                 boxes. Set to a high value to produce circles.
         """
         self.border_radius: int = border_radius
@@ -222,7 +224,10 @@ class VertexLabelAnnotator:
         self.text_padding: int = text_padding
 
     def annotate(
-        self, scene: ImageType, key_points: KeyPoints, labels: List[str] = None
+        self,
+        scene: ImageType,
+        key_points: KeyPoints,
+        labels: Optional[List[str]] = None,
     ) -> ImageType:
         """
         A class that draws labels of skeleton vertices on images. It uses specified key
@@ -234,7 +239,7 @@ class VertexLabelAnnotator:
                 `PIL.Image.Image`.
             key_points (KeyPoints): A collection of key points where each key point
                 consists of x and y coordinates.
-            labels (List[str], optional): A list of labels to be displayed on the
+            labels (Optional[List[str]]): A list of labels to be displayed on the
                 annotated image. If not provided, keypoint indices will be used.
 
         Returns:
@@ -305,6 +310,7 @@ class VertexLabelAnnotator:
         ![vertex-label-annotator-custom-example](https://media.roboflow.com/
         supervision-annotator-examples/vertex-label-annotator-custom-example.png)
         """
+        assert isinstance(scene, np.ndarray)
         font = cv2.FONT_HERSHEY_SIMPLEX
 
         skeletons_count, points_count, _ = key_points.xy.shape
@@ -400,7 +406,7 @@ class VertexLabelAnnotator:
     @staticmethod
     def preprocess_and_validate_labels(
         labels: Optional[List[str]], points_count: int, skeletons_count: int
-    ) -> np.array:
+    ) -> np.ndarray:
         if labels and len(labels) != points_count:
             raise ValueError(
                 f"Number of labels ({len(labels)}) must match number of key points "
@@ -416,7 +422,7 @@ class VertexLabelAnnotator:
         colors: Optional[Union[Color, List[Color]]],
         points_count: int,
         skeletons_count: int,
-    ) -> np.array:
+    ) -> np.ndarray:
         if isinstance(colors, list) and len(colors) != points_count:
             raise ValueError(
                 f"Number of colors ({len(colors)}) must match number of key points "
diff --git a/supervision/keypoint/core.py b/supervision/keypoint/core.py
index 6d2a69c6..39b2f08c 100644
--- a/supervision/keypoint/core.py
+++ b/supervision/keypoint/core.py
@@ -88,15 +88,17 @@ class KeyPoints:
         ```
 
     Attributes:
-        xy (np.ndarray): An array of shape `(n, 2)` containing
-            the bounding boxes coordinates in format `[x1, y1]`
+        xy (np.ndarray): An array of shape `(n, m, 2)` containing
+            `n` detected objects, each composed of `m` equally-sized
+            sets of keypoints, where each point is `[x, y]`.
         confidence (Optional[np.ndarray]): An array of shape
-            `(n,)` containing the confidence scores of the keypoint keypoints.
+            `(n, m)` containing the confidence scores of each keypoint.
         class_id (Optional[np.ndarray]): An array of shape
-            `(n,)` containing the class ids of the keypoint keypoints.
+            `(n,)` containing the class ids of the detected objects.
         data (Dict[str, Union[np.ndarray, List]]): A dictionary containing additional
             data where each key is a string representing the data type, and the value
-            is either a NumPy array or a list of corresponding data.
+            is either a NumPy array or a list of corresponding data of length `n`
+            (one entry per detected object).
     """  # noqa: E501 // docs
 
     xy: npt.NDArray[np.float32]
@@ -132,7 +134,7 @@ class KeyPoints:
     ]:
         """
         Iterates over the Keypoint object and yield a tuple of
-        `(xy, confidence, class_id, data)` for each keypoint detection.
+        `(xy, confidence, class_id, data)` for each object detection.
         """
         for i in range(len(self.xy)):
             yield (
diff --git a/supervision/metrics/detection.py b/supervision/metrics/detection.py
index bbbfa711..5b9e6ca4 100644
--- a/supervision/metrics/detection.py
+++ b/supervision/metrics/detection.py
@@ -806,7 +806,7 @@ class MeanAveragePrecision:
             prediction_confidence (np.ndarray): Objectness value from 0-1.
             prediction_class_ids (np.ndarray): Predicted object classes.
             true_class_ids (np.ndarray): True object classes.
-            eps (float, optional): Small value to prevent division by zero.
+            eps (float): Small value to prevent division by zero.
 
         Returns:
             np.ndarray: Average precision for different IoU levels.
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index bd9bae50..4be3b184 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -197,19 +197,19 @@ class ByteTrack:
     </video>
 
     Parameters:
-        track_activation_threshold (float, optional): Detection confidence threshold
+        track_activation_threshold (float): Detection confidence threshold
             for track activation. Increasing track_activation_threshold improves accuracy
             and stability but might miss true detections. Decreasing it increases
             completeness but risks introducing noise and instability.
-        lost_track_buffer (int, optional): Number of frames to buffer when a track is lost.
+        lost_track_buffer (int): Number of frames to buffer when a track is lost.
             Increasing lost_track_buffer enhances occlusion handling, significantly
             reducing the likelihood of track fragmentation or disappearance caused
             by brief detection gaps.
-        minimum_matching_threshold (float, optional): Threshold for matching tracks with detections.
+        minimum_matching_threshold (float): Threshold for matching tracks with detections.
             Increasing minimum_matching_threshold improves accuracy but risks fragmentation.
             Decreasing it improves completeness but risks false positives and drift.
-        frame_rate (int, optional): The frame rate of the video.
-        minimum_consecutive_frames (int, optional): Number of consecutive frames that an object must
+        frame_rate (int): The frame rate of the video.
+        minimum_consecutive_frames (int): Number of consecutive frames that an object must
             be tracked before it is considered a 'valid' track.
             Increasing minimum_consecutive_frames prevents the creation of accidental tracks from
             false detection or double detection, but risks missing shorter tracks.
diff --git a/supervision/utils/image.py b/supervision/utils/image.py
index ef6cb226..0bd2dbaa 100644
--- a/supervision/utils/image.py
+++ b/supervision/utils/image.py
@@ -158,7 +158,7 @@ def resize_image(
             accepting either `numpy.ndarray` or `PIL.Image.Image`.
         resolution_wh (Tuple[int, int]): The target resolution as
             `(width, height)`.
-        keep_aspect_ratio (bool, optional): Flag to maintain the image's original
+        keep_aspect_ratio (bool): Flag to maintain the image's original
             aspect ratio. Defaults to `False`.
 
     Returns:
@@ -380,9 +380,9 @@ class ImageSink:
 
         Args:
             target_dir_path (str): The target directory where images will be saved.
-            overwrite (bool, optional): Whether to overwrite the existing directory.
+            overwrite (bool): Whether to overwrite the existing directory.
                 Defaults to False.
-            image_name_pattern (str, optional): The image file name pattern.
+            image_name_pattern (str): The image file name pattern.
                 Defaults to "image_{:05d}.png".
 
         Examples:
@@ -419,7 +419,7 @@ class ImageSink:
         Args:
             image (np.ndarray): The image to be saved. The image must be in BGR color
                 format.
-            image_name (str, optional): The name to use for the saved image.
+            image_name (Optional[str]): The name to use for the saved image.
                 If not provided, a name will be
                 generated using the `image_name_pattern`.
         """
diff --git a/supervision/utils/internal.py b/supervision/utils/internal.py
index 4a09e0f7..10f4a450 100644
--- a/supervision/utils/internal.py
+++ b/supervision/utils/internal.py
@@ -2,7 +2,7 @@ import functools
 import inspect
 import os
 import warnings
-from typing import Any, Callable, Set
+from typing import Any, Callable, Generic, Optional, Set, TypeVar
 
 
 class SupervisionWarnings(Warning):
@@ -56,9 +56,9 @@ def deprecated_parameter(
     Parameters:
         old_parameter (str): The name of the deprecated parameter.
         new_parameter (str): The name of the parameter that should be used instead.
-        map_function (Callable, optional): A function used to map the value of the old
+        map_function (Callable): A function used to map the value of the old
             parameter to the new parameter. Defaults to the identity function.
-        warning_message (str, optional): The warning message to be displayed when the
+        warning_message (str): The warning message to be displayed when the
             deprecated parameter is used. Defaults to a generic warning message with
             placeholders for the old parameter, new parameter, and function name.
         **message_kwargs: Additional keyword arguments that can be used to customize
@@ -122,7 +122,10 @@ def deprecated(reason: str):
     return decorator
 
 
-class classproperty(property):
+T = TypeVar("T")
+
+
+class classproperty(Generic[T]):
     """
     A decorator that combines @classmethod and @property.
     It allows a method to be accessed as a property of the class,
@@ -134,17 +137,27 @@ class classproperty(property):
             ...
     """
 
-    def __get__(self, owner_self: object, owner_cls: type) -> object:
+    def __init__(self, fget: Callable[..., T]):
+        """
+        Args:
+            The function that is called when the property is accessed.
+        """
+        self.fget = fget
+
+    def __get__(self, owner_self: Any, owner_cls: Optional[type] = None) -> T:
         """
         Override the __get__ method to return the result of the function call.
 
         Args:
-        owner_self: The instance through which the attribute was accessed, or None.
-        owner_cls: The class through which the attribute was accessed.
+            owner_self: The instance through which the attribute was accessed, or None.
+                Irrelevant for class properties.
+            owner_cls: The class through which the attribute was accessed.
 
         Returns:
-        The result of calling the function stored in 'fget' with 'owner_cls'.
+            The result of calling the function stored in 'fget' with 'owner_cls'.
         """
+        if self.fget is None:
+            raise AttributeError("unreadable attribute")
         return self.fget(owner_cls)
 
 
diff --git a/supervision/utils/video.py b/supervision/utils/video.py
index 455ca21c..2e502cf2 100644
--- a/supervision/utils/video.py
+++ b/supervision/utils/video.py
@@ -19,7 +19,7 @@ class VideoInfo:
         width (int): width of the video in pixels
         height (int): height of the video in pixels
         fps (int): frames per second of the video
-        total_frames (int, optional): total number of frames in the video,
+        total_frames (Optional[int]): total number of frames in the video,
             default is None
 
     Examples:
diff --git a/test/dataset/formats/test_yolo.py b/test/dataset/formats/test_yolo.py
index f84e275b..70f0e362 100644
--- a/test/dataset/formats/test_yolo.py
+++ b/test/dataset/formats/test_yolo.py
@@ -13,7 +13,7 @@ from supervision.dataset.formats.yolo import (
 from supervision.detection.core import Detections
 
 
-def _mock_simple_mask(resolution_wh: Tuple[int, int], box: List[int]) -> np.array:
+def _mock_simple_mask(resolution_wh: Tuple[int, int], box: List[int]) -> np.ndarray:
     x_min, y_min, x_max, y_max = box
     mask = np.full(resolution_wh, False, dtype=bool)
     mask[y_min:y_max, x_min:x_max] = True