diff --git a/.github/workflows/test-min.yml b/.github/workflows/test-min.yml
index 5c9fa7a4..ae4bb14b 100644
--- a/.github/workflows/test-min.yml
+++ b/.github/workflows/test-min.yml
@@ -38,7 +38,6 @@ jobs:
               numpy==1.21.2 \
               opencv-python==4.5.5.64 \
               packaging==23.2 \
-              Pillow==10.1.0 \
               pluggy==1.3.0 \
               pyparsing==3.1.1 \
               pytest==7.2.0 \
diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 5421004b..ce88b6e0 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -13,6 +13,7 @@ repos:
       - id: trailing-whitespace
         exclude: test/.*\.py
       - id: check-yaml
+        exclude: mkdocs.yml
       - id: check-docstring-first
       - id: check-executables-have-shebangs
       - id: check-toml
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
index dab07a14..70d3c04a 100644
--- a/CONTRIBUTING.md
+++ b/CONTRIBUTING.md
@@ -52,7 +52,7 @@ git push -u origin main
 
 ### Pre-commit tool
 
-This project utilizes the [pre-commit](https://pre-commit.com/) tool to maintain code quality and consistency. Before submitting a pull request or making any commits, it is important to run the pre-commit tool to ensure that your changes meet the project's guidelines.
+This project uses the [pre-commit](https://pre-commit.com/) tool to maintain code quality and consistency. Before submitting a pull request or making any commits, it is important to run the pre-commit tool to ensure that your changes meet the project's guidelines.
 
 Furthermore, we have integrated a pre-commit GitHub Action into our workflow. This means that with every pull request opened, the pre-commit checks will be automatically enforced, streamlining the code review process and ensuring that all contributions adhere to our quality standards.
 
diff --git a/README.md b/README.md
index 1fd46c2c..4ac35a81 100644
--- a/README.md
+++ b/README.md
@@ -46,17 +46,17 @@ Read more about desktop, headless, and local installation in our [guide](https:/
 Supervision was designed to be model agnostic. Just plug in any classification, detection, or segmentation model. For your convenience, we have created [connectors](https://supervision.roboflow.com/detection/core/#detections) for the most popular libraries like Ultralytics, Transformers, or MMDetection.
 
 ```python
->>> import cv2
->>> import supervision as sv
->>> from ultralytics import YOLO
+import cv2
+import supervision as sv
+from ultralytics import YOLO
 
->>> image = cv2.imread(...)
->>> model = YOLO('yolov8s.pt')
->>> result = model(image)[0]
->>> detections = sv.Detections.from_ultralytics(result)
+image = cv2.imread(...)
+model = YOLO('yolov8s.pt')
+result = model(image)[0]
+detections = sv.Detections.from_ultralytics(result)
 
->>> len(detections)
-5
+len(detections)
+# 5
 ```
 
 <details>
@@ -67,17 +67,17 @@ Supervision was designed to be model agnostic. Just plug in any classification,
     Running with [Inference](https://github.com/roboflow/inference) requires a [Roboflow API KEY](https://docs.roboflow.com/api-reference/authentication#retrieve-an-api-key).
 
     ```python
-    >>> import cv2
-    >>> import supervision as sv
-    >>> from inference.models.utils import get_roboflow_model
+    import cv2
+    import supervision as sv
+    from inference.models.utils import get_roboflow_model
 
-    >>> image = cv2.imread(...)
-    >>> model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
-    >>> result = model.infer(image)[0]
-    >>> detections = sv.Detections.from_inference(result)
+    image = cv2.imread(...)
+    model = get_roboflow_model(model_id="yolov8s-640", api_key=<ROBOFLOW API KEY>)
+    result = model.infer(image)[0]
+    detections = sv.Detections.from_inference(result)
 
-    >>> len(detections)
-    >>> 5
+    len(detections)
+    # 5
 
     ```
 
@@ -88,17 +88,17 @@ Supervision was designed to be model agnostic. Just plug in any classification,
 Supervision offers a wide range of highly customizable [annotators](https://supervision.roboflow.com/annotators/), allowing you to compose the perfect visualization for your use case.
 
 ```python
->>> import cv2
->>> import supervision as sv
+import cv2
+import supervision as sv
 
->>> image = cv2.imread(...)
->>> detections = sv.Detections(...)
+image = cv2.imread(...)
+detections = sv.Detections(...)
 
->>> bounding_box_annotator = sv.BoundingBoxAnnotator()
->>> annotated_frame = bounding_box_annotator.annotate(
-...     scene=image.copy(),
-...     detections=detections
-... )
+bounding_box_annotator = sv.BoundingBoxAnnotator()
+annotated_frame = bounding_box_annotator.annotate(
+    scene=image.copy(),
+    detections=detections
+)
 ```
 
 https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-ab5644f39bce
@@ -108,19 +108,19 @@ https://github.com/roboflow/supervision/assets/26109316/691e219c-0565-4403-9218-
 Supervision provides a set of [utils](https://supervision.roboflow.com/datasets/) that allow you to load, split, merge, and save datasets in one of the supported formats.
 
 ```python
->>> import supervision as sv
+import supervision as sv
 
->>> dataset = sv.DetectionDataset.from_yolo(
-...     images_directory_path=...,
-...     annotations_directory_path=...,
-...     data_yaml_path=...
-... )
+dataset = sv.DetectionDataset.from_yolo(
+    images_directory_path=...,
+    annotations_directory_path=...,
+    data_yaml_path=...
+)
 
->>> dataset.classes
+dataset.classes
 ['dog', 'person']
 
->>> len(dataset)
-1000
+len(dataset)
+# 1000
 ```
 
 <details close>
@@ -129,86 +129,86 @@ Supervision provides a set of [utils](https://supervision.roboflow.com/datasets/
 - load
 
   ```python
-  >>> dataset = sv.DetectionDataset.from_yolo(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...,
-  ...     data_yaml_path=...
-  ... )
+  dataset = sv.DetectionDataset.from_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  )
 
-  >>> dataset = sv.DetectionDataset.from_pascal_voc(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...
-  ... )
+  dataset = sv.DetectionDataset.from_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )
 
-  >>> dataset = sv.DetectionDataset.from_coco(
-  ...     images_directory_path=...,
-  ...     annotations_path=...
-  ... )
+  dataset = sv.DetectionDataset.from_coco(
+      images_directory_path=...,
+      annotations_path=...
+  )
   ```
 
 - split
 
   ```python
-  >>> train_dataset, test_dataset = dataset.split(split_ratio=0.7)
-  >>> test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
+  train_dataset, test_dataset = dataset.split(split_ratio=0.7)
+  test_dataset, valid_dataset = test_dataset.split(split_ratio=0.5)
 
-  >>> len(train_dataset), len(test_dataset), len(valid_dataset)
-  (700, 150, 150)
+  len(train_dataset), len(test_dataset), len(valid_dataset)
+  # (700, 150, 150)
   ```
 
 - merge
 
   ```python
-  >>> ds_1 = sv.DetectionDataset(...)
-  >>> len(ds_1)
-  100
-  >>> ds_1.classes
-  ['dog', 'person']
+  ds_1 = sv.DetectionDataset(...)
+  len(ds_1)
+  # 100
+  ds_1.classes
+  # ['dog', 'person']
 
-  >>> ds_2 = sv.DetectionDataset(...)
-  >>> len(ds_2)
-  200
-  >>> ds_2.classes
-  ['cat']
+  ds_2 = sv.DetectionDataset(...)
+  len(ds_2)
+  # 200
+  ds_2.classes
+  # ['cat']
 
-  >>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
-  >>> len(ds_merged)
-  300
-  >>> ds_merged.classes
-  ['cat', 'dog', 'person']
+  ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
+  len(ds_merged)
+  # 300
+  ds_merged.classes
+  # ['cat', 'dog', 'person']
   ```
 
 - save
 
   ```python
-  >>> dataset.as_yolo(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...,
-  ...     data_yaml_path=...
-  ... )
+  dataset.as_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  )
 
-  >>> dataset.as_pascal_voc(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...
-  ... )
+  dataset.as_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )
 
-  >>> dataset.as_coco(
-  ...     images_directory_path=...,
-  ...     annotations_path=...
-  ... )
+  dataset.as_coco(
+      images_directory_path=...,
+      annotations_path=...
+  )
   ```
 
 - convert
 
   ```python
-  >>> sv.DetectionDataset.from_yolo(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...,
-  ...     data_yaml_path=...
-  ... ).as_pascal_voc(
-  ...     images_directory_path=...,
-  ...     annotations_directory_path=...
-  ... )
+  sv.DetectionDataset.from_yolo(
+      images_directory_path=...,
+      annotations_directory_path=...,
+      data_yaml_path=...
+  ).as_pascal_voc(
+      images_directory_path=...,
+      annotations_directory_path=...
+  )
   ```
 
 </details>
diff --git a/docs/annotators.md b/docs/annotators.md
index 0c521f1e..796f1b24 100644
--- a/docs/annotators.md
+++ b/docs/annotators.md
@@ -1,20 +1,21 @@
 ---
 comments: true
+status: new
 ---
 
 === "BoundingBox"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> bounding_box_annotator = sv.BoundingBoxAnnotator()
-    >>> annotated_frame = bounding_box_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    bounding_box_annotator = sv.BoundingBoxAnnotator()
+    annotated_frame = bounding_box_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -26,16 +27,16 @@ comments: true
 === "RoundBox"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> round_box_annotator = sv.RoundBoxAnnotator()
-    >>> annotated_frame = round_box_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    round_box_annotator = sv.RoundBoxAnnotator()
+    annotated_frame = round_box_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -47,16 +48,16 @@ comments: true
 === "BoxCorner"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> corner_annotator = sv.BoxCornerAnnotator()
-    >>> annotated_frame = corner_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    corner_annotator = sv.BoxCornerAnnotator()
+    annotated_frame = corner_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -89,16 +90,16 @@ comments: true
 === "Color"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> color_annotator = sv.ColorAnnotator()
-    >>> annotated_frame = color_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    color_annotator = sv.ColorAnnotator()
+    annotated_frame = color_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -110,16 +111,16 @@ comments: true
 === "Circle"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> circle_annotator = sv.CircleAnnotator()
-    >>> annotated_frame = circle_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    circle_annotator = sv.CircleAnnotator()
+    annotated_frame = circle_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -131,16 +132,16 @@ comments: true
 === "Dot"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> dot_annotator = sv.DotAnnotator()
-    >>> annotated_frame = dot_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    dot_annotator = sv.DotAnnotator()
+    annotated_frame = dot_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -152,16 +153,16 @@ comments: true
 === "Triangle"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> triangle_annotator = sv.TriangleAnnotator()
-    >>> annotated_frame = triangle_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    triangle_annotator = sv.TriangleAnnotator()
+    annotated_frame = triangle_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -173,16 +174,16 @@ comments: true
 === "Ellipse"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> ellipse_annotator = sv.EllipseAnnotator()
-    >>> annotated_frame = ellipse_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    ellipse_annotator = sv.EllipseAnnotator()
+    annotated_frame = ellipse_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -194,16 +195,16 @@ comments: true
 === "Halo"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> halo_annotator = sv.HaloAnnotator()
-    >>> annotated_frame = halo_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    halo_annotator = sv.HaloAnnotator()
+    annotated_frame = halo_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -215,16 +216,16 @@ comments: true
 === "PercentageBar"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> percentage_bar_annotator = sv.PercentageBarAnnotator()
-    >>> annotated_frame = percentage_bar_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    percentage_bar_annotator = sv.PercentageBarAnnotator()
+    annotated_frame = percentage_bar_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -236,16 +237,16 @@ comments: true
 === "Mask"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> mask_annotator = sv.MaskAnnotator()
-    >>> annotated_frame = mask_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    mask_annotator = sv.MaskAnnotator()
+    annotated_frame = mask_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -257,16 +258,16 @@ comments: true
 === "Polygon"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> polygon_annotator = sv.PolygonAnnotator()
-    >>> annotated_frame = polygon_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    polygon_annotator = sv.PolygonAnnotator()
+    annotated_frame = polygon_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -278,16 +279,16 @@ comments: true
 === "Label"
 
     ```python
-    >>> import supervision as sv
+     import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
-    >>> annotated_frame = label_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
+    annotated_frame = label_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -299,16 +300,16 @@ comments: true
 === "Blur"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> blur_annotator = sv.BlurAnnotator()
-    >>> annotated_frame = blur_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    blur_annotator = sv.BlurAnnotator()
+    annotated_frame = blur_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -320,16 +321,16 @@ comments: true
 === "Pixelate"
 
     ```python
-    >>> import supervision as sv
+    import supervision as sv
 
-    >>> image = ...
-    >>> detections = sv.Detections(...)
+    image = ...
+    detections = sv.Detections(...)
 
-    >>> pixelate_annotator = sv.PixelateAnnotator()
-    >>> annotated_frame = pixelate_annotator.annotate(
-    ...     scene=image.copy(),
-    ...     detections=detections
-    ... )
+    pixelate_annotator = sv.PixelateAnnotator()
+    annotated_frame = pixelate_annotator.annotate(
+        scene=image.copy(),
+        detections=detections
+    )
     ```
 
     <div class="result" markdown>
@@ -341,26 +342,26 @@ comments: true
 === "Trace"
 
     ```python
-    >>> import supervision as sv
-    >>> from ultralytics import YOLO
+    import supervision as sv
+    from ultralytics import YOLO
 
-    >>> model = YOLO('yolov8x.pt')
+    model = YOLO('yolov8x.pt')
 
-    >>> trace_annotator = sv.TraceAnnotator()
+    trace_annotator = sv.TraceAnnotator()
 
-    >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-    >>> frames_generator = get_video_frames_generator(source_path='...')
-    >>> tracker = sv.ByteTrack()
+    video_info = sv.VideoInfo.from_video_path(video_path='...')
+    frames_generator = get_video_frames_generator(source_path='...')
+    tracker = sv.ByteTrack()
 
-    >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-    ...    for frame in frames_generator:
-    ...        result = model(frame)[0]
-    ...        detections = sv.Detections.from_ultralytics(result)
-    ...        detections = tracker.update_with_detections(detections)
-    ...        annotated_frame = trace_annotator.annotate(
-    ...            scene=frame.copy(),
-    ...            detections=detections)
-    ...        sink.write_frame(frame=annotated_frame)
+    with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+        for frame in frames_generator:
+            result = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(result)
+            detections = tracker.update_with_detections(detections)
+            annotated_frame = trace_annotator.annotate(
+                scene=frame.copy(),
+                detections=detections)
+            sink.write_frame(frame=annotated_frame)
     ```
 
     <div class="result" markdown>
@@ -372,24 +373,24 @@ comments: true
 === "HeatMap"
 
     ```python
-    >>> import supervision as sv
-    >>> from ultralytics import YOLO
+    import supervision as sv
+    from ultralytics import YOLO
 
-    >>> model = YOLO('yolov8x.pt')
+    model = YOLO('yolov8x.pt')
 
-    >>> heat_map_annotator = sv.HeatMapAnnotator()
+    heat_map_annotator = sv.HeatMapAnnotator()
 
-    >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-    >>> frames_generator = get_video_frames_generator(source_path='...')
+    video_info = sv.VideoInfo.from_video_path(video_path='...')
+    frames_generator = get_video_frames_generator(source_path='...')
 
-    >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-    ...    for frame in frames_generator:
-    ...        result = model(frame)[0]
-    ...        detections = sv.Detections.from_ultralytics(result)
-    ...        annotated_frame = heat_map_annotator.annotate(
-    ...            scene=frame.copy(),
-    ...            detections=detections)
-    ...        sink.write_frame(frame=annotated_frame)
+    with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+        for frame in frames_generator:
+            result = model(frame)[0]
+            detections = sv.Detections.from_ultralytics(result)
+            annotated_frame = heat_map_annotator.annotate(
+                scene=frame.copy(),
+                detections=detections)
+            sink.write_frame(frame=annotated_frame)
     ```
 
     <div class="result" markdown>
diff --git a/docs/detection/core.md b/docs/detection/core.md
index e966d60b..94d641e8 100644
--- a/docs/detection/core.md
+++ b/docs/detection/core.md
@@ -1,5 +1,6 @@
 ---
 comments: true
+status: new
 ---
 
 ## Detections
diff --git a/docs/detection/tools/smoother.md b/docs/detection/tools/smoother.md
new file mode 100644
index 00000000..e19778d3
--- /dev/null
+++ b/docs/detection/tools/smoother.md
@@ -0,0 +1,8 @@
+---
+comments: true
+status: new
+---
+
+## Detection Smoother
+
+:::supervision.detection.tools.smoother.DetectionsSmoother
diff --git a/docs/how_to/evaluate_model.md b/docs/how_to/evaluate_model.md
deleted file mode 100644
index 7adab821..00000000
--- a/docs/how_to/evaluate_model.md
+++ /dev/null
@@ -1,5 +0,0 @@
----
-comments: true
----
-
-🚧 Page under construction.
diff --git a/docs/how_to/process_video.md b/docs/how_to/process_video.md
deleted file mode 100644
index 7adab821..00000000
--- a/docs/how_to/process_video.md
+++ /dev/null
@@ -1,5 +0,0 @@
----
-comments: true
----
-
-🚧 Page under construction.
diff --git a/docs/index.md b/docs/index.md
index d3c48e20..2e04331d 100644
--- a/docs/index.md
+++ b/docs/index.md
@@ -17,6 +17,38 @@ comments: true
 
 We write your reusable computer vision tools. Whether you need to load your dataset from your hard drive, draw detections on an image or video, or count how many detections are in a zone. You can count on us!
 
+<div class="grid cards" markdown>
+
+-   __Detect and Annotate__
+
+    ---
+
+    Annotate predictions from a range of object detection and segmentation models
+
+    [:octicons-arrow-right-24: Tutorial](how_to/detect_and_annotate)
+
+-   __Track Objects__
+
+    ---
+
+    Discover how to enhance video analysis by implementing seamless object tracking
+
+    [:octicons-arrow-right-24: Tutorial](how_to/track_objects)
+
+-   > __Count Objects Crossing Line__
+
+    ---
+
+    Explore methods to accurately count and analyze objects crossing a predefined line
+
+-   > __Filter Objects in Zone__
+
+    ---
+
+    Master the techniques to selectively filter and focus on objects within a specific zone
+
+</div>
+
 ## 💻 Install
 
 You can install `supervision` with pip in a
diff --git a/mkdocs.yml b/mkdocs.yml
index efef520b..220737c8 100644
--- a/mkdocs.yml
+++ b/mkdocs.yml
@@ -28,10 +28,8 @@ nav:
   - Home: index.md
   - How to:
       - Detect and Annotate: how_to/detect_and_annotate.md
-      - Process Video: how_to/process_video.md
       - Track Objects: how_to/track_objects.md
       - Filter Detections: how_to/filter_detections.md
-      - Evaluate Model: how_to/evaluate_model.md
   - API:
     - Classifications:
         - Core: classification/core.md
@@ -42,6 +40,7 @@ nav:
         - Line Zone: detection/tools/line_zone.md
         - Polygon Zone: detection/tools/polygon_zone.md
         - Inference Slicer: detection/tools/inference_slicer.md
+        - Detection Smoother: detection/tools/smoother.md
     - Annotators: annotators.md
     - Trackers: trackers.md
     - Datasets: datasets.md
@@ -82,6 +81,7 @@ theme:
     code: Roboto Mono
   features:
     - content.code.copy
+    - content.code.annotate
 
 plugins:
   - mkdocstrings
@@ -97,6 +97,9 @@ markdown_extensions:
       alternate_style: true
   - toc:
       permalink: true
+  - pymdownx.emoji:
+      emoji_index: !!python/name:material.extensions.emoji.twemoji
+      emoji_generator: !!python/name:material.extensions.emoji.to_svg
 
 extra_javascript:
   - "https://widget.kapa.ai/kapa-widget.bundle.js"
diff --git a/poetry.lock b/poetry.lock
index 835455b2..9e1a54c6 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -672,47 +672,56 @@ test-no-images = ["pytest", "pytest-cov", "wurlitzer"]
 
 [[package]]
 name = "cryptography"
-version = "41.0.7"
+version = "42.0.0"
 description = "cryptography is a package which provides cryptographic recipes and primitives to Python developers."
 optional = false
 python-versions = ">=3.7"
 files = [
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 ]
 
 [package.dependencies]
-cffi = ">=1.12"
+cffi = {version = ">=1.12", markers = "platform_python_implementation != \"PyPy\""}
 
 [package.extras]
 docs = ["sphinx (>=5.3.0)", "sphinx-rtd-theme (>=1.1.1)"]
-docstest = ["pyenchant (>=1.6.11)", "sphinxcontrib-spelling (>=4.0.1)", "twine (>=1.12.0)"]
+docstest = ["pyenchant (>=1.6.11)", "readme-renderer", "sphinxcontrib-spelling (>=4.0.1)"]
 nox = ["nox"]
-pep8test = ["black", "check-sdist", "mypy", "ruff"]
+pep8test = ["check-sdist", "click", "mypy", "ruff"]
 sdist = ["build"]
 ssh = ["bcrypt (>=3.1.5)"]
-test = ["pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
+test = ["certifi", "pretend", "pytest (>=6.2.0)", "pytest-benchmark", "pytest-cov", "pytest-xdist"]
 test-randomorder = ["pytest-randomly"]
 
 [[package]]
@@ -1254,13 +1263,13 @@ files = [
 
 [[package]]
 name = "jsonschema"
-version = "4.21.0"
+version = "4.21.1"
 description = "An implementation of JSON Schema validation for Python"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -1419,13 +1428,13 @@ test = ["flaky", "ipykernel", "pre-commit", "pytest (>=7.0)", "pytest-console-sc
 
 [[package]]
 name = "jupyter-server-terminals"
-version = "0.5.1"
+version = "0.5.2"
 description = "A Jupyter Server Extension Providing Terminals."
 optional = false
 python-versions = ">=3.8"
 files = [
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+    {file = "jupyter_server_terminals-0.5.2.tar.gz", hash = "sha256:396b5ccc0881e550bf0ee7012c6ef1b53edbde69e67cab1d56e89711b46052e8"},
 ]
 
 [package.dependencies]
@@ -1687,61 +1696,71 @@ testing = ["coverage", "pytest", "pytest-cov", "pytest-regressions"]
 
 [[package]]
 name = "markupsafe"
-version = "2.1.3"
+version = "2.1.4"
 description = "Safely add untrusted strings to HTML/XML markup."
 optional = false
 python-versions = ">=3.7"
 files = [
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+    {file = "MarkupSafe-2.1.4-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7653fa39578957bc42e5ebc15cf4361d9e0ee4b702d7d5ec96cdac860953c5b4"},
+    {file = "MarkupSafe-2.1.4-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:47bb5f0142b8b64ed1399b6b60f700a580335c8e1c57f2f15587bd072012decc"},
+    {file = "MarkupSafe-2.1.4-cp39-cp39-musllinux_1_1_aarch64.whl", hash = "sha256:fe8512ed897d5daf089e5bd010c3dc03bb1bdae00b35588c49b98268d4a01e00"},
+    {file = "MarkupSafe-2.1.4-cp39-cp39-musllinux_1_1_i686.whl", hash = "sha256:36d7626a8cca4d34216875aee5a1d3d654bb3dac201c1c003d182283e3205949"},
+    {file = "MarkupSafe-2.1.4-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:b6f14a9cd50c3cb100eb94b3273131c80d102e19bb20253ac7bd7336118a673a"},
+    {file = "MarkupSafe-2.1.4-cp39-cp39-win32.whl", hash = "sha256:c8f253a84dbd2c63c19590fa86a032ef3d8cc18923b8049d91bcdeeb2581fbf6"},
+    {file = "MarkupSafe-2.1.4-cp39-cp39-win_amd64.whl", hash = "sha256:8b570a1537367b52396e53325769608f2a687ec9a4363647af1cded8928af959"},
+    {file = "MarkupSafe-2.1.4.tar.gz", hash = "sha256:3aae9af4cac263007fd6309c64c6ab4506dd2b79382d9d19a1994f9240b8db4f"},
 ]
 
 [[package]]
@@ -1907,13 +1926,13 @@ mkdocs = ">=1.1"
 
 [[package]]
 name = "mkdocs-material"
-version = "9.5.4"
+version = "9.5.5"
 description = "Documentation that simply works"
 optional = false
 python-versions = ">=3.8"
 files = [
-    {file = "mkdocs_material-9.5.4-py3-none-any.whl", hash = "sha256:efd7cc8ae03296d728da9bd38f4db8b07ab61f9738a0cbd0dfaf2a15a50e7343"},
-    {file = "mkdocs_material-9.5.4.tar.gz", hash = "sha256:3d196ee67fad16b2df1a458d650a8ac1890294eaae368d26cee71bc24ad41c40"},
+    {file = "mkdocs_material-9.5.5-py3-none-any.whl", hash = "sha256:ac50b2431a79a3b160fdefbba37c9132485f1a69166aba115ad49fafdbbbc5df"},
+    {file = "mkdocs_material-9.5.5.tar.gz", hash = "sha256:4480d9580faf42fed0123d0465502bfc1c0c239ecc9c4d66159cf0459ea1b4ae"},
 ]
 
 [package.dependencies]
@@ -1925,7 +1944,7 @@ markdown = ">=3.2,<4.0"
 mkdocs = ">=1.5.3,<1.6.0"
 mkdocs-material-extensions = ">=1.3,<2.0"
 paginate = ">=0.5,<1.0"
-pillow = {version = ">=9.4,<10.0", optional = true, markers = "extra == \"imaging\""}
+pillow = {version = ">=10.2,<11.0", optional = true, markers = "extra == \"imaging\""}
 pygments = ">=2.16,<3.0"
 pymdown-extensions = ">=10.2,<11.0"
 regex = ">=2022.4"
@@ -1933,7 +1952,7 @@ requests = ">=2.26,<3.0"
 
 [package.extras]
 git = ["mkdocs-git-committers-plugin-2 (>=1.1,<2.0)", "mkdocs-git-revision-date-localized-plugin (>=1.2,<2.0)"]
-imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=9.4,<10.0)"]
+imaging = ["cairosvg (>=2.6,<3.0)", "pillow (>=10.2,<11.0)"]
 recommended = ["mkdocs-minify-plugin (>=0.7,<1.0)", "mkdocs-redirects (>=1.2,<2.0)", "mkdocs-rss-plugin (>=1.6,<2.0)"]
 
 [[package]]
@@ -2143,13 +2162,13 @@ test = ["pep440", "pre-commit", "pytest", "testpath"]
 
 [[package]]
 name = "nest-asyncio"
-version = "1.5.9"
+version = "1.6.0"
 description = "Patch asyncio to allow nested event loops"
 optional = false
 python-versions = ">=3.5"
 files = [
-    {file = "nest_asyncio-1.5.9-py3-none-any.whl", hash = "sha256:61ec07ef052e72e3de22045b81b2cc7d71fceb04c568ba0b2e4b2f9f5231bec2"},
-    {file = "nest_asyncio-1.5.9.tar.gz", hash = "sha256:d1e1144e9c6e3e6392e0fcf5211cb1c8374b5648a98f1ebe48e5336006b41907"},
+    {file = "nest_asyncio-1.6.0-py3-none-any.whl", hash = "sha256:87af6efd6b5e897c81050477ef65c62e2b2f35d51703cae01aff2905b1852e1c"},
+    {file = "nest_asyncio-1.6.0.tar.gz", hash = "sha256:6f172d5449aca15afd6c646851f4e31e02c598d553a667e38cafa997cfec55fe"},
 ]
 
 [[package]]
@@ -2284,12 +2303,31 @@ files = [
     {file = "opencv_python-4.8.1.78-cp37-abi3-win_amd64.whl", hash = "sha256:b983197f97cfa6fcb74e1da1802c7497a6f94ed561aba6980f1f33123f904956"},
 ]
 
+[package.dependencies]
+numpy = {version = ">=1.23.5", markers = "python_version >= \"3.11\""}
+
+[[package]]
+name = "opencv-python"
+version = "4.9.0.80"
+description = "Wrapper package for OpenCV python bindings."
+optional = true
+python-versions = ">=3.6"
+files = [
+    {file = "opencv-python-4.9.0.80.tar.gz", hash = "sha256:1a9f0e6267de3a1a1db0c54213d022c7c8b5b9ca4b580e80bdc58516c922c9e1"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:7e5f7aa4486651a6ebfa8ed4b594b65bd2d2f41beeb4241a3e4b1b85acbbbadb"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:71dfb9555ccccdd77305fc3dcca5897fbf0cf28b297c51ee55e079c065d812a3"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7b34a52e9da36dda8c151c6394aed602e4b17fa041df0b9f5b93ae10b0fcca2a"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e4088cab82b66a3b37ffc452976b14a3c599269c247895ae9ceb4066d8188a57"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:dcf000c36dd1651118a2462257e3a9e76db789a78432e1f303c7bac54f63ef6c"},
+    {file = "opencv_python-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:3f16f08e02b2a2da44259c7cc712e779eff1dd8b55fdb0323e8cab09548086c0"},
+]
+
 [package.dependencies]
 numpy = [
     {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
     {version = ">=1.17.3", markers = "(platform_system != \"Darwin\" and platform_system != \"Linux\") and python_version >= \"3.8\" and python_version < \"3.9\" or platform_system != \"Darwin\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_machine != \"aarch64\" or platform_machine != \"arm64\" and python_version >= \"3.8\" and python_version < \"3.9\" and platform_system != \"Linux\" or (platform_machine != \"arm64\" and platform_machine != \"aarch64\") and python_version >= \"3.8\" and python_version < \"3.9\""},
-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
@@ -2310,25 +2348,44 @@ files = [
     {file = "opencv_python_headless-4.8.1.78-cp37-abi3-win_amd64.whl", hash = "sha256:0a0f1e9f836f7d5bad1dd164694944c8761711cbdf4b36ebbd4815a8ef731079"},
 ]
 
+[package.dependencies]
+numpy = {version = ">=1.23.5", markers = "python_version >= \"3.11\""}
+
+[[package]]
+name = "opencv-python-headless"
+version = "4.9.0.80"
+description = "Wrapper package for OpenCV python bindings."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "opencv-python-headless-4.9.0.80.tar.gz", hash = "sha256:71a4cd8cf7c37122901d8e81295db7fb188730e33a0e40039a4e59c1030b0958"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-macosx_10_16_x86_64.whl", hash = "sha256:2ea8a2edc4db87841991b2fbab55fc07b97ecb602e0f47d5d485bd75cee17c1a"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-macosx_11_0_arm64.whl", hash = "sha256:e0ee54e27be493e8f7850847edae3128e18b540dac1d7b2e4001b8944e11e1c6"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:57ce2865e8fec431c6f97a81e9faaf23fa5be61011d0a75ccf47a3c0d65fa73d"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:976656362d68d9f40a5c66f83901430538002465f7db59142784f3893918f3df"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-win32.whl", hash = "sha256:11e3849d83e6651d4e7699aadda9ec7ed7c38957cbbcb99db074f2a2d2de9670"},
+    {file = "opencv_python_headless-4.9.0.80-cp37-abi3-win_amd64.whl", hash = "sha256:a8056c2cb37cd65dfcdf4153ca16f7362afcf3a50d600d6bb69c660fc61ee29c"},
+]
+
 [package.dependencies]
 numpy = [
     {version = ">=1.21.0", markers = "python_version <= \"3.9\" and platform_system == \"Darwin\" and platform_machine == \"arm64\" and python_version >= \"3.8\""},
     {version = ">=1.19.3", markers = "platform_system == \"Linux\" and platform_machine == \"aarch64\" and python_version >= \"3.8\" and python_version < \"3.10\" or python_version > \"3.9\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_system != \"Darwin\" and python_version < \"3.10\" or python_version >= \"3.9\" and platform_machine != \"arm64\" and python_version < \"3.10\""},
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-    {version = ">=1.23.5", markers = "python_version >= \"3.11\""},
+    {version = ">=1.23.5", markers = "python_version >= \"3.11\" and python_version < \"3.12\""},
     {version = ">=1.21.4", markers = "python_version >= \"3.10\" and platform_system == \"Darwin\" and python_version < \"3.11\""},
     {version = ">=1.21.2", markers = "platform_system != \"Darwin\" and python_version >= \"3.10\" and python_version < \"3.11\""},
 ]
 
 [[package]]
 name = "overrides"
-version = "7.4.0"
+version = "7.6.0"
 description = "A decorator to automatically detect mismatch when overriding a method."
 optional = false
 python-versions = ">=3.6"
 files = [
-    {file = "overrides-7.4.0-py3-none-any.whl", hash = "sha256:3ad24583f86d6d7a49049695efe9933e67ba62f0c7625d53c59fa832ce4b8b7d"},
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+    {file = "overrides-7.6.0-py3-none-any.whl", hash = "sha256:c36e6635519ea9c5b043b65c36d4b886aee8bd45b7d4681d2a6df0898df4b654"},
+    {file = "overrides-7.6.0.tar.gz", hash = "sha256:01e15bbbf15b766f0675c275baa1878bd1c7dc9bc7b9ee13e677cdba93dc1bd9"},
 ]
 
 [[package]]
@@ -2416,82 +2473,88 @@ files = [
 
 [[package]]
 name = "pillow"
-version = "9.5.0"
+version = "10.2.0"
 description = "Python Imaging Library (Fork)"
 optional = false
-python-versions = ">=3.7"
+python-versions = ">=3.8"
 files = [
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 name = "pkginfo"
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+    {file = "scipy-1.12.0-cp39-cp39-musllinux_1_1_x86_64.whl", hash = "sha256:196ebad3a4882081f62a5bf4aeb7326aa34b110e533aab23e4374fcccb0890dc"},
+    {file = "scipy-1.12.0-cp39-cp39-win_amd64.whl", hash = "sha256:b360f1b6b2f742781299514e99ff560d1fe9bd1bff2712894b52abe528d1fd1e"},
+    {file = "scipy-1.12.0.tar.gz", hash = "sha256:4bf5abab8a36d20193c698b0f1fc282c1d083c94723902c447e5d2f1780936a3"},
 ]
 
 [package.dependencies]
-numpy = ">=1.21.6,<1.28.0"
+numpy = ">=1.22.4,<1.29.0"
 
 [package.extras]
 dev = ["click", "cython-lint (>=0.12.2)", "doit (>=0.36.0)", "mypy", "pycodestyle", "pydevtool", "rich-click", "ruff", "types-psutil", "typing_extensions"]
 doc = ["jupytext", "matplotlib (>2)", "myst-nb", "numpydoc", "pooch", "pydata-sphinx-theme (==0.9.0)", "sphinx (!=4.1.0)", "sphinx-design (>=0.2.0)"]
-test = ["asv", "gmpy2", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
+test = ["asv", "gmpy2", "hypothesis", "mpmath", "pooch", "pytest", "pytest-cov", "pytest-timeout", "pytest-xdist", "scikit-umfpack", "threadpoolctl"]
 
 [[package]]
 name = "secretstorage"
@@ -3909,4 +3972,4 @@ desktop = ["opencv-python"]
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.8"
-content-hash = "3d59673b11422c0c463ec7b7e20b8a295155d71a6c42ad97f819e9798643b0ef"
+content-hash = "5e5a6f753fc524c096453bc89bb70514d1a7b4ffda3a9c8ed4938b549d194b3f"
diff --git a/pyproject.toml b/pyproject.toml
index 5437ff3c..f6aba471 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -37,9 +37,8 @@ scipy = [
     { version = "1.10.0", python = "<3.9" },
     { version = "^1.10.0", python = ">=3.9" }
 ]
-matplotlib = ">=3.5.0"
+matplotlib = ">=3.6.0"
 pyyaml = ">=5.3"
-pillow = ">=9.4"
 opencv-python = { version = ">=4.5.5.64", optional = true }
 opencv-python-headless = ">=4.5.5.64"
 requests = { version = ">=2.26.0,<=2.31.0", optional = true }
@@ -47,6 +46,7 @@ tqdm = { version = ">=4.62.3,<=4.66.1", optional = true }
 defusedxml = "^0.7.1"
 
 
+
 [tool.poetry.extras]
 desktop = ["opencv-python"]
 assets = ["requests","tqdm"]
@@ -65,7 +65,7 @@ tox = "^4.11.4"
 
 
 [tool.poetry.group.docs.dependencies]
-mkdocs-material = {extras = ["imaging"], version = "^9.5.4"}
+mkdocs-material = {extras = ["imaging"], version = "^9.5.5"}
 mkdocstrings = {extras = ["python"], version = ">=0.20,<0.25"}
 
 
diff --git a/supervision/__init__.py b/supervision/__init__.py
index 3370e13d..d44f6723 100644
--- a/supervision/__init__.py
+++ b/supervision/__init__.py
@@ -38,6 +38,7 @@ from supervision.detection.core import Detections
 from supervision.detection.line_counter import LineZone, LineZoneAnnotator
 from supervision.detection.tools.inference_slicer import InferenceSlicer
 from supervision.detection.tools.polygon_zone import PolygonZone, PolygonZoneAnnotator
+from supervision.detection.tools.smoother import DetectionsSmoother
 from supervision.detection.utils import (
     box_iou_batch,
     calculate_masks_centroids,
diff --git a/supervision/annotators/core.py b/supervision/annotators/core.py
index 631ca41f..5b206e35 100644
--- a/supervision/annotators/core.py
+++ b/supervision/annotators/core.py
@@ -57,16 +57,16 @@ class BoundingBoxAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> bounding_box_annotator = sv.BoundingBoxAnnotator()
-            >>> annotated_frame = bounding_box_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            bounding_box_annotator = sv.BoundingBoxAnnotator()
+            annotated_frame = bounding_box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![bounding-box-annotator-example](https://media.roboflow.com/
@@ -176,7 +176,7 @@ class MaskAnnotator(BaseAnnotator):
 
     !!! warning
 
-        This annotator utilizes the `sv.Detections.mask`.
+        This annotator uses `sv.Detections.mask`.
     """
 
     def __init__(
@@ -217,16 +217,16 @@ class MaskAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> mask_annotator = sv.MaskAnnotator()
-            >>> annotated_frame = mask_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            mask_annotator = sv.MaskAnnotator()
+            annotated_frame = mask_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![mask-annotator-example](https://media.roboflow.com/
@@ -259,7 +259,7 @@ class PolygonAnnotator(BaseAnnotator):
 
     !!! warning
 
-        This annotator utilizes the `sv.Detections.mask`.
+        This annotator uses `sv.Detections.mask`.
     """
 
     def __init__(
@@ -300,16 +300,16 @@ class PolygonAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> polygon_annotator = sv.PolygonAnnotator()
-            >>> annotated_frame = polygon_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            polygon_annotator = sv.PolygonAnnotator()
+            annotated_frame = polygon_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![polygon-annotator-example](https://media.roboflow.com/
@@ -382,16 +382,16 @@ class ColorAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> color_annotator = sv.ColorAnnotator()
-            >>> annotated_frame = color_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            color_annotator = sv.ColorAnnotator()
+            annotated_frame = color_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![box-mask-annotator-example](https://media.roboflow.com/
@@ -427,7 +427,7 @@ class HaloAnnotator(BaseAnnotator):
 
     !!! warning
 
-        This annotator utilizes the `sv.Detections.mask`.
+        This annotator uses `sv.Detections.mask`.
     """
 
     def __init__(
@@ -472,16 +472,16 @@ class HaloAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> halo_annotator = sv.HaloAnnotator()
-            >>> annotated_frame = halo_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            halo_annotator = sv.HaloAnnotator()
+            annotated_frame = halo_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![halo-annotator-example](https://media.roboflow.com/
@@ -566,16 +566,16 @@ class EllipseAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> ellipse_annotator = sv.EllipseAnnotator()
-            >>> annotated_frame = ellipse_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            ellipse_annotator = sv.EllipseAnnotator()
+            annotated_frame = ellipse_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![ellipse-annotator-example](https://media.roboflow.com/
@@ -653,16 +653,16 @@ class BoxCornerAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> corner_annotator = sv.BoxCornerAnnotator()
-            >>> annotated_frame = corner_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            corner_annotator = sv.BoxCornerAnnotator()
+            annotated_frame = corner_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![box-corner-annotator-example](https://media.roboflow.com/
@@ -737,16 +737,16 @@ class CircleAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> circle_annotator = sv.CircleAnnotator()
-            >>> annotated_frame = circle_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            circle_annotator = sv.CircleAnnotator()
+            annotated_frame = circle_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
 
@@ -823,16 +823,16 @@ class DotAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> dot_annotator = sv.DotAnnotator()
-            >>> annotated_frame = dot_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            dot_annotator = sv.DotAnnotator()
+            annotated_frame = dot_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![dot-annotator-example](https://media.roboflow.com/
@@ -950,16 +950,16 @@ class LabelAnnotator:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
-            >>> annotated_frame = label_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            label_annotator = sv.LabelAnnotator(text_position=sv.Position.CENTER)
+            annotated_frame = label_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![label-annotator-example](https://media.roboflow.com/
@@ -1064,16 +1064,16 @@ class BlurAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> blur_annotator = sv.BlurAnnotator()
-            >>> annotated_frame = circle_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            blur_annotator = sv.BlurAnnotator()
+            annotated_frame = circle_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![blur-annotator-example](https://media.roboflow.com/
@@ -1098,7 +1098,7 @@ class TraceAnnotator:
 
     !!! warning
 
-        This annotator utilizes the `sv.Detections.tracker_id`. Read
+        This annotator uses the `sv.Detections.tracker_id`. Read
         [here](https://supervision.roboflow.com/trackers/) to learn how to plug
         tracking into your inference pipeline.
     """
@@ -1149,26 +1149,25 @@ class TraceAnnotator:
 
         Example:
             ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO
 
-            >>> model = YOLO('yolov8x.pt')
+            model = YOLO('yolov8x.pt')
+            trace_annotator = sv.TraceAnnotator()
 
-            >>> trace_annotator = sv.TraceAnnotator()
+            video_info = sv.VideoInfo.from_video_path(video_path='...')
+            frames_generator = sv.get_video_frames_generator(source_path='...')
+            tracker = sv.ByteTrack()
 
-            >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-            >>> frames_generator = sv.get_video_frames_generator(source_path='...')
-            >>> tracker = sv.ByteTrack()
-
-            >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-            ...    for frame in frames_generator:
-            ...        result = model(frame)[0]
-            ...        detections = sv.Detections.from_ultralytics(result)
-            ...        detections = tracker.update_with_detections(detections)
-            ...        annotated_frame = trace_annotator.annotate(
-            ...            scene=frame.copy(),
-            ...            detections=detections)
-            ...        sink.write_frame(frame=annotated_frame)
+            with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+               for frame in frames_generator:
+                   result = model(frame)[0]
+                   detections = sv.Detections.from_ultralytics(result)
+                   detections = tracker.update_with_detections(detections)
+                   annotated_frame = trace_annotator.annotate(
+                       scene=frame.copy(),
+                       detections=detections)
+                   sink.write_frame(frame=annotated_frame)
             ```
 
         ![trace-annotator-example](https://media.roboflow.com/
@@ -1245,24 +1244,24 @@ class HeatMapAnnotator:
 
         Example:
             ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO
 
-            >>> model = YOLO('yolov8x.pt')
+            model = YOLO('yolov8x.pt')
 
-            >>> heat_map_annotator = sv.HeatMapAnnotator()
+            heat_map_annotator = sv.HeatMapAnnotator()
 
-            >>> video_info = sv.VideoInfo.from_video_path(video_path='...')
-            >>> frames_generator = get_video_frames_generator(source_path='...')
+            video_info = sv.VideoInfo.from_video_path(video_path='...')
+            frames_generator = get_video_frames_generator(source_path='...')
 
-            >>> with sv.VideoSink(target_path='...', video_info=video_info) as sink:
-            ...    for frame in frames_generator:
-            ...        result = model(frame)[0]
-            ...        detections = sv.Detections.from_ultralytics(result)
-            ...        annotated_frame = heat_map_annotator.annotate(
-            ...            scene=frame.copy(),
-            ...            detections=detections)
-            ...        sink.write_frame(frame=annotated_frame)
+            with sv.VideoSink(target_path='...', video_info=video_info) as sink:
+               for frame in frames_generator:
+                   result = model(frame)[0]
+                   detections = sv.Detections.from_ultralytics(result)
+                   annotated_frame = heat_map_annotator.annotate(
+                       scene=frame.copy(),
+                       detections=detections)
+                   sink.write_frame(frame=annotated_frame)
             ```
 
         ![heatmap-annotator-example](https://media.roboflow.com/
@@ -1322,16 +1321,16 @@ class PixelateAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> pixelate_annotator = sv.PixelateAnnotator()
-            >>> annotated_frame = pixelate_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            pixelate_annotator = sv.PixelateAnnotator()
+            annotated_frame = pixelate_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![pixelate-annotator-example](https://media.roboflow.com/
@@ -1408,16 +1407,16 @@ class TriangleAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> triangle_annotator = sv.TriangleAnnotator()
-            >>> annotated_frame = triangle_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            triangle_annotator = sv.TriangleAnnotator()
+            annotated_frame = triangle_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![triangle-annotator-example](https://media.roboflow.com/
@@ -1501,16 +1500,16 @@ class RoundBoxAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> round_box_annotator = sv.RoundBoxAnnotator()
-            >>> annotated_frame = round_box_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            round_box_annotator = sv.RoundBoxAnnotator()
+            annotated_frame = round_box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![round-box-annotator-example](https://media.roboflow.com/
@@ -1641,16 +1640,16 @@ class PercentageBarAnnotator(BaseAnnotator):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> percentage_bar_annotator = sv.BoundingBoxAnnotator()
-            >>> annotated_frame = percentage_bar_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections
-            ... )
+            percentage_bar_annotator = sv.BoundingBoxAnnotator()
+            annotated_frame = percentage_bar_annotator.annotate(
+                scene=image.copy(),
+                detections=detections
+            )
             ```
 
         ![percentage-bar-example](https://media.roboflow.com/
diff --git a/supervision/assets/downloader.py b/supervision/assets/downloader.py
index e37aca40..8e86359a 100644
--- a/supervision/assets/downloader.py
+++ b/supervision/assets/downloader.py
@@ -54,9 +54,9 @@ def download_assets(asset_name: Union[VideoAssets, str]) -> str:
 
     Example:
         ```python
-        >>> from supervision.assets import download_assets, VideoAssets
+        from supervision.assets import download_assets, VideoAssets
 
-        >>> download_assets(VideoAssets.VEHICLES)
+        download_assets(VideoAssets.VEHICLES)
         "vehicles.mp4"
         ```
     """
diff --git a/supervision/classification/core.py b/supervision/classification/core.py
index 279d358a..34df85e3 100644
--- a/supervision/classification/core.py
+++ b/supervision/classification/core.py
@@ -59,18 +59,18 @@ class Classifications:
 
         Example:
             ```python
-            >>> from PIL import Image
-            >>> import clip
-            >>> import supervision as sv
+            from PIL import Image
+            import clip
+            import supervision as sv
 
-            >>> model, preprocess = clip.load('ViT-B/32')
+            model, preprocess = clip.load('ViT-B/32')
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> image = preprocess(image).unsqueeze(0)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            image = preprocess(image).unsqueeze(0)
 
-            >>> text = clip.tokenize(["a diagram", "a dog", "a cat"])
-            >>> output, _ = model(image, text)
-            >>> classifications = sv.Classifications.from_clip(output)
+            text = clip.tokenize(["a diagram", "a dog", "a cat"])
+            output, _ = model(image, text)
+            classifications = sv.Classifications.from_clip(output)
             ```
         """
 
@@ -97,15 +97,15 @@ class Classifications:
 
         Example:
             ```python
-            >>> import cv2
-            >>> from ultralytics import YOLO
-            >>> import supervision as sv
+            import cv2
+            from ultralytics import YOLO
+            import supervision as sv
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = YOLO('yolov8n-cls.pt')
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            model = YOLO('yolov8n-cls.pt')
 
-            >>> output = model(image)[0]
-            >>> classifications = sv.Classifications.from_ultralytics(output)
+            output = model(image)[0]
+            classifications = sv.Classifications.from_ultralytics(output)
             ```
         """
         confidence = ultralytics_results.probs.data.cpu().numpy()
@@ -125,25 +125,25 @@ class Classifications:
 
         Example:
             ```python
-            >>> from PIL import Image
-            >>> import timm
-            >>> from timm.data import resolve_data_config, create_transform
-            >>> import supervision as sv
+            from PIL import Image
+            import timm
+            from timm.data import resolve_data_config, create_transform
+            import supervision as sv
 
-            >>> model = timm.create_model(
-            ...     model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
-            ...     pretrained=True
-            ... ).eval()
+            model = timm.create_model(
+                model_name='hf-hub:nateraw/resnet50-oxford-iiit-pet',
+                pretrained=True
+            ).eval()
 
-            >>> config = resolve_data_config({}, model=model)
-            >>> transform = create_transform(**config)
+            config = resolve_data_config({}, model=model)
+            transform = create_transform(**config)
 
-            >>> image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
-            >>> x = transform(image).unsqueeze(0)
+            image = Image.open(SOURCE_IMAGE_PATH).convert('RGB')
+            x = transform(image).unsqueeze(0)
 
-            >>> output = model(x)
+            output = model(x)
 
-            >>> classifications = sv.Classifications.from_timm(output)
+            classifications = sv.Classifications.from_timm(output)
             ```
         """
         confidence = timm_results.cpu().detach().numpy()[0]
@@ -168,11 +168,11 @@ class Classifications:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> classifications = sv.Classifications(...)
+            classifications = sv.Classifications(...)
 
-            >>> classifications.get_top_k(1)
+            classifications.get_top_k(1)
 
             (array([1]), array([0.9]))
             ```
diff --git a/supervision/dataset/core.py b/supervision/dataset/core.py
index 48043dbb..551e96da 100644
--- a/supervision/dataset/core.py
+++ b/supervision/dataset/core.py
@@ -118,13 +118,13 @@ class DetectionDataset(BaseDataset):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> ds = sv.DetectionDataset(...)
-            >>> train_ds, test_ds = ds.split(split_ratio=0.7,
-            ...                              random_state=42, shuffle=True)
-            >>> len(train_ds), len(test_ds)
-            (700, 300)
+            ds = sv.DetectionDataset(...)
+            train_ds, test_ds = ds.split(split_ratio=0.7,
+                                         random_state=42, shuffle=True)
+            len(train_ds), len(test_ds)
+            # (700, 300)
             ```
         """
 
@@ -231,24 +231,24 @@ class DetectionDataset(BaseDataset):
 
         Example:
             ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv
 
-            >>> roboflow.login()
+            roboflow.login()
 
-            >>> rf = Roboflow()
+            rf = Roboflow()
 
-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("voc")
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("voc")
 
-            >>> ds = sv.DetectionDataset.from_pascal_voc(
-            ...     images_directory_path=f"{dataset.location}/train/images",
-            ...     annotations_directory_path=f"{dataset.location}/train/labels"
-            ... )
+            ds = sv.DetectionDataset.from_pascal_voc(
+                images_directory_path=f"{dataset.location}/train/images",
+                annotations_directory_path=f"{dataset.location}/train/labels"
+            )
 
-            >>> ds.classes
-            ['dog', 'person']
+            ds.classes
+            # ['dog', 'person']
             ```
         """
 
@@ -288,25 +288,24 @@ class DetectionDataset(BaseDataset):
 
         Example:
             ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv
 
-            >>> roboflow.login()
+            roboflow.login()
+            rf = Roboflow()
 
-            >>> rf = Roboflow()
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("yolov5")
 
-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("yolov5")
+            ds = sv.DetectionDataset.from_yolo(
+                images_directory_path=f"{dataset.location}/train/images",
+                annotations_directory_path=f"{dataset.location}/train/labels",
+                data_yaml_path=f"{dataset.location}/data.yaml"
+            )
 
-            >>> ds = sv.DetectionDataset.from_yolo(
-            ...     images_directory_path=f"{dataset.location}/train/images",
-            ...     annotations_directory_path=f"{dataset.location}/train/labels",
-            ...     data_yaml_path=f"{dataset.location}/data.yaml"
-            ... )
-
-            >>> ds.classes
-            ['dog', 'person']
+            ds.classes
+            # ['dog', 'person']
             ```
         """
         classes, images, annotations = load_yolo_annotations(
@@ -394,24 +393,23 @@ class DetectionDataset(BaseDataset):
 
         Example:
             ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv
 
-            >>> roboflow.login()
+            roboflow.login()
+            rf = Roboflow()
 
-            >>> rf = Roboflow()
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("coco")
 
-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("coco")
+            ds = sv.DetectionDataset.from_coco(
+                images_directory_path=f"{dataset.location}/train",
+                annotations_path=f"{dataset.location}/train/_annotations.coco.json",
+            )
 
-            >>> ds = sv.DetectionDataset.from_coco(
-            ...     images_directory_path=f"{dataset.location}/train",
-            ...     annotations_path=f"{dataset.location}/train/_annotations.coco.json",
-            ... )
-
-            >>> ds.classes
-            ['dog', 'person']
+            ds.classes
+            # ['dog', 'person']
             ```
         """
         classes, images, annotations = load_coco_annotations(
@@ -486,25 +484,25 @@ class DetectionDataset(BaseDataset):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> ds_1 = sv.DetectionDataset(...)
-            >>> len(ds_1)
-            100
-            >>> ds_1.classes
-            ['dog', 'person']
+            ds_1 = sv.DetectionDataset(...)
+            len(ds_1)
+            # 100
+            ds_1.classes
+            # ['dog', 'person']
 
-            >>> ds_2 = sv.DetectionDataset(...)
-            >>> len(ds_2)
-            200
-            >>> ds_2.classes
-            ['cat']
+            ds_2 = sv.DetectionDataset(...)
+            len(ds_2)
+            # 200
+            ds_2.classes
+            # ['cat']
 
-            >>> ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
-            >>> len(ds_merged)
-            300
-            >>> ds_merged.classes
-            ['cat', 'dog', 'person']
+            ds_merged = sv.DetectionDataset.merge([ds_1, ds_2])
+            len(ds_merged)
+            # 300
+            ds_merged.classes
+            # ['cat', 'dog', 'person']
             ```
         """
         merged_images, merged_annotations = {}, {}
@@ -571,13 +569,13 @@ class ClassificationDataset(BaseDataset):
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> cd = sv.ClassificationDataset(...)
-            >>> train_cd,test_cd = cd.split(split_ratio=0.7,
-            ...                             random_state=42,shuffle=True)
-            >>> len(train_cd), len(test_cd)
-            (700, 300)
+            cd = sv.ClassificationDataset(...)
+            train_cd,test_cd = cd.split(split_ratio=0.7,
+                                        random_state=42,shuffle=True)
+            len(train_cd), len(test_cd)
+            # (700, 300)
             ```
         """
         image_names = list(self.images.keys())
@@ -639,20 +637,19 @@ class ClassificationDataset(BaseDataset):
 
         Example:
             ```python
-            >>> import roboflow
-            >>> from roboflow import Roboflow
-            >>> import supervision as sv
+            import roboflow
+            from roboflow import Roboflow
+            import supervision as sv
 
-            >>> roboflow.login()
+            roboflow.login()
+            rf = Roboflow()
 
-            >>> rf = Roboflow()
+            project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
+            dataset = project.version(PROJECT_VERSION).download("folder")
 
-            >>> project = rf.workspace(WORKSPACE_ID).project(PROJECT_ID)
-            >>> dataset = project.version(PROJECT_VERSION).download("folder")
-
-            >>> cd = sv.ClassificationDataset.from_folder_structure(
-            ...     root_directory_path=f"{dataset.location}/train"
-            ... )
+            cd = sv.ClassificationDataset.from_folder_structure(
+                root_directory_path=f"{dataset.location}/train"
+            )
             ```
         """
         classes = os.listdir(root_directory_path)
diff --git a/supervision/detection/annotate.py b/supervision/detection/annotate.py
index f3c80653..ff8a3ca2 100644
--- a/supervision/detection/annotate.py
+++ b/supervision/detection/annotate.py
@@ -63,23 +63,22 @@ class BoxAnnotator:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> classes = ['person', ...]
-            >>> image = ...
-            >>> detections = sv.Detections(...)
+            classes = ['person', ...]
+            image = ...
+            detections = sv.Detections(...)
 
-            >>> box_annotator = sv.BoxAnnotator()
-            >>> labels = [
-            ...     f"{classes[class_id]} {confidence:0.2f}"
-            ...     for _, _, confidence, class_id, _
-            ...     in detections
-            ... ]
-            >>> annotated_frame = box_annotator.annotate(
-            ...     scene=image.copy(),
-            ...     detections=detections,
-            ...     labels=labels
-            ... )
+            box_annotator = sv.BoxAnnotator()
+            labels = [
+                f"{classes[class_id]} {confidence:0.2f}"
+                for _, _, confidence, class_id, _ in detections
+            ]
+            annotated_frame = box_annotator.annotate(
+                scene=image.copy(),
+                detections=detections,
+                labels=labels
+            )
             ```
         """
         font = cv2.FONT_HERSHEY_SIMPLEX
diff --git a/supervision/detection/core.py b/supervision/detection/core.py
index 53c1e46c..44575253 100644
--- a/supervision/detection/core.py
+++ b/supervision/detection/core.py
@@ -126,14 +126,14 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> import torch
-            >>> import supervision as sv
+            import cv2
+            import torch
+            import supervision as sv
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
-            >>> result = model(image)
-            >>> detections = sv.Detections.from_yolov5(result)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
+            result = model(image)
+            detections = sv.Detections.from_yolov5(result)
             ```
         """
         yolov5_detections_predictions = yolov5_results.pred[0].cpu().cpu().numpy()
@@ -159,14 +159,14 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import cv2
+            import supervision as sv
+            from ultralytics import YOLO
 
-            >>> image = cv2.imread(...)
-            >>> model = YOLO('yolov8s.pt')
-            >>> result = model(image)[0]
-            >>> detections = sv.Detections.from_ultralytics(result)
+            image = cv2.imread()
+            model = YOLO('yolov8s.pt')
+            result = model(image)[0]
+            detections = sv.Detections.from_ultralytics(result)
             ```
         """
 
@@ -209,14 +209,14 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> from super_gradients.training import models
-            >>> import supervision as sv
+            import cv2
+            from super_gradients.training import models
+            import supervision as sv
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = models.get('yolo_nas_l', pretrained_weights="coco")
-            >>> result = list(model.predict(image, conf=0.35))[0]
-            >>> detections = sv.Detections.from_yolo_nas(result)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            model = models.get('yolo_nas_l', pretrained_weights="coco")
+            result = list(model.predict(image, conf=0.35))[0]
+            detections = sv.Detections.from_yolo_nas(result)
             ```
         """
         if np.asarray(yolo_nas_results.prediction.bboxes_xyxy).shape[0] == 0:
@@ -246,20 +246,16 @@ class Detections:
 
         Example:
             ```python
-            >>> import tensorflow as tf
-            >>> import tensorflow_hub as hub
-            >>> import numpy as np
-            >>> import cv2
+            import tensorflow as tf
+            import tensorflow_hub as hub
+            import numpy as np
+            import cv2
 
-            >>> module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
-
-            >>> model = hub.load(module_handle)
-
-            >>> img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
-
-            >>> result = model(img)
-
-            >>> detections = sv.Detections.from_tensorflow(result)
+            module_handle = "https://tfhub.dev/tensorflow/centernet/hourglass_512x512_kpts/1"
+            model = hub.load(module_handle)
+            img = np.array(cv2.imread(SOURCE_IMAGE_PATH))
+            result = model(img)
+            detections = sv.Detections.from_tensorflow(result)
             ```
         """  # noqa: E501 // docs
 
@@ -289,15 +285,15 @@ class Detections:
 
         Example:
             ```python
-            >>> import supervision as sv
-            >>> from deepsparse import Pipeline
+            import supervision as sv
+            from deepsparse import Pipeline
 
-            >>> yolo_pipeline = Pipeline.create(
-            ...     task="yolo",
-            ...     model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
-            ... )
-            >>> result = yolo_pipeline(<SOURCE IMAGE PATH>)
-            >>> detections = sv.Detections.from_deepsparse(result)
+            yolo_pipeline = Pipeline.create(
+                task="yolo",
+                model_path = "zoo:cv/detection/yolov5-l/pytorch/ultralytics/coco/pruned80_quant-none"
+             )
+            result = yolo_pipeline(<SOURCE IMAGE PATH>)
+            detections = sv.Detections.from_deepsparse(result)
             ```
         """  # noqa: E501 // docs
 
@@ -326,14 +322,14 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from mmdet.apis import DetInferencer
+            import cv2
+            import supervision as sv
+            from mmdet.apis import DetInferencer
 
-            >>> inferencer = DetInferencer(model_name, checkpoint, device)
-            >>> mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output',
-            ...                           return_datasamples=True)["predictions"][0]
-            >>> detections = sv.Detections.from_mmdetection(mmdet_result)
+            inferencer = DetInferencer(model_name, checkpoint, device)
+            mmdet_result = inferencer(SOURCE_IMAGE_PATH, out_dir='./output',
+                                      return_datasamples=True)["predictions"][0]
+            detections = sv.Detections.from_mmdetection(mmdet_result)
             ```
         """
 
@@ -375,18 +371,18 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> from detectron2.engine import DefaultPredictor
-            >>> from detectron2.config import get_cfg
-            >>> import supervision as sv
+            import cv2
+            from detectron2.engine import DefaultPredictor
+            from detectron2.config import get_cfg
+            import supervision as sv
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> cfg = get_cfg()
-            >>> cfg.merge_from_file("path/to/config.yaml")
-            >>> cfg.MODEL.WEIGHTS = "path/to/model_weights.pth"
-            >>> predictor = DefaultPredictor(cfg)
-            >>> result = predictor(image)
-            >>> detections = sv.Detections.from_detectron2(result)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            cfg = get_cfg()
+            cfg.merge_from_file("path/to/config.yaml")
+            cfg.MODEL.WEIGHTS = "path/to/model_weights.pth"
+            predictor = DefaultPredictor(cfg)
+            result = predictor(image)
+            detections = sv.Detections.from_detectron2(result)
             ```
         """
 
@@ -423,14 +419,14 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from inference.models.utils import get_roboflow_model
+            import cv2
+            import supervision as sv
+            from inference.models.utils import get_roboflow_model
 
-            >>> image = cv2.imread(...)
-            >>> model = get_roboflow_model(model_id="yolov8s-640")
-            >>> result = model.infer(image)[0]
-            >>> detections = sv.Detections.from_inference(result)
+            image = cv2.imread()
+            model = get_roboflow_model(model_id="yolov8s-640")
+            result = model.infer(image)[0]
+            detections = sv.Detections.from_inference(result)
             ```
         """
         with suppress(AttributeError):
@@ -472,14 +468,14 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from inference.models.utils import get_roboflow_model
+            import cv2
+            import supervision as sv
+            from inference.models.utils import get_roboflow_model
 
-            >>> image = cv2.imread(...)
-            >>> model = get_roboflow_model(model_id="yolov8s-640")
-            >>> result = model.infer(image)[0]
-            >>> detections = sv.Detections.from_roboflow(result)
+            image = cv2.imread()
+            model = get_roboflow_model(model_id="yolov8s-640")
+            result = model.infer(image)[0]
+            detections = sv.Detections.from_roboflow(result)
             ```
         """
         return cls.from_inference(roboflow_result)
@@ -499,17 +495,17 @@ class Detections:
 
         Example:
             ```python
-            >>> import supervision as sv
-            >>> from segment_anything import (
-            ...     sam_model_registry,
-            ...     SamAutomaticMaskGenerator
-            ... )
+            import supervision as sv
+            from segment_anything import (
+                sam_model_registry,
+                SamAutomaticMaskGenerator
+             )
 
-            >>> sam_model_reg = sam_model_registry[MODEL_TYPE]
-            >>> sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
-            >>> mask_generator = SamAutomaticMaskGenerator(sam)
-            >>> sam_result = mask_generator.generate(IMAGE)
-            >>> detections = sv.Detections.from_sam(sam_result=sam_result)
+            sam_model_reg = sam_model_registry[MODEL_TYPE]
+            sam = sam_model_reg(checkpoint=CHECKPOINT_PATH).to(device=DEVICE)
+            mask_generator = SamAutomaticMaskGenerator(sam)
+            sam_result = mask_generator.generate(IMAGE)
+            detections = sv.Detections.from_sam(sam_result=sam_result)
             ```
         """
 
@@ -546,25 +542,25 @@ class Detections:
 
         Example:
             ```python
-            >>> import requests
-            >>> import supervision as sv
+            import requests
+            import supervision as sv
 
-            >>> image = open(input, "rb").read()
+            image = open(input, "rb").read()
 
-            >>> endpoint = "https://.cognitiveservices.azure.com/"
-            >>> subscription_key = "..."
+            endpoint = "https://.cognitiveservices.azure.com/"
+            subscription_key = ""
 
-            >>> headers = {
-            ...    "Content-Type": "application/octet-stream",
-            ...    "Ocp-Apim-Subscription-Key": subscription_key
-            ... }
+            headers = {
+                "Content-Type": "application/octet-stream",
+                "Ocp-Apim-Subscription-Key": subscription_key
+             }
 
-            >>> response = requests.post(endpoint,
-            ...     headers=self.headers,
-            ...     data=image
-            ... ).json()
+            response = requests.post(endpoint,
+                headers=self.headers,
+                data=image
+             ).json()
 
-            >>> detections = sv.Detections.from_azure_analyze_image(response)
+            detections = sv.Detections.from_azure_analyze_image(response)
             ```
         """
         if "error" in azure_result:
@@ -628,21 +624,21 @@ class Detections:
 
         Example:
             ```python
-            >>> import supervision as sv
-            >>> import paddle
-            >>> from ppdet.engine import Trainer
-            >>> from ppdet.core.workspace import load_config
+            import supervision as sv
+            import paddle
+            from ppdet.engine import Trainer
+            from ppdet.core.workspace import load_config
 
-            >>> weights = (...)
-            >>> config = (...)
+            weights = ()
+            config = ()
 
-            >>> cfg = load_config(config)
-            >>> trainer = Trainer(cfg, mode='test')
-            >>> trainer.load_weights(weights)
+            cfg = load_config(config)
+            trainer = Trainer(cfg, mode='test')
+            trainer.load_weights(weights)
 
-            >>> paddledet_result = trainer.predict([images])[0]
+            paddledet_result = trainer.predict([images])[0]
 
-            >>> detections = sv.Detections.from_paddledet(paddledet_result)
+            detections = sv.Detections.from_paddledet(paddledet_result)
             ```
         """
 
@@ -666,9 +662,9 @@ class Detections:
 
         Example:
             ```python
-            >>> from supervision import Detections
+            from supervision import Detections
 
-            >>> empty_detections = Detections.empty()
+            empty_detections = Detections.empty()
             ```
         """
         return cls(
@@ -700,29 +696,29 @@ class Detections:
             import numpy as np
             import supervision as sv
 
-            >>> detections_1 = sv.Detections(
-            ...     xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
-            ...     class_id=np.array([1, 2]),
-            ...     data={'feature_vector': np.array([0.1, 0.2)])}
-            ... )
+            detections_1 = sv.Detections(
+                xyxy=np.array([[15, 15, 100, 100], [200, 200, 300, 300]]),
+                class_id=np.array([1, 2]),
+                data={'feature_vector': np.array([0.1, 0.2)])}
+             )
 
-            >>> detections_2 = sv.Detections(
-            ...     xyxy=np.array([[30, 30, 120, 120]]),
-            ...     class_id=np.array([1]),
-            ...     data={'feature_vector': [np.array([0.3])]}
-            ... )
+            detections_2 = sv.Detections(
+                xyxy=np.array([[30, 30, 120, 120]]),
+                class_id=np.array([1]),
+                data={'feature_vector': [np.array([0.3])]}
+             )
 
-            >>> merged_detections = Detections.merge([detections_1, detections_2])
+            merged_detections = Detections.merge([detections_1, detections_2])
 
-            >>> merged_detections.xyxy
+            merged_detections.xyxy
             array([[ 15,  15, 100, 100],
                    [200, 200, 300, 300],
                    [ 30,  30, 120, 120]])
 
-            >>> merged_detections.class_id
+            merged_detections.class_id
             array([1, 2, 1])
 
-            >>> merged_detections.data['feature_vector']
+            merged_detections.data['feature_vector']
             array([0.1, 0.2, 0.3])
             ```
         """
@@ -855,17 +851,17 @@ class Detections:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> detections = sv.Detections(...)
+            detections = sv.Detections()
 
-            >>> first_detection = detections[0]
-            >>> first_10_detections = detections[0:10]
-            >>> some_detections = detections[[0, 2, 4]]
-            >>> class_0_detections = detections[detections.class_id == 0]
-            >>> high_confidence_detections = detections[detections.confidence > 0.5]
+            first_detection = detections[0]
+            first_10_detections = detections[0:10]
+            some_detections = detections[[0, 2, 4]]
+            class_0_detections = detections[detections.class_id == 0]
+            high_confidence_detections = detections[detections.confidence > 0.5]
 
-            >>> feature_vector = detections['feature_vector']
+            feature_vector = detections['feature_vector']
             ```
         """
         if isinstance(index, str):
@@ -891,22 +887,22 @@ class Detections:
 
         Example:
             ```python
-            >>> import cv2
-            >>> from ultralytics import YOLO
-            >>> import supervision as sv
+            import cv2
+            from ultralytics import YOLO
+            import supervision as sv
 
-            >>> model = YOLO('yolov8s.pt')
+            model = YOLO('yolov8s.pt')
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
 
-            >>> result = model(image)[0]
-            >>> detections = sv.Detections.from_ultralytics(result)
+            result = model(image)[0]
+            detections = sv.Detections.from_ultralytics(result)
 
-            >>> detections['names'] = [
-            ...     model.model.names[class_id]
-            ...     for class_id
-            ...     in detections.class_id
-            ... ]
+            detections['names'] = [
+                 model.model.names[class_id]
+                 for class_id
+                 in detections.class_id
+             ]
             ```
         """
         if not isinstance(value, (np.ndarray, list)):
@@ -926,7 +922,7 @@ class Detections:
 
         Returns:
           np.ndarray: An array of floats containing the area of each detection
-            in the format of `(area_1, area_2, ..., area_n)`,
+            in the format of `(area_1, area_2, , area_n)`,
             where n is the number of detections.
         """
         if self.mask is not None:
@@ -941,7 +937,7 @@ class Detections:
 
         Returns:
             np.ndarray: An array of floats containing the area of each bounding
-                box in the format of `(area_1, area_2, ..., area_n)`,
+                box in the format of `(area_1, area_2, , area_n)`,
                 where n is the number of detections.
         """
         return (self.xyxy[:, 3] - self.xyxy[:, 1]) * (self.xyxy[:, 2] - self.xyxy[:, 0])
diff --git a/supervision/detection/line_counter.py b/supervision/detection/line_counter.py
index 8fd3de4c..7cf40a4e 100644
--- a/supervision/detection/line_counter.py
+++ b/supervision/detection/line_counter.py
@@ -15,7 +15,7 @@ class LineZone:
 
     !!! warning
 
-        LineZone utilizes the `tracker_id`. Read
+        LineZone uses the `tracker_id`. Read
         [here](https://supervision.roboflow.com/trackers/) to learn how to plug
         tracking into your inference pipeline.
 
diff --git a/supervision/detection/tools/inference_slicer.py b/supervision/detection/tools/inference_slicer.py
index 2c0c48a6..7157723f 100644
--- a/supervision/detection/tools/inference_slicer.py
+++ b/supervision/detection/tools/inference_slicer.py
@@ -77,20 +77,20 @@ class InferenceSlicer:
 
         Example:
             ```python
-            >>> import cv2
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import cv2
+            import supervision as sv
+            from ultralytics import YOLO
 
-            >>> image = cv2.imread(SOURCE_IMAGE_PATH)
-            >>> model = YOLO(...)
+            image = cv2.imread(SOURCE_IMAGE_PATH)
+            model = YOLO(...)
 
-            >>> def callback(image_slice: np.ndarray) -> sv.Detections:
-            ...     result = model(image_slice)[0]
-            ...     return sv.Detections.from_ultralytics(result)
+            def callback(image_slice: np.ndarray) -> sv.Detections:
+                result = model(image_slice)[0]
+                return sv.Detections.from_ultralytics(result)
 
-            >>> slicer = sv.InferenceSlicer(callback = callback)
+            slicer = sv.InferenceSlicer(callback = callback)
 
-            >>> detections = slicer(image)
+            detections = slicer(image)
             ```
         """
         detections_list = []
diff --git a/supervision/detection/tools/smoother.py b/supervision/detection/tools/smoother.py
new file mode 100644
index 00000000..3913a9d3
--- /dev/null
+++ b/supervision/detection/tools/smoother.py
@@ -0,0 +1,118 @@
+from collections import defaultdict, deque
+from copy import deepcopy
+from typing import Optional
+
+import numpy as np
+
+from supervision.detection.core import Detections
+
+
+class DetectionsSmoother:
+    """
+    A utility class for smoothing detections over multiple frames in video tracking.
+    It maintains a history of detections for each track and provides smoothed
+    predictions based on these histories.
+
+    <video controls>
+        <source
+            src="https://media.roboflow.com/supervision-detection-smoothing.mp4"
+            type="video/mp4">
+    </video>
+
+    !!! warning
+
+        - `DetectionsSmoother` requires the `tracker_id` for each detection. Refer to
+          [Roboflow Trackers](https://supervision.roboflow.com/trackers/) for
+          information on integrating tracking into your inference pipeline.
+        - This class is not compatible with segmentation models.
+
+    Example:
+        ```python
+        import supervision as sv
+
+        from ultralytics import YOLO
+
+        video_info = sv.VideoInfo.from_video_path(video_path=<SOURCE_FILE_PATH>)
+        frame_generator = sv.get_video_frames_generator(source_path=<SOURCE_FILE_PATH>)
+
+        model = YOLO(<MODEL_PATH>)
+        tracker = sv.ByteTrack(frame_rate=video_info.fps)
+        smoother = sv.DetectionsSmoother()
+
+        annotator = sv.BoundingBoxAnnotator()
+
+        with sv.VideoSink(<TARGET_FILE_PATH>, video_info=video_info) as sink:
+            for frame in frame_generator:
+                result = model(frame)[0]
+                detections = sv.Detections.from_ultralytics(result)
+                detections = tracker.update_with_detections(detections)
+                detections = smoother.update_with_detections(detections)
+
+                annotated_frame = bounding_box_annotator.annotate(frame.copy(), detections)
+                sink.write_frame(annotated_frame)
+        ```
+    """  # noqa: E501 // docs
+
+    def __init__(self, length: int = 5) -> None:
+        """
+        Args:
+            length (int): The maximum number of frames to consider for smoothing
+                detections. Defaults to 5.
+        """
+        self.tracks = defaultdict(lambda: deque(maxlen=length))
+
+    def update_with_detections(self, detections: Detections) -> Detections:
+        """
+        Updates the smoother with a new set of detections from a frame.
+
+        Args:
+            detections (Detections): The detections to add to the smoother.
+        """
+
+        if detections.tracker_id is None:
+            print(
+                "Smoothing skipped. DetectionsSmoother requires tracker_id. Refer to "
+                "https://supervision.roboflow.com/trackers for more information."
+            )
+            return detections
+
+        for detection_idx in range(len(detections)):
+            tracker_id = detections.tracker_id[detection_idx]
+            if tracker_id is None:
+                continue
+
+            self.tracks[tracker_id].append(detections[detection_idx])
+
+        for track_id in self.tracks.keys():
+            if track_id not in detections.tracker_id:
+                self.tracks[track_id].append(None)
+
+        for track_id in list(self.tracks.keys()):
+            if all([d is None for d in self.tracks[track_id]]):
+                del self.tracks[track_id]
+
+        return self.get_smoothed_detections()
+
+    def get_track(self, track_id: int) -> Optional[Detections]:
+        track = self.tracks.get(track_id, None)
+        if track is None:
+            return None
+
+        track = [d for d in track if d is not None]
+        if len(track) == 0:
+            return None
+
+        ret = deepcopy(track[0])
+        ret.xyxy = np.mean([d.xyxy for d in track], axis=0)
+        ret.confidence = np.mean([d.confidence for d in track], axis=0)
+
+        return ret
+
+    def get_smoothed_detections(self) -> Detections:
+        tracked_detections = []
+        for track_id in self.tracks:
+            track = self.get_track(track_id)
+            if track is not None:
+                tracked_detections.append(track)
+
+        return Detections.merge(tracked_detections)
diff --git a/supervision/detection/utils.py b/supervision/detection/utils.py
index f7af7797..e8db4627 100644
--- a/supervision/detection/utils.py
+++ b/supervision/detection/utils.py
@@ -415,16 +415,17 @@ def move_boxes(xyxy: np.ndarray, offset: np.ndarray) -> np.ndarray:
 
     Example:
         ```python
-        >>> import numpy as np
-        >>> import supervision as sv
+        import numpy as np
+        import supervision as sv
 
-        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
-        >>> offset = np.array([5, 5])
-        >>> sv.move_boxes(boxes, offset)
-        ... array([
-        ...     [15, 15, 25, 25],
-        ...     [35, 35, 45, 45]
-        ... ])
+        boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        offset = np.array([5, 5])
+        moved_box = sv.move_boxes(boxes, offset)
+        print(moved_box)
+        # np.array([
+        #    [15, 15, 25, 25],
+        #     [35, 35, 45, 45]
+        # ])
         ```
     """
     return xyxy + np.hstack([offset, offset])
@@ -446,16 +447,17 @@ def scale_boxes(xyxy: np.ndarray, factor: float) -> np.ndarray:
 
     Example:
         ```python
-        >>> import numpy as np
-        >>> import supervision as sv
+        import numpy as np
+        import supervision as sv
 
-        >>> boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
-        >>> factor = 1.5
-        >>> sv.scale_boxes(boxes, factor)
-        ... array([
-        ...     [ 7.5,  7.5, 22.5, 22.5],
-        ...     [27.5, 27.5, 42.5, 42.5]
-        ... ])
+        boxes = np.array([[10, 10, 20, 20], [30, 30, 40, 40]])
+        factor = 1.5
+        scaled_bb = sv.scale_boxes(boxes, factor)
+        print(scaled_bb)
+        # np.array([
+        #    [ 7.5,  7.5, 22.5, 22.5],
+        #    [27.5, 27.5, 42.5, 42.5]
+        # ])
         ```
     """
     centers = (xyxy[:, :2] + xyxy[:, 2:]) / 2
diff --git a/supervision/draw/utils.py b/supervision/draw/utils.py
index 10f5c932..a6e600e3 100644
--- a/supervision/draw/utils.py
+++ b/supervision/draw/utils.py
@@ -135,9 +135,11 @@ def draw_text(
 
     Examples:
         ```python
-        >>> scene = np.zeros((100, 100, 3), dtype=np.uint8)
-        >>> text_anchor = Point(x=50, y=50)
-        >>> scene = draw_text(scene=scene, text="Hello, world!",text_anchor=text_anchor)
+        import numpy as np
+
+        scene = np.zeros((100, 100, 3), dtype=np.uint8)
+        text_anchor = Point(x=50, y=50)
+        scene = draw_text(scene=scene, text="Hello, world!",text_anchor=text_anchor)
         ```
     """
     text_width, text_height = cv2.getTextSize(
diff --git a/supervision/geometry/utils.py b/supervision/geometry/utils.py
index f1b66db2..dd4e64db 100644
--- a/supervision/geometry/utils.py
+++ b/supervision/geometry/utils.py
@@ -22,10 +22,11 @@ def get_polygon_center(polygon: np.ndarray) -> Point:
 
     Examples:
         ```python
-        >>> from supervision.geometry.utils import get_polygon_center
+        from supervision.geometry.utils import get_polygon_center
+        import numpy as np
 
-        >>> vertices = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
-        >>> get_center(vertices)
+        vertices = np.array([[0, 0], [0, 1], [1, 1], [1, 0]])
+        get_center(vertices)
         Point(x=0.5, y=0.5)
         ```
     """
diff --git a/supervision/metrics/detection.py b/supervision/metrics/detection.py
index c332ce9a..71007d71 100644
--- a/supervision/metrics/detection.py
+++ b/supervision/metrics/detection.py
@@ -116,31 +116,31 @@ class ConfusionMatrix:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> targets = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            targets = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]
 
-            >>> predictions = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            predictions = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]
 
-            >>> confusion_matrix = sv.ConfusionMatrix.from_detections(
-            ...     predictions=predictions,
-            ...     targets=target,
-            ...     classes=['person', ...]
-            ... )
+            confusion_matrix = sv.ConfusionMatrix.from_detections(
+                predictions=predictions,
+                targets=target,
+                classes=['person', ...]
+            )
 
-            >>> confusion_matrix.matrix
-            array([
-                [0., 0., 0., 0.],
-                [0., 1., 0., 1.],
-                [0., 1., 1., 0.],
-                [1., 1., 0., 0.]
-            ])
+            print(confusion_matrix.matrix)
+            # np.array([
+            #    [0., 0., 0., 0.],
+            #    [0., 1., 0., 1.],
+            #    [0., 1., 1., 0.],
+            #    [1., 1., 0., 0.]
+            # ])
             ```
         """
 
@@ -191,46 +191,47 @@ class ConfusionMatrix:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
+            import numpy as np
 
-            >>> targets = (
-            ...     [
-            ...         array(
-            ...             [
-            ...                 [0.0, 0.0, 3.0, 3.0, 1],
-            ...                 [2.0, 2.0, 5.0, 5.0, 1],
-            ...                 [6.0, 1.0, 8.0, 3.0, 2],
-            ...             ]
-            ...         ),
-            ...         array([1.0, 1.0, 2.0, 2.0, 2]),
-            ...     ]
-            ... )
+            targets = (
+                [
+                    np.array(
+                        [
+                            [0.0, 0.0, 3.0, 3.0, 1],
+                            [2.0, 2.0, 5.0, 5.0, 1],
+                            [6.0, 1.0, 8.0, 3.0, 2],
+                        ]
+                    ),
+                    np.array([1.0, 1.0, 2.0, 2.0, 2]),
+                ]
+            )
 
-            >>> predictions = [
-            ...     array(
-            ...         [
-            ...             [0.0, 0.0, 3.0, 3.0, 1, 0.9],
-            ...             [0.1, 0.1, 3.0, 3.0, 0, 0.9],
-            ...             [6.0, 1.0, 8.0, 3.0, 1, 0.8],
-            ...             [1.0, 6.0, 2.0, 7.0, 1, 0.8],
-            ...         ]
-            ...     ),
-            ...     array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
-            ... ]
+            predictions = [
+                np.array(
+                    [
+                        [0.0, 0.0, 3.0, 3.0, 1, 0.9],
+                        [0.1, 0.1, 3.0, 3.0, 0, 0.9],
+                        [6.0, 1.0, 8.0, 3.0, 1, 0.8],
+                        [1.0, 6.0, 2.0, 7.0, 1, 0.8],
+                    ]
+                ),
+                np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
+            ]
 
-            >>> confusion_matrix = sv.ConfusionMatrix.from_tensors(
-            ...     predictions=predictions,
-            ...     targets=targets,
-            ...     classes=['person', ...]
-            ... )
+            confusion_matrix = sv.ConfusionMatrix.from_tensors(
+                predictions=predictions,
+                targets=targets,
+                classes=['person', ...]
+            )
 
-            >>> confusion_matrix.matrix
-            array([
-                [0., 0., 0., 0.],
-                [0., 1., 0., 1.],
-                [0., 1., 1., 0.],
-                [1., 1., 0., 0.]
-            ])
+            print(confusion_matrix.matrix)
+            # np.array([
+            #     [0., 0., 0., 0.],
+            #     [0., 1., 0., 1.],
+            #     [0., 1., 1., 0.],
+            #     [1., 1., 0., 0.]
+            # ])
             ```
         """
         validate_input_tensors(predictions, targets)
@@ -365,28 +366,28 @@ class ConfusionMatrix:
 
         Example:
             ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO
 
-            >>> dataset = sv.DetectionDataset.from_yolo(...)
+            dataset = sv.DetectionDataset.from_yolo(...)
 
-            >>> model = YOLO(...)
-            >>> def callback(image: np.ndarray) -> sv.Detections:
-            ...     result = model(image)[0]
-            ...     return sv.Detections.from_ultralytics(result)
+            model = YOLO(...)
+            def callback(image: np.ndarray) -> sv.Detections:
+                result = model(image)[0]
+                return sv.Detections.from_ultralytics(result)
 
-            >>> confusion_matrix = sv.ConfusionMatrix.benchmark(
-            ...     dataset = dataset,
-            ...     callback = callback
-            ... )
+            confusion_matrix = sv.ConfusionMatrix.benchmark(
+                dataset = dataset,
+                callback = callback
+            )
 
-            >>> confusion_matrix.matrix
-            array([
-                [0., 0., 0., 0.],
-                [0., 1., 0., 1.],
-                [0., 1., 1., 0.],
-                [1., 1., 0., 0.]
-            ])
+            print(confusion_matrix.matrix)
+            # np.array([
+            #     [0., 0., 0., 0.],
+            #     [0., 1., 0., 1.],
+            #     [0., 1., 1., 0.],
+            #     [1., 1., 0., 0.]
+            # ])
             ```
         """
         predictions, targets = [], []
@@ -532,25 +533,25 @@ class MeanAveragePrecision:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> targets = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            targets = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]
 
-            >>> predictions = [
-            ...     sv.Detections(...),
-            ...     sv.Detections(...)
-            ... ]
+            predictions = [
+                sv.Detections(...),
+                sv.Detections(...)
+            ]
 
-            >>> mean_average_precision = sv.MeanAveragePrecision.from_detections(
-            ...     predictions=predictions,
-            ...     targets=target,
-            ... )
+            mean_average_precision = sv.MeanAveragePrecision.from_detections(
+                predictions=predictions,
+                targets=target,
+            )
 
-            >>> mean_average_precison.map50_95
-            0.2899
+            print(mean_average_precison.map50_95)
+            # 0.2899
             ```
         """
         prediction_tensors = []
@@ -583,23 +584,23 @@ class MeanAveragePrecision:
 
         Example:
             ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO
 
-            >>> dataset = sv.DetectionDataset.from_yolo(...)
+            dataset = sv.DetectionDataset.from_yolo(...)
 
-            >>> model = YOLO(...)
-            >>> def callback(image: np.ndarray) -> sv.Detections:
-            ...     result = model(image)[0]
-            ...     return sv.Detections.from_ultralytics(result)
+            model = YOLO(...)
+            def callback(image: np.ndarray) -> sv.Detections:
+                result = model(image)[0]
+                return sv.Detections.from_ultralytics(result)
 
-            >>> mean_average_precision = sv.MeanAveragePrecision.benchmark(
-            ...     dataset = dataset,
-            ...     callback = callback
-            ... )
+            mean_average_precision = sv.MeanAveragePrecision.benchmark(
+                dataset = dataset,
+                callback = callback
+            )
 
-            >>> mean_average_precision.map50_95
-            0.433
+            print(mean_average_precision.map50_95)
+            # 0.433
             ```
         """
         predictions, targets = [], []
@@ -637,40 +638,41 @@ class MeanAveragePrecision:
 
         Example:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
+            import numpy as np
 
-            >>> targets = (
-            ...     [
-            ...         array(
-            ...             [
-            ...                 [0.0, 0.0, 3.0, 3.0, 1],
-            ...                 [2.0, 2.0, 5.0, 5.0, 1],
-            ...                 [6.0, 1.0, 8.0, 3.0, 2],
-            ...             ]
-            ...         ),
-            ...         array([1.0, 1.0, 2.0, 2.0, 2]),
-            ...     ]
-            ... )
+            targets = (
+                [
+                    np.array(
+                        [
+                            [0.0, 0.0, 3.0, 3.0, 1],
+                            [2.0, 2.0, 5.0, 5.0, 1],
+                            [6.0, 1.0, 8.0, 3.0, 2],
+                        ]
+                    ),
+                    np.array([[1.0, 1.0, 2.0, 2.0, 2]]),
+                ]
+            )
 
-            >>> predictions = [
-            ...     array(
-            ...         [
-            ...             [0.0, 0.0, 3.0, 3.0, 1, 0.9],
-            ...             [0.1, 0.1, 3.0, 3.0, 0, 0.9],
-            ...             [6.0, 1.0, 8.0, 3.0, 1, 0.8],
-            ...             [1.0, 6.0, 2.0, 7.0, 1, 0.8],
-            ...         ]
-            ...     ),
-            ...     array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
-            ... ]
+            predictions = [
+                np.array(
+                    [
+                        [0.0, 0.0, 3.0, 3.0, 1, 0.9],
+                        [0.1, 0.1, 3.0, 3.0, 0, 0.9],
+                        [6.0, 1.0, 8.0, 3.0, 1, 0.8],
+                        [1.0, 6.0, 2.0, 7.0, 1, 0.8],
+                    ]
+                ),
+                np.array([[1.0, 1.0, 2.0, 2.0, 2, 0.8]])
+            ]
 
-            >>> mean_average_precison = sv.MeanAveragePrecision.from_tensors(
-            ...     predictions=predictions,
-            ...     targets=targets,
-            ... )
+            mean_average_precison = sv.MeanAveragePrecision.from_tensors(
+                predictions=predictions,
+                targets=targets,
+            )
 
-            >>> mean_average_precison.map50_95
-            0.2899
+            print(mean_average_precison.map50_95)
+            # 0.6649
             ```
         """
         validate_input_tensors(predictions, targets)
diff --git a/supervision/tracker/byte_tracker/core.py b/supervision/tracker/byte_tracker/core.py
index 34a90138..bf831daa 100644
--- a/supervision/tracker/byte_tracker/core.py
+++ b/supervision/tracker/byte_tracker/core.py
@@ -205,30 +205,29 @@ class ByteTrack:
             Detection: The updated detection results that now include tracking IDs.
         Example:
             ```python
-            >>> import supervision as sv
-            >>> from ultralytics import YOLO
+            import supervision as sv
+            from ultralytics import YOLO
 
-            >>> model = YOLO(...)
-            >>> byte_tracker = sv.ByteTrack()
-            >>> annotator = sv.BoxAnnotator()
+            model = YOLO(...)
+            byte_tracker = sv.ByteTrack()
+            annotator = sv.BoxAnnotator()
 
-            >>> def callback(frame: np.ndarray, index: int) -> np.ndarray:
-            ...     results = model(frame)[0]
-            ...     detections = sv.Detections.from_ultralytics(results)
-            ...     detections = byte_tracker.update_with_detections(detections)
-            ...     labels = [
-            ...         f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
-            ...         for _, _, confidence, class_id, tracker_id
-            ...         in detections
-            ...     ]
-            ...     return annotator.annotate(scene=frame.copy(),
-            ...                               detections=detections, labels=labels)
+            def callback(frame: np.ndarray, index: int) -> np.ndarray:
+                results = model(frame)[0]
+                detections = sv.Detections.from_ultralytics(results)
+                detections = byte_tracker.update_with_detections(detections)
+                labels = [
+                    f"#{tracker_id} {model.model.names[class_id]} {confidence:0.2f}"
+                    for _, _, confidence, class_id, tracker_id in detections
+                ]
+                return annotator.annotate(scene=frame.copy(),
+                                          detections=detections, labels=labels)
 
-            >>> sv.process_video(
-            ...     source_path='...',
-            ...     target_path='...',
-            ...     callback=callback
-            ... )
+            sv.process_video(
+                source_path='...',
+                target_path='...',
+                callback=callback
+            )
             ```
         """
 
diff --git a/supervision/utils/file.py b/supervision/utils/file.py
index c0521236..dfce7eb0 100644
--- a/supervision/utils/file.py
+++ b/supervision/utils/file.py
@@ -34,14 +34,14 @@ def list_files_with_extensions(
 
     Examples:
         ```python
-        >>> import supervision as sv
+        import supervision as sv
 
-        >>> # List all files in the directory
-        >>> files = sv.list_files_with_extensions(directory='my_directory')
+        # List all files in the directory
+        files = sv.list_files_with_extensions(directory='my_directory')
 
-        >>> # List only files with '.txt' and '.md' extensions
-        >>> files = sv.list_files_with_extensions(
-        ...     directory='my_directory', extensions=['txt', 'md'])
+        # List only files with '.txt' and '.md' extensions
+        files = sv.list_files_with_extensions(
+            directory='my_directory', extensions=['txt', 'md'])
         ```
     """
 
diff --git a/supervision/utils/image.py b/supervision/utils/image.py
index 8abfbba9..442e9b6a 100644
--- a/supervision/utils/image.py
+++ b/supervision/utils/image.py
@@ -20,13 +20,13 @@ def crop_image(image: np.ndarray, xyxy: np.ndarray) -> np.ndarray:
 
     Examples:
         ```python
-        >>> import supervision as sv
+        import supervision as sv
 
-        >>> detection = sv.Detections(...)
-        >>> with sv.ImageSink(target_dir_path='target/directory/path') as sink:
-        ...     for xyxy in detection.xyxy:
-        ...         cropped_image = sv.crop_image(image=image, xyxy=xyxy)
-        ...         sink.save_image(image=image)
+        detection = sv.Detections(...)
+        with sv.ImageSink(target_dir_path='target/directory/path') as sink:
+            for xyxy in detection.xyxy:
+                cropped_image = sv.crop_image(image=image, xyxy=xyxy)
+                sink.save_image(image=image)
         ```
     """
 
@@ -55,13 +55,13 @@ class ImageSink:
 
         Examples:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> with sv.ImageSink(target_dir_path='target/directory/path',
-            ...                   overwrite=True) as sink:
-            ...     for image in sv.get_video_frames_generator(
-            ...         source_path='source_video.mp4', stride=2):
-            ...         sink.save_image(image=image)
+            with sv.ImageSink(target_dir_path='target/directory/path',
+                              overwrite=True) as sink:
+                for image in sv.get_video_frames_generator(
+                    source_path='source_video.mp4', stride=2):
+                    sink.save_image(image=image)
             ```
         """
 
diff --git a/supervision/utils/notebook.py b/supervision/utils/notebook.py
index 97b6659c..e8a0c7aa 100644
--- a/supervision/utils/notebook.py
+++ b/supervision/utils/notebook.py
@@ -18,13 +18,13 @@ def plot_image(
 
     Examples:
         ```python
-        >>> import cv2
-        >>> import supervision as sv
+        import cv2
+        import supervision as sv
 
-        >>> image = cv2.imread("path/to/image.jpg")
+        image = cv2.imread("path/to/image.jpg")
 
         %matplotlib inline
-        >>> sv.plot_image(image=image, size=(16, 16))
+        sv.plot_image(image=image, size=(16, 16))
         ```
     """
     plt.figure(figsize=size)
@@ -63,18 +63,18 @@ def plot_images_grid(
 
     Examples:
         ```python
-        >>> import cv2
-        >>> import supervision as sv
+        import cv2
+        import supervision as sv
 
-        >>> image1 = cv2.imread("path/to/image1.jpg")
-        >>> image2 = cv2.imread("path/to/image2.jpg")
-        >>> image3 = cv2.imread("path/to/image3.jpg")
+        image1 = cv2.imread("path/to/image1.jpg")
+        image2 = cv2.imread("path/to/image2.jpg")
+        image3 = cv2.imread("path/to/image3.jpg")
 
-        >>> images = [image1, image2, image3]
-        >>> titles = ["Image 1", "Image 2", "Image 3"]
+        images = [image1, image2, image3]
+        titles = ["Image 1", "Image 2", "Image 3"]
 
         %matplotlib inline
-        >>> plot_images_grid(images, grid_size=(2, 2), titles=titles, size=(16, 16))
+        plot_images_grid(images, grid_size=(2, 2), titles=titles, size=(16, 16))
         ```
     """
     nrows, ncols = grid_size
diff --git a/supervision/utils/video.py b/supervision/utils/video.py
index 1687c453..6be3bffe 100644
--- a/supervision/utils/video.py
+++ b/supervision/utils/video.py
@@ -24,15 +24,15 @@ class VideoInfo:
 
     Examples:
         ```python
-        >>> import supervision as sv
+        import supervision as sv
 
-        >>> video_info = sv.VideoInfo.from_video_path(video_path='video.mp4')
+        video_info = sv.VideoInfo.from_video_path(video_path='video.mp4')
 
-        >>> video_info
-        VideoInfo(width=3840, height=2160, fps=25, total_frames=538)
+        video_info
+        # VideoInfo(width=3840, height=2160, fps=25, total_frames=538)
 
-        >>> video_info.resolution_wh
-        (3840, 2160)
+        video_info.resolution_wh
+        # (3840, 2160)
         ```
     """
 
@@ -71,14 +71,14 @@ class VideoSink:
 
     Example:
         ```python
-        >>> import supervision as sv
+        import supervision as sv
 
-        >>> video_info = sv.VideoInfo.from_video_path('source.mp4')
-        >>> frames_generator = sv.get_video_frames_generator('source.mp4')
+        video_info = sv.VideoInfo.from_video_path('source.mp4')
+        frames_generator = sv.get_video_frames_generator('source.mp4')
 
-        >>> with sv.VideoSink(target_path='target.mp4', video_info=video_info) as sink:
-        ...     for frame in frames_generator:
-        ...         sink.write_frame(frame=frame)
+        with sv.VideoSink(target_path='target.mp4', video_info=video_info) as sink:
+            for frame in frames_generator:
+                sink.write_frame(frame=frame)
         ```
     """
 
@@ -143,10 +143,10 @@ def get_video_frames_generator(
 
     Examples:
         ```python
-        >>> import supervision as sv
+        import supervision as sv
 
-        >>> for frame in sv.get_video_frames_generator(source_path='source_video.mp4'):
-        ...     ...
+        for frame in sv.get_video_frames_generator(source_path='source_video.mp4'):
+            ...
         ```
     """
     video, start, end = _validate_and_setup_video(source_path, start, end)
@@ -183,16 +183,16 @@ def process_video(
 
     Examples:
         ```python
-        >>> import supervision as sv
+        import supervision as sv
 
-        >>> def callback(scene: np.ndarray, index: int) -> np.ndarray:
-        ...     ...
+        def callback(scene: np.ndarray, index: int) -> np.ndarray:
+            ...
 
-        >>> process_video(
-        ...     source_path='...',
-        ...     target_path='...',
-        ...     callback=callback
-        ... )
+        process_video(
+            source_path='...',
+            target_path='...',
+            callback=callback
+        )
         ```
     """
     source_video_info = VideoInfo.from_video_path(video_path=source_path)
@@ -217,15 +217,15 @@ class FPSMonitor:
 
         Examples:
             ```python
-            >>> import supervision as sv
+            import supervision as sv
 
-            >>> frames_generator = sv.get_video_frames_generator('source.mp4')
-            >>> fps_monitor = sv.FPSMonitor()
+            frames_generator = sv.get_video_frames_generator('source.mp4')
+            fps_monitor = sv.FPSMonitor()
 
-            >>> for frame in frames_generator:
-            ...     # your processing code here
-            ...     fps_monitor.tick()
-            ...     fps = fps_monitor()
+            for frame in frames_generator:
+                # your processing code here
+                fps_monitor.tick()
+                fps = fps_monitor()
             ```
         """
         self.all_timestamps = deque(maxlen=sample_size)