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>