diff --git a/docs/en/guides/distance-calculation.md b/docs/en/guides/distance-calculation.md
index 3a4f99f4244..8b48cc0a200 100644
--- a/docs/en/guides/distance-calculation.md
+++ b/docs/en/guides/distance-calculation.md
@@ -23,6 +23,7 @@ Measuring the gap between two objects is known as distance calculation within a
 !!! Example "Distance Calculation using YOLOv8 Example"
 
     === "Video Stream"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import distance_calculation
@@ -68,13 +69,13 @@ Measuring the gap between two objects is known as distance calculation within a
 
 ### Optional Arguments `set_args`
 
-| Name           | Type   | Default         | Description                                            |
-|----------------|--------|-----------------|--------------------------------------------------------|
-| names          | `dict` | `None`          | Classes names                                          |
-| view_img       | `bool` | `False`         | Display frames with counts                             |
-| line_thickness | `int`  | `2`             | Increase bounding boxes thickness                      |
-| line_color     | `RGB`  | `(255, 255, 0)` | Line Color for centroids mapping on two bounding boxes |
-| centroid_color | `RGB`  | `(255, 0, 255)` | Centroid color for each bounding box                   |
+| Name             | Type   | Default         | Description                                            |
+|------------------|--------|-----------------|--------------------------------------------------------|
+| `names`          | `dict` | `None`          | Classes names                                          |
+| `view_img`       | `bool` | `False`         | Display frames with counts                             |
+| `line_thickness` | `int`  | `2`             | Increase bounding boxes thickness                      |
+| `line_color`     | `RGB`  | `(255, 255, 0)` | Line Color for centroids mapping on two bounding boxes |
+| `centroid_color` | `RGB`  | `(255, 0, 255)` | Centroid color for each bounding box                   |
 
 ### Arguments `model.track`
 
diff --git a/docs/en/guides/heatmaps.md b/docs/en/guides/heatmaps.md
index e52f2383354..100638839de 100644
--- a/docs/en/guides/heatmaps.md
+++ b/docs/en/guides/heatmaps.md
@@ -42,6 +42,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 !!! Example "Heatmaps using Ultralytics YOLOv8 Example"
 
     === "Heatmap"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import heatmap
@@ -83,6 +84,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
         ```
 
     === "Line Counting"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import heatmap
@@ -126,6 +128,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
         ```
 
     === "Region Counting"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import heatmap
@@ -170,6 +173,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
         ```
 
     === "Im0"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import heatmap
@@ -194,6 +198,7 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
         ```
 
     === "Specific Classes"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import heatmap
@@ -238,22 +243,22 @@ A heatmap generated with [Ultralytics YOLOv8](https://github.com/ultralytics/ult
 
 ### Arguments `set_args`
 
-| Name                | Type           | Default           | Description                                               |
-|---------------------|----------------|-------------------|-----------------------------------------------------------|
-| view_img            | `bool`         | `False`           | Display the frame with heatmap                            |
-| colormap            | `cv2.COLORMAP` | `None`            | cv2.COLORMAP for heatmap                                  |
-| imw                 | `int`          | `None`            | Width of Heatmap                                          |
-| imh                 | `int`          | `None`            | Height of Heatmap                                         |
-| heatmap_alpha       | `float`        | `0.5`             | Heatmap alpha value                                       |
-| count_reg_pts       | `list`         | `None`            | Object counting region points                             |
-| count_txt_thickness | `int`          | `2`               | Count values text size                                    |
-| count_txt_color     | `RGB Color`    | `(0, 0, 0)`       | Foreground color for Object counts text                   |
-| count_color         | `RGB Color`    | `(255, 255, 255)` | Background color for Object counts text                   |
-| count_reg_color     | `RGB Color`    | `(255, 0, 255)`   | Counting region color                                     |
-| region_thickness    | `int`          | `5`               | Counting region thickness value                           |
-| decay_factor        | `float`        | `0.99`            | Decay factor for heatmap area removal after specific time |
-| shape               | `str`          | `circle`          | Heatmap shape for display "rect" or "circle" supported    |
-| line_dist_thresh    | `int`          | `15`              | Euclidean Distance threshold for line counter             |
+| Name                  | Type           | Default           | Description                                               |
+|-----------------------|----------------|-------------------|-----------------------------------------------------------|
+| `view_img`            | `bool`         | `False`           | Display the frame with heatmap                            |
+| `colormap`            | `cv2.COLORMAP` | `None`            | cv2.COLORMAP for heatmap                                  |
+| `imw`                 | `int`          | `None`            | Width of Heatmap                                          |
+| `imh`                 | `int`          | `None`            | Height of Heatmap                                         |
+| `heatmap_alpha`       | `float`        | `0.5`             | Heatmap alpha value                                       |
+| `count_reg_pts`       | `list`         | `None`            | Object counting region points                             |
+| `count_txt_thickness` | `int`          | `2`               | Count values text size                                    |
+| `count_txt_color`     | `RGB Color`    | `(0, 0, 0)`       | Foreground color for Object counts text                   |
+| `count_color`         | `RGB Color`    | `(255, 255, 255)` | Background color for Object counts text                   |
+| `count_reg_color`     | `RGB Color`    | `(255, 0, 255)`   | Counting region color                                     |
+| `region_thickness`    | `int`          | `5`               | Counting region thickness value                           |
+| `decay_factor`        | `float`        | `0.99`            | Decay factor for heatmap area removal after specific time |
+| `shape`               | `str`          | `circle`          | Heatmap shape for display "rect" or "circle" supported    |
+| `line_dist_thresh`    | `int`          | `15`              | Euclidean Distance threshold for line counter             |
 
 ### Arguments `model.track`
 
diff --git a/docs/en/guides/instance-segmentation-and-tracking.md b/docs/en/guides/instance-segmentation-and-tracking.md
index 5c6fb589714..0339ab900d0 100644
--- a/docs/en/guides/instance-segmentation-and-tracking.md
+++ b/docs/en/guides/instance-segmentation-and-tracking.md
@@ -26,6 +26,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
 !!! Example "Instance Segmentation and Tracking"
 
     === "Instance Segmentation"
+
         ```python
         import cv2
         from ultralytics import YOLO
@@ -68,6 +69,7 @@ There are two types of instance segmentation tracking available in the Ultralyti
         ```
 
     === "Instance Segmentation with Object Tracking"
+
         ```python
         import cv2
         from ultralytics import YOLO
diff --git a/docs/en/guides/object-blurring.md b/docs/en/guides/object-blurring.md
index bb2f7443ee6..0b6faa3ec6d 100644
--- a/docs/en/guides/object-blurring.md
+++ b/docs/en/guides/object-blurring.md
@@ -19,6 +19,7 @@ Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 !!! Example "Object Blurring using YOLOv8 Example"
 
     === "Object Blurring"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
diff --git a/docs/en/guides/object-counting.md b/docs/en/guides/object-counting.md
index b0f10c65e2d..bb44d397036 100644
--- a/docs/en/guides/object-counting.md
+++ b/docs/en/guides/object-counting.md
@@ -37,6 +37,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 !!! Example "Object Counting using YOLOv8 Example"
 
     === "Region"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import object_counter
@@ -76,10 +77,10 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         cap.release()
         video_writer.release()
         cv2.destroyAllWindows()
-
         ```
 
     === "Line"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import object_counter
@@ -122,6 +123,7 @@ Object counting with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
         ```
 
     === "Specific Classes"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import object_counter
diff --git a/docs/en/guides/object-cropping.md b/docs/en/guides/object-cropping.md
index 6400454f19b..554eeb8c91a 100644
--- a/docs/en/guides/object-cropping.md
+++ b/docs/en/guides/object-cropping.md
@@ -19,6 +19,7 @@ Object cropping with [Ultralytics YOLOv8](https://github.com/ultralytics/ultraly
 !!! Example "Object Cropping using YOLOv8 Example"
 
     === "Object Cropping"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.utils.plotting import Annotator, colors
diff --git a/docs/en/guides/speed-estimation.md b/docs/en/guides/speed-estimation.md
index 0e2cffe2faa..a597f522d07 100644
--- a/docs/en/guides/speed-estimation.md
+++ b/docs/en/guides/speed-estimation.md
@@ -26,6 +26,7 @@ Speed estimation is the process of calculating the rate of movement of an object
 !!! Example "Speed Estimation using YOLOv8 Example"
 
     === "Speed Estimation"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import speed_estimation
@@ -76,14 +77,14 @@ Speed estimation is the process of calculating the rate of movement of an object
 
 ### Optional Arguments `set_args`
 
-| Name             | Type   | Default                    | Description                                       |
-|------------------|--------|----------------------------|---------------------------------------------------|
-| reg_pts          | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area                   |
-| names            | `dict` | `None`                     | Classes names                                     |
-| view_img         | `bool` | `False`                    | Display frames with counts                        |
-| line_thickness   | `int`  | `2`                        | Increase bounding boxes thickness                 |
-| region_thickness | `int`  | `5`                        | Thickness for object counter region or line       |
-| spdl_dist_thresh | `int`  | `10`                       | Euclidean Distance threshold for speed check line |
+| Name               | Type   | Default                    | Description                                       |
+|--------------------|--------|----------------------------|---------------------------------------------------|
+| `reg_pts`          | `list` | `[(20, 400), (1260, 400)]` | Points defining the Region Area                   |
+| `names`            | `dict` | `None`                     | Classes names                                     |
+| `view_img`         | `bool` | `False`                    | Display frames with counts                        |
+| `line_thickness`   | `int`  | `2`                        | Increase bounding boxes thickness                 |
+| `region_thickness` | `int`  | `5`                        | Thickness for object counter region or line       |
+| `spdl_dist_thresh` | `int`  | `10`                       | Euclidean Distance threshold for speed check line |
 
 ### Arguments `model.track`
 
diff --git a/docs/en/guides/vision-eye.md b/docs/en/guides/vision-eye.md
index c1734ff7e04..16314a82e6a 100644
--- a/docs/en/guides/vision-eye.md
+++ b/docs/en/guides/vision-eye.md
@@ -20,6 +20,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
 !!! Example "VisionEye Object Mapping using YOLOv8"
 
     === "VisionEye Object Mapping"
+
         ```python
         import cv2
         from ultralytics import YOLO
@@ -62,6 +63,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Tracking, IDetection, Vi
         ```
 
     === "VisionEye Object Mapping with Object Tracking"
+
         ```python
         import cv2
         from ultralytics import YOLO
diff --git a/docs/en/guides/workouts-monitoring.md b/docs/en/guides/workouts-monitoring.md
index c35cd0a81d1..b2297803f18 100644
--- a/docs/en/guides/workouts-monitoring.md
+++ b/docs/en/guides/workouts-monitoring.md
@@ -26,6 +26,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 !!! Example "Workouts Monitoring Example"
 
     === "Workouts Monitoring"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import ai_gym
@@ -56,6 +57,7 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
         ```
 
     === "Workouts Monitoring with Save Output"
+
         ```python
         from ultralytics import YOLO
         from ultralytics.solutions import ai_gym
@@ -102,14 +104,14 @@ Monitoring workouts through pose estimation with [Ultralytics YOLOv8](https://gi
 
 ### Arguments `set_args`
 
-| Name            | Type   | Default  | Description                                                                            |
-|-----------------|--------|----------|----------------------------------------------------------------------------------------|
-| kpts_to_check   | `list` | `None`   | List of three keypoints index, for counting specific workout, followed by keypoint Map |
-| view_img        | `bool` | `False`  | Display the frame with counts                                                          |
-| line_thickness  | `int`  | `2`      | Increase the thickness of count value                                                  |
-| pose_type       | `str`  | `pushup` | Pose that need to be monitored, "pullup" and "abworkout" also supported                |
-| pose_up_angle   | `int`  | `145`    | Pose Up Angle value                                                                    |
-| pose_down_angle | `int`  | `90`     | Pose Down Angle value                                                                  |
+| Name              | Type   | Default  | Description                                                                            |
+|-------------------|--------|----------|----------------------------------------------------------------------------------------|
+| `kpts_to_check`   | `list` | `None`   | List of three keypoints index, for counting specific workout, followed by keypoint Map |
+| `view_img`        | `bool` | `False`  | Display the frame with counts                                                          |
+| `line_thickness`  | `int`  | `2`      | Increase the thickness of count value                                                  |
+| `pose_type`       | `str`  | `pushup` | Pose that need to be monitored, "pullup" and "abworkout" also supported                |
+| `pose_up_angle`   | `int`  | `145`    | Pose Up Angle value                                                                    |
+| `pose_down_angle` | `int`  | `90`     | Pose Down Angle value                                                                  |
 
 ### Arguments `model.predict`
 
diff --git a/docs/en/hub/inference_api.md b/docs/en/hub/inference_api.md
index e18574c55d8..b258d375eed 100644
--- a/docs/en/hub/inference_api.md
+++ b/docs/en/hub/inference_api.md
@@ -108,6 +108,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
 !!! Example "Detect Model JSON Response"
 
     === "Local"
+
         ```python
         from ultralytics import YOLO
 
@@ -122,6 +123,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
         ```
 
     === "CLI API"
+
         ```bash
         curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
             -H "x-api-key: API_KEY" \
@@ -132,6 +134,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
         ```
 
     === "Python API"
+
         ```python
         import requests
 
@@ -153,6 +156,7 @@ YOLO detection models, such as `yolov8n.pt`, can return JSON responses from loca
         ```
 
     === "JSON Response"
+
         ```json
         {
           "success": True,
@@ -202,6 +206,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
 !!! Example "Segment Model JSON Response"
 
     === "Local"
+
         ```python
         from ultralytics import YOLO
 
@@ -216,6 +221,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
         ```
 
     === "CLI API"
+
         ```bash
         curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
             -H "x-api-key: API_KEY" \
@@ -226,6 +232,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
         ```
 
     === "Python API"
+
         ```python
         import requests
 
@@ -247,6 +254,7 @@ YOLO segmentation models, such as `yolov8n-seg.pt`, can return JSON responses fr
         ```
 
     === "JSON Response"
+
         Note `segments` `x` and `y` lengths may vary from one object to another. Larger or more complex objects may have more segment points.
         ```json
         {
@@ -339,6 +347,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
 !!! Example "Pose Model JSON Response"
 
     === "Local"
+
         ```python
         from ultralytics import YOLO
 
@@ -353,6 +362,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
         ```
 
     === "CLI API"
+
         ```bash
         curl -X POST "https://api.ultralytics.com/v1/predict/MODEL_ID" \
             -H "x-api-key: API_KEY" \
@@ -363,6 +373,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
         ```
 
     === "Python API"
+
         ```python
         import requests
 
@@ -384,6 +395,7 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
         ```
 
     === "JSON Response"
+
         Note COCO-keypoints pretrained models will have 17 human keypoints. The `visible` part of the keypoints indicates whether a keypoint is visible or obscured. Obscured keypoints may be outside the image or may not be visible, i.e. a person's eyes facing away from the camera.
         ```json
         {
diff --git a/docs/en/integrations/weights-biases.md b/docs/en/integrations/weights-biases.md
index e2990ff2111..6a69a18b67a 100644
--- a/docs/en/integrations/weights-biases.md
+++ b/docs/en/integrations/weights-biases.md
@@ -46,6 +46,7 @@ Start by initializing the Weights & Biases environment in your workspace. You ca
 !!! Tip "Initial SDK Setup"
 
     === "CLI"
+
         ```bash
         # Initialize your Weights & Biases environment
         import wandb
diff --git a/docs/en/models/fast-sam.md b/docs/en/models/fast-sam.md
index 16d8453b732..5e881d6ff08 100644
--- a/docs/en/models/fast-sam.md
+++ b/docs/en/models/fast-sam.md
@@ -50,6 +50,7 @@ To perform object detection on an image, use the `predict` method as shown below
 !!! Example
 
     === "Python"
+
         ```python
         from ultralytics import FastSAM
         from ultralytics.models.fastsam import FastSAMPrompt
@@ -83,6 +84,7 @@ To perform object detection on an image, use the `predict` method as shown below
         ```
 
     === "CLI"
+
         ```bash
         # Load a FastSAM model and segment everything with it
         yolo segment predict model=FastSAM-s.pt source=path/to/bus.jpg imgsz=640
@@ -97,6 +99,7 @@ Validation of the model on a dataset can be done as follows:
 !!! Example
 
     === "Python"
+
         ```python
         from ultralytics import FastSAM
 
@@ -108,6 +111,7 @@ Validation of the model on a dataset can be done as follows:
         ```
 
     === "CLI"
+
         ```bash
         # Load a FastSAM model and validate it on the COCO8 example dataset at image size 640
         yolo segment val model=FastSAM-s.pt data=coco8.yaml imgsz=640
diff --git a/docs/en/models/mobile-sam.md b/docs/en/models/mobile-sam.md
index 51700f6a4f3..dfed1dda7c8 100644
--- a/docs/en/models/mobile-sam.md
+++ b/docs/en/models/mobile-sam.md
@@ -72,6 +72,7 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
 !!! Example
 
     === "Python"
+
         ```python
         from ultralytics import SAM
 
@@ -87,6 +88,7 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
 !!! Example
 
     === "Python"
+
         ```python
         from ultralytics import SAM
 
diff --git a/docs/en/models/sam.md b/docs/en/models/sam.md
index 7b9135b3229..654726d41c0 100644
--- a/docs/en/models/sam.md
+++ b/docs/en/models/sam.md
@@ -148,6 +148,7 @@ Tests run on a 2023 Apple M2 Macbook with 16GB of RAM. To reproduce this test:
 !!! Example
 
     === "Python"
+
         ```python
         from ultralytics import FastSAM, SAM, YOLO
 
@@ -183,6 +184,7 @@ To auto-annotate your dataset with the Ultralytics framework, use the `auto_anno
 !!! Example
 
     === "Python"
+
         ```python
         from ultralytics.data.annotator import auto_annotate
 
diff --git a/docs/en/models/yolov5.md b/docs/en/models/yolov5.md
index 50accac02c0..af43d321cd3 100644
--- a/docs/en/models/yolov5.md
+++ b/docs/en/models/yolov5.md
@@ -97,6 +97,7 @@ If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv
 !!! Quote ""
 
     === "BibTeX"
+
         ```bibtex
         @software{yolov5,
           title = {Ultralytics YOLOv5},
diff --git a/docs/en/modes/benchmark.md b/docs/en/modes/benchmark.md
index c845c621e15..1322d05686f 100644
--- a/docs/en/modes/benchmark.md
+++ b/docs/en/modes/benchmark.md
@@ -62,6 +62,7 @@ Run YOLOv8n benchmarks on all supported export formats including ONNX, TensorRT
         # Benchmark on GPU
         benchmark(model='yolov8n.pt', data='coco8.yaml', imgsz=640, half=False, device=0)
         ```
+
     === "CLI"
 
         ```bash
diff --git a/docs/en/modes/export.md b/docs/en/modes/export.md
index 29993918e04..55e6214c9b2 100644
--- a/docs/en/modes/export.md
+++ b/docs/en/modes/export.md
@@ -62,6 +62,7 @@ Export a YOLOv8n model to a different format like ONNX or TensorRT. See Argument
         # Export the model
         model.export(format='onnx')
         ```
+
     === "CLI"
 
         ```bash
diff --git a/docs/en/modes/predict.md b/docs/en/modes/predict.md
index a2bb61582ba..07c1ce98db4 100644
--- a/docs/en/modes/predict.md
+++ b/docs/en/modes/predict.md
@@ -53,6 +53,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
 !!! Example "Predict"
 
     === "Return a list with `stream=False`"
+
         ```python
         from ultralytics import YOLO
 
@@ -71,6 +72,7 @@ Ultralytics YOLO models return either a Python list of `Results` objects, or a m
         ```
 
     === "Return a generator with `stream=True`"
+
         ```python
         from ultralytics import YOLO
 
@@ -118,6 +120,7 @@ Below are code examples for using each source type:
 !!! Example "Prediction sources"
 
     === "image"
+
         Run inference on an image file.
         ```python
         from ultralytics import YOLO
@@ -133,6 +136,7 @@ Below are code examples for using each source type:
         ```
 
     === "screenshot"
+
         Run inference on the current screen content as a screenshot.
         ```python
         from ultralytics import YOLO
@@ -148,6 +152,7 @@ Below are code examples for using each source type:
         ```
 
     === "URL"
+
         Run inference on an image or video hosted remotely via URL.
         ```python
         from ultralytics import YOLO
@@ -163,6 +168,7 @@ Below are code examples for using each source type:
         ```
 
     === "PIL"
+
         Run inference on an image opened with Python Imaging Library (PIL).
         ```python
         from PIL import Image
@@ -179,6 +185,7 @@ Below are code examples for using each source type:
         ```
 
     === "OpenCV"
+
         Run inference on an image read with OpenCV.
         ```python
         import cv2
@@ -195,6 +202,7 @@ Below are code examples for using each source type:
         ```
 
     === "numpy"
+
         Run inference on an image represented as a numpy array.
         ```python
         import numpy as np
@@ -211,6 +219,7 @@ Below are code examples for using each source type:
         ```
 
     === "torch"
+
         Run inference on an image represented as a PyTorch tensor.
         ```python
         import torch
@@ -227,6 +236,7 @@ Below are code examples for using each source type:
         ```
 
     === "CSV"
+
         Run inference on a collection of images, URLs, videos and directories listed in a CSV file.
         ```python
         import torch
@@ -243,6 +253,7 @@ Below are code examples for using each source type:
         ```
 
     === "video"
+
         Run inference on a video file. By using `stream=True`, you can create a generator of Results objects to reduce memory usage.
         ```python
         from ultralytics import YOLO
@@ -258,6 +269,7 @@ Below are code examples for using each source type:
         ```
 
     === "directory"
+
         Run inference on all images and videos in a directory. To also capture images and videos in subdirectories use a glob pattern, i.e. `path/to/dir/**/*`.
         ```python
         from ultralytics import YOLO
@@ -273,6 +285,7 @@ Below are code examples for using each source type:
         ```
 
     === "glob"
+
         Run inference on all images and videos that match a glob expression with `*` characters.
         ```python
         from ultralytics import YOLO
@@ -291,6 +304,7 @@ Below are code examples for using each source type:
         ```
 
     === "YouTube"
+
         Run inference on a YouTube video. By using `stream=True`, you can create a generator of Results objects to reduce memory usage for long videos.
         ```python
         from ultralytics import YOLO
@@ -306,6 +320,7 @@ Below are code examples for using each source type:
         ```
 
     === "Streams"
+
         Run inference on remote streaming sources using RTSP, RTMP, TCP and IP address protocols. If multiple streams are provided in a `*.streams` text file then batched inference will run, i.e. 8 streams will run at batch-size 8, otherwise single streams will run at batch-size 1.
         ```python
         from ultralytics import YOLO
@@ -384,16 +399,16 @@ The below table contains valid Ultralytics image formats.
 
 | Image Suffixes | Example Predict Command          | Reference                                                                     |
 |----------------|----------------------------------|-------------------------------------------------------------------------------|
-| .bmp           | `yolo predict source=image.bmp`  | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format)    |
-| .dng           | `yolo predict source=image.dng`  | [Adobe DNG](https://www.adobe.com/products/photoshop/extend.displayTab2.html) |
-| .jpeg          | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG)                                    |
-| .jpg           | `yolo predict source=image.jpg`  | [JPEG](https://en.wikipedia.org/wiki/JPEG)                                    |
-| .mpo           | `yolo predict source=image.mpo`  | [Multi Picture Object](https://fileinfo.com/extension/mpo)                    |
-| .png           | `yolo predict source=image.png`  | [Portable Network Graphics](https://en.wikipedia.org/wiki/PNG)                |
-| .tif           | `yolo predict source=image.tif`  | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF)                   |
-| .tiff          | `yolo predict source=image.tiff` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF)                   |
-| .webp          | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP)                                    |
-| .pfm           | `yolo predict source=image.pfm`  | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats)        |
+| `.bmp`         | `yolo predict source=image.bmp`  | [Microsoft BMP File Format](https://en.wikipedia.org/wiki/BMP_file_format)    |
+| `.dng`         | `yolo predict source=image.dng`  | [Adobe DNG](https://www.adobe.com/products/photoshop/extend.displayTab2.html) |
+| `.jpeg`        | `yolo predict source=image.jpeg` | [JPEG](https://en.wikipedia.org/wiki/JPEG)                                    |
+| `.jpg`         | `yolo predict source=image.jpg`  | [JPEG](https://en.wikipedia.org/wiki/JPEG)                                    |
+| `.mpo`         | `yolo predict source=image.mpo`  | [Multi Picture Object](https://fileinfo.com/extension/mpo)                    |
+| `.png`         | `yolo predict source=image.png`  | [Portable Network Graphics](https://en.wikipedia.org/wiki/PNG)                |
+| `.tif`         | `yolo predict source=image.tif`  | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF)                   |
+| `.tiff`        | `yolo predict source=image.tiff` | [Tag Image File Format](https://en.wikipedia.org/wiki/TIFF)                   |
+| `.webp`        | `yolo predict source=image.webp` | [WebP](https://en.wikipedia.org/wiki/WebP)                                    |
+| `.pfm`         | `yolo predict source=image.pfm`  | [Portable FloatMap](https://en.wikipedia.org/wiki/Netpbm#File_formats)        |
 
 ### Videos
 
@@ -401,18 +416,18 @@ The below table contains valid Ultralytics video formats.
 
 | Video Suffixes | Example Predict Command          | Reference                                                                        |
 |----------------|----------------------------------|----------------------------------------------------------------------------------|
-| .asf           | `yolo predict source=video.asf`  | [Advanced Systems Format](https://en.wikipedia.org/wiki/Advanced_Systems_Format) |
-| .avi           | `yolo predict source=video.avi`  | [Audio Video Interleave](https://en.wikipedia.org/wiki/Audio_Video_Interleave)   |
-| .gif           | `yolo predict source=video.gif`  | [Graphics Interchange Format](https://en.wikipedia.org/wiki/GIF)                 |
-| .m4v           | `yolo predict source=video.m4v`  | [MPEG-4 Part 14](https://en.wikipedia.org/wiki/M4V)                              |
-| .mkv           | `yolo predict source=video.mkv`  | [Matroska](https://en.wikipedia.org/wiki/Matroska)                               |
-| .mov           | `yolo predict source=video.mov`  | [QuickTime File Format](https://en.wikipedia.org/wiki/QuickTime_File_Format)     |
-| .mp4           | `yolo predict source=video.mp4`  | [MPEG-4 Part 14 - Wikipedia](https://en.wikipedia.org/wiki/MPEG-4_Part_14)       |
-| .mpeg          | `yolo predict source=video.mpeg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1)                            |
-| .mpg           | `yolo predict source=video.mpg`  | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1)                            |
-| .ts            | `yolo predict source=video.ts`   | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream)     |
-| .wmv           | `yolo predict source=video.wmv`  | [Windows Media Video](https://en.wikipedia.org/wiki/Windows_Media_Video)         |
-| .webm          | `yolo predict source=video.webm` | [WebM Project](https://en.wikipedia.org/wiki/WebM)                               |
+| `.asf`         | `yolo predict source=video.asf`  | [Advanced Systems Format](https://en.wikipedia.org/wiki/Advanced_Systems_Format) |
+| `.avi`         | `yolo predict source=video.avi`  | [Audio Video Interleave](https://en.wikipedia.org/wiki/Audio_Video_Interleave)   |
+| `.gif`         | `yolo predict source=video.gif`  | [Graphics Interchange Format](https://en.wikipedia.org/wiki/GIF)                 |
+| `.m4v`         | `yolo predict source=video.m4v`  | [MPEG-4 Part 14](https://en.wikipedia.org/wiki/M4V)                              |
+| `.mkv`         | `yolo predict source=video.mkv`  | [Matroska](https://en.wikipedia.org/wiki/Matroska)                               |
+| `.mov`         | `yolo predict source=video.mov`  | [QuickTime File Format](https://en.wikipedia.org/wiki/QuickTime_File_Format)     |
+| `.mp4`         | `yolo predict source=video.mp4`  | [MPEG-4 Part 14 - Wikipedia](https://en.wikipedia.org/wiki/MPEG-4_Part_14)       |
+| `.mpeg`        | `yolo predict source=video.mpeg` | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1)                            |
+| `.mpg`         | `yolo predict source=video.mpg`  | [MPEG-1 Part 2](https://en.wikipedia.org/wiki/MPEG-1)                            |
+| `.ts`          | `yolo predict source=video.ts`   | [MPEG Transport Stream](https://en.wikipedia.org/wiki/MPEG_transport_stream)     |
+| `.wmv`         | `yolo predict source=video.wmv`  | [Windows Media Video](https://en.wikipedia.org/wiki/Windows_Media_Video)         |
+| `.webm`        | `yolo predict source=video.webm` | [WebM Project](https://en.wikipedia.org/wiki/WebM)                               |
 
 ## Working with Results
 
diff --git a/docs/en/modes/train.md b/docs/en/modes/train.md
index 331d7cdd378..c9f3e09bba0 100644
--- a/docs/en/modes/train.md
+++ b/docs/en/modes/train.md
@@ -241,6 +241,7 @@ To use Comet:
 !!! Example
 
     === "Python"
+
         ```python
         # pip install comet_ml
         import comet_ml
@@ -259,6 +260,7 @@ To use ClearML:
 !!! Example
 
     === "Python"
+
         ```python
         # pip install clearml
         import clearml
@@ -277,6 +279,7 @@ To use TensorBoard in [Google Colab](https://colab.research.google.com/github/ul
 !!! Example
 
     === "CLI"
+
         ```bash
         load_ext tensorboard
         tensorboard --logdir ultralytics/runs  # replace with 'runs' directory
@@ -287,6 +290,7 @@ To use TensorBoard locally run the below command and view results at http://loca
 !!! Example
 
     === "CLI"
+
         ```bash
         tensorboard --logdir ultralytics/runs  # replace with 'runs' directory
         ```
diff --git a/docs/en/modes/val.md b/docs/en/modes/val.md
index d9e2c96a6be..626aa7b9d0c 100644
--- a/docs/en/modes/val.md
+++ b/docs/en/modes/val.md
@@ -67,6 +67,7 @@ Validate trained YOLOv8n model accuracy on the COCO128 dataset. No argument need
         metrics.box.map75  # map75
         metrics.box.maps   # a list contains map50-95 of each category
         ```
+
     === "CLI"
 
         ```bash
diff --git a/docs/en/quickstart.md b/docs/en/quickstart.md
index 865520b2288..3ea839a5494 100644
--- a/docs/en/quickstart.md
+++ b/docs/en/quickstart.md
@@ -22,6 +22,7 @@ Ultralytics provides various installation methods including pip, conda, and Dock
 !!! Example "Install"
 
     === "Pip install (recommended)"
+
         Install the `ultralytics` package using pip, or update an existing installation by running `pip install -U ultralytics`. Visit the Python Package Index (PyPI) for more details on the `ultralytics` package: [https://pypi.org/project/ultralytics/](https://pypi.org/project/ultralytics/).
 
         [![PyPI version](https://badge.fury.io/py/ultralytics.svg)](https://badge.fury.io/py/ultralytics) [![Downloads](https://static.pepy.tech/badge/ultralytics)](https://pepy.tech/project/ultralytics)
@@ -38,8 +39,8 @@ Ultralytics provides various installation methods including pip, conda, and Dock
         pip install git+https://github.com/ultralytics/ultralytics.git@main
         ```
 
-
     === "Conda install"
+
         Conda is an alternative package manager to pip which may also be used for installation. Visit Anaconda for more details at [https://anaconda.org/conda-forge/ultralytics](https://anaconda.org/conda-forge/ultralytics). Ultralytics feedstock repository for updating the conda package is at [https://github.com/conda-forge/ultralytics-feedstock/](https://github.com/conda-forge/ultralytics-feedstock/).
 
 
diff --git a/docs/en/tasks/pose.md b/docs/en/tasks/pose.md
index 401459d4572..44db387f053 100644
--- a/docs/en/tasks/pose.md
+++ b/docs/en/tasks/pose.md
@@ -64,6 +64,7 @@ Train a YOLOv8-pose model on the COCO128-pose dataset.
         # Train the model
         results = model.train(data='coco8-pose.yaml', epochs=100, imgsz=640)
         ```
+
     === "CLI"
 
         ```bash
diff --git a/docs/en/usage/cli.md b/docs/en/usage/cli.md
index 98b2dbbde33..62b79e43b30 100644
--- a/docs/en/usage/cli.md
+++ b/docs/en/usage/cli.md
@@ -193,18 +193,21 @@ Default arguments can be overridden by simply passing them as arguments in the C
 !!! Tip ""
 
     === "Train"
+
         Train a detection model for `10 epochs` with `learning_rate` of `0.01`
         ```bash
         yolo detect train data=coco128.yaml model=yolov8n.pt epochs=10 lr0=0.01
         ```
 
     === "Predict"
+
         Predict a YouTube video using a pretrained segmentation model at image size 320:
         ```bash
         yolo segment predict model=yolov8n-seg.pt source='https://youtu.be/LNwODJXcvt4' imgsz=320
         ```
 
     === "Val"
+
         Validate a pretrained detection model at batch-size 1 and image size 640:
         ```bash
         yolo detect val model=yolov8n.pt data=coco128.yaml batch=1 imgsz=640
@@ -221,6 +224,7 @@ This will create `default_copy.yaml`, which you can then pass as `cfg=default_co
 !!! Example
 
     === "CLI"
+
         ```bash
         yolo copy-cfg
         yolo cfg=default_copy.yaml imgsz=320
diff --git a/docs/en/usage/python.md b/docs/en/usage/python.md
index ac5a712b6fe..2659907a7b0 100644
--- a/docs/en/usage/python.md
+++ b/docs/en/usage/python.md
@@ -52,6 +52,7 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
 !!! Example "Train"
 
     === "From pretrained(recommended)"
+
         ```python
         from ultralytics import YOLO
 
@@ -60,6 +61,7 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
         ```
 
     === "From scratch"
+
         ```python
         from ultralytics import YOLO
 
@@ -68,6 +70,7 @@ Train mode is used for training a YOLOv8 model on a custom dataset. In this mode
         ```
 
     === "Resume"
+
         ```python
         model = YOLO("last.pt")
         results = model.train(resume=True)
@@ -82,6 +85,7 @@ Val mode is used for validating a YOLOv8 model after it has been trained. In thi
 !!! Example "Val"
 
     === "Val after training"
+
         ```python
           from ultralytics import YOLO
 
@@ -91,6 +95,7 @@ Val mode is used for validating a YOLOv8 model after it has been trained. In thi
         ```
 
     === "Val independently"
+
         ```python
           from ultralytics import YOLO
 
@@ -110,6 +115,7 @@ Predict mode is used for making predictions using a trained YOLOv8 model on new
 !!! Example "Predict"
 
     === "From source"
+
         ```python
         from ultralytics import YOLO
         from PIL import Image
@@ -133,6 +139,7 @@ Predict mode is used for making predictions using a trained YOLOv8 model on new
         ```
 
     === "Results usage"
+
         ```python
         # results would be a list of Results object including all the predictions by default
         # but be careful as it could occupy a lot memory when there're many images,