Update YOLOv3 and YOLOv5 YAMLs (#7574)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/guides/isolating-segmentation-objects.md

@@ -14,7 +14,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
 ## Recipe Walk Through
 
-1. Begin with the necessary imports
+1.  Begin with the necessary imports
 
     ```py
     from pathlib import Path
@@ -28,9 +28,9 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
         See the Ultralytics [Quickstart](../quickstart.md/#install-ultralytics) Installation section for a quick walkthrough on installing the required libraries.
 
-    ---
+    ***
 
-2. Load a model and run `predict()` method on a source.
+2.  Load a model and run `predict()` method on a source.
 
     ```py
     from ultralytics import YOLO
@@ -55,11 +55,11 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
     For additional information about Segmentation Models, visit the [Segment Task](../tasks/segment.md#models) page. To learn more about `predict()` method, see [Predict Mode](../modes/predict.md) section of the Documentation.
 
-    ---
+    ***
 
-3. Now iterate over the results and the contours. For workflows that want to save an image to file, the source image `base-name` and the detection `class-label` are retrieved for later use (optional).
+3.  Now iterate over the results and the contours. For workflows that want to save an image to file, the source image `base-name` and the detection `class-label` are retrieved for later use (optional).
 
-    ``` { .py .annotate }
+    ```{ .py .annotate }
     # (2) Iterate detection results (helpful for multiple images)
     for r in res:
         img = np.copy(r.orig_img)
@@ -79,13 +79,13 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
         A single image will only iterate the first loop once. A single image with only a single detection will iterate each loop _only_ once.
 
-    ---
+    ***
 
-4. Start with generating a binary mask from the source image and then draw a filled contour onto the mask. This will allow the object to be isolated from the other parts of the image. An example from `bus.jpg` for one of the detected `person` class objects is shown on the right.
+4.  Start with generating a binary mask from the source image and then draw a filled contour onto the mask. This will allow the object to be isolated from the other parts of the image. An example from `bus.jpg` for one of the detected `person` class objects is shown on the right.
 
     ![Binary Mask Image](https://github.com/ultralytics/ultralytics/assets/62214284/59bce684-fdda-4b17-8104-0b4b51149aca){ width="240", align="right" }
 
-    ``` { .py .annotate }
+    ```{ .py .annotate }
     # Create binary mask
     b_mask = np.zeros(img.shape[:2], np.uint8)
 
@@ -116,7 +116,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
     <summary> Expand to understand what is happening when defining the <code>contour</code> variable.</summary>
     <p>
 
-    - `c.masks.xy` :: Provides the coordinates of the mask contour points in the format  `(x, y)`. For more details, refer to the [Masks Section from Predict Mode](../modes/predict.md#masks).
+    - `c.masks.xy` :: Provides the coordinates of the mask contour points in the format `(x, y)`. For more details, refer to the [Masks Section from Predict Mode](../modes/predict.md#masks).
 
     - `.pop()` :: As `masks.xy` is a list containing a single element, this element is extracted using the `pop()` method.
 
@@ -143,9 +143,9 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
     </details>
     <p></p>
 
-    ---
+    ***
 
-5. Next the there are 2 options for how to move forward with the image from this point and a subsequent option for each.
+5.  Next the there are 2 options for how to move forward with the image from this point and a subsequent option for each.
 
     ### Object Isolation Options
 
@@ -256,9 +256,9 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
         This is a built in feature for the Ultralytics library. See the `save_crop` argument for  [Predict Mode Inference Arguments](../modes/predict.md/#inference-arguments) for details.
 
-    ---
+    ***
 
-6. <u>What to do next is entirely left to you as the developer.</u> A basic example of one possible next step (saving the image to file for future use) is shown.
+6.  <u>What to do next is entirely left to you as the developer.</u> A basic example of one possible next step (saving the image to file for future use) is shown.
 
     - **NOTE:** this step is optional and can be skipped if not required for your specific use case.
 
@@ -275,7 +275,7 @@ After performing the [Segment Task](../tasks/segment.md), it's sometimes desirab
 
 Here, all steps from the previous section are combined into a single block of code. For repeated use, it would be optimal to define a function to do some or all commands contained in the `for`-loops, but that is an exercise left to the reader.
 
-``` { .py .annotate }
+```{ .py .annotate }
 from pathlib import Path
 
 import cv2 as cv

docs/en/guides/model-deployment-options.md

@@ -8,7 +8,7 @@ keywords: YOLOv8, Deployment, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, Co
 
 ## Introduction
 
-*Setting the Scene:* You've come a long way on your journey with YOLOv8. You've diligently collected data, meticulously annotated it, and put in the hours to train and rigorously evaluate your custom YOLOv8 model. Now, it’s time to put your model to work for your specific application, use case, or project. But there's a critical decision that stands before you: how to export and deploy your model effectively.
+You've come a long way on your journey with YOLOv8. You've diligently collected data, meticulously annotated it, and put in the hours to train and rigorously evaluate your custom YOLOv8 model. Now, it’s time to put your model to work for your specific application, use case, or project. But there's a critical decision that stands before you: how to export and deploy your model effectively.
 
 This guide walks you through YOLOv8’s deployment options and the essential factors to consider to choose the right option for your project.
 

docs/en/guides/object-blurring.md

@@ -8,7 +8,7 @@ keywords: Ultralytics, YOLOv8, Object Detection, Object Blurring, Privacy Protec
 
 ## What is Object Blurring?
 
-Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/)  involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLOv8 model capabilities to identify and manipulate objects within a given scene.
+Object blurring with [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics/) involves applying a blurring effect to specific detected objects in an image or video. This can be achieved using the YOLOv8 model capabilities to identify and manipulate objects within a given scene.
 
 ## Advantages of Object Blurring?