PyCharm Code and Docs Inspect fixes v1 (#18461)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Ultralytics Assistant <135830346+UltralyticsAssistant@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>

docs/en/integrations/albumentations.md

@@ -87,7 +87,7 @@ Next, let's take look a closer look at the specific augmentations that are appli
 
 ### Blur
 
-The Blur transformation in Albumentations applies a simple blur effect to the image by averaging pixel values within a small square area, or kernel. This is done using OpenCV's `cv2.blur` function, which helps reduce noise in the image, though it also slightly reduces image details.
+The Blur transformation in Albumentations applies a simple blur effect to the image by averaging pixel values within a small square area, or kernel. This is done using OpenCV `cv2.blur` function, which helps reduce noise in the image, though it also slightly reduces image details.
 
 Here are the parameters and values used in this integration:
 
@@ -117,7 +117,7 @@ The ToGray transformation in Albumentations converts an image to grayscale, redu
 
 Here are the parameters and values used in this integration:
 
-- **num_output_channels**: Sets the number of channels in the output image. If this value is more than 1, the single grayscale channel will be replicated to create a multi-channel grayscale image. By default, it's set to 3, giving a grayscale image with three identical channels.
+- **num_output_channels**: Sets the number of channels in the output image. If this value is more than 1, the single grayscale channel will be replicated to create a multichannel grayscale image. By default, it's set to 3, giving a grayscale image with three identical channels.
 
 - **method**: Defines the grayscale conversion method. The default method, "weighted_average", applies a formula (0.299R + 0.587G + 0.114B) that closely aligns with human perception, providing a natural-looking grayscale effect. Other options, like "from_lab", "desaturation", "average", "max", and "pca", offer alternative ways to create grayscale images based on various needs for speed, brightness emphasis, or detail preservation.
 
@@ -135,7 +135,7 @@ Here are the parameters and values used in this integration:
 
 - **clip_limit**: Controls the contrast enhancement range. Set to a default range of (1, 4), it determines the maximum contrast allowed in each tile. Higher values are used for more contrast but may also introduce noise.
 
-- **tile_grid_size**: Defines the size of the grid of tiles, typically as (rows, columns). The default value is (8, 8), meaning the image is divided into an 8x8 grid. Smaller tile sizes provide more localized adjustments, while larger ones create effects closer to global equalization.
+- **tile_grid_size**: Defines the size of the grid of tiles, typically as (rows, columns). The default value is (8, 8), meaning the image is divided into a 8x8 grid. Smaller tile sizes provide more localized adjustments, while larger ones create effects closer to global equalization.
 
 - **p**: The probability of applying CLAHE. Here, p=0.01 introduces the enhancement effect only 1% of the time, ensuring that contrast adjustments are applied sparingly for occasional variation in training images.
 

docs/en/integrations/paddlepaddle.md

@@ -114,7 +114,7 @@ For more details about supported export options, visit the [Ultralytics document
 
 ## Deploying Exported YOLO11 PaddlePaddle Models
 
-After successfully exporting your Ultralytics YOLO11 models to PaddlePaddle format, you can now deploy them. The primary and recommended first step for running a PaddlePaddle model is to use the YOLO("./model_paddle_model") method, as outlined in the previous usage code snippet.
+After successfully exporting your Ultralytics YOLO11 models to PaddlePaddle format, you can now deploy them. The primary and recommended first step for running a PaddlePaddle model is to use the YOLO("yolo11n_paddle_model/") method, as outlined in the previous usage code snippet.
 
 However, for in-depth instructions on deploying your PaddlePaddle models in various other settings, take a look at the following resources:
 

docs/en/integrations/tf-savedmodel.md

@@ -101,7 +101,7 @@ For more details about supported export options, visit the [Ultralytics document
 
 ## Deploying Exported YOLO11 TF SavedModel Models
 
-Now that you have exported your YOLO11 model to the TF SavedModel format, the next step is to deploy it. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("./yolo11n_saved_model") method, as previously shown in the usage code snippet.
+Now that you have exported your YOLO11 model to the TF SavedModel format, the next step is to deploy it. The primary and recommended first step for running a TF GraphDef model is to use the YOLO("yolo11n_saved_model/") method, as previously shown in the usage code snippet.
 
 However, for in-depth instructions on deploying your TF SavedModel models, take a look at the following resources:
 

docs/en/integrations/vscode.md

@@ -125,7 +125,7 @@ These are the current snippet categories available to the Ultralytics-snippets e
 
 ### Learning with Examples
 
-The `ultra.examples` snippets are to useful for anyone looking to learn how to get started with the basics of working with Ultralytics YOLO. Example snippets are intended to run once inserted (some have dropdown options as well). An example of this is shown at the animation at the [top] of this page, where after the snippet is inserted, all code is selected and run interactively using <kbd>Shift ⇑</kbd>+<kbd>Enter ↵</kbd>.
+The `ultra.examples` snippets are very useful for anyone looking to learn how to get started with the basics of working with Ultralytics YOLO. Example snippets are intended to run once inserted (some have dropdown options as well). An example of this is shown at the animation at the [top] of this page, where after the snippet is inserted, all code is selected and run interactively using <kbd>Shift ⇑</kbd>+<kbd>Enter ↵</kbd>.
 
 !!! example
 
@@ -168,7 +168,7 @@ However, since Ultralytics supports numerous [tasks], when [working with inferen
 
 ### Keywords Arguments
 
-There are over 💯 keyword arguments for all of the various Ultralytics [tasks] and [modes]! That's a lot to remember and it can be easy to forget if the argument is `save_frame` or `save_frames` (it's definitely `save_frames` by the way). This is where the `ultra.kwargs` snippets can help out!
+There are over 💯 keyword arguments for all the various Ultralytics [tasks] and [modes]! That's a lot to remember, and it can be easy to forget if the argument is `save_frame` or `save_frames` (it's definitely `save_frames` by the way). This is where the `ultra.kwargs` snippets can help out!
 
 !!! example
 
@@ -229,7 +229,7 @@ If you use VS Code and have started to see a message prompting you to install th
 
 1. Install Ultralytics-snippets and the message will no longer be shown 😆!
 
-2. You can using `yolo settings vscode_msg False` to disable the message from showing without having to install the extension. You can learn more about the [Ultralytics Settings] on the [quickstart] page if you're unfamiliar.
+2. You can be using `yolo settings vscode_msg False` to disable the message from showing without having to install the extension. You can learn more about the [Ultralytics Settings] on the [quickstart] page if you're unfamiliar.
 
 ### I have an idea for a new Ultralytics code snippet, how can I get one added?