Docs Prettier reformat (#13483)

Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>

docs/en/guides/preprocessing_annotated_data.md

@@ -8,7 +8,7 @@ keywords: data preprocessing, computer vision, image resizing, normalization, da
 
 ## Introduction
 
-After you've defined your computer vision [project's goals](./defining-project-goals.md) and [collected and annotated data](./data-collection-and-annotation.md), the next step is to preprocess annotated data and prepare it for model training. Clean and consistent data are vital to creating a model that performs well. 
+After you've defined your computer vision [project's goals](./defining-project-goals.md) and [collected and annotated data](./data-collection-and-annotation.md), the next step is to preprocess annotated data and prepare it for model training. Clean and consistent data are vital to creating a model that performs well.
 
 Preprocessing is a step in the [computer vision project workflow](./steps-of-a-cv-project.md) that includes resizing images, normalizing pixel values, augmenting the dataset, and splitting the data into training, validation, and test sets. Let's explore the essential techniques and best practices for cleaning your data!
 
@@ -22,7 +22,7 @@ We are already collecting and annotating our data carefully with multiple consid
 
 ## Data Preprocessing Techniques
 
-One of the first and foremost steps in data preprocessing is resizing.  Some models are designed to handle variable input sizes, but many models require a consistent input size. Resizing images makes them uniform and reduces computational complexity.
+One of the first and foremost steps in data preprocessing is resizing. Some models are designed to handle variable input sizes, but many models require a consistent input size. Resizing images makes them uniform and reduces computational complexity.
 
 ### Resizing Images
 
@@ -31,12 +31,12 @@ You can resize your images using the following methods:
 - **Bilinear Interpolation**: Smooths pixel values by taking a weighted average of the four nearest pixel values.
 - **Nearest Neighbor**: Assigns the nearest pixel value without averaging, leading to a blocky image but faster computation.
 
-To make resizing a simpler task, you can use the following tools: 
+To make resizing a simpler task, you can use the following tools:
 
 - **OpenCV**: A popular computer vision library with extensive functions for image processing.
 - **PIL (Pillow)**: A Python Imaging Library for opening, manipulating, and saving image files.
 
-With respect to YOLOv8, the 'imgsz' parameter during [model training](../modes/train.md) allows for flexible input sizes. When set to a specific size, such as 640, the model will resize input images so their largest dimension is 640 pixels while maintaining the original aspect ratio. 
+With respect to YOLOv8, the 'imgsz' parameter during [model training](../modes/train.md) allows for flexible input sizes. When set to a specific size, such as 640, the model will resize input images so their largest dimension is 640 pixels while maintaining the original aspect ratio.
 
 By evaluating your model's and dataset's specific needs, you can determine whether resizing is a necessary preprocessing step or if your model can efficiently handle images of varying sizes.
 
@@ -47,16 +47,16 @@ Another preprocessing technique is normalization. Normalization scales the pixel
 - **Min-Max Scaling**: Scales pixel values to a range of 0 to 1.
 - **Z-Score Normalization**: Scales pixel values based on their mean and standard deviation.
 
-With respect to YOLOv8, normalization is seamlessly handled as part of its preprocessing pipeline during model training. YOLOv8 automatically performs several preprocessing steps, including conversion to RGB, scaling pixel values to the range [0, 1], and normalization using predefined mean and standard deviation values. 
+With respect to YOLOv8, normalization is seamlessly handled as part of its preprocessing pipeline during model training. YOLOv8 automatically performs several preprocessing steps, including conversion to RGB, scaling pixel values to the range [0, 1], and normalization using predefined mean and standard deviation values.
 
 ### Splitting the Dataset
 
-Once you've cleaned the data, you are ready to split the dataset. Splitting the data into training, validation, and test sets is done to ensure that the model can be evaluated on unseen data to assess its generalization performance. A common split is 70% for training, 20% for validation, and 10% for testing. There are various tools and libraries that you can use to split your data like scikit-learn or TensorFlow. 
+Once you've cleaned the data, you are ready to split the dataset. Splitting the data into training, validation, and test sets is done to ensure that the model can be evaluated on unseen data to assess its generalization performance. A common split is 70% for training, 20% for validation, and 10% for testing. There are various tools and libraries that you can use to split your data like scikit-learn or TensorFlow.
 
 Consider the following when splitting your dataset:
+
 - **Maintaining Data Distribution**: Ensure that the data distribution of classes is maintained across training, validation, and test sets.
-- **Avoiding Data Leakage**: Typically, data augmentation is done after the dataset is split. Data augmentation and any other preprocessing should only be applied to the training set to prevent information from the validation or test sets from influencing the model training.
--**Balancing Classes**: For imbalanced datasets, consider techniques such as oversampling the minority class or under-sampling the majority class within the training set.
+- **Avoiding Data Leakage**: Typically, data augmentation is done after the dataset is split. Data augmentation and any other preprocessing should only be applied to the training set to prevent information from the validation or test sets from influencing the model training. -**Balancing Classes**: For imbalanced datasets, consider techniques such as oversampling the minority class or under-sampling the majority class within the training set.
 
 ### What is Data Augmentation?
 
@@ -89,13 +89,13 @@ Also, you can adjust the intensity of these augmentation techniques through spec
 
 ## A Case Study of Preprocessing
 
-Consider a project aimed at developing a model to detect and classify different types of vehicles in traffic images using YOLOv8. We've collected traffic images and annotated them with bounding boxes and labels. 
+Consider a project aimed at developing a model to detect and classify different types of vehicles in traffic images using YOLOv8. We've collected traffic images and annotated them with bounding boxes and labels.
 
 Here's what each step of preprocessing would look like for this project:
 
 - Resizing Images: Since YOLOv8 handles flexible input sizes and performs resizing automatically, manual resizing is not required. The model will adjust the image size according to the specified 'imgsz' parameter during training.
 - Normalizing Pixel Values: YOLOv8 automatically normalizes pixel values to a range of 0 to 1 during preprocessing, so it's not required.
-- Splitting the Dataset: Divide the dataset into training (70%), validation (20%), and test (10%) sets using tools like scikit-learn. 
+- Splitting the Dataset: Divide the dataset into training (70%), validation (20%), and test (10%) sets using tools like scikit-learn.
 - Data Augmentation: Modify the dataset configuration file (.yaml) to include data augmentation techniques such as random crops, horizontal flips, and brightness adjustments.
 
 These steps make sure the dataset is prepared without any potential issues and is ready for Exploratory Data Analysis (EDA).
@@ -104,11 +104,11 @@ These steps make sure the dataset is prepared without any potential issues and i
 
 After preprocessing and augmenting your dataset, the next step is to gain insights through Exploratory Data Analysis. EDA uses statistical techniques and visualization tools to understand the patterns and distributions in your data. You can identify issues like class imbalances or outliers and make informed decisions about further data preprocessing or model training adjustments.
 
-### Statistical EDA Techniques 
+### Statistical EDA Techniques
 
 Statistical techniques often begin with calculating basic metrics such as mean, median, standard deviation, and range. These metrics provide a quick overview of your image dataset's properties, such as pixel intensity distributions. Understanding these basic statistics helps you grasp the overall quality and characteristics of your data, allowing you to spot any irregularities early on.
 
-### Visual EDA Techniques 
+### Visual EDA Techniques
 
 Visualizations are key in EDA for image datasets. For example, class imbalance analysis is another vital aspect of EDA. It helps determine if certain classes are underrepresented in your dataset, Visualizing the distribution of different image classes or categories using bar charts can quickly reveal any imbalances. Similarly, outliers can be identified using visualization tools like box plots, which highlight anomalies in pixel intensity or feature distributions. Outlier detection prevents unusual data points from skewing your results.
 
@@ -131,9 +131,11 @@ For a more advanced approach to EDA, you can use the Ultralytics Explorer tool.
 Here are some questions that might come up while you prepare your dataset:
 
 - **Q1:** How much preprocessing is too much?
+
     - **A1:** Preprocessing is essential but should be balanced. Overdoing it can lead to loss of critical information, overfitting, increased complexity, and higher computational costs. Focus on necessary steps like resizing, normalization, and basic augmentation, adjusting based on model performance.
 
 - **Q2:** What are the common pitfalls in EDA?
+
     - **A2:** Common pitfalls in Exploratory Data Analysis (EDA) include ignoring data quality issues like missing values and outliers, confirmation bias, overfitting visualizations, neglecting data distribution, and overlooking correlations. A systematic approach helps gain accurate and valuable insights.
 
 ## Reach Out and Connect