Update docs metadata (#3781)

docs/usage/callbacks.md

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-description: Learn how to leverage callbacks in Ultralytics YOLO framework to perform custom tasks in trainer, validator, predictor and exporter modes.
-keywords: callbacks, Ultralytics framework, Trainer, Validator, Predictor, Exporter, train, val, export, predict, YOLO, Object Detection
+description: Learn how to utilize callbacks in the Ultralytics framework during train, val, export, and predict modes for enhanced functionality.
+keywords: Ultralytics, YOLO, callbacks guide, training callback, validation callback, export callback, prediction callback
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 ## Callbacks

docs/usage/cfg.md

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-description: Learn about YOLO settings and modes for different tasks like detection, segmentation etc. Train and predict with custom argparse commands.
-keywords: YOLO settings, hyperparameters, YOLOv8, Ultralytics, YOLO guide, YOLO commands, YOLO tasks, YOLO modes, YOLO training, YOLO detect, YOLO segment, YOLO classify, YOLO pose, YOLO train, YOLO val, YOLO predict, YOLO export, YOLO track, YOLO benchmark
+description: Master YOLOv8 settings and hyperparameters for improved model performance. Learn to use YOLO CLI commands, adjust training settings, and optimize YOLO tasks & modes.
+keywords: YOLOv8, settings, hyperparameters, YOLO CLI commands, YOLO tasks, YOLO modes, Ultralytics documentation, model optimization, YOLOv8 training
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 YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings

docs/usage/cli.md

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-description: Learn how to use YOLOv8 from the Command Line Interface (CLI) through simple, single-line commands with `yolo` without Python code.
-keywords: YOLO, CLI, command line interface, detect, segment, classify, train, validate, predict, export, Ultralytics Docs
+description: 'Learn how to use Ultralytics YOLO through Command Line: train models, run predictions and exports models to different formats easily using terminal commands.'
+keywords: Ultralytics, YOLO, CLI, train, validation, prediction, command line interface, YOLO CLI, YOLO terminal, model training, prediction, exporting
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 # Command Line Interface Usage

docs/usage/engine.md

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-description: Learn how to train and customize your models fast with the Ultralytics YOLO 'DetectionTrainer' and 'CustomTrainer'. Read more here!
-keywords: Ultralytics, YOLO, DetectionTrainer, BaseTrainer, engine components, trainers, customizing, callbacks, validators, predictors
+description: Discover how to customize and extend base Ultralytics YOLO Trainer engines. Support your custom model and dataloader by overriding built-in functions.
+keywords: Ultralytics, YOLO, trainer engines, BaseTrainer, DetectionTrainer, customizing trainers, extending trainers, custom model, custom dataloader
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 Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine

docs/usage/hyperparameter_tuning.md

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-description: Learn to integrate hyperparameter tuning using Ray Tune with Ultralytics YOLOv8, and optimize your model's performance efficiently.
-keywords: yolov8, ray tune, hyperparameter tuning, hyperparameter optimization, machine learning, computer vision, deep learning, image recognition
+description: Discover how to streamline hyperparameter tuning for YOLOv8 models with Ray Tune. Learn to accelerate tuning, integrate with Weights & Biases, and analyze results.
+keywords: Ultralytics, YOLOv8, Ray Tune, hyperparameter tuning, machine learning optimization, Weights & Biases integration, result analysis
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 # Efficient Hyperparameter Tuning with Ray Tune and YOLOv8
@@ -166,4 +166,4 @@ plt.show()
 
 In this documentation, we covered common workflows to analyze the results of experiments run with Ray Tune using Ultralytics. The key steps include loading the experiment results from a directory, performing basic experiment-level and trial-level analysis and plotting metrics.
 
-Explore further by looking into Ray Tune’s [Analyze Results](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html) docs page to get the most out of your hyperparameter tuning experiments.
+Explore further by looking into Ray Tune’s [Analyze Results](https://docs.ray.io/en/latest/tune/examples/tune_analyze_results.html) docs page to get the most out of your hyperparameter tuning experiments.

docs/usage/python.md

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-description: Integrate YOLOv8 in Python. Load, use pretrained models, train, and infer images. Export to ONNX. Track objects in videos.
-keywords: yolov8, python usage, object detection, segmentation, classification, pretrained models, train models, image predictions
+description: Boost your Python projects with object detection, segmentation and classification using YOLOv8. Explore how to load, train, validate, predict, export, track and benchmark models with ease.
+keywords: YOLOv8, Ultralytics, Python, object detection, segmentation, classification, model training, validation, prediction, model export, benchmark, real-time tracking
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 # Python Usage