Docs updates for HUB, YOLOv4, YOLOv7, NAS (#3174)

Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/usage/callbacks.md

@@ -1,6 +1,7 @@
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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
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 ## Callbacks

docs/usage/cfg.md

@@ -1,6 +1,7 @@
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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.'
+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
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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

@@ -1,6 +1,7 @@
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 comments: true
 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
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 # Command Line Interface Usage

docs/usage/engine.md

@@ -1,6 +1,7 @@
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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
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 Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine
@@ -83,4 +84,4 @@ To know more about Callback triggering events and entry point, checkout our [Cal
 ## Other engine components
 
 There are other components that can be customized similarly like `Validators` and `Predictors`
-See Reference section for more information on these.
+See Reference section for more information on these.

docs/usage/hyperparameter_tuning.md

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 description: Discover how to integrate hyperparameter tuning with Ray Tune and Ultralytics YOLOv8. Speed up the tuning process and optimize your model's performance.
+keywords: yolov8, ray tune, hyperparameter tuning, hyperparameter optimization, machine learning, computer vision, deep learning, image recognition
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 # Hyperparameter Tuning with Ray Tune and YOLOv8

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
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 # Python Usage