Update docs metadata (#3781)

docs/yolov5/environments/aws_quickstart_tutorial.md

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-description: Get started with YOLOv5 on AWS. Our comprehensive guide provides everything you need to know to run YOLOv5 on an Amazon Deep Learning instance.
-keywords: YOLOv5, AWS, Deep Learning, Instance, Guide, Quickstart
+description: Step-by-step guide to run YOLOv5 on AWS Deep Learning instance. Learn how to create an instance, connect to it and train, validate and deploy models.
+keywords: AWS, YOLOv5, instance, deep learning, Ultralytics, guide, training, deployment, object detection
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 # YOLOv5 🚀 on AWS Deep Learning Instance: A Comprehensive Guide

docs/yolov5/environments/docker_image_quickstart_tutorial.md

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-description: Get started with YOLOv5 in a Docker container. Learn to set up and run YOLOv5 models and explore other quickstart options. 🚀
-keywords: YOLOv5, Docker, tutorial, setup, training, testing, detection
+description: Learn how to set up and run YOLOv5 in a Docker container. This tutorial includes the prerequisites and step-by-step instructions.
+keywords: YOLOv5, Docker, Ultralytics, Image Detection, YOLOv5 Docker Image, Docker Container, Machine Learning, AI
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 # Get Started with YOLOv5 🚀 in Docker

docs/yolov5/environments/google_cloud_quickstart_tutorial.md

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-description: Set up YOLOv5 on a Google Cloud Platform (GCP) Deep Learning VM. Train, test, detect, and export YOLOv5 models. Tutorial updated April 2023.
-keywords: YOLOv5, GCP, deep learning, tutorial, Google Cloud Platform, virtual machine, VM, setup, free credit, Colab Notebook, AWS, Docker
+description: Step-by-step tutorial on how to set up and run YOLOv5 on Google Cloud Platform Deep Learning VM. Perfect guide for beginners and GCP new users!.
+keywords: YOLOv5, Google Cloud Platform, GCP, Deep Learning VM, Ultralytics
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 # Run YOLOv5 🚀 on Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐

docs/yolov5/index.md

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-description: Explore the extensive functionalities of the YOLOv5 object detection model, renowned for its speed and precision. Dive into our comprehensive guide for installation, architectural insights, use-cases, and more to unlock the full potential of YOLOv5 for your computer vision applications.
-keywords: ultralytics, yolov5, object detection, deep learning, pytorch, computer vision, tutorial, architecture, documentation, frameworks, real-time, model training, multicore, multithreading
+description: Deep dive into Ultralytics' YOLOv5. Learn about object detection model - YOLOv5, how to train it on custom data, multi-GPU training and more.
+keywords: Ultralytics, YOLOv5, Deep Learning, Object detection, PyTorch, Tutorial, Multi-GPU training, Custom data training
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 # Comprehensive Guide to Ultralytics YOLOv5

docs/yolov5/quickstart_tutorial.md

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-description: Learn how to quickly start using YOLOv5 including installation, inference, and training on this Ultralytics Docs page.
-keywords: YOLOv5, object detection, PyTorch, quickstart, detect.py, training, Ultralytics Docs
+description: Kickstart your journey with YOLOv5. Learn how to install, run inference, and train models on your own images. Dive headfirst into object detection with PyTorch.
+keywords: YOLOv5, Quickstart, Installation, Inference, Training, Object detection, PyTorch, Ultralytics
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 # YOLOv5 Quickstart

docs/yolov5/tutorials/architecture_description.md

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-description: Explore the details of Ultralytics YOLOv5 architecture, a comprehensive guide to its model structure, data augmentation techniques, training strategies, and various features. Understand the intricacies of object detection algorithms and improve your skills in the machine learning field.
-keywords: yolov5 architecture, data augmentation, training strategies, object detection, yolo docs, ultralytics
+description: Explore the architecture of YOLOv5, an object detection algorithm by Ultralytics. Understand the model structure, data augmentation methods, training strategies, and loss computation techniques.
+keywords: Ultralytics, YOLOv5, Object Detection, Architecture, Model Structure, Data Augmentation, Training Strategies, Loss Computation
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 # Ultralytics YOLOv5 Architecture

docs/yolov5/tutorials/clearml_logging_integration.md

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-description: Integrate ClearML with YOLOv5 to track experiments and manage data versions. Optimize hyperparameters and remotely monitor your runs.
-keywords: YOLOv5, ClearML, experiment manager, remotely train, monitor, hyperparameter optimization, data versioning tool, HPO, data version management, optimization locally, agent, training progress, custom YOLOv5, AI development, model building
+description: Learn how ClearML can enhance your YOLOv5 pipeline – track your training runs, version your data, remotely monitor your models and optimize performance.
+keywords: ClearML, YOLOv5, Ultralytics, AI toolbox, training data, remote training, hyperparameter optimization, YOLOv5 model
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 # ClearML Integration

docs/yolov5/tutorials/comet_logging_integration.md

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-description: Learn how to use YOLOv5 with Comet, a tool for logging and visualizing machine learning model metrics in real-time. Install, log and analyze seamlessly.
-keywords: object detection, YOLOv5, Comet, model metrics, deep learning, image classification, Colab notebook, machine learning, datasets, hyperparameters tracking, training script, checkpoint
+description: Learn how to set up and use Comet to enhance your YOLOv5 model training, metrics tracking and visualization. Includes a step by step guide to integrate Comet with YOLOv5.
+keywords: YOLOv5, Comet, Machine Learning, Ultralytics, Real time metrics tracking, Hyperparameters, Model checkpoints, Model predictions, YOLOv5 training, Comet Credentials
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 <img src="https://cdn.comet.ml/img/notebook_logo.png">

docs/yolov5/tutorials/hyperparameter_evolution.md

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-description: Learn to find optimum YOLOv5 hyperparameters via **evolution**. A guide to learn hyperparameter tuning with Genetic Algorithms.
-keywords: YOLOv5, Hyperparameter Evolution, Genetic Algorithm, Hyperparameter Optimization, Fitness, Evolve, Visualize
+description: Learn how to optimize YOLOv5 with hyperparameter evolution using Genetic Algorithm. This guide provides steps to initialize, define, evolve and visualize hyperparameters for top performance.
+keywords: Ultralytics, YOLOv5, Hyperparameter Optimization, Genetic Algorithm, Machine Learning, Deep Learning, AI, Object Detection, Image Classification, Python
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 📚 This guide explains **hyperparameter evolution** for YOLOv5 🚀. Hyperparameter evolution is a method of [Hyperparameter Optimization](https://en.wikipedia.org/wiki/Hyperparameter_optimization) using a [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) (GA) for optimization. UPDATED 25 September 2022.

docs/yolov5/tutorials/model_export.md

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-description: Export YOLOv5 models to TFLite, ONNX, CoreML, and TensorRT formats. Achieve up to 5x GPU speedup using TensorRT. Benchmarks included.
-keywords: YOLOv5, object detection, export, ONNX, CoreML, TensorFlow, TensorRT, OpenVINO
+description: Learn how to export a trained YOLOv5 model from PyTorch to different formats including TorchScript, ONNX, OpenVINO, TensorRT, and CoreML, and how to use these models.
+keywords: Ultralytics, YOLOv5, model export, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TensorFlow
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 # TFLite, ONNX, CoreML, TensorRT Export

docs/yolov5/tutorials/model_pruning_and_sparsity.md

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-description: Learn how to apply pruning to your YOLOv5 models. See the before and after performance with an explanation of sparsity and more.
-keywords: YOLOv5, ultralytics, pruning, deep learning, computer vision, object detection, AI, tutorial
+description: Improve YOLOv5 model efficiency by pruning with Ultralytics. Understand the process, conduct tests and view the impact on accuracy and sparsity. Test-maintained API environments.
+keywords: YOLOv5, YOLO, Ultralytics, model pruning, PyTorch, machine learning, deep learning, computer vision, object detection
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 📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models.  

docs/yolov5/tutorials/multi_gpu_training.md

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-description: Learn how to train your dataset on single or multiple machines using YOLOv5 on multiple GPUs. Use simple commands with DDP mode for faster performance.
-keywords: ultralytics, yolo, yolov5, multi-gpu, training, dataset, dataloader, data parallel, distributed data parallel, docker, pytorch
+description: Learn how to train datasets on single or multiple GPUs using YOLOv5. Includes setup, training modes and result profiling for efficient leveraging of multiple GPUs.
+keywords: YOLOv5, multi-GPU Training, YOLOv5 training, deep learning, machine learning, object detection, Ultralytics
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 📚 This guide explains how to properly use **multiple** GPUs to train a dataset with YOLOv5 🚀 on single or multiple machine(s).  

docs/yolov5/tutorials/neural_magic_pruning_quantization.md

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-description: Learn how to deploy YOLOv5 with DeepSparse to achieve exceptional CPU performance close to GPUs, using pruning, and quantization.<br>
-keywords: YOLOv5, DeepSparse, Neural Magic, CPU, Production, Performance, Deployments, APIs, SparseZoo, Ultralytics, Model Sparsity, Inference, Open-source, ONNX, Server
+description: Explore how to achieve exceptional AI performance with DeepSparse's incredible inference speed. Discover how to deploy YOLOv5, and learn about model sparsification and fine-tuning with SparseML.
+keywords: YOLOv5, DeepSparse, Ultralytics, Neural Magic, sparsification, inference runtime, deep learning, deployment, model fine-tuning, SparseML, AI performance, GPU-class performance
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 <!--

docs/yolov5/tutorials/pytorch_hub_model_loading.md

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-description: Learn how to load YOLOv5🚀 from PyTorch Hub at https://pytorch.org/hub/ultralytics_yolov5 and perform image inference. UPDATED 26 March 2023.
-keywords: YOLOv5, PyTorch Hub, object detection, computer vision, machine learning, artificial intelligence
+description: Detailed guide on loading YOLOv5 from PyTorch Hub. Includes examples & tips on inference settings, multi-GPU inference, training and more.
+keywords: Ultralytics, YOLOv5, PyTorch, loading YOLOv5, PyTorch Hub, inference, multi-GPU inference, training
 ---
 
 📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).  

docs/yolov5/tutorials/roboflow_datasets_integration.md

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-description: Use Roboflow to organize, label, prepare, version & host datasets for training YOLOv5 models. Upload via UI, API, or Python, making versions with custom preprocessing and offline augmentation. Export in YOLOv5 format and access custom training tutorials. Use active learning to improve model deployments.
-keywords: YOLOv5, Roboflow, Dataset, Labeling, Versioning, Darknet, Export, Python, Upload, Active Learning, Preprocessing
+description: Learn how to use Roboflow for organizing, labelling, preparing, and hosting your datasets for YOLOv5 models. Enhance your model deployments with our platform.
+keywords: Ultralytics, YOLOv5, Roboflow, data organization, data labelling, data preparation, model deployment, active learning, machine learning pipeline
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 # Roboflow Datasets

docs/yolov5/tutorials/running_on_jetson_nano.md

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-description: Deploy YOLOv5 on NVIDIA Jetson using TensorRT and DeepStream SDK for high performance inference. Step-by-step guide with code snippets.
-keywords: YOLOv5, NVIDIA Jetson, TensorRT, DeepStream SDK, deployment, AI at edge, PyTorch, computer vision, object detection, CUDA
+description: Detailed guide on deploying trained models on NVIDIA Jetson using TensorRT and DeepStream SDK. Optimize the inference performance on Jetson with Ultralytics.
+keywords: TensorRT, NVIDIA Jetson, DeepStream SDK, deployment, Ultralytics, YOLO, Machine Learning, AI, Deep Learning, model optimization, inference performance
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 # Deploy on NVIDIA Jetson using TensorRT and DeepStream SDK

docs/yolov5/tutorials/test_time_augmentation.md

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-description: Learn how to use Test Time Augmentation (TTA) with YOLOv5 to improve mAP and Recall during testing and inference. Code examples included.
-keywords: YOLOv5, test time augmentation, TTA, mAP, recall, object detection, deep learning, computer vision, PyTorch
+description: Boost your YOLOv5 performance with our step-by-step guide on Test-Time Augmentation (TTA). Learn to enhance your model's mAP and Recall during testing and inference.
+keywords: YOLOv5, Ultralytics, Test-Time Augmentation, TTA, mAP, Recall, model performance, guide
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 # Test-Time Augmentation (TTA)

docs/yolov5/tutorials/tips_for_best_training_results.md

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-description: Get the most out of YOLOv5 with this guide; producing best results, checking dataset, hypertuning & more. Updated May 2022.
-keywords: YOLOv5 training guide, mAP, best results, dataset, model selection, training settings, hyperparameters, Ultralytics Docs
+description: Our comprehensive guide provides insights on how to train your YOLOv5 system to get the best mAP. Master dataset preparation, model selection, training settings, and more.
+keywords: Ultralytics, YOLOv5, Training guide, dataset preparation, model selection, training settings, mAP results, Machine Learning, Object Detection
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 📚 This guide explains how to produce the best mAP and training results with YOLOv5 🚀.  

docs/yolov5/tutorials/train_custom_data.md

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-description: Train your custom dataset with YOLOv5. Learn to collect, label and annotate images, and train and deploy models. Get started now.
-keywords: YOLOv5, train custom dataset, object detection, artificial intelligence, deep learning, computer vision
+description: Learn how to train your data on custom datasets using YOLOv5. Simple and updated guide on collection and organization of images, labelling, model training and deployment.
+keywords: YOLOv5, train on custom dataset, image collection, model training, object detection, image labelling, Ultralytics, PyTorch, machine learning
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 📚 This guide explains how to train your own **custom dataset** with [YOLOv5](https://github.com/ultralytics/yolov5) 🚀.  

docs/yolov5/tutorials/transfer_learning_with_frozen_layers.md

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-description: Learn how to freeze YOLOv5 when transfer learning. Retrain a pre-trained model on new data faster and with fewer resources.
-keywords: Freeze YOLOv5, Transfer Learning YOLOv5, Freeze Layers, Reduce Resources, Speed up Training, Increase Accuracy
+description: Learn to freeze YOLOv5 layers for efficient transfer learning. Optimize your model retraining with less resources and faster training times.
+keywords: YOLOv5, freeze layers, transfer learning, model retraining, Ultralytics
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 📚 This guide explains how to **freeze** YOLOv5 🚀 layers when **transfer learning**. Transfer learning is a useful way to quickly retrain a model on new data without having to retrain the entire network. Instead, part of the initial weights are frozen in place, and the rest of the weights are used to compute loss and are updated by the optimizer. This requires less resources than normal training and allows for faster training times, though it may also result in reductions to final trained accuracy.