Update HUB SDK Docs (#13309)

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

docs/en/yolov5/tutorials/architecture_description.md

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-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
+description: Dive deep into the powerful YOLOv5 architecture by Ultralytics, exploring its model structure, data augmentation techniques, training strategies, and loss computations.
+keywords: YOLOv5 architecture, object detection, Ultralytics, YOLO, model structure, data augmentation, training strategies, loss computations, deep learning, machine learning
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 # Ultralytics YOLOv5 Architecture

docs/en/yolov5/tutorials/clearml_logging_integration.md

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-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
+description: Learn how to use ClearML for tracking YOLOv5 experiments, data versioning, hyperparameter optimization, and remote execution with ease.
+keywords: ClearML, YOLOv5, machine learning, experiment tracking, data versioning, hyperparameter optimization, remote execution, ML pipeline
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 # ClearML Integration

docs/en/yolov5/tutorials/comet_logging_integration.md

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-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
+description: Learn to track, visualize and optimize YOLOv5 model metrics with Comet for seamless machine learning workflows.
+keywords: YOLOv5, Comet, machine learning, model tracking, hyperparameters, visualization, deep learning, logging, metrics
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 ![Comet](https://cdn.comet.ml/img/notebook_logo.png)

docs/en/yolov5/tutorials/hyperparameter_evolution.md

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-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
+description: Learn how to optimize YOLOv5 hyperparameters using genetic algorithms for improved training performance. Step-by-step instructions included.
+keywords: YOLOv5, hyperparameter evolution, genetic algorithms, machine learning, optimization, Ultralytics
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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.

docs/en/yolov5/tutorials/model_ensembling.md

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-description: Learn how to ensemble YOLOv5 models for improved mAP and Recall! Clone the repo, install requirements, and start testing and inference.
-keywords: YOLOv5, object detection, ensemble learning, mAP, Recall
+description: Learn how to use YOLOv5 model ensembling during testing and inference to enhance mAP and Recall for more accurate predictions.
+keywords: YOLOv5, model ensembling, testing, inference, mAP, Recall, Ultralytics, object detection, PyTorch
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 📚 This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and Recall.

docs/en/yolov5/tutorials/model_export.md

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-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
+description: Learn to export YOLOv5 models to various formats like TFLite, ONNX, CoreML and TensorRT. Increase model efficiency and deployment flexibility with our step-by-step guide.
+keywords: YOLOv5 export, TFLite, ONNX, CoreML, TensorRT, model conversion, YOLOv5 tutorial, PyTorch export
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 # TFLite, ONNX, CoreML, TensorRT Export

docs/en/yolov5/tutorials/model_pruning_and_sparsity.md

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-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
+description: Learn how to prune YOLOv5 models for improved performance. Follow this step-by-step guide to optimize your YOLOv5 models effectively.
+keywords: YOLOv5 pruning, model pruning, YOLOv5 optimization, YOLOv5 guide, machine learning pruning
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 📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models.

docs/en/yolov5/tutorials/multi_gpu_training.md

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-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
+description: Learn how to train YOLOv5 on multiple GPUs for optimal performance. Guide covers single and multiple machine setups.
+keywords: YOLOv5, multiple GPUs, machine learning, deep learning, PyTorch, data parallel, distributed data parallel
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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/en/yolov5/tutorials/neural_magic_pruning_quantization.md

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-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
+description: Learn how to deploy YOLOv5 using Neural Magic's DeepSparse for GPU-class performance on CPUs. Discover easy integration, flexible deployments, and more.
+keywords: YOLOv5, DeepSparse, Neural Magic, YOLO deployment, Sparse inference, Deep learning, Model sparsity, CPU optimization, No hardware accelerators, AI deployment
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 <!--

docs/en/yolov5/tutorials/pytorch_hub_model_loading.md

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-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
+description: Learn how to load YOLOv5 from PyTorch Hub for seamless model inference and customization. Follow our step-by-step guide at Ultralytics Docs.
+keywords: YOLOv5, PyTorch Hub, model loading, Ultralytics, object detection, machine learning, AI, tutorial, inference
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 📚 This guide explains how to load YOLOv5 🚀 from PyTorch Hub at [https://pytorch.org/hub/ultralytics_yolov5](https://pytorch.org/hub/ultralytics_yolov5).

docs/en/yolov5/tutorials/roboflow_datasets_integration.md

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-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
+description: Learn how to use Roboflow for organizing, labeling, and versioning datasets to train YOLOv5 models. Free for public workspaces.
+keywords: Roboflow, YOLOv5, data management, dataset labeling, dataset versioning, Ultralytics, machine learning, AI training
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 # Roboflow Datasets

docs/en/yolov5/tutorials/running_on_jetson_nano.md

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-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
+description: Learn how to deploy models on NVIDIA Jetson using TensorRT and DeepStream SDK. Follow our step-by-step guide for optimized AI inference.
+keywords: NVIDIA Jetson, TensorRT, DeepStream SDK, AI deployment, Jetson Nano, Jetson Xavier NX, YOLOv5, AI inference, Ultralytics
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 # Deploy on NVIDIA Jetson using TensorRT and DeepStream SDK

docs/en/yolov5/tutorials/test_time_augmentation.md

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-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
+description: Boost your YOLOv5 performance with Test-Time Augmentation (TTA). Learn setup, testing, and inference techniques to elevate mAP and Recall.
+keywords: YOLOv5, Test-Time Augmentation, TTA, machine learning, deep learning, object detection, mAP, Recall, PyTorch
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 # Test-Time Augmentation (TTA)

docs/en/yolov5/tutorials/tips_for_best_training_results.md

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-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
+description: Discover how to achieve optimal mAP and training results using YOLOv5. Learn essential dataset, model selection, and training settings best practices.
+keywords: YOLOv5 training, mAP, dataset best practices, model selection, training settings, YOLOv5 guide, YOLOv5 tutorial, machine learning
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 📚 This guide explains how to produce the best mAP and training results with YOLOv5 🚀.

docs/en/yolov5/tutorials/train_custom_data.md

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-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
+description: Learn how to train YOLOv5 on your own custom datasets with easy-to-follow steps. Detailed guide on dataset preparation, model selection, and training process.
+keywords: YOLOv5, custom dataset, model training, object detection, machine learning, AI, YOLO model, PyTorch, dataset preparation
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 📚 This guide explains how to train your own **custom dataset** with [YOLOv5](https://github.com/ultralytics/yolov5) 🚀.

docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md

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-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
+description: Learn to freeze YOLOv5 layers for efficient transfer learning, reducing resources and speeding up training while maintaining accuracy.
+keywords: YOLOv5, transfer learning, freeze layers, machine learning, deep learning, model training, PyTorch, 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.