Update HUB SDK Docs (#13309)

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

docs/en/integrations/amazon-sagemaker.md

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-description: Learn how to deploy YOLOv8 models on Amazon SageMaker Endpoints. This guide covers the essentials of AWS environment setup, model preparation, and deployment using AWS CloudFormation and the AWS Cloud Development Kit (CDK).
-keywords: YOLOv8, Amazon SageMaker, deploy YOLOv8, AWS deployment, machine learning, real-time inference, AWS CloudFormation, AWS CDK
+description: Learn step-by-step how to deploy Ultralytics' YOLOv8 on Amazon SageMaker Endpoints, from setup to testing, for powerful real-time inference with AWS services.
+keywords: YOLOv8, Amazon SageMaker, AWS, Ultralytics, machine learning, computer vision, model deployment, AWS CloudFormation, AWS CDK, real-time inference
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 # A Guide to Deploying YOLOv8 on Amazon SageMaker Endpoints

docs/en/integrations/clearml.md

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-description: Learn how to streamline and optimize your YOLOv8 model training with ClearML. This guide provides insights into integrating ClearML's MLOps tools for efficient model training, from initial setup to advanced experiment tracking and model management.
-keywords: Ultralytics, YOLOv8, Object Detection, ClearML, Model Training, MLOps, Experiment Tracking, Workflow Optimization
+description: Discover how to integrate YOLOv8 with ClearML to streamline your MLOps workflow, automate experiments, and enhance model management effortlessly.
+keywords: YOLOv8, ClearML, MLOps, Ultralytics, machine learning, object detection, model training, automation, experiment management
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 # Training YOLOv8 with ClearML: Streamlining Your MLOps Workflow

docs/en/integrations/comet.md

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-description: Discover how to track and enhance YOLOv8 model training with Comet ML's logging tools, from setup to monitoring key metrics and managing experiments for in-depth analysis.
-keywords: Ultralytics, YOLOv8, Object Detection, Comet ML, Model Training, Model Metrics Logging, Experiment Tracking, Offline Experiment Management
+description: Learn to simplify the logging of YOLOv8 training with Comet ML. This guide covers installation, setup, real-time insights, and custom logging.
+keywords: YOLOv8, Comet ML, logging, machine learning, training, model checkpoints, metrics, installation, configuration, real-time insights, custom logging
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 # Elevating YOLOv8 Training: Simplify Your Logging Process with Comet ML

docs/en/integrations/coreml.md

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-description: Explore the process of exporting Ultralytics YOLOv8 models to CoreML format, enabling efficient object detection capabilities for iOS and macOS applications on Apple devices.
-keywords: Ultralytics, YOLOv8, CoreML Export, Model Deployment, Apple Devices, Object Detection, Machine Learning
+description: Learn how to export YOLOv8 models to CoreML for optimized, on-device machine learning on iOS and macOS. Follow step-by-step instructions.
+keywords: CoreML export, YOLOv8 models, CoreML conversion, Ultralytics, iOS object detection, macOS machine learning, AI deployment, machine learning integration
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 # CoreML Export for YOLOv8 Models

docs/en/integrations/dvc.md

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-description: This guide provides a step-by-step approach to integrating DVCLive with Ultralytics YOLOv8 for advanced experiment tracking. Learn how to set up your environment, run experiments with varied configurations, and analyze results using DVCLive's powerful tracking and visualization tools.
-keywords: DVCLive, Ultralytics, YOLOv8, Machine Learning, Experiment Tracking, Data Version Control, ML Workflows, Model Training, Hyperparameter Tuning
+description: Unlock seamless YOLOv8 tracking with DVCLive. Discover how to log, visualize, and analyze experiments for optimized ML model performance.
+keywords: YOLOv8, DVCLive, experiment tracking, machine learning, model training, data visualization, Git integration
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 # Advanced YOLOv8 Experiment Tracking with DVCLive

docs/en/integrations/edge-tpu.md

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-description: Discover how to uplift your Ultralytics YOLOv8 model's overall performance with the TFLite Edge TPU export format, which is perfect for mobile and embedded devices.
-keywords: Ultralytics, YOLOv8, TFLite edge TPU format, Export YOLOv8, Model Deployment, Flexible Deployment
+description: Learn how to export YOLOv8 models to TFLite Edge TPU format for high-speed, low-power inferencing on mobile and embedded devices.
+keywords: YOLOv8, TFLite Edge TPU, TensorFlow Lite, model export, machine learning, edge computing, neural networks, Ultralytics
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 # Learn to Export to TFLite Edge TPU Format From YOLOv8 Model

docs/en/integrations/google-colab.md

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-description: A guide on how to train Ultralytics YOLOv8 models quickly, perform data processing directly in your web browser, and collaborate with others using Google Colab.
-keywords: Ultralytics YOLOv8, Google Colab, CPU, GPU, TPU, Browser-based, Hardware Acceleration, Machine Learning, Google Colaboratory
+description: Learn how to efficiently train Ultralytics YOLOv8 models using Google Colab's powerful cloud-based environment. Start your project with ease.
+keywords: YOLOv8, Google Colab, machine learning, deep learning, model training, GPU, TPU, cloud computing, Jupyter Notebook, Ultralytics
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 # Accelerating YOLOv8 Projects with Google Colab

docs/en/integrations/gradio.md

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-description: Learn to use Gradio and Ultralytics YOLOv8 for interactive object detection. Upload images and adjust detection parameters in real-time.
-keywords: Gradio, Ultralytics YOLOv8, object detection, interactive AI, Python
+description: Discover an interactive way to perform object detection with Ultralytics YOLOv8 using Gradio. Upload images and adjust settings for real-time results.
+keywords: Ultralytics, YOLOv8, Gradio, object detection, interactive, real-time, image processing, AI
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 # Interactive Object Detection: Gradio & Ultralytics YOLOv8 🚀

docs/en/integrations/index.md

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-description: Explore Ultralytics integrations with tools for dataset management, model optimization, ML workflows automation, experiment tracking, version control, and more. Learn about our support for various model export formats for deployment.
-keywords: Ultralytics integrations, Roboflow, Neural Magic, ClearML, Comet ML, DVC, Ultralytics HUB, MLFlow, Neptune, Ray Tune, TensorBoard, W&B, model export formats, PyTorch, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TF Lite, TF Edge TPU, TF.js, PaddlePaddle, NCNN
+description: Discover Ultralytics integrations for streamlined ML workflows, dataset management, optimized model training, and robust deployment solutions.
+keywords: Ultralytics, machine learning, ML workflows, dataset management, model training, model deployment, Roboflow, ClearML, Comet ML, DVC, MLFlow, Ultralytics HUB, Neptune, Ray Tune, TensorBoard, Weights & Biases, Amazon SageMaker, Paperspace Gradient, Google Colab, Neural Magic, Gradio, TorchScript, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, TF GraphDef, TFLite, TFLite Edge TPU, TF.js, PaddlePaddle, NCNN
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 # Ultralytics Integrations

docs/en/integrations/mlflow.md

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-description: Uncover the utility of MLflow for effective experiment logging in your Ultralytics YOLO projects.
-keywords: ultralytics docs, YOLO, MLflow, experiment logging, metrics tracking, parameter logging, artifact logging
+description: Learn how to set up and use MLflow logging with Ultralytics YOLO for enhanced experiment tracking, model reproducibility, and performance improvements.
+keywords: MLflow, Ultralytics YOLO, machine learning, experiment tracking, metrics logging, parameter logging, artifact logging
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 # MLflow Integration for Ultralytics YOLO

docs/en/integrations/ncnn.md

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-description: Uncover how to improve your Ultralytics YOLOv8 model's performance using the NCNN export format that is suitable for devices with limited computation resources.
-keywords: Ultralytics, YOLOv8, NCNN Export, Export YOLOv8, Model Deployment
+description: Optimize YOLOv8 models for mobile and embedded devices by exporting to NCNN format. Enhance performance in resource-constrained environments.
+keywords: Ultralytics, YOLOv8, NCNN, model export, machine learning, deployment, mobile, embedded systems, deep learning, AI models
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 # How to Export to NCNN from YOLOv8 for Smooth Deployment

docs/en/integrations/neural-magic.md

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-description: Learn how to deploy your YOLOv8 models rapidly using Neural Magic's DeepSparse. This guide focuses on integrating Ultralytics YOLOv8 with the DeepSparse Engine for high-speed, CPU-based inference, leveraging advanced neural network sparsity techniques.
-keywords: YOLOv8, DeepSparse Engine, Ultralytics, CPU Inference, Neural Network Sparsity, Object Detection, Model Optimization
+description: Enhance YOLOv8 performance using Neural Magic's DeepSparse Engine. Learn how to deploy and benchmark YOLOv8 models on CPUs for efficient object detection.
+keywords: YOLOv8, DeepSparse, Neural Magic, model optimization, object detection, inference speed, CPU performance, sparsity, pruning, quantization
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 # Optimizing YOLOv8 Inferences with Neural Magic's DeepSparse Engine

docs/en/integrations/onnx.md

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-description: Explore how to improve your Ultralytics YOLOv8 model's performance and interoperability using the ONNX (Open Neural Network Exchange) export format that is suitable for diverse hardware and software environments.
-keywords: Ultralytics, YOLOv8, ONNX Format, Export YOLOv8, CUDA Support, Model Deployment
+description: Learn how to export YOLOv8 models to ONNX format for flexible deployment across various platforms with enhanced performance.
+keywords: YOLOv8, ONNX, model export, Ultralytics, ONNX Runtime, machine learning, model deployment, computer vision, deep learning
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 # ONNX Export for YOLOv8 Models

docs/en/integrations/openvino.md

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-description: Discover the power of deploying your Ultralytics YOLOv8 model using OpenVINO format for up to 10x speedup vs PyTorch.
-keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, OpenVINO, OpenVINO format
+description: Learn to export YOLOv8 models to OpenVINO format for up to 3x CPU speedup and hardware acceleration on Intel GPU and NPU.
+keywords: YOLOv8, OpenVINO, model export, Intel, AI inference, CPU speedup, GPU acceleration, NPU, deep learning
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 # Intel OpenVINO Export

docs/en/integrations/paddlepaddle.md

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-description: This guide explains how to export Ultralytics YOLOv8 models to the PaddlePaddle format for wide device support and harnessing the power of Baidu's ML framework.
-keywords: Ultralytics, YOLOv8, PaddlePaddle Export, Model Deployment, Flexible Deployment,  Industrial-Grade Deep Learning, Baidu, Cross-Platform Compatibility
+description: Learn how to export YOLOv8 models to PaddlePaddle format for enhanced performance, flexibility, and deployment across various platforms and devices.
+keywords: YOLOv8, PaddlePaddle, export models, computer vision, deep learning, model deployment, performance optimization
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 # How to Export to PaddlePaddle Format from YOLOv8 Models
@@ -119,4 +119,4 @@ In this guide, we explored the process of exporting Ultralytics YOLOv8 models to
 
 For further details on usage, visit the [PaddlePaddle official documentation](https://www.paddlepaddle.org.cn/documentation/docs/en/guides/index_en.html)
 
-Want to explore more ways to integrate your Ultralytics YOLOv8 models? Our [integration guide page](index.md) explores various options, equipping you with valuable resources and insights. 
+Want to explore more ways to integrate your Ultralytics YOLOv8 models? Our [integration guide page](index.md) explores various options, equipping you with valuable resources and insights.

docs/en/integrations/paperspace.md

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-description: Explore how to enhance your YOLOv8 projects with the Paperspace Gradient integration for streamlined model training, evaluation, and deployment on the cloud.
-keywords: Ultralytics, YOLOv8, Object Detection, Paperspace, Paperspace Gradient, Model Training, Model Deployment, Cloud Computing
+description: Simplify YOLOv8 training with Paperspace Gradient's all-in-one MLOps platform. Access GPUs, automate workflows, and deploy with ease.
+keywords: YOLOv8, Paperspace Gradient, MLOps, machine learning, training, GPUs, Jupyter notebooks, model deployment, AI, cloud platform
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 # YOLOv8 Model Training Made Simple with Paperspace Gradient

docs/en/integrations/ray-tune.md

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-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
+description: Optimize YOLOv8 model performance with Ray Tune. Learn efficient hyperparameter tuning using advanced search strategies, parallelism, and early stopping.
+keywords: YOLOv8, Ray Tune, hyperparameter tuning, model optimization, machine learning, deep learning, AI, Ultralytics, Weights & Biases
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 # Efficient Hyperparameter Tuning with Ray Tune and YOLOv8

docs/en/integrations/roboflow.md

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-description: Learn how to use Roboflow with Ultralytics for labeling and managing images for use in training, and for evaluating model performance.
-keywords: Ultralytics, YOLOv8, Roboflow, vector analysis, confusion matrix, data management, image labeling
+description: Learn how to gather, label, and deploy data for custom YOLOv8 models using Roboflow's powerful tools. Optimize your computer vision pipeline effortlessly.
+keywords: Roboflow, YOLOv8, data labeling, computer vision, model training, model deployment, dataset management, automated image annotation, AI tools
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 # Roboflow

docs/en/integrations/tensorboard.md

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-description: Walk through the integration of YOLOv8 with TensorBoard to be able to use TensorFlow's visualization toolkit for enhanced model training analysis, offering capabilities like metric tracking, model graph visualization, and more.
-keywords: TensorBoard, YOLOv8, Visualization, TensorFlow, Training Analysis, Metric Tracking, Model Graphs, Experimentation, Ultralytics
+description: Learn how to integrate YOLOv8 with TensorBoard for real-time visual insights into your model's training metrics, performance graphs, and debugging workflows.
+keywords: YOLOv8, TensorBoard, model training, visualization, machine learning, deep learning, Ultralytics, training metrics, performance analysis
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 # Gain Visual Insights with YOLOv8's Integration with TensorBoard

docs/en/integrations/tensorrt.md

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-description: Discover the power and flexibility of exporting Ultralytics YOLOv8 models to TensorRT format for enhanced performance and efficiency on NVIDIA GPUs.
-keywords: Ultralytics, YOLOv8, TensorRT Export, Model Deployment, GPU Acceleration, NVIDIA Support, CUDA Deployment
+description: Learn to convert YOLOv8 models to TensorRT for high-speed NVIDIA GPU inference. Boost efficiency and deploy optimized models with our step-by-step guide.
+keywords: YOLOv8, TensorRT, NVIDIA, GPU, deep learning, model optimization, high-speed inference, model export
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 # TensorRT Export for YOLOv8 Models

docs/en/integrations/tf-graphdef.md

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-description: A guide that walks you step-by-step through how to export Ultralytics YOLOv8 models to TF GraphDef format for smooth deployment and efficient model performance.
-keywords: Ultralytics, YOLOv8, TF GraphDef Export, Model Deployment, TensorFlow Ecosystem, Cross-Platform Compatibility, Performance Optimization  
+description: Learn how to export YOLOv8 models to the TF GraphDef format for seamless deployment on various platforms, including mobile and web.
+keywords: YOLOv8, export, TensorFlow, GraphDef, model deployment, TensorFlow Serving, TensorFlow Lite, TensorFlow.js, machine learning, AI, computer vision
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 # How to Export to TF GraphDef from YOLOv8 for Deployment

docs/en/integrations/tf-savedmodel.md

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-description: A guide that goes through exporting from Ultralytics YOLOv8 models to TensorFlow SavedModel format for streamlined deployments and optimized model performance.
-keywords: Ultralytics YOLOv8, TensorFlow SavedModel, Model Deployment, TensorFlow Serving, TensorFlow Lite, Model Optimization, Computer Vision, Performance Optimization
+description: Learn how to export Ultralytics YOLOv8 models to TensorFlow SavedModel format for easy deployment across various platforms and environments.
+keywords: YOLOv8, TF SavedModel, Ultralytics, TensorFlow, model export, model deployment, machine learning, AI
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 # Understand How to Export to TF SavedModel Format From YOLOv8

docs/en/integrations/tfjs.md

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-description: A guide that showcases how to export from an Ultralytics YOLOv8 model to TF.js model format for streamlined browser deployments and optimized model performance.
-keywords: Ultralytics YOLOv8, TensorFlow.js, TF.js, Model Deployment, Node.js, Model Format, Export Format, Model Conversion
+description: Convert your Ultralytics YOLOv8 models to TensorFlow.js for high-speed, local object detection. Learn how to optimize ML models for browser and Node.js apps.
+keywords: YOLOv8, TensorFlow.js, TF.js, model export, machine learning, object detection, browser ML, Node.js, Ultralytics, YOLO, export models
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 # Export to TF.js Model Format From a YOLOv8 Model Format

docs/en/integrations/tflite.md

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-description: Explore how to improve your Ultralytics YOLOv8 model's performance and interoperability using the TFLite export format suitable for edge computing environments.
-keywords: Ultralytics, YOLOv8, TFLite Export, Export YOLOv8, Model Deployment
+description: Learn how to convert YOLOv8 models to TFLite for edge device deployment. Optimize performance and ensure seamless execution on various platforms.
+keywords: YOLOv8, TFLite, model export, TensorFlow Lite, edge devices, deployment, Ultralytics, machine learning, on-device inference, model optimization
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 # A Guide on YOLOv8 Model Export to TFLite for Deployment

docs/en/integrations/torchscript.md

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-description: Learn to export your Ultralytics YOLOv8 models to TorchScript format for deployment through platforms like embedded systems, web browsers, and C++ applications.
-keywords: Ultralytics, YOLOv8, Export to Torchscript, Model Optimization, Deployment, PyTorch, C++, Faster Inference
+description: Learn how to export Ultralytics YOLOv8 models to TorchScript for flexible, cross-platform deployment. Boost performance and utilize in various environments.
+keywords: YOLOv8, TorchScript, model export, Ultralytics, PyTorch, deep learning, AI deployment, cross-platform, performance optimization
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 # YOLOv8 Model Export to TorchScript for Quick Deployment

docs/en/integrations/weights-biases.md

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-description: Discover how to train your YOLOv8 models efficiently with Weights & Biases. This guide walks through integrating Weights & Biases with YOLOv8 to enable seamless experiment tracking, result visualization, and model explainability.
-keywords: Ultralytics, YOLOv8, Object Detection, Weights & Biases, Model Training, Experiment Tracking, Visualizing Results
+description: Learn how to enhance YOLOv8 experiment tracking and visualization with Weights & Biases for better model performance and management.
+keywords: YOLOv8, Weights & Biases, model training, experiment tracking, Ultralytics, machine learning, computer vision, model visualization
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 # Enhancing YOLOv8 Experiment Tracking and Visualization with Weights & Biases