Update docs metadata (#3781)

docs/README.md

@@ -1,6 +1,6 @@
 ---
-description: Learn how to install the Ultralytics package in developer mode and build/serve locally using MkDocs. Deploy your project to your host easily.
+description: Learn how to install Ultralytics in developer mode, build and serve it locally for testing, and deploy your documentation site on platforms like GitHub Pages, GitLab Pages, and Amazon S3.
-keywords: install Ultralytics package, deploy documentation, building locally, deploy site, GitHub Pages, GitLab Pages, Amazon S3, MkDocs documentation
+keywords: Ultralytics, documentation, mkdocs, installation, developer mode, building, deployment, local server, GitHub Pages, GitLab Pages, Amazon S3
 ---
 
 # Ultralytics Docs

docs/SECURITY.md

@@ -1,6 +1,6 @@
 ---
-description: Ensure robust security with Ultralytics' open-source projects. We use advanced vulnerability scans and actively address potential risks. Your safety is our priority.
+description: Discover how Ultralytics ensures the safety of user data and systems. Check out the measures we have implemented, including Snyk and GitHub CodeQL Scanning.
-keywords: Ultralytics, security policy, Snyk, CodeQL scanning, security vulnerability, security issues, report security issue
+keywords: Ultralytics, Security Policy, data security, open-source projects, Snyk scanning, CodeQL scanning, vulnerability detection, threat prevention
 ---
 
 # Security Policy

docs/datasets/classify/caltech101.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the Caltech-101 dataset, a collection of images for object recognition tasks in machine learning and computer vision algorithms.
+description: Learn about the Caltech-101 dataset, its structure and uses in machine learning. Includes instructions to train a YOLO model using this dataset.
-keywords: Caltech-101 Dataset, Object recognition tasks, Ultralytics YOLO Docs, training, testing, code snippets & examples, machine learning, computer vision
+keywords: Caltech-101, dataset, YOLO training, machine learning, object recognition, ultralytics
 ---
 
 # Caltech-101 Dataset

docs/datasets/classify/caltech256.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the Caltech-256 dataset, a broad collection of images used for object classification tasks in machine learning and computer vision algorithms.
+description: Explore the Caltech-256 dataset, a diverse collection of images used for object recognition tasks in machine learning. Learn to train a YOLO model on the dataset.
-keywords: Caltech-256, Dataset, Object Recognition, Image Classification, Convolutional Neural Networks, SVMs, YOLO, Deep Learning Models
+keywords: Ultralytics, YOLO, Caltech-256, dataset, object recognition, machine learning, computer vision, deep learning
 ---
 
 # Caltech-256 Dataset

docs/datasets/classify/cifar10.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the CIFAR-10 dataset, a collection of images that are commonly used to train machine learning and computer vision algorithms.
+description: Explore the CIFAR-10 dataset, widely used for training in machine learning and computer vision, and learn how to use it with Ultralytics YOLO.
-keywords: CIFAR-10 dataset, YOLO model training, image classification, deep learning, computer vision, object detection, machine learning, convolutional neural networks, Alex Krizhevsky
+keywords: CIFAR-10, dataset, machine learning, image classification, computer vision, YOLO, Ultralytics, training, testing, deep learning, Convolutional Neural Networks, Support Vector Machines
 ---
 
 # CIFAR-10 Dataset

docs/datasets/classify/cifar100.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the CIFAR-100 dataset, a collection of images that are commonly used to train machine learning and computer vision algorithms.
+description: Discover how to leverage the CIFAR-100 dataset for machine learning and computer vision tasks with YOLO. Gain insights on its structure, use, and utilization for model training.
-keywords: CIFAR-100 dataset, CIFAR-100 classes, CIFAR-100 structure, CIFAR-100 applications, CIFAR-100 usage, YOLO model training, machine learning, computer vision
+keywords: Ultralytics, YOLO, CIFAR-100 dataset, image classification, machine learning, computer vision, YOLO model training
 ---
 
 # CIFAR-100 Dataset

docs/datasets/classify/fashion-mnist.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the Fashion-MNIST dataset, a large database of Zalando's article images used for training various image processing systems and machine learning models.
+description: Learn how to use the Fashion-MNIST dataset for image classification with the Ultralytics YOLO model. Covers dataset structure, labels, applications, and usage.
-keywords: Fashion-MNIST, dataset, machine learning, image classification, convolutional neural networks, benchmarking, Zalando's article images
+keywords: Ultralytics, YOLO, Fashion-MNIST, dataset, image classification, machine learning, deep learning, neural networks, training, testing
 ---
 
 # Fashion-MNIST Dataset

docs/datasets/classify/imagenet.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the ImageNet dataset, a large-scale database of annotated images commonly used for training deep learning models in computer vision tasks.
+description: Understand how to use ImageNet, an extensive annotated image dataset for object recognition research, with Ultralytics YOLO models. Learn about its structure, usage, and significance in computer vision.
-keywords: ImageNet, dataset, deep learning, computer vision, YOLO models, training, object recognition, image classification, object detection, WordNet, synsets, ILSVRC
+keywords: Ultralytics, YOLO, ImageNet, dataset, object recognition, deep learning, computer vision, machine learning, dataset training, model training, image classification, object detection
 ---
 
 # ImageNet Dataset

docs/datasets/classify/imagenet10.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the ImageNet10 dataset, a compact subset of the original ImageNet dataset designed for quick testing, CI tests, and sanity checks.
+description: Explore the compact ImageNet10 Dataset developed by Ultralytics. Ideal for fast testing of computer vision training pipelines and CV model sanity checks.
-keywords: ImageNet10 dataset, ImageNet, small scale, subset, computer vision models, pipelines, testing, debugging, synsets, annotations, applications, structure, sample images, citations, acknowledgments, Ultralytics Docs
+keywords: Ultralytics, YOLO, ImageNet10 Dataset, Image detection, Deep Learning, ImageNet, AI model testing, Computer vision, Machine learning
 ---
 
 # ImageNet10 Dataset

docs/datasets/classify/imagenette.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the ImageNette dataset, a subset of 10 easily classified classes from the Imagenet dataset commonly used for training various image processing systems and machine learning models.
+description: Learn about the ImageNette dataset and its usage in deep learning model training. Find code snippets for model training and explore ImageNette datatypes.
-keywords: ImageNette Dataset, ImageNette, training set, validation set, image classification, convolutional neural networks, machine learning, computer vision, ultralytics, yolov8n-cls.pt, python
+keywords: ImageNette dataset, Ultralytics, YOLO, Image classification, Machine Learning, Deep learning, Training code snippets, CNN, ImageNette160, ImageNette320
 ---
 
 # ImageNette Dataset

docs/datasets/classify/imagewoof.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the ImageWoof dataset, a subset of the ImageNet consisting of 10 challenging-to-classify dog breed classes.
+description: Explore the ImageWoof dataset, designed for challenging dog breed classification. Train AI models with Ultralytics YOLO using this dataset.
-keywords: ImageWoof dataset, dog breed images, image classification, noisy labels, deep learning models, CNN training, fastai
+keywords: ImageWoof, image classification, dog breeds, machine learning, deep learning, Ultralytics, YOLO, dataset
 ---
 
 # ImageWoof Dataset

docs/datasets/classify/index.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn how torchvision organizes classification image datasets. Use this code to create and train models. CLI and Python code shown.
+description: Explore image classification datasets supported by Ultralytics, learn the standard dataset format, and set up your own dataset for training models.
-keywords: image classification, datasets, format, torchvision, YOLO, Ultralytics
+keywords: Ultralytics, image classification, dataset, machine learning, CIFAR-10, ImageNet, MNIST, torchvision
 ---
 
 # Image Classification Datasets Overview

docs/datasets/classify/mnist.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the MNIST dataset, a large database of handwritten digits commonly used for training various image processing systems and machine learning models.
+description: Detailed guide on the MNIST Dataset, a benchmark in the machine learning community for image classification tasks. Learn about its structure, usage and application.
-keywords: MNIST, EMNIST, dataset, handwritten digits, convolutional neural networks, support vector machines, machine learning, computer vision, image processing, benchmark data, Ultralytics
+keywords: MNIST dataset, Ultralytics, image classification, machine learning, computer vision, deep learning, AI, dataset guide
 ---
 
 # MNIST Dataset

docs/datasets/detect/argoverse.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the Argoverse dataset, a rich dataset designed to support research in autonomous driving tasks such as 3D tracking, motion forecasting, and stereo depth estimation.
+description: Explore Argoverse, a comprehensive dataset for autonomous driving tasks including 3D tracking, motion forecasting and depth estimation used in YOLO.
-keywords: Argoverse Dataset, Sensor Dataset, Autonomous Driving Research, Deep Learning Models, YOLOv8n Model, 3D Tracking, Motion Forecasting, Stereo Depth Estimation, Labeled 3D Object Tracks, High-Quality Sensor Data, Richly Annotated HD Maps
+keywords: Argoverse dataset, autonomous driving, YOLO, 3D tracking, motion forecasting, LiDAR data, HD maps, ultralytics documentation
 ---
 
 # Argoverse Dataset

docs/datasets/detect/coco.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the COCO dataset, designed to encourage research on object detection, segmentation, and captioning with standardized evaluation metrics.
+description: Learn how COCO, a leading dataset for object detection and segmentation, integrates with Ultralytics. Discover ways to use it for training YOLO models.
-keywords: COCO dataset, object detection, segmentation, captioning, deep learning models, computer vision, benchmarking, data annotations, COCO Consortium
+keywords: Ultralytics, COCO dataset, object detection, YOLO, YOLO model training, image segmentation, computer vision, deep learning models
 ---
 
 # COCO Dataset

docs/datasets/detect/coco8.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Get started with Ultralytics COCO8. Ideal for testing and debugging object detection models or experimenting with new detection approaches.
+description: Discover the benefits of using the practical and diverse COCO8 dataset for object detection model testing. Learn to configure and use it via Ultralytics HUB and YOLOv8.
-keywords: Ultralytics, COCO8, object detection dataset, YAML file format, dataset usage, COCO dataset, acknowledgments
+keywords: Ultralytics, COCO8 dataset, object detection, model testing, dataset configuration, detection approaches, sanity check, training pipelines, YOLOv8
 ---
 
 # COCO8 Dataset

docs/datasets/detect/globalwheat2020.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the Global Wheat Head Dataset, aimed at supporting the development of accurate wheat head models for applications in wheat phenotyping and crop management.
+description: Understand how to utilize the vast Global Wheat Head Dataset for building wheat head detection models. Features, structure, applications, usage, sample data, and citation.
-keywords: Global Wheat Head Dataset, wheat head detection, wheat phenotyping, crop management, object detection, deep learning models, dataset structure, annotations, sample data, citations and acknowledgments
+keywords: Ultralytics, YOLO, Global Wheat Head Dataset, wheat head detection, plant phenotyping, crop management, deep learning, outdoor images, annotations, YAML configuration
 ---
 
 # Global Wheat Head Dataset

docs/datasets/detect/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore supported dataset formats for training YOLO detection models, including Ultralytics YOLO and COCO. This guide covers various dataset formats and their specific configurations for effective object detection training.
+description: Navigate through supported dataset formats, methods to utilize them and how to add your own datasets. Get insights on porting or converting label formats.
-keywords: object detection, datasets, formats, Ultralytics YOLO, COCO, label format, dataset file format, dataset definition, YOLO dataset, model configuration
+keywords: Ultralytics, YOLO, datasets, object detection, dataset formats, label formats, data conversion
 ---
 
 # Object Detection Datasets Overview

docs/datasets/detect/objects365.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the Objects365 dataset, designed for object detection research with a focus on diverse objects, featuring 365 categories, 2 million images, and 30 million bounding boxes.
+description: Discover the Objects365 dataset, a wide-scale, high-quality resource for object detection research. Learn to use it with the Ultralytics YOLO model.
-keywords: Objects365 dataset, object detection, computer vision, deep learning, Ultralytics Docs
+keywords: Objects365, object detection, Ultralytics, dataset, YOLO, bounding boxes, annotations, computer vision, deep learning, training models
 ---
 
 # Objects365 Dataset

docs/datasets/detect/sku-110k.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Explore the SKU-110k dataset, designed for object detection in densely packed retail shelf images, featuring over 110k unique SKU categories and annotations.
+description: 'Explore the SKU-110k dataset: densely packed retail shelf images for object detection research. Learn how to use it with Ultralytics.'
-keywords: SKU-110k, object detection, retail shelves, dataset, computer vision
+keywords: SKU-110k dataset, object detection, retail shelf images, Ultralytics, YOLO, computer vision, deep learning models
 ---
 
 # SKU-110k Dataset

docs/datasets/detect/visdrone.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Discover the VisDrone dataset, a comprehensive benchmark for drone-based computer vision tasks, including object detection, tracking, and crowd counting.
+description: Explore the VisDrone Dataset, a large-scale benchmark for drone-based image analysis, and learn how to train a YOLO model using it.
-keywords: VisDrone Dataset, Ultralytics YOLO Docs, AISKYEYE, Lab of Machine Learning and Data Mining, Computer Vision tasks, drone-based image analysis, object detection, object tracking, crowd counting, YOLOv8n model
+keywords: VisDrone Dataset, Ultralytics, drone-based image analysis, YOLO model, object detection, object tracking, crowd counting
 ---
 
 # VisDrone Dataset

docs/datasets/detect/voc.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Learn about the VOC dataset, designed to encourage research on object detection, segmentation, and classification with standardized evaluation metrics.
+description: A complete guide to the PASCAL VOC dataset used for object detection, segmentation and classification tasks with relevance to YOLO model training.
-keywords: PASCAL VOC dataset, object detection, segmentation, classification, computer vision, deep learning, benchmarking, VOC2007, VOC2012, mean Average Precision, mAP, PASCAL VOC evaluation server, trained models, YAML, YAML file, VOC.yaml, training, YOLOv8n model, model training, image size, annotations, object bounding boxes, segmentation masks, instance segmentation, SSD, Mask R-CNN, yolov8n.pt, mosaicing, PASCAL VOC Consortium
+keywords: Ultralytics, PASCAL VOC dataset, object detection, segmentation, image classification, YOLO, model training, VOC.yaml, deep learning
 ---
 
 # VOC Dataset

docs/datasets/detect/xview.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Discover the xView Dataset, a large-scale overhead imagery dataset for object detection tasks, featuring 1M instances, 60 classes, and high-resolution images.
+description: Explore xView, a large-scale, high resolution satellite imagery dataset for object detection. Dive into dataset structure, usage examples & its potential applications.
-keywords: xView dataset, overhead imagery, computer vision, deep learning models, satellite imagery analysis, object detection
+keywords: Ultralytics, YOLO, computer vision, xView dataset, satellite imagery, object detection, overhead imagery, training, deep learning, dataset YAML
 ---
 
 # xView Dataset

docs/datasets/index.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Ultralytics provides support for various datasets to facilitate multiple computer vision tasks. Check out our list of main datasets and their summaries.
+description: Explore various computer vision datasets supported by Ultralytics for object detection, segmentation, pose estimation, image classification, and multi-object tracking.
-keywords: ultralytics, computer vision, object detection, instance segmentation, pose estimation, image classification, multi-object tracking
+keywords: computer vision, datasets, Ultralytics, YOLO, object detection, instance segmentation, pose estimation, image classification, multi-object tracking
 ---
 
 # Datasets Overview

docs/datasets/pose/coco.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the COCO-Pose dataset, designed to encourage research on pose estimation tasks with standardized evaluation metrics.
+description: Detailed guide on the special COCO-Pose Dataset in Ultralytics. Learn about its key features, structure, and usage in pose estimation tasks with YOLO.
-keywords: COCO-Pose, COCO dataset, pose estimation, keypoints detection, computer vision, deep learning, YOLOv8n-pose, dataset configuration
+keywords: Ultralytics YOLO, COCO-Pose Dataset, Deep Learning, Pose Estimation, Training Models, Dataset YAML, openpose, YOLO
 ---
 
 # COCO-Pose Dataset

docs/datasets/pose/coco8-pose.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Test and debug object detection models with Ultralytics COCO8-Pose Dataset - a small, versatile pose detection dataset with 8 images.
+description: Discover the versatile COCO8-Pose dataset, perfect for testing and debugging pose detection models. Learn how to get started with YOLOv8-pose model training.
-keywords: coco8-pose dataset, ultralytics, object detection, pose detection, yolo, hub
+keywords: Ultralytics, YOLOv8, pose detection, COCO8-Pose dataset, dataset, model training, YAML
 ---
 
 # COCO8-Pose Dataset

docs/datasets/pose/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to format your dataset for training YOLO models with Ultralytics YOLO format using our concise tutorial and example YAML files.
+description: Understand the YOLO pose dataset format and learn to use Ultralytics datasets to train your pose estimation models effectively.
-keywords: pose estimation, datasets, supported formats, YAML file, object class index, keypoints, ultralytics YOLO format
+keywords: Ultralytics, YOLO, pose estimation, datasets, training, YAML, keypoints, COCO-Pose, COCO8-Pose, data conversion
 ---
 
 # Pose Estimation Datasets Overview
@@ -125,4 +125,4 @@ from ultralytics.data.converter import convert_coco
 convert_coco(labels_dir='../coco/annotations/', use_keypoints=True)
 ```
 
-This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.
+This conversion tool can be used to convert the COCO dataset or any dataset in the COCO format to the Ultralytics YOLO format. The `use_keypoints` parameter specifies whether to include keypoints (for pose estimation) in the converted labels.

docs/datasets/segment/coco.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the COCO-Seg dataset, designed for simple training of YOLO models on instance segmentation tasks.
+description: Explore the possibilities of the COCO-Seg dataset, designed for object instance segmentation and YOLO model training. Discover key features, dataset structure, applications, and usage.
-keywords: COCO-Seg, COCO, instance segmentation, segmentation annotations, computer vision, deep learning, data science, YOLO models, image size, open-source datasets
+keywords: Ultralytics, YOLO, COCO-Seg, dataset, instance segmentation, model training, deep learning, computer vision
 ---
 
 # COCO-Seg Dataset

docs/datasets/segment/coco8-seg.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Test and debug segmentation models on small, versatile COCO8-Seg instance segmentation dataset, now available for use with YOLOv8 and Ultralytics HUB.
+description: 'Discover the COCO8-Seg: a compact but versatile instance segmentation dataset ideal for testing Ultralytics YOLOv8 detection approaches. Complete usage guide included.'
-keywords: Ultralytics, COCO8-Seg, instance segmentation dataset, segmentation models, new detection approaches, COCO train 2017 set
+keywords: COCO8-Seg dataset, Ultralytics, YOLOv8, instance segmentation, dataset configuration, YAML, YOLOv8n-seg model, mosaiced dataset images
 ---
 
 # COCO8-Seg Dataset

docs/datasets/segment/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the Ultralytics YOLO dataset format for segmentation models. Use YAML to train Detection Models. Convert COCO to YOLO format using Python.
+description: Learn how Ultralytics YOLO supports various dataset formats for instance segmentation. This guide includes information on data conversions, auto-annotations, and dataset usage.
-keywords: instance segmentation datasets, yolov8 segmentations, yaml dataset format, auto annotation, convert label formats
+keywords: Ultralytics, YOLO, Instance Segmentation, Dataset, YAML, COCO, Auto-Annotation, Image Segmentation
 ---
 
 # Instance Segmentation Datasets Overview

docs/datasets/track/index.md

@@ -1,7 +1,7 @@
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 comments: true
-description: Discover the datasets compatible with Multi-Object Detector. Train your trackers and make your detections more efficient with Ultralytics' YOLO.
+description: Understand multi-object tracking datasets, upcoming features and how to use them with YOLO in Python and CLI. Dive in now!.
-keywords: multi-object tracking, dataset format, ultralytics yolo, object detection, segmentation, pose model, python, cli
+keywords: Ultralytics, YOLO, multi-object tracking, datasets, detection, segmentation, pose models, Python, CLI
 ---
 
 # Multi-object Tracking Datasets Overview

docs/help/CI.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Understand all the Continuous Integration (CI) tests for Ultralytics repositories and see their statuses in a clear, concise table.
+description: Learn how Ultralytics leverages Continuous Integration (CI) for maintaining high-quality code. Explore our CI tests and the status of these tests for our repositories.
-keywords: Ultralytics, CI Tests, Continuous Integration, Docker Deployment, Broken Links, CodeQL, PyPI Publishing
+keywords: continuous integration, software development, CI tests, Ultralytics repositories, high-quality code, Docker Deployment, Broken Links, CodeQL, PyPi Publishing
 ---
 
 # Continuous Integration (CI)

docs/help/CLA.md

@@ -1,6 +1,6 @@
 ---
-description: Individual Contributor License Agreement. Settle Intellectual Property issues for Contributions made to anything open source released by Ultralytics.
+description: Understand terms governing contributions to Ultralytics projects including source code, bug fixes, documentation and more. Read our Contributor License Agreement.
-keywords: Ultralytics, Individual, Contributor, License, Agreement, open source, software, projects, contributions
+keywords: Ultralytics, Contributor License Agreement, Open Source Software, Contributions, Copyright License, Patent License, Moral Rights
 ---
 
 # Ultralytics Individual Contributor License Agreement

docs/help/FAQ.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: 'Get quick answers to common Ultralytics YOLO questions: Hardware requirements, fine-tuning, conversion, real-time detection, and accuracy tips.'
+description: Find solutions to your common Ultralytics YOLO related queries. Learn about hardware requirements, fine-tuning YOLO models, conversion to ONNX/TensorFlow, and more.
-keywords: Ultralytics YOLO, Frequently Asked Questions, hardware requirements, model fine-tuning, converting to ONNX, TensorFlow, real-time detection, improving model accuracy
+keywords: Ultralytics, YOLO, FAQ, hardware requirements, ONNX, TensorFlow, real-time detection, YOLO accuracy
 ---
 
 # Ultralytics YOLO Frequently Asked Questions (FAQ)

docs/help/code_of_conduct.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Read the Ultralytics Contributor Covenant Code of Conduct. Learn ways to create a welcoming community & consequences for inappropriate conduct.
+description: Explore Ultralytics community’s Code of Conduct, ensuring a supportive, inclusive environment for contributors & members at all levels. Find our guidelines on acceptable behavior & enforcement.
-keywords: Ultralytics, contributor, covenant, code, conduct, pledge, standards, enforcement, harassment-free, community, guidelines
+keywords: Ultralytics, code of conduct, community, contribution, behavior guidelines, enforcement, open source contributions
 ---
 
 # Ultralytics Contributor Covenant Code of Conduct

docs/help/contributing.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to contribute to Ultralytics Open-Source YOLO Repositories with contributions guidelines, pull requests requirements, and GitHub CI tests.
+description: Learn how to contribute to Ultralytics YOLO projects – guidelines for pull requests, reporting bugs, code conduct and CLA signing.
-keywords: Ultralytics YOLO, Open source, Contribution guidelines, Pull requests, CLA, GitHub Actions CI Tests, Google-style docstrings
+keywords: Ultralytics, YOLO, open-source, contribute, pull request, bug report, coding guidelines, CLA, code of conduct, GitHub
 ---
 
 # Contributing to Ultralytics Open-Source YOLO Repositories

docs/help/environmental-health-safety.md

@@ -1,7 +1,7 @@
 ---
 comments: false
-description: Discover Ultralytics' commitment to Environmental, Health, and Safety (EHS). Learn about our policy, principles, and strategies for ensuring a sustainable and safe working environment.
+description: Discover Ultralytics’ EHS policy principles and implementation measures. Committed to safety, environment, and continuous improvement for a sustainable future.
-keywords: Ultralytics, Environmental Policy, Health and Safety, EHS, Sustainability, Workplace Safety, Environmental Compliance
+keywords: Ultralytics policy, EHS, environment, health and safety, compliance, prevention, continuous improvement, risk management, emergency preparedness, resource allocation, communication
 ---
 
 # Ultralytics Environmental, Health and Safety (EHS) Policy
@@ -34,4 +34,4 @@ At Ultralytics, we recognize that the long-term success of our company relies no
 
 This policy reflects our commitment to minimizing our environmental footprint, ensuring the safety and well-being of our employees, and continuously improving our performance.
 
-Please remember that the implementation of an effective EHS policy requires the involvement and commitment of everyone working at or with Ultralytics. We encourage you to take personal responsibility for your safety and the safety of others, and to take care of the environment in which we live and work.
+Please remember that the implementation of an effective EHS policy requires the involvement and commitment of everyone working at or with Ultralytics. We encourage you to take personal responsibility for your safety and the safety of others, and to take care of the environment in which we live and work.

docs/help/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Get comprehensive resources for Ultralytics YOLO repositories. Find guides, FAQs, MRE creation, CLA & more. Join the supportive community now!
+description: Find comprehensive guides and documents on Ultralytics YOLO tasks. Includes FAQs, contributing guides, CI guide, CLA, MRE guide, code of conduct & more.
-keywords: ultralytics, yolo, help, guide, resources, faq, contributing, continuous integration, contributor license agreement, minimum reproducible example, code of conduct, security policy
+keywords: Ultralytics, YOLO, guides, documents, FAQ, contributing, CI guide, CLA, MRE guide, code of conduct, EHS policy, security policy
 ---
 
 Welcome to the Ultralytics Help page! We are committed to providing you with comprehensive resources to make your experience with Ultralytics YOLO repositories as smooth and enjoyable as possible. On this page, you'll find essential links to guides and documents that will help you navigate through common tasks and address any questions you might have while using our repositories.

docs/help/minimum_reproducible_example.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to create a Minimum Reproducible Example (MRE) for Ultralytics YOLO bug reports to help maintainers and contributors understand your issue better.
+description: Learn how to create minimum reproducible examples (MRE) for efficient bug reporting in Ultralytics YOLO repositories with this step-by-step guide.
-keywords: Ultralytics, YOLO, bug report, minimum reproducible example, MRE, isolate problem, public models, public datasets, necessary dependencies, clear description, format code properly, test code, GitHub code block, error message
+keywords: Ultralytics, YOLO, minimum reproducible example, MRE, bug reports, guide, dependencies, code, troubleshooting
 ---
 
 # Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories

docs/hub/app/android.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Run YOLO models on your Android device for real-time object detection with Ultralytics Android App. Utilizes TensorFlow Lite and hardware delegates.
+description: Learn about the Ultralytics Android App, enabling real-time object detection using YOLO models. Discover in-app features, quantization methods, and delegate options for optimal performance.
-keywords: Ultralytics, Android, app, YOLO models, real-time object detection, TensorFlow Lite, quantization, acceleration, delegates, performance variability
+keywords: Ultralytics, Android App, real-time object detection, YOLO models, TensorFlow Lite, FP16 quantization, INT8 quantization, CPU, GPU, Hexagon, NNAPI
 ---
 
 # Ultralytics Android App: Real-time Object Detection with YOLO Models

docs/hub/app/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Experience the power of YOLOv5 and YOLOv8 models with Ultralytics HUB app. Download from Google Play and App Store now.
+description: Explore the Ultralytics HUB App, offering the ability to run YOLOv5 and YOLOv8 models on your iOS and Android devices with optimized performance.
-keywords: Ultralytics, HUB, App, Mobile, Object Detection, Image Recognition, YOLOv5, YOLOv8, Hardware Acceleration, Custom Model Training, iOS, Android
+keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object detection, image recognition, mobile device, hardware acceleration, Apple Neural Engine, Android GPU, NNAPI, custom model training
 ---
 
 # Ultralytics HUB App

docs/hub/app/ios.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Get started with the Ultralytics iOS app and run YOLO models in real-time for object detection on your iPhone or iPad with the Apple Neural Engine.
+description: Execute object detection in real-time on your iOS devices utilizing YOLO models. Leverage the power of the Apple Neural Engine and Core ML for fast and efficient object detection.
-keywords: YOLO, object detection, iOS app, Ultralytics, Apple Neural Engine, quantization, FP16, INT8, Core ML, machine learning
+keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple Neural Engine, Core ML, FP16, INT8, quantization
 ---
 
 # Ultralytics iOS App: Real-time Object Detection with YOLO Models

docs/hub/datasets.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Efficiently manage and use custom datasets on Ultralytics HUB for streamlined training with YOLOv5 and YOLOv8 models.
+description: Learn how Ultralytics HUB datasets streamline your ML workflow. Upload, format, validate, access, share, edit or delete datasets for Ultralytics YOLO model training.
-keywords: Ultralytics, HUB, Datasets, Upload, Visualize, Train, Custom Data, YAML, YOLOv5, YOLOv8
+keywords: Ultralytics, HUB datasets, YOLO model training, upload datasets, dataset validation, ML workflow, share datasets
 ---
 
 # HUB Datasets
@@ -156,4 +156,4 @@ Navigate to the Dataset page of the dataset you want to delete, open the dataset
 
     If you change your mind, you can restore the dataset from the [Trash](https://hub.ultralytics.com/trash) page.
 
-    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_3.jpg)
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the datasets](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_delete_dataset_3.jpg)

docs/hub/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: 'Ultralytics HUB: Train & deploy YOLO models from one spot! Use drag-and-drop interface with templates & pre-training models. Check quickstart, datasets, and more.'
+description: Gain seamless experience in training and deploying your YOLOv5 and YOLOv8 models with Ultralytics HUB. Explore pre-trained models, templates and various integrations.
-keywords: Ultralytics HUB, YOLOv5, YOLOv8, object detection, instance segmentation, classification, drag-and-drop interface, pre-trained models, integrations, mobile app, Inference API, datasets, projects, models
+keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pretrained models, model integrations
 ---
 
 # Ultralytics HUB

docs/hub/inference_api.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore Ultralytics YOLOv8 Inference API for efficient object detection. Check out our Python and CLI examples to streamline your image analysis.
+description: Access object detection capabilities of YOLOv8 via our RESTful API. Learn how to use the YOLO Inference API with Python or CLI for swift object detection.
-keywords: YOLO, object detection, Ultralytics, inference API, RESTful API
+keywords: Ultralytics, YOLOv8, Inference API, object detection, RESTful API, Python, CLI, Quickstart
 ---
 
 # YOLO Inference API

docs/hub/models.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Train and Deploy your Model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle or directly on Mobile.
+description: Learn how to use Ultralytics HUB models for efficient and user-friendly AI model training. For easy model creation, training, evaluation and deployment, follow our detailed guide.
-keywords: Ultralytics, HUB, models, artificial intelligence, APIs, export models, TensorFlow, ONNX, Paddle, OpenVINO, CoreML, iOS, Android
+keywords: Ultralytics, HUB Models, AI model training, model creation, model training, model evaluation, model deployment
 ---
 
 # Ultralytics HUB Models
@@ -210,4 +210,4 @@ Navigate to the Model page of the model you want to delete, open the model actio
 
     If you change your mind, you can restore the model from the [Trash](https://hub.ultralytics.com/trash) page.
 
-    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_delete_model_3.jpg)
+    ![Ultralytics HUB screenshot of the Trash page with an arrow pointing to the Restore option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/models/hub_delete_model_3.jpg)

docs/hub/projects.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Efficiently manage and compare AI models with Ultralytics HUB Projects. Create, share, and edit projects for streamlined model development.
+description: Learn how to manage Ultralytics HUB projects. Understand effective strategies to create, share, edit, delete, and compare models in an organized workspace.
-keywords: Ultralytics HUB projects, model management, model comparison, create project, share project, edit project, delete project, compare models
+keywords: Ultralytics, HUB projects, Create project, Edit project, Share project, Delete project, Compare Models, Model Management
 ---
 
 # Ultralytics HUB Projects
@@ -166,4 +166,4 @@ Navigate to the Project page of the project where the model you want to mode is
 
 Select the project you want to transfer the model to and click **Save**.
 
-![Ultralytics HUB screenshot of the Transfer Model dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_3.jpg)
+![Ultralytics HUB screenshot of the Transfer Model dialog with an arrow pointing to the dropdown and one to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_3.jpg)

docs/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore Ultralytics YOLOv8, a cutting-edge real-time object detection and image segmentation model for various applications and hardware platforms.
+description: Explore a complete guide to Ultralytics YOLOv8, a high-speed, high-accuracy object detection & image segmentation model. Installation, prediction, training tutorials and more.
-keywords: YOLOv8, object detection, image segmentation, computer vision, machine learning, deep learning, AGPL-3.0 License, Enterprise License
+keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine learning, deep learning, computer vision, YOLOv8 installation, YOLOv8 prediction, YOLOv8 training, YOLO history, YOLO licenses
 ---
 
 <div align="center">

docs/models/fast-sam.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore the Fast Segment Anything Model (FastSAM), a real-time solution for the segment anything task that leverages a Convolutional Neural Network (CNN) for segmenting any object within an image, guided by user interaction prompts.
+description: Explore FastSAM, a CNN-based solution for real-time object segmentation in images. Enhanced user interaction, computational efficiency and adaptable across vision tasks.
-keywords: FastSAM, Segment Anything Model, SAM, Convolutional Neural Network, CNN, image segmentation, real-time image processing
+keywords: FastSAM, machine learning, CNN-based solution, object segmentation, real-time solution, Ultralytics, vision tasks, image processing, industrial applications, user interaction
 ---
 
 # Fast Segment Anything Model (FastSAM)
@@ -166,4 +166,4 @@ We would like to acknowledge the FastSAM authors for their significant contribut
 }
 ```
 
-The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
+The original FastSAM paper can be found on [arXiv](https://arxiv.org/abs/2306.12156). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/CASIA-IVA-Lab/FastSAM). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

docs/models/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about the supported models and architectures, such as YOLOv3, YOLOv5, and YOLOv8, and how to contribute your own model to Ultralytics.
+description: Learn about the YOLO family, SAM, MobileSAM, FastSAM, YOLO-NAS, and RT-DETR models supported by Ultralytics, with examples on how to use them via CLI and Python.
-keywords: Ultralytics YOLO, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, SAM, YOLO-NAS, RT-DETR, object detection, instance segmentation, detection transformers, real-time detection, computer vision, CLI, Python
+keywords: Ultralytics, documentation, YOLO, SAM, MobileSAM, FastSAM, YOLO-NAS, RT-DETR, models, architectures, Python, CLI
 ---
 
 # Models
@@ -45,4 +45,4 @@ model.info()  # display model information
 model.train(data="coco128.yaml", epochs=100)  # train the model
 ```
 
-For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.
+For more details on each model, their supported tasks, modes, and performance, please visit their respective documentation pages linked above.

docs/models/mobile-sam.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: MobileSAM is a lightweight adaptation of the Segment Anything Model (SAM) designed for mobile applications. It maintains the full functionality of the original SAM while significantly improving speed, making it suitable for CPU-only edge devices, such as mobile phones.
+description: Learn more about MobileSAM, its implementation, comparison with the original SAM, and how to download and test it in the Ultralytics framework. Improve your mobile applications today.
-keywords: MobileSAM, Faster Segment Anything, Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI
+keywords: MobileSAM, Ultralytics, SAM, mobile applications, Arxiv, GPU, API, image encoder, mask decoder, model download, testing method
 ---
 
 ![MobileSAM Logo](https://github.com/ChaoningZhang/MobileSAM/blob/master/assets/logo2.png?raw=true)
@@ -96,4 +96,4 @@ If you find MobileSAM useful in your research or development work, please consid
   journal={arXiv preprint arXiv:2306.14289},
   year={2023}
 }
-```
+```

docs/models/rtdetr.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Dive into Baidu's RT-DETR, a revolutionary real-time object detection model built on the foundation of Vision Transformers (ViT). Learn how to use pre-trained PaddlePaddle RT-DETR models with the Ultralytics Python API for various tasks.
+description: Discover the features and benefits of RT-DETR, Baidu’s efficient and adaptable real-time object detector powered by Vision Transformers, including pre-trained models.
-keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API, object detector
+keywords: RT-DETR, Baidu, Vision Transformers, object detection, real-time performance, CUDA, TensorRT, IoU-aware query selection, Ultralytics, Python API, PaddlePaddle
 ---
 
 # Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector
@@ -71,4 +71,4 @@ If you use Baidu's RT-DETR in your research or development work, please cite the
 
 We would like to acknowledge Baidu and the [PaddlePaddle](https://github.com/PaddlePaddle/PaddleDetection) team for creating and maintaining this valuable resource for the computer vision community. Their contribution to the field with the development of the Vision Transformers-based real-time object detector, RT-DETR, is greatly appreciated.
 
-*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*
+*Keywords: RT-DETR, Transformer, ViT, Vision Transformers, Baidu RT-DETR, PaddlePaddle, Paddle Paddle RT-DETR, real-time object detection, Vision Transformers-based object detection, pre-trained PaddlePaddle RT-DETR models, Baidu's RT-DETR usage, Ultralytics Python API*

docs/models/sam.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover the Segment Anything Model (SAM), a revolutionary promptable image segmentation model, and delve into the details of its advanced architecture and the large-scale SA-1B dataset.
+description: Explore the cutting-edge Segment Anything Model (SAM) from Ultralytics that allows real-time image segmentation. Learn about its promptable segmentation, zero-shot performance, and how to use it.
-keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI
+keywords: Ultralytics, image segmentation, Segment Anything Model, SAM, SA-1B dataset, real-time performance, zero-shot transfer, object detection, image analysis, machine learning
 ---
 
 # Segment Anything Model (SAM)
@@ -218,4 +218,4 @@ If you find SAM useful in your research or development work, please consider cit
 
 We would like to express our gratitude to Meta AI for creating and maintaining this valuable resource for the computer vision community.
 
-*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*
+*keywords: Segment Anything, Segment Anything Model, SAM, Meta SAM, image segmentation, promptable segmentation, zero-shot performance, SA-1B dataset, advanced architecture, auto-annotation, Ultralytics, pre-trained models, SAM base, SAM large, instance segmentation, computer vision, AI, artificial intelligence, machine learning, data annotation, segmentation masks, detection model, YOLO detection model, bibtex, Meta AI.*

docs/models/yolo-nas.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Dive into YOLO-NAS, Deci's next-generation object detection model, offering breakthroughs in speed and accuracy. Learn how to utilize pre-trained models using the Ultralytics Python API for various tasks.
+description: Explore detailed documentation of YOLO-NAS, a superior object detection model. Learn about its features, pre-trained models, usage with Ultralytics Python API, and more.
-keywords: YOLO-NAS, Deci AI, Ultralytics, object detection, deep learning, neural architecture search, Python API, pre-trained models, quantization
+keywords: YOLO-NAS, Deci AI, object detection, deep learning, neural architecture search, Ultralytics Python API, YOLO model, pre-trained models, quantization, optimization, COCO, Objects365, Roboflow 100
 ---
 
 # YOLO-NAS

docs/models/yolov3.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: YOLOv3, YOLOv3-Ultralytics and YOLOv3u by Ultralytics explained. Learn the evolution of these models and their specifications.
+description: Get an overview of YOLOv3, YOLOv3-Ultralytics and YOLOv3u. Learn about their key features, usage, and supported tasks for object detection.
-keywords: YOLOv3, Ultralytics YOLOv3, YOLO v3, YOLOv3 models, object detection, models, machine learning, AI, image recognition, object recognition
+keywords: YOLOv3, YOLOv3-Ultralytics, YOLOv3u, Object Detection, Inferencing, Training, Ultralytics
 ---
 
 # YOLOv3, YOLOv3-Ultralytics, and YOLOv3u

docs/models/yolov4.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore YOLOv4, a state-of-the-art, real-time object detector. Learn about its architecture, features, and performance.
+description: Explore our detailed guide on YOLOv4, a state-of-the-art real-time object detector. Understand its architectural highlights, innovative features, and application examples.
-keywords: YOLOv4, object detection, real-time, CNN, GPU, Ultralytics, documentation, YOLOv4 architecture, YOLOv4 features, YOLOv4 performance
+keywords: ultralytics, YOLOv4, object detection, neural network, real-time detection, object detector, machine learning
 ---
 
 # YOLOv4: High-Speed and Precise Object Detection

docs/models/yolov5.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: YOLOv5 by Ultralytics explained. Discover the evolution of this model and its key specifications. Experience faster and more accurate object detection.
+description: Discover YOLOv5u, a boosted version of the YOLOv5 model featuring an improved accuracy-speed tradeoff and numerous pre-trained models for various object detection tasks.
-keywords: YOLOv5, Ultralytics YOLOv5, YOLO v5, YOLOv5 models, YOLO, object detection, model, neural network, accuracy, speed, pre-trained weights, inference, validation, training
+keywords: YOLOv5u, object detection, pre-trained models, Ultralytics, Inference, Validation, YOLOv5, YOLOv8, anchor-free, objectness-free, real-time applications, machine learning
 ---
 
 # YOLOv5

docs/models/yolov6.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover Meituan YOLOv6, a robust real-time object detector. Learn how to utilize pre-trained models with Ultralytics Python API for a variety of tasks.
+description: Explore Meituan YOLOv6, a state-of-the-art object detection model striking a balance between speed and accuracy. Dive into features, pre-trained models, and Python usage.
-keywords: Meituan, YOLOv6, object detection, Bi-directional Concatenation (BiC), anchor-aided training (AAT), pre-trained models, high-resolution input, real-time, ultra-fast computations
+keywords: Meituan YOLOv6, object detection, Ultralytics, YOLOv6 docs, Bi-directional Concatenation, Anchor-Aided Training, pretrained models, real-time applications
 ---
 
 # Meituan YOLOv6

docs/models/yolov7.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Discover YOLOv7, a cutting-edge real-time object detector that surpasses competitors in speed and accuracy. Explore its unique trainable bag-of-freebies.
+description: Explore the YOLOv7, a real-time object detector. Understand its superior speed, impressive accuracy, and unique trainable bag-of-freebies optimization focus.
-keywords: object detection, real-time object detector, YOLOv7, MS COCO, computer vision, neural networks, AI, deep learning, deep neural networks, real-time, GPU, GitHub, arXiv
+keywords: YOLOv7, real-time object detector, state-of-the-art, Ultralytics, MS COCO dataset, model re-parameterization, dynamic label assignment, extended scaling, compound scaling
 ---
 
 # YOLOv7: Trainable Bag-of-Freebies

docs/models/yolov8.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn about YOLOv8's pre-trained weights supporting detection, instance segmentation, pose, and classification tasks. Get performance details.
+description: Explore the thrilling features of YOLOv8, the latest version of our real-time object detector! Learn how advanced architectures, pre-trained models and optimal balance between accuracy & speed make YOLOv8 the perfect choice for your object detection tasks.
-keywords: YOLOv8, real-time object detection, object detection, deep learning, machine learning
+keywords: YOLOv8, Ultralytics, real-time object detector, pre-trained models, documentation, object detection, YOLO series, advanced architectures, accuracy, speed
 ---
 
 # YOLOv8

docs/modes/benchmark.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Benchmark mode compares speed and accuracy of various YOLOv8 export formats like ONNX or OpenVINO. Optimize formats for speed or accuracy.
+description: Learn how to profile speed and accuracy of YOLOv8 across various export formats; get insights on mAP50-95, accuracy_top5 metrics, and more.
-keywords: YOLOv8, Benchmark Mode, Export Formats, ONNX, OpenVINO, TensorRT, Ultralytics Docs
+keywords: Ultralytics, YOLOv8, benchmarking, speed profiling, accuracy profiling, mAP50-95, accuracy_top5, ONNX, OpenVINO, TensorRT, YOLO export formats
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/export.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: 'Export mode: Create a deployment-ready YOLOv8 model by converting it to various formats. Export to ONNX or OpenVINO for up to 3x CPU speedup.'
+description: Step-by-step guide on exporting your YOLOv8 models to various format like ONNX, TensorRT, CoreML and more for deployment. Explore now!.
-keywords: ultralytics docs, YOLOv8, export YOLOv8, YOLOv8 model deployment, exporting YOLOv8, ONNX, OpenVINO, TensorRT, CoreML, TF SavedModel, PaddlePaddle, TorchScript, ONNX format, OpenVINO format, TensorRT format, CoreML format, TF SavedModel format, PaddlePaddle format, Tencent ncnn format
+keywords: YOLO, YOLOv8, Ultralytics, Model export, ONNX, TensorRT, CoreML, TensorFlow SavedModel, OpenVINO, PyTorch, export model
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/index.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Use Ultralytics YOLOv8 Modes (Train, Val, Predict, Export, Track, Benchmark) to train, validate, predict, track, export or benchmark.
+description: From training to tracking, make the most of YOLOv8 with Ultralytics. Get insights and examples for each supported mode including validation, export, and benchmarking.
-keywords: yolov8, yolo, ultralytics, training, validation, prediction, export, tracking, benchmarking, real-time object detection, object tracking
+keywords: Ultralytics, YOLOv8, Machine Learning, Object Detection, Training, Validation, Prediction, Export, Tracking, Benchmarking
 ---
 
 # Ultralytics YOLOv8 Modes

docs/modes/predict.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Get started with YOLOv8 Predict mode and input sources. Accepts various input sources such as images, videos, and directories.
+description: Discover how to use YOLOv8 predict mode for various tasks. Learn about different inference sources like images, videos, and data formats.
-keywords: YOLOv8, predict mode, generator, streaming mode, input sources, video formats, arguments customization
+keywords: Ultralytics, YOLOv8, predict mode, inference sources, prediction tasks, streaming mode, image processing, video processing, machine learning, AI
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/track.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Explore YOLOv8n-based object tracking with Ultralytics' BoT-SORT and ByteTrack. Learn configuration, usage, and customization tips.
+description: Learn how to use Ultralytics YOLO for object tracking in video streams. Guides to use different trackers and customise tracker configurations.
-keywords: object tracking, YOLO, trackers, BoT-SORT, ByteTrack
+keywords: Ultralytics, YOLO, object tracking, video streams, BoT-SORT, ByteTrack, Python guide, CLI guide
 ---
 
 <img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png">

docs/modes/train.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Learn how to train custom YOLOv8 models on various datasets, configure hyperparameters, and use Ultralytics' YOLO for seamless training.
+description: Step-by-step guide to train YOLOv8 models with Ultralytics YOLO with examples of single-GPU and multi-GPU training. Efficient way for object detection training.
-keywords: YOLOv8, train mode, train a custom YOLOv8 model, hyperparameters, train a model, Comet, ClearML, TensorBoard, logging, loggers
+keywords: Ultralytics, YOLOv8, YOLO, object detection, train mode, custom dataset, GPU training, multi-GPU, hyperparameters, CLI examples, Python examples
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/val.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Validate and improve YOLOv8n model accuracy on COCO128 and other datasets using hyperparameter & configuration tuning, in Val mode.
+description: 'Guide for Validating YOLOv8 Models: Learn how to evaluate the performance of your YOLO models using validation settings and metrics with Python and CLI examples.'
-keywords: Ultralytics, YOLO, YOLOv8, Val, Validation, Hyperparameters, Performance, Accuracy, Generalization, COCO, Export Formats, PyTorch
+keywords: Ultralytics, YOLO Docs, YOLOv8, validation, model evaluation, hyperparameters, accuracy, metrics, Python, CLI
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/quickstart.md

@@ -1,7 +1,7 @@
 ---
 comments: true
-description: Install and use YOLOv8 via CLI or Python. Run single-line commands or integrate with Python projects for object detection, segmentation, and classification.
+description: Explore various methods to install Ultralytics using pip, conda, git and Docker. Learn how to use Ultralytics with command line interface or within your Python projects.
-keywords: YOLOv8, object detection, segmentation, classification, pip, git, CLI, Python
+keywords: Ultralytics installation, pip install Ultralytics, Docker install Ultralytics, Ultralytics command line interface, Ultralytics Python interface
 ---
 
 ## Install Ultralytics

docs/reference/cfg/__init__.md

@@ -1,3 +1,8 @@
+---
+description: Explore Ultralytics cfg functions like cfg2dict, handle_deprecation, merge_equal_args & more to handle YOLO settings and configurations efficiently.
+keywords: Ultralytics, YOLO, Configuration, cfg2dict, handle_deprecation, merge_equals_args, handle_yolo_settings, copy_default_cfg, Image Detection
+---
+
 ## cfg2dict
 ---
 ### ::: ultralytics.cfg.cfg2dict
@@ -41,4 +46,4 @@
 ## copy_default_cfg
 ---
 ### ::: ultralytics.cfg.copy_default_cfg
-<br><br>
+<br><br>

docs/reference/data/annotator.md

@@ -1,4 +1,9 @@
+---
+description: Enhance your machine learning model with Ultralytics’ auto_annotate function. Simplify data annotation for improved model training.
+keywords: Ultralytics, Auto-Annotate, Machine Learning, AI, Annotation, Data Processing, Model Training
+---
+
 ## auto_annotate
 ---
 ### ::: ultralytics.data.annotator.auto_annotate
-<br><br>
+<br><br>

docs/reference/data/augment.md

@@ -1,3 +1,8 @@
+---
+description: Detailed exploration into Ultralytics data augmentation methods including BaseTransform, MixUp, LetterBox, ToTensor, and more for enhancing model performance.
+keywords: Ultralytics, Data Augmentation, BaseTransform, MixUp, RandomHSV, LetterBox, Albumentations, classify_transforms, classify_albumentations
+---
+
 ## BaseTransform
 ---
 ### ::: ultralytics.data.augment.BaseTransform
@@ -91,4 +96,4 @@
 ## classify_albumentations
 ---
 ### ::: ultralytics.data.augment.classify_albumentations
-<br><br>
+<br><br>

docs/reference/data/base.md

@@ -1,4 +1,9 @@
+---
+description: Explore BaseDataset in Ultralytics docs. Learn how this implementation simplifies dataset creation and manipulation.
+keywords: Ultralytics, docs, BaseDataset, data manipulation, dataset creation
+---
+
 ## BaseDataset
 ---
 ### ::: ultralytics.data.base.BaseDataset
-<br><br>
+<br><br>

docs/reference/data/build.md

@@ -1,3 +1,8 @@
+---
+description: Explore the Ultralytics YOLO v3 data build procedures, including the InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source.
+keywords: Ultralytics, YOLO v3, Data build, DataLoader, InfiniteDataLoader, seed_worker, build_dataloader, load_inference_source
+---
+
 ## InfiniteDataLoader
 ---
 ### ::: ultralytics.data.build.InfiniteDataLoader
@@ -31,4 +36,4 @@
 ## load_inference_source
 ---
 ### ::: ultralytics.data.build.load_inference_source
-<br><br>
+<br><br>

docs/reference/data/converter.md

@@ -1,3 +1,8 @@
+---
+description: Explore Ultralytics data converter functions like coco91_to_coco80_class, merge_multi_segment, rle2polygon for efficient data handling.
+keywords: Ultralytics, Data Converter, coco91_to_coco80_class, merge_multi_segment, rle2polygon
+---
+
 ## coco91_to_coco80_class
 ---
 ### ::: ultralytics.data.converter.coco91_to_coco80_class
@@ -26,4 +31,4 @@
 ## delete_dsstore
 ---
 ### ::: ultralytics.data.converter.delete_dsstore
-<br><br>
+<br><br>

docs/reference/data/dataset.md

@@ -1,3 +1,8 @@
+---
+description: Explore the YOLODataset and SemanticDataset classes in YOLO data. Learn how to efficiently handle and manipulate your data with Ultralytics.
+keywords: Ultralytics, YOLO, YOLODataset, SemanticDataset, data handling, data manipulation
+---
+
 ## YOLODataset
 ---
 ### ::: ultralytics.data.dataset.YOLODataset
@@ -11,4 +16,4 @@
 ## SemanticDataset
 ---
 ### ::: ultralytics.data.dataset.SemanticDataset
-<br><br>
+<br><br>

docs/reference/data/loaders.md

@@ -1,3 +1,8 @@
+---
+description: Find detailed guides on Ultralytics YOLO data loaders, including LoadStreams, LoadImages and LoadTensor. Learn how to get the best YouTube URLs.
+keywords: Ultralytics, data loaders, LoadStreams, LoadImages, LoadTensor, YOLO, YouTube URLs
+---
+
 ## SourceTypes
 ---
 ### ::: ultralytics.data.loaders.SourceTypes
@@ -36,4 +41,4 @@
 ## get_best_youtube_url
 ---
 ### ::: ultralytics.data.loaders.get_best_youtube_url
-<br><br>
+<br><br>

docs/reference/data/utils.md

@@ -1,3 +1,8 @@
+---
+description: Uncover a detailed guide to Ultralytics data utilities. Learn functions from img2label_paths to autosplit, all boosting your YOLO model’s efficiency.
+keywords: Ultralytics, data utils, YOLO, img2label_paths, exif_size, polygon2mask, polygons2masks_overlap, check_cls_dataset, delete_dsstore, autosplit
+---
+
 ## HUBDatasetStats
 ---
 ### ::: ultralytics.data.utils.HUBDatasetStats
@@ -66,4 +71,4 @@
 ## autosplit
 ---
 ### ::: ultralytics.data.utils.autosplit
-<br><br>
+<br><br>

docs/reference/engine/exporter.md

@@ -1,3 +1,8 @@
+---
+description: Explore the exporter functionality of Ultralytics. Learn about exporting formats, iOSDetectModel, and try exporting with examples.
+keywords: Ultralytics, Exporter, iOSDetectModel, Export Formats, Try export
+---
+
 ## Exporter
 ---
 ### ::: ultralytics.engine.exporter.Exporter
@@ -26,4 +31,4 @@
 ## export
 ---
 ### ::: ultralytics.engine.exporter.export
-<br><br>
+<br><br>

docs/reference/engine/model.md

@@ -1,4 +1,9 @@
+---
+description: Explore the detailed guide on using the Ultralytics YOLO Engine Model. Learn better ways to implement, train and evaluate YOLO models.
+keywords: Ultralytics, YOLO, engine model, documentation, guide, implementation, training, evaluation
+---
+
 ## YOLO
 ---
 ### ::: ultralytics.engine.model.YOLO
-<br><br>
+<br><br>

docs/reference/engine/predictor.md

@@ -1,4 +1,9 @@
+---
+description: Learn about Ultralytics BasePredictor, an essential component of our engine that serves as the foundation for all prediction operations.
+keywords: Ultralytics, BasePredictor, YOLO, prediction, engine
+---
+
 ## BasePredictor
 ---
 ### ::: ultralytics.engine.predictor.BasePredictor
-<br><br>
+<br><br>

docs/reference/engine/results.md

@@ -1,3 +1,8 @@
+---
+description: Master Ultralytics engine results including base tensors, boxes, and keypoints with our thorough documentation.
+keywords: Ultralytics, engine, results, base tensor, boxes, keypoints
+---
+
 ## BaseTensor
 ---
 ### ::: ultralytics.engine.results.BaseTensor
@@ -26,4 +31,4 @@
 ## Probs
 ---
 ### ::: ultralytics.engine.results.Probs
-<br><br>
+<br><br>

docs/reference/engine/trainer.md

@@ -1,4 +1,9 @@
+---
+description: Learn about the BaseTrainer class in the Ultralytics library. From training control, customization to advanced usage.
+keywords: Ultralytics, BaseTrainer, Machine Learning, Training Control, Python library
+---
+
 ## BaseTrainer
 ---
 ### ::: ultralytics.engine.trainer.BaseTrainer
-<br><br>
+<br><br>

docs/reference/engine/validator.md

@@ -1,4 +1,9 @@
+---
+description: Learn about the Ultralytics BaseValidator module. Understand its principles, uses, and how it interacts with other components.
+keywords: Ultralytics, BaseValidator, Ultralytics engine, module, components
+---
+
 ## BaseValidator
 ---
 ### ::: ultralytics.engine.validator.BaseValidator
-<br><br>
+<br><br>

docs/reference/hub/__init__.md

@@ -1,3 +1,8 @@
+---
+description: Explore Ultralytics hub functions for model resetting, checking datasets, model exporting and more. Easy-to-follow instructions provided.
+keywords: Ultralytics, hub functions, model export, dataset check, reset model, YOLO Docs
+---
+
 ## login
 ---
 ### ::: ultralytics.hub.login
@@ -36,4 +41,4 @@
 ## check_dataset
 ---
 ### ::: ultralytics.hub.check_dataset
-<br><br>
+<br><br>

docs/reference/hub/auth.md

@@ -1,4 +1,9 @@
+---
+description: Dive into the Ultralytics Auth API documentation & learn how to manage authentication in your AI & ML projects easily and effectively.
+keywords: Ultralytics, Auth, API documentation, User Authentication, AI, Machine Learning
+---
+
 ## Auth
 ---
 ### ::: ultralytics.hub.auth.Auth
-<br><br>
+<br><br>

docs/reference/hub/session.md

@@ -1,4 +1,9 @@
+---
+description: Explore details about the HUBTrainingSession in Ultralytics framework. Learn to utilize this functionality for effective model training.
+keywords: Ultralytics, HUBTrainingSession, Documentation, Model Training, AI, Machine Learning, YOLO
+---
+
 ## HUBTrainingSession
 ---
 ### ::: ultralytics.hub.session.HUBTrainingSession
-<br><br>
+<br><br>

docs/reference/hub/utils.md

@@ -1,3 +1,8 @@
+---
+description: Explore Ultralytics docs for various Events, including "request_with_credentials" and "requests_with_progress". Also, understand the use of the "smart_request".
+keywords: Ultralytics, Events, request_with_credentials, smart_request, Ultralytics hub utils, requests_with_progress
+---
+
 ## Events
 ---
 ### ::: ultralytics.hub.utils.Events
@@ -16,4 +21,4 @@
 ## smart_request
 ---
 ### ::: ultralytics.hub.utils.smart_request
-<br><br>
+<br><br>

docs/reference/models/fastsam/model.md

@@ -1,4 +1,9 @@
+---
+description: Learn all about Ultralytics FastSAM model. Dive into our comprehensive guide for seamless integration and efficient model training.
+keywords: Ultralytics, FastSAM model, Model documentation, Efficient model training
+---
+
 ## FastSAM
 ---
 ### ::: ultralytics.models.fastsam.model.FastSAM
-<br><br>
+<br><br>

docs/reference/models/fastsam/predict.md

@@ -1,4 +1,9 @@
+---
+description: Get detailed insights about Ultralytics FastSAMPredictor. Learn to predict and optimize your AI models with our properly documented guidelines.
+keywords: Ultralytics, FastSAMPredictor, predictive modeling, AI optimization, machine learning, deep learning, Ultralytics documentation
+---
+
 ## FastSAMPredictor
 ---
 ### ::: ultralytics.models.fastsam.predict.FastSAMPredictor
-<br><br>
+<br><br>

docs/reference/models/fastsam/prompt.md

@@ -1,4 +1,9 @@
+---
+description: Learn to effectively utilize FastSAMPrompt model from Ultralytics. Detailed guide to help you get the most out of your machine learning models.
+keywords: Ultralytics, FastSAMPrompt, machine learning, model, guide, documentation
+---
+
 ## FastSAMPrompt
 ---
 ### ::: ultralytics.models.fastsam.prompt.FastSAMPrompt
-<br><br>
+<br><br>

docs/reference/models/fastsam/utils.md

@@ -1,3 +1,8 @@
+---
+description: Learn how to adjust bounding boxes to image borders in Ultralytics models using the bbox_iou utility. Enhance your object detection performance.
+keywords: Ultralytics, bounding boxes, Bboxes, image borders, object detection, bbox_iou, model utilities
+---
+
 ## adjust_bboxes_to_image_border
 ---
 ### ::: ultralytics.models.fastsam.utils.adjust_bboxes_to_image_border
@@ -6,4 +11,4 @@
 ## bbox_iou
 ---
 ### ::: ultralytics.models.fastsam.utils.bbox_iou
-<br><br>
+<br><br>

docs/reference/models/fastsam/val.md

@@ -1,4 +1,9 @@
+---
+description: Learn about FastSAMValidator in Ultralytics models. Comprehensive guide to enhancing AI capabilities with Ultralytics.
+keywords: Ultralytics, FastSAMValidator, model, synthetic, AI, machine learning, validation
+---
+
 ## FastSAMValidator
 ---
 ### ::: ultralytics.models.fastsam.val.FastSAMValidator
-<br><br>
+<br><br>

docs/reference/models/nas/model.md

@@ -1,4 +1,9 @@
+---
+description: Learn how our NAS model operates in Ultralytics. Comprehensive guide with detailed examples. Master the nuances of Ultralytics NAS model.
+keywords: Ultralytics, NAS model, NAS guide, machine learning, model documentation
+---
+
 ## NAS
 ---
 ### ::: ultralytics.models.nas.model.NAS
-<br><br>
+<br><br>

docs/reference/models/nas/predict.md

@@ -1,4 +1,9 @@
+---
+description: Explore Ultralytics NASPredictor. Understand high-level architecture of the model for effective implementation and efficient predictions.
+keywords: NASPredictor, Ultralytics, Ultralytics model, model architecture, efficient predictions
+---
+
 ## NASPredictor
 ---
 ### ::: ultralytics.models.nas.predict.NASPredictor
-<br><br>
+<br><br>

docs/reference/models/nas/val.md

@@ -1,4 +1,9 @@
+---
+description: Explore the utilities and functions of the Ultralytics NASValidator. Find out how it benefits allocation and optimization in AI models.
+keywords: Ultralytics, NASValidator, models.nas.val.NASValidator, AI models, allocation, optimization
+---
+
 ## NASValidator
 ---
 ### ::: ultralytics.models.nas.val.NASValidator
-<br><br>
+<br><br>

docs/reference/models/rtdetr/model.md

@@ -1,4 +1,9 @@
+---
+description: Explore the specifics of using the RTDETR model in Ultralytics. Detailed documentation layered with explanations and examples.
+keywords: Ultralytics, RTDETR model, Ultralytics models, object detection, Ultralytics documentation
+---
+
 ## RTDETR
 ---
 ### ::: ultralytics.models.rtdetr.model.RTDETR
-<br><br>
+<br><br>

docs/reference/models/rtdetr/predict.md

@@ -1,4 +1,9 @@
+---
+description: Learn how to use the RTDETRPredictor model of the Ultralytics package. Detailed documentation, usage instructions, and advice.
+keywords: Ultralytics, RTDETRPredictor, model documentation, guide, real-time object detection
+---
+
 ## RTDETRPredictor
 ---
 ### ::: ultralytics.models.rtdetr.predict.RTDETRPredictor
-<br><br>
+<br><br>

docs/reference/models/rtdetr/train.md

@@ -1,3 +1,8 @@
+---
+description: Get insights into RTDETRTrainer, a crucial component of Ultralytics for effective model training. Explore detailed documentation at Ultralytics.
+keywords: Ultralytics, RTDETRTrainer, model training, Ultralytics models, PyTorch models, neural networks, machine learning, deep learning
+---
+
 ## RTDETRTrainer
 ---
 ### ::: ultralytics.models.rtdetr.train.RTDETRTrainer
@@ -6,4 +11,4 @@
 ## train
 ---
 ### ::: ultralytics.models.rtdetr.train.train
-<br><br>
+<br><br>

docs/reference/models/rtdetr/val.md

@@ -1,3 +1,8 @@
+---
+description: Explore RTDETRDataset in Ultralytics Models. Learn about the RTDETRValidator function, understand its usage in real-time object detection.
+keywords: Ultralytics, RTDETRDataset, RTDETRValidator, real-time object detection, models documentation
+---
+
 ## RTDETRDataset
 ---
 ### ::: ultralytics.models.rtdetr.val.RTDETRDataset
@@ -6,4 +11,4 @@
 ## RTDETRValidator
 ---
 ### ::: ultralytics.models.rtdetr.val.RTDETRValidator
-<br><br>
+<br><br>

docs/reference/models/sam/amg.md

@@ -1,3 +1,8 @@
+---
+description: Explore Ultralytics methods for mask data processing, transformation and encoding. Deepen your understanding of RLE encoding, image cropping and more.
+keywords: Ultralytics, Mask Data, Transformation, Encoding, RLE encoding, Image cropping, Pytorch, SAM, AMG, Ultralytics model
+---
+
 ## MaskData
 ---
 ### ::: ultralytics.models.sam.amg.MaskData
@@ -81,4 +86,4 @@
 ## batched_mask_to_box
 ---
 ### ::: ultralytics.models.sam.amg.batched_mask_to_box
-<br><br>
+<br><br>