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

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

.github/ISSUE_TEMPLATE/config.yml

@@ -7,5 +7,5 @@ contact_links:
     url: https://community.ultralytics.com/
     about: Ask on Ultralytics Community Forum
   - name: 🎧 Discord
-    url: https://discord.gg/n6cFeSPZdD
+    url: https://discord.gg/7aegy5d8
     about: Ask on Ultralytics Discord

.github/workflows/ci.yaml

@@ -141,7 +141,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu-latest]
-        python-version: ['3.7', '3.8', '3.9', '3.10']
+        python-version: ['3.8', '3.9', '3.10']
         model: [yolov8n]
         torch: [latest]
         include:

.github/workflows/publish.yml

@@ -63,7 +63,7 @@ jobs:
           python -m twine upload dist/* -u __token__ -p $PYPI_TOKEN
       - name: Deploy Docs
         continue-on-error: true
-        if: (github.event_name == 'push' && steps.check_pypi.outputs.increment == 'True') || github.event.inputs.docs == 'true'
+        if: ((github.event_name == 'push' && (contains(github.event.head_commit.message, 'docs/') || contains(github.event.head_commit.message, 'mkdocs.yaml'))) || github.event.inputs.docs == 'true') && github.repository == 'ultralytics/ultralytics' && github.actor == 'glenn-jocher'
         env:
           PERSONAL_ACCESS_TOKEN: ${{ secrets.PERSONAL_ACCESS_TOKEN }}
         run: |

README.md

@@ -20,7 +20,7 @@
 
 [Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO versions and introduces new features and improvements to further boost performance and flexibility. YOLOv8 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.
 
-We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.gg/n6cFeSPZdD">Discord</a> community for questions and discussions!
+We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 <a href="https://docs.ultralytics.com/">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.gg/7aegy5d8">Discord</a> community for questions and discussions!
 
 To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
 
@@ -45,7 +45,7 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://discord.gg/7aegy5d8" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
 </div>
 </div>
@@ -237,7 +237,7 @@ YOLOv8 is available under two different licenses:
 
 ## <div align="center">Contact</div>
 
-For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://discord.gg/n6cFeSPZdD) community for questions and discussions!
+For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues), and join our [Discord](https://discord.gg/7aegy5d8) community for questions and discussions!
 
 <br>
 <div align="center">
@@ -259,6 +259,6 @@ For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https:/
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://discord.gg/7aegy5d8" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
 </div>

README.zh-CN.md

@@ -20,7 +20,7 @@
 
 [Ultralytics](https://ultralytics.com) [YOLOv8](https://github.com/ultralytics/ultralytics) 是一款前沿、最先进（SOTA）的模型，基于先前 YOLO 版本的成功，引入了新功能和改进，进一步提升性能和灵活性。YOLOv8 设计快速、准确且易于使用，使其成为各种物体检测与跟踪、实例分割、图像分类和姿态估计任务的绝佳选择。
 
-我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 <a href="https://docs.ultralytics.com/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://discord.gg/n6cFeSPZdD">Discord</a> 社区进行问题和讨论！
+我们希望这里的资源能帮助您充分利用 YOLOv8。请浏览 YOLOv8 <a href="https://docs.ultralytics.com/">文档</a> 了解详细信息，在 <a href="https://github.com/ultralytics/ultralytics/issues/new/choose">GitHub</a> 上提交问题以获得支持，并加入我们的 <a href="https://discord.gg/7aegy5d8">Discord</a> 社区进行问题和讨论！
 
 如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格
 
@@ -45,7 +45,7 @@
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://discord.gg/7aegy5d8" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="2%" alt="" /></a>
 </div>
 </div>
@@ -236,7 +236,7 @@ YOLOv8 提供两种不同的许可证：
 
 ## <div align="center">联系方式</div>
 
-对于 YOLOv8 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)，并加入我们的 [Discord](https://discord.gg/n6cFeSPZdD) 社区进行问题和讨论！
+对于 YOLOv8 的错误报告和功能请求，请访问 [GitHub Issues](https://github.com/ultralytics/ultralytics/issues)，并加入我们的 [Discord](https://discord.gg/7aegy5d8) 社区进行问题和讨论！
 
 <br>
 <div align="center">
@@ -257,6 +257,6 @@ YOLOv8 提供两种不同的许可证：
   <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
   <a href="https://www.instagram.com/ultralytics/" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="" /></a>
-  <a href="https://discord.gg/n6cFeSPZdD" style="text-decoration:none;">
+  <a href="https://discord.gg/7aegy5d8" style="text-decoration:none;">
     <img src="https://github.com/ultralytics/assets/blob/main/social/logo-social-discord.png" width="3%" alt="" /></a>
 </div>

docs/README.md

@@ -1,5 +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.
+keywords: install Ultralytics package, deploy documentation, building locally, deploy site, GitHub Pages, GitLab Pages, Amazon S3, MkDocs documentation
 ---
 
 # Ultralytics Docs

docs/SECURITY.md

@@ -1,5 +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.
+keywords: Ultralytics, security policy, Snyk, CodeQL scanning, security vulnerability, security issues, report security issue
 ---
 
 # Security Policy

docs/datasets/classify/caltech101.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the Caltech-101 dataset, a collection of images for object recognition tasks in machine learning and computer vision algorithms.
+keywords: Caltech-101 Dataset, Object recognition tasks, Ultralytics YOLO Docs, training, testing, code snippets & examples, machine learning, computer vision
 ---
 
 # Caltech-101 Dataset

docs/datasets/classify/caltech256.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: Caltech-256, Dataset, Object Recognition, Image Classification, Convolutional Neural Networks, SVMs, YOLO, Deep Learning Models
 ---
 
 # Caltech-256 Dataset

docs/datasets/classify/cifar10.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: CIFAR-10 dataset, YOLO model training, image classification, deep learning, computer vision, object detection, machine learning, convolutional neural networks, Alex Krizhevsky
 ---
 
 # CIFAR-10 Dataset

docs/datasets/classify/cifar100.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: CIFAR-100 dataset, CIFAR-100 classes, CIFAR-100 structure, CIFAR-100 applications, CIFAR-100 usage, YOLO model training, machine learning, computer vision
 ---
 
 # CIFAR-100 Dataset

docs/datasets/classify/fashion-mnist.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: Fashion-MNIST, dataset, machine learning, image classification, convolutional neural networks, benchmarking, Zalando's article images
 ---
 
 # Fashion-MNIST Dataset
@@ -75,4 +76,4 @@ The example showcases the variety and complexity of the images in the Fashion-MN
 
 ## Acknowledgments
 
-If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.
+If you use the Fashion-MNIST dataset in your research or development work, please acknowledge the dataset by linking to the [GitHub repository](https://github.com/zalandoresearch/fashion-mnist). This dataset was made available by Zalando Research.

docs/datasets/classify/imagenet.md

@@ -1,6 +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.
+keywords: ImageNet, dataset, deep learning, computer vision, YOLO models, training, object recognition, image classification, object detection, WordNet, synsets, ILSVRC
 ---
 
 # ImageNet Dataset

docs/datasets/classify/imagenet10.md

@@ -1,6 +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.
+keywords: ImageNet10 dataset, ImageNet, small scale, subset, computer vision models, pipelines, testing, debugging, synsets, annotations, applications, structure, sample images, citations, acknowledgments, Ultralytics Docs
 ---
 
 # ImageNet10 Dataset

docs/datasets/classify/imagenette.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: ImageNette Dataset, ImageNette, training set, validation set, image classification, convolutional neural networks, machine learning, computer vision, ultralytics, yolov8n-cls.pt, python
 ---
 
 # ImageNette Dataset

docs/datasets/classify/imagewoof.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the ImageWoof dataset, a subset of the ImageNet consisting of 10 challenging-to-classify dog breed classes.
+keywords: ImageWoof dataset, dog breed images, image classification, noisy labels, deep learning models, CNN training, fastai
 ---
 
 # ImageWoof Dataset

docs/datasets/classify/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn how torchvision organizes classification image datasets. Use this code to create and train models. CLI and Python code shown.
+keywords: image classification, datasets, format, torchvision, YOLO, Ultralytics
 ---
 
 # Image Classification Datasets Overview

docs/datasets/classify/mnist.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: MNIST, EMNIST, dataset, handwritten digits, convolutional neural networks, support vector machines, machine learning, computer vision, image processing, benchmark data, Ultralytics
 ---
 
 # MNIST Dataset

docs/datasets/detect/argoverse.md

@@ -1,6 +1,7 @@
 ---
 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.
+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
 ---
 
 # Argoverse Dataset

docs/datasets/detect/coco.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the COCO dataset, designed to encourage research on object detection, segmentation, and captioning with standardized evaluation metrics.
+keywords: COCO dataset, object detection, segmentation, captioning, deep learning models, computer vision, benchmarking, data annotations, COCO Consortium
 ---
 
 # COCO Dataset

docs/datasets/detect/coco8.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Get started with Ultralytics COCO8. Ideal for testing and debugging object detection models or experimenting with new detection approaches.
+keywords: Ultralytics, COCO8, object detection dataset, YAML file format, dataset usage, COCO dataset, acknowledgments
 ---
 
 # COCO8 Dataset

docs/datasets/detect/globalwheat2020.md

@@ -1,6 +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.
+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
 ---
 
 # Global Wheat Head Dataset

docs/datasets/detect/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about supported dataset formats for training YOLO detection models, including Ultralytics YOLO and COCO, in this Object Detection Datasets Overview.
+keywords: object detection, datasets, formats, Ultralytics YOLO, label format, dataset file format, dataset definition, YOLO dataset, model configuration
 ---
 
 # Object Detection Datasets Overview

docs/datasets/detect/objects365.md

@@ -1,6 +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.
+keywords: Objects365 dataset, object detection, computer vision, deep learning, Ultralytics Docs
 ---
 
 # Objects365 Dataset
@@ -84,4 +85,4 @@ If you use the Objects365 dataset in your research or development work, please c
 }
 ```
 
-We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).
+We would like to acknowledge the team of researchers who created and maintain the Objects365 dataset as a valuable resource for the computer vision research community. For more information about the Objects365 dataset and its creators, visit the [Objects365 dataset website](https://www.objects365.org/).

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

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: SKU-110k, object detection, retail shelves, dataset, computer vision
 ---
 
 # SKU-110k Dataset

docs/datasets/detect/visdrone.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Discover the VisDrone dataset, a comprehensive benchmark for drone-based computer vision tasks, including object detection, tracking, and crowd counting.
+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
 ---
 
 # VisDrone Dataset

docs/datasets/detect/voc.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the VOC dataset, designed to encourage research on object detection, segmentation, and classification with standardized evaluation metrics.
+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
 ---
 
 # VOC Dataset

docs/datasets/detect/xview.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: xView dataset, overhead imagery, computer vision, deep learning models, satellite imagery analysis, object detection
 ---
 
 # xView Dataset
@@ -89,4 +90,4 @@ If you use the xView dataset in your research or development work, please cite t
 }
 ```
 
-We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).
+We would like to acknowledge the [Defense Innovation Unit](https://www.diu.mil/) (DIU) and the creators of the xView dataset for their valuable contribution to the computer vision research community. For more information about the xView dataset and its creators, visit the [xView dataset website](http://xviewdataset.org/).

docs/datasets/index.md

@@ -1,6 +1,7 @@
 ---
 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.
+keywords: ultralytics, computer vision, object detection, instance segmentation, pose estimation, image classification, multi-object tracking
 ---
 
 # Datasets Overview

docs/datasets/pose/coco.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the COCO-Pose dataset, designed to encourage research on pose estimation tasks with standardized evaluation metrics.
+keywords: COCO-Pose, COCO dataset, pose estimation, keypoints detection, computer vision, deep learning, YOLOv8n-pose, dataset configuration
 ---
 
 # COCO-Pose Dataset

docs/datasets/pose/coco8-pose.md

@@ -1,6 +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.
+keywords: coco8-pose dataset, ultralytics, object detection, pose detection, yolo, hub
 ---
 
 # COCO8-Pose Dataset

docs/datasets/pose/index.md

@@ -1,6 +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.
+keywords: pose estimation, datasets, supported formats, YAML file, object class index, keypoints, ultralytics YOLO format
 ---
 
 # Pose Estimation Datasets Overview

docs/datasets/segment/coco.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about the COCO-Seg dataset, designed for simple training of YOLO models on instance segmentation tasks.
+keywords: COCO-Seg, COCO, instance segmentation, segmentation annotations, computer vision, deep learning, data science, YOLO models, image size, open-source datasets
 ---
 
 # COCO-Seg Dataset

docs/datasets/segment/coco8-seg.md

@@ -1,6 +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.
+keywords: Ultralytics, COCO8-Seg, instance segmentation dataset, segmentation models, new detection approaches, COCO train 2017 set
 ---
 
 # COCO8-Seg Dataset

docs/datasets/segment/index.md

@@ -1,6 +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.
+keywords: instance segmentation datasets, yolov8 segmentations, yaml dataset format, auto annotation, convert label formats
 ---
 
 # Instance Segmentation Datasets Overview

docs/datasets/track/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Discover the datasets compatible with Multi-Object Detector. Train your trackers and make your detections more efficient with Ultralytics' YOLO.
+keywords: multi-object tracking, dataset format, ultralytics yolo, object detection, segmentation, pose model, python, cli
 ---
 
 # Multi-object Tracking Datasets Overview

docs/help/CI.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Understand all the Continuous Integration (CI) tests for Ultralytics repositories and see their statuses in a clear, concise table.
+keywords: Ultralytics, CI Tests, Continuous Integration, Docker Deployment, Broken Links, CodeQL, PyPI Publishing
 ---
 
 # Continuous Integration (CI)
@@ -31,4 +32,4 @@ If you notice a test failing, it would be a great help if you could report it th
 
 Remember, a successful CI test does not mean that everything is perfect. It is always recommended to manually review the code before deployment or merging changes.
 
-Happy coding!
+Happy coding!

docs/help/CLA.md

@@ -1,5 +1,6 @@
 ---
 description: Individual Contributor License Agreement. Settle Intellectual Property issues for Contributions made to anything open source released by Ultralytics.
+keywords: Ultralytics, Individual, Contributor, License, Agreement, open source, software, projects, contributions
 ---
 
 # Ultralytics Individual Contributor License Agreement

docs/help/FAQ.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: 'Get quick answers to common Ultralytics YOLO questions: Hardware requirements, fine-tuning, conversion, real-time detection, and accuracy tips.'
+keywords: Ultralytics YOLO, Frequently Asked Questions, hardware requirements, model fine-tuning, converting to ONNX, TensorFlow, real-time detection, improving model accuracy
 ---
 
 # Ultralytics YOLO Frequently Asked Questions (FAQ)

docs/help/code_of_conduct.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Read the Ultralytics Contributor Covenant Code of Conduct. Learn ways to create a welcoming community & consequences for inappropriate conduct.
+keywords: Ultralytics, contributor, covenant, code, conduct, pledge, standards, enforcement, harassment-free, community, guidelines
 ---
 
 # Ultralytics Contributor Covenant Code of Conduct

docs/help/contributing.md

@@ -1,6 +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.
+keywords: Ultralytics YOLO, Open source, Contribution guidelines, Pull requests, CLA, GitHub Actions CI Tests, Google-style docstrings
 ---
 
 # Contributing to Ultralytics Open-Source YOLO Repositories

docs/help/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Get comprehensive resources for Ultralytics YOLO repositories. Find guides, FAQs, MRE creation, CLA & more. Join the supportive community now!
+keywords: ultralytics, yolo, help, guide, resources, faq, contributing, continuous integration, contributor license agreement, minimum reproducible example, code of conduct, 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

@@ -1,6 +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.
+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
 ---
 
 # Creating a Minimum Reproducible Example for Bug Reports in Ultralytics YOLO Repositories

docs/hub/app/android.md

@@ -1,6 +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.
+keywords: Ultralytics, Android, app, YOLO models, real-time object detection, TensorFlow Lite, quantization, acceleration, delegates, performance variability
 ---
 
 # Ultralytics Android App: Real-time Object Detection with YOLO Models

docs/hub/app/index.md

@@ -1,6 +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.
+keywords: Ultralytics, HUB, App, Mobile, Object Detection, Image Recognition, YOLOv5, YOLOv8, Hardware Acceleration, Custom Model Training, iOS, Android
 ---
 
 # Ultralytics HUB App

docs/hub/app/ios.md

@@ -1,6 +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.
+keywords: YOLO, object detection, iOS app, Ultralytics, Apple Neural Engine, quantization, FP16, INT8, Core ML, machine learning
 ---
 
 # Ultralytics iOS App: Real-time Object Detection with YOLO Models

docs/hub/datasets.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Upload custom datasets to Ultralytics HUB for YOLOv5 and YOLOv8 models. Follow YAML structure, zip and upload. Scan & train new models.
+keywords: Ultralytics, HUB, Datasets, Upload, Visualize, Train, Custom Data, YAML, YOLOv5, YOLOv8
 ---
 
 # HUB Datasets

docs/hub/index.md

@@ -1,6 +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.'
+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
 ---
 
 # Ultralytics HUB
@@ -28,7 +29,7 @@ easily upload their data and train new models quickly. It offers a range of pre-
 templates to choose from, making it easy for users to get started with training their own models. Once a model is
 trained, it can be easily deployed and used for real-time object detection, instance segmentation and classification tasks.
 
-We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.gg/n6cFeSPZdD">Discord</a> community for questions and discussions!
+We hope that the resources here will help you get the most out of HUB. Please browse the HUB <a href="https://docs.ultralytics.com/hub">Docs</a> for details, raise an issue on <a href="https://github.com/ultralytics/hub/issues/new/choose">GitHub</a> for support, and join our <a href="https://discord.gg/7aegy5d8">Discord</a> community for questions and discussions!
 
 - [**Quickstart**](./quickstart.md). Start training and deploying YOLO models with HUB in seconds.
 - [**Datasets: Preparing and Uploading**](./datasets.md). Learn how to prepare and upload your datasets to HUB in YOLO format.

docs/hub/inference_api.md

@@ -1,6 +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.
+keywords: YOLO, object detection, Ultralytics, inference API, RESTful API
 ---
 
 # YOLO Inference API
@@ -454,4 +455,4 @@ YOLO pose models, such as `yolov8n-pose.pt`, can return JSON responses from loca
             }
           ]
         }
-        ```
+        ```

docs/hub/models.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Train and Deploy your Model to 13 different formats, including TensorFlow, ONNX, OpenVINO, CoreML, Paddle or directly on Mobile.
+keywords: Ultralytics, HUB, models, artificial intelligence, APIs, export models, TensorFlow, ONNX, Paddle, OpenVINO, CoreML, iOS, Android
 ---
 
 # HUB Models

docs/hub/projects.md

@@ -1,7 +1,169 @@
 ---
 comments: true
+description: Efficiently manage and compare AI models with Ultralytics HUB Projects. Create, share, and edit projects for streamlined model development.
+keywords: Ultralytics HUB projects, model management, model comparison, create project, share project, edit project, delete project, compare models
 ---
 
-# 🚧 Page Under Construction ⚒
+# Ultralytics HUB Projects
 
-This page is currently under construction!️ 👷Please check back later for updates. 😃🔜
+Ultralytics HUB projects provide an effective solution for consolidating and managing your models. If you are working with several models that perform similar tasks or have related purposes, Ultralytics HUB projects allow you to group these models together.
+
+This creates a unified and organized workspace that facilitates easier model management, comparison and development. Having similar models or various iterations together can facilitate rapid benchmarking, as you can compare their effectiveness. This can lead to faster, more insightful iterative development and refinement of your models.
+
+## Create Project
+
+Navigate to the [Projects](https://hub.ultralytics.com/projects) page by clicking on the **Projects** button in the sidebar.
+
+![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Projects button in the sidebar](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also create a project directly from the [Home](https://hub.ultralytics.com/home) page.
+
+    ![Ultralytics HUB screenshot of the Home page with an arrow pointing to the Create Project card](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_2.jpg)
+
+Click on the **Create Project** button on the top right of the page. This action will trigger the **Create Project** dialog, opening up a suite of options for tailoring your project to your needs.
+
+![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Create Project button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_3.jpg)
+
+Type the name of your project in the *Project name* field or keep the default name and finalize the project creation with a single click.
+
+You have the additional option to enrich your project with a description and a unique image, enhancing its recognizability on the Projects page.
+
+When you're happy with your project configuration, click **Create**.
+
+![Ultralytics HUB screenshot of the Create Project dialog with an arrow pointing to the Create button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_4.jpg)
+
+After your project is created, you will be able to access it from the Projects page.
+
+![Ultralytics HUB screenshot of the Projects page with an arrow pointing to one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_5.jpg)
+
+Next, [create a model](./models.md) inside your project.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Create Model button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_create_project_6.jpg)
+
+## Share Project
+
+!!! info "Info"
+
+    Ultralytics HUB's sharing functionality provides a convenient way to share projects with others. This feature is designed to accommodate both existing Ultralytics HUB users and those who have yet to create an account.
+
+??? note "Note"
+
+    You have control over the general access of your projects.
+
+    You can choose to set the general access to "Private", in which case, only you will have access to it. Alternatively, you can set the general access to "Unlisted" which grants viewing access to anyone who has the direct link to the project, regardless of whether they have an Ultralytics HUB account or not.
+
+Navigate to the Project page of the project you want to share, open the project actions dropdown and click on the **Share** option. This action will trigger the **Share Project** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Share option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also share a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Share option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_2.jpg)
+
+Set the general access to "Unlisted" and click **Save**.
+
+![Ultralytics HUB screenshot of the Share Project 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_share_project_3.jpg)
+
+!!! warning "Warning"
+
+    When changing the general access of a project, the general access of the models inside the project will be changed as well.
+
+Now, anyone who has the direct link to your project can view it.
+
+??? tip "Tip"
+
+    You can easily click on the project's link shown in the **Share Project** dialog to copy it.
+
+    ![Ultralytics HUB screenshot of the Share Project dialog with an arrow pointing to the project's link](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_share_project_4.jpg)
+
+## Edit Project
+
+Navigate to the Project page of the project you want to edit, open the project actions dropdown and click on the **Edit** option. This action will trigger the **Update Project** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Edit option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also edit a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Edit option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_2.jpg)
+
+Apply the desired modifications to your project and then confirm the changes by clicking **Save**.
+
+![Ultralytics HUB screenshot of the Update Project dialog with an arrow pointing to the Save button](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_edit_project_3.jpg)
+
+## Delete Project
+
+Navigate to the Project page of the project you want to delete, open the project actions dropdown and click on the **Delete** option. This action will delete the project.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Delete option](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_1.jpg)
+
+??? tip "Tip"
+
+    You can also delete a project directly from the [Projects](https://hub.ultralytics.com/projects) page.
+
+    ![Ultralytics HUB screenshot of the Projects page with an arrow pointing to the Delete option of one of the projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_2.jpg)
+
+!!! warning "Warning"
+
+    When deleting a project, the the models inside the project will be deleted as well.
+
+??? note "Note"
+
+    If you change your mind, you can restore the project 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 projects](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_delete_project_3.jpg)
+
+## Compare Models
+
+Navigate to the Project page of the project where the models you want to compare are located. To use the model comparison feature, click on the  **Charts** tab.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Charts tab](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_1.jpg)
+
+This will display all the relevant charts. Each chart corresponds to a different metric and contains the performance of each model for that metric. The models are represented by different colors and you can hover over each data point to get more information.
+
+![Ultralytics HUB screenshot of the Charts tab inside the Project page](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_2.jpg)
+
+??? tip "Tip"
+
+    Each chart can be enlarged for better visualization.
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with an arrow pointing to the expand icon](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_3.jpg)
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with one of the charts expanded](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_4.jpg)
+
+??? tip "Tip"
+
+    You have the flexibility to customize your view by selectively hiding certain models. This feature allows you to concentrate on the models of interest.
+
+    ![Ultralytics HUB screenshot of the Charts tab inside the Project page with an arrow pointing to the hide/unhide icon of one of the model](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_compare_models_5.jpg)
+
+## Reorder Models
+
+??? note "Note"
+
+    Ultralytics HUB's reordering functionality works only inside projects you own.
+
+Navigate to the Project page of the project where the models you want to reorder are located. Click on the designated reorder icon of the model you want to move and drag it to the desired location.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the reorder icon](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_reorder_models_1.jpg)
+
+## Transfer Models
+
+Navigate to the Project page of the project where the model you want to mode is located, open the project actions dropdown and click on the **Transfer** option. This action will trigger the **Transfer Model** dialog.
+
+![Ultralytics HUB screenshot of the Project page with an arrow pointing to the Transfer option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_1.jpg)
+
+??? tip "Tip"
+
+    You can also transfer a model directly from the [Models](https://hub.ultralytics.com/models) page.
+
+    ![Ultralytics HUB screenshot of the Models page with an arrow pointing to the Transfer option of one of the models](https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/projects/hub_transfer_models_2.jpg)
+
+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)

docs/index.md

@@ -1,6 +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.
+keywords: YOLOv8, object detection, image segmentation, computer vision, machine learning, deep learning, AGPL-3.0 License, Enterprise License
 ---
 
 <div align="center">
@@ -47,4 +48,4 @@ Ultralytics YOLO repositories like YOLOv3, YOLOv5, or YOLOv8 are available under
 - **AGPL-3.0 License**: See [LICENSE](https://github.com/ultralytics/ultralytics/blob/main/LICENSE) file for details.
 - **Enterprise License**: Provides greater flexibility for commercial product development without the open-source requirements of AGPL-3.0. Typical use cases are embedding Ultralytics software and AI models in commercial products and applications. Request an Enterprise License at [Ultralytics Licensing](https://ultralytics.com/license).
 
-Please note our licensing approach ensures that any enhancements made to our open-source projects are shared back to the community. We firmly believe in the principles of open source, and we are committed to ensuring that our work can be used and improved upon in a manner that benefits everyone.
+Please note our licensing approach ensures that any enhancements made to our open-source projects are shared back to the community. We firmly believe in the principles of open source, and we are committed to ensuring that our work can be used and improved upon in a manner that benefits everyone.

docs/models/index.md

@@ -1,6 +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.
+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
 ---
 
 # Models
@@ -9,13 +10,15 @@ Ultralytics supports many models and architectures with more to come in the futu
 
 In this documentation, we provide information on four major models:
 
-1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family, known for its efficient real-time object detection capabilities.
-2. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture, offering better performance and speed tradeoffs compared to previous versions.
-3. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
-4. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
-5. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
-6. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
-7. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
+1. [YOLOv3](./yolov3.md): The third iteration of the YOLO model family originally by Joseph Redmon, known for its efficient real-time object detection capabilities.
+2. [YOLOv4](./yolov3.md): A darknet-native update to YOLOv3 released by Alexey Bochkovskiy in 2020.
+3. [YOLOv5](./yolov5.md): An improved version of the YOLO architecture by Ultralytics, offering better performance and speed tradeoffs compared to previous versions.
+4. [YOLOv6](./yolov6.md): Released by [Meituan](https://about.meituan.com/) in 2022 and is in use in many of the company's autonomous delivery robots.
+5. [YOLOv7](./yolov7.md): Updated YOLO models released in 2022 by the authors of YOLOv4.
+6. [YOLOv8](./yolov8.md): The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
+7. [Segment Anything Model (SAM)](./sam.md): Meta's Segment Anything Model (SAM).
+8. [YOLO-NAS](./yolo-nas.md): YOLO Neural Architecture Search (NAS) Models.
+9. [Realtime Detection Transformers (RT-DETR)](./rtdetr.md): Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
 
 You can use these models directly in the Command Line Interface (CLI) or in a Python environment. Below are examples of how to use the models with CLI and Python:
 
@@ -36,4 +39,4 @@ 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/rtdetr.md

@@ -1,6 +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.
+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
 ---
 
 # Baidu's RT-DETR: A Vision Transformer-Based Real-Time Object Detector

docs/models/sam.md

@@ -1,6 +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.
+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
 ---
 
 # Segment Anything Model (SAM)
@@ -95,4 +96,4 @@ 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

@@ -1,6 +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.
+keywords: YOLO-NAS, Deci AI, Ultralytics, object detection, deep learning, neural architecture search, Python API, pre-trained models, quantization
 ---
 
 # YOLO-NAS

docs/models/yolov3.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: YOLOv3, YOLOv3-Ultralytics and YOLOv3u by Ultralytics explained. Learn the evolution of these models and their specifications.
+keywords: YOLOv3, Ultralytics YOLOv3, YOLO v3, YOLOv3 models, object detection, models, machine learning, AI, image recognition, object recognition
 ---
 
 # YOLOv3, YOLOv3-Ultralytics, and YOLOv3u

docs/models/yolov4.md

@@ -0,0 +1,67 @@
+---
+comments: true
+description: Explore YOLOv4, a state-of-the-art, real-time object detector. Learn about its architecture, features, and performance.
+keywords: YOLOv4, object detection, real-time, CNN, GPU, Ultralytics, documentation, YOLOv4 architecture, YOLOv4 features, YOLOv4 performance
+---
+
+# YOLOv4: High-Speed and Precise Object Detection
+
+Welcome to the Ultralytics documentation page for YOLOv4, a state-of-the-art, real-time object detector launched in 2020 by Alexey Bochkovskiy at [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet). YOLOv4 is designed to provide the optimal balance between speed and accuracy, making it an excellent choice for many applications.
+
+![YOLOv4 architecture diagram](https://user-images.githubusercontent.com/26833433/246185689-530b7fe8-737b-4bb0-b5dd-de10ef5aface.png)
+**YOLOv4 architecture diagram**. Showcasing the intricate network design of YOLOv4, including the backbone, neck, and head components, and their interconnected layers for optimal real-time object detection.
+
+## Introduction
+
+YOLOv4 stands for You Only Look Once version 4. It is a real-time object detection model developed to address the limitations of previous YOLO versions like [YOLOv3](./yolov3.md) and other object detection models. Unlike other convolutional neural network (CNN) based object detectors, YOLOv4 is not only applicable for recommendation systems but also for standalone process management and human input reduction. Its operation on conventional graphics processing units (GPUs) allows for mass usage at an affordable price, and it is designed to work in real-time on a conventional GPU while requiring only one such GPU for training.
+
+## Architecture
+
+YOLOv4 makes use of several innovative features that work together to optimize its performance. These include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT), Mish-activation, Mosaic data augmentation, DropBlock regularization, and CIoU loss. These features are combined to achieve state-of-the-art results.
+
+A typical object detector is composed of several parts including the input, the backbone, the neck, and the head. The backbone of YOLOv4 is pre-trained on ImageNet and is used to predict classes and bounding boxes of objects. The backbone could be from several models including VGG, ResNet, ResNeXt, or DenseNet. The neck part of the detector is used to collect feature maps from different stages and usually includes several bottom-up paths and several top-down paths. The head part is what is used to make the final object detections and classifications.
+
+## Bag of Freebies
+
+YOLOv4 also makes use of methods known as "bag of freebies," which are techniques that improve the accuracy of the model during training without increasing the cost of inference. Data augmentation is a common bag of freebies technique used in object detection, which increases the variability of the input images to improve the robustness of the model. Some examples of data augmentation include photometric distortions (adjusting the brightness, contrast, hue, saturation, and noise of an image) and geometric distortions (adding random scaling, cropping, flipping, and rotating). These techniques help the model to generalize better to different types of images.
+
+## Features and Performance
+
+YOLOv4 is designed for optimal speed and accuracy in object detection. The architecture of YOLOv4 includes CSPDarknet53 as the backbone, PANet as the neck, and YOLOv3 as the detection head. This design allows YOLOv4 to perform object detection at an impressive speed, making it suitable for real-time applications. YOLOv4 also excels in accuracy, achieving state-of-the-art results in object detection benchmarks.
+
+## Usage Examples
+
+As of the time of writing, Ultralytics does not currently support YOLOv4 models. Therefore, any users interested in using YOLOv4 will need to refer directly to the YOLOv4 GitHub repository for installation and usage instructions.
+
+Here is a brief overview of the typical steps you might take to use YOLOv4:
+
+1. Visit the YOLOv4 GitHub repository: [https://github.com/AlexeyAB/darknet](https://github.com/AlexeyAB/darknet).
+
+2. Follow the instructions provided in the README file for installation. This typically involves cloning the repository, installing necessary dependencies, and setting up any necessary environment variables.
+
+3. Once installation is complete, you can train and use the model as per the usage instructions provided in the repository. This usually involves preparing your dataset, configuring the model parameters, training the model, and then using the trained model to perform object detection.
+
+Please note that the specific steps may vary depending on your specific use case and the current state of the YOLOv4 repository. Therefore, it is strongly recommended to refer directly to the instructions provided in the YOLOv4 GitHub repository.
+
+We regret any inconvenience this may cause and will strive to update this document with usage examples for Ultralytics once support for YOLOv4 is implemented.
+
+## Conclusion
+
+YOLOv4 is a powerful and efficient object detection model that strikes a balance between speed and accuracy. Its use of unique features and bag of freebies techniques during training allows it to perform excellently in real-time object detection tasks. YOLOv4 can be trained and used by anyone with a conventional GPU, making it accessible and practical for a wide range of applications.
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the YOLOv4 authors for their significant contributions in the field of real-time object detection:
+
+```bibtex
+@misc{bochkovskiy2020yolov4,
+      title={YOLOv4: Optimal Speed and Accuracy of Object Detection}, 
+      author={Alexey Bochkovskiy and Chien-Yao Wang and Hong-Yuan Mark Liao},
+      year={2020},
+      eprint={2004.10934},
+      archivePrefix={arXiv},
+      primaryClass={cs.CV}
+}
+```
+
+The original YOLOv4 paper can be found on [arXiv](https://arxiv.org/pdf/2004.10934.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/AlexeyAB/darknet). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

docs/models/yolov5.md

@@ -1,6 +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.
+keywords: YOLOv5, Ultralytics YOLOv5, YOLO v5, YOLOv5 models, YOLO, object detection, model, neural network, accuracy, speed, pre-trained weights, inference, validation, training
 ---
 
 # YOLOv5

docs/models/yolov6.md

@@ -1,6 +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.
+keywords: Meituan, YOLOv6, object detection, Bi-directional Concatenation (BiC), anchor-aided training (AAT), pre-trained models, high-resolution input, real-time, ultra-fast computations
 ---
 
 # Meituan YOLOv6
@@ -78,4 +79,4 @@ We would like to acknowledge the authors for their significant contributions in
 }
 ```
 
-The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.
+The original YOLOv6 paper can be found on [arXiv](https://arxiv.org/abs/2301.05586). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/meituan/YOLOv6). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

docs/models/yolov7.md

@@ -0,0 +1,61 @@
+---
+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.
+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
+---
+
+# YOLOv7: Trainable Bag-of-Freebies
+
+YOLOv7 is a state-of-the-art real-time object detector that surpasses all known object detectors in both speed and accuracy in the range from 5 FPS to 160 FPS. It has the highest accuracy (56.8% AP) among all known real-time object detectors with 30 FPS or higher on GPU V100. Moreover, YOLOv7 outperforms other object detectors such as YOLOR, YOLOX, Scaled-YOLOv4, YOLOv5, and many others in speed and accuracy. The model is trained on the MS COCO dataset from scratch without using any other datasets or pre-trained weights. Source code for YOLOv7 is available on GitHub.
+
+![YOLOv7 comparison with SOTA object detectors](https://github.com/ultralytics/ultralytics/assets/26833433/5e1e0420-8122-4c79-b8d0-2860aa79af92)
+**Comparison of state-of-the-art object detectors.** From the results in Table 2 we know that the proposed method has the best speed-accuracy trade-off comprehensively. If we compare YOLOv7-tiny-SiLU with YOLOv5-N (r6.1), our method is 127 fps faster and 10.7% more accurate on AP. In addition, YOLOv7 has 51.4% AP at frame rate of 161 fps, while PPYOLOE-L with the same AP has only 78 fps frame rate. In terms of parameter usage, YOLOv7 is 41% less than PPYOLOE-L. If we compare YOLOv7-X with 114 fps inference speed to YOLOv5-L (r6.1) with 99 fps inference speed, YOLOv7-X can improve AP by 3.9%. If YOLOv7-X is compared with YOLOv5-X (r6.1) of similar scale, the inference speed of YOLOv7-X is 31 fps faster. In addition, in terms the amount of parameters and computation, YOLOv7-X reduces 22% of parameters and 8% of computation compared to YOLOv5-X (r6.1), but improves AP by 2.2% ([Source](https://arxiv.org/pdf/2207.02696.pdf)).
+
+## Overview
+
+Real-time object detection is an important component in many computer vision systems, including multi-object tracking, autonomous driving, robotics, and medical image analysis. In recent years, real-time object detection development has focused on designing efficient architectures and improving the inference speed of various CPUs, GPUs, and neural processing units (NPUs). YOLOv7 supports both mobile GPU and GPU devices, from the edge to the cloud.
+
+Unlike traditional real-time object detectors that focus on architecture optimization, YOLOv7 introduces a focus on the optimization of the training process. This includes modules and optimization methods designed to improve the accuracy of object detection without increasing the inference cost, a concept known as the "trainable bag-of-freebies".
+
+## Key Features
+
+YOLOv7 introduces several key features:
+
+1. **Model Re-parameterization**: YOLOv7 proposes a planned re-parameterized model, which is a strategy applicable to layers in different networks with the concept of gradient propagation path.
+
+2. **Dynamic Label Assignment**: The training of the model with multiple output layers presents a new issue: "How to assign dynamic targets for the outputs of different branches?" To solve this problem, YOLOv7 introduces a new label assignment method called coarse-to-fine lead guided label assignment.
+
+3. **Extended and Compound Scaling**: YOLOv7 proposes "extend" and "compound scaling" methods for the real-time object detector that can effectively utilize parameters and computation.
+
+4. **Efficiency**: The method proposed by YOLOv7 can effectively reduce about 40% parameters and 50% computation of state-of-the-art real-time object detector, and has faster inference speed and higher detection accuracy.
+
+## Usage Examples
+
+As of the time of writing, Ultralytics does not currently support YOLOv7 models. Therefore, any users interested in using YOLOv7 will need to refer directly to the YOLOv7 GitHub repository for installation and usage instructions.
+
+Here is a brief overview of the typical steps you might take to use YOLOv7:
+
+1. Visit the YOLOv7 GitHub repository: [https://github.com/WongKinYiu/yolov7](https://github.com/WongKinYiu/yolov7).
+
+2. Follow the instructions provided in the README file for installation. This typically involves cloning the repository, installing necessary dependencies, and setting up any necessary environment variables.
+
+3. Once installation is complete, you can train and use the model as per the usage instructions provided in the repository. This usually involves preparing your dataset, configuring the model parameters, training the model, and then using the trained model to perform object detection.
+
+Please note that the specific steps may vary depending on your specific use case and the current state of the YOLOv7 repository. Therefore, it is strongly recommended to refer directly to the instructions provided in the YOLOv7 GitHub repository.
+
+We regret any inconvenience this may cause and will strive to update this document with usage examples for Ultralytics once support for YOLOv7 is implemented.
+
+## Citations and Acknowledgements
+
+We would like to acknowledge the YOLOv7 authors for their significant contributions in the field of real-time object detection:
+
+```bibtex
+@article{wang2022yolov7,
+  title={{YOLOv7}: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors},
+  author={Wang, Chien-Yao and Bochkovskiy, Alexey and Liao, Hong-Yuan Mark},
+  journal={arXiv preprint arXiv:2207.02696},
+  year={2022}
+}
+```
+
+The original YOLOv7 paper can be found on [arXiv](https://arxiv.org/pdf/2207.02696.pdf). The authors have made their work publicly available, and the codebase can be accessed on [GitHub](https://github.com/WongKinYiu/yolov7). We appreciate their efforts in advancing the field and making their work accessible to the broader community.

docs/models/yolov8.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn about YOLOv8's pre-trained weights supporting detection, instance segmentation, pose, and classification tasks. Get performance details.
+keywords: YOLOv8, real-time object detection, object detection, deep learning, machine learning
 ---
 
 # YOLOv8

docs/modes/benchmark.md

@@ -1,6 +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.
+keywords: YOLOv8, Benchmark Mode, Export Formats, ONNX, OpenVINO, TensorRT, Ultralytics Docs
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/export.md

@@ -1,6 +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.'
+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
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/index.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Use Ultralytics YOLOv8 Modes (Train, Val, Predict, Export, Track, Benchmark) to train, validate, predict, track, export or benchmark.
+keywords: yolov8, yolo, ultralytics, training, validation, prediction, export, tracking, benchmarking, real-time object detection, object tracking
 ---
 
 # Ultralytics YOLOv8 Modes

docs/modes/predict.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Get started with YOLOv8 Predict mode and input sources. Accepts various input sources such as images, videos, and directories.
+keywords: YOLOv8, predict mode, generator, streaming mode, input sources, video formats, arguments customization
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">
@@ -300,4 +301,4 @@ Here's a Python script using OpenCV (cv2) and YOLOv8 to run inference on video f
     # Release the video capture object and close the display window
     cap.release()
     cv2.destroyAllWindows()
-    ```
+    ```

docs/modes/track.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Explore YOLOv8n-based object tracking with Ultralytics' BoT-SORT and ByteTrack. Learn configuration, usage, and customization tips.
+keywords: object tracking, YOLO, trackers, BoT-SORT, ByteTrack
 ---
 
 <img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png">
@@ -97,5 +98,4 @@ any configurations(expect the `tracker_type`) you need to.
         ```
 
 Please refer to [ultralytics/tracker/cfg](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/tracker/cfg)
-page
-
+page

docs/modes/train.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Learn how to train custom YOLOv8 models on various datasets, configure hyperparameters, and use Ultralytics' YOLO for seamless training.
+keywords: YOLOv8, train mode, train a custom YOLOv8 model, hyperparameters, train a model, Comet, ClearML, TensorBoard, logging, loggers
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/modes/val.md

@@ -1,6 +1,7 @@
 ---
 comments: true
 description: Validate and improve YOLOv8n model accuracy on COCO128 and other datasets using hyperparameter & configuration tuning, in Val mode.
+keywords: Ultralytics, YOLO, YOLOv8, Val, Validation, Hyperparameters, Performance, Accuracy, Generalization, COCO, Export Formats, PyTorch
 ---
 
 <img width="1024" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png">

docs/quickstart.md

@@ -1,6 +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.
+keywords: YOLOv8, object detection, segmentation, classification, pip, git, CLI, Python
 ---
 
 ## Install

docs/reference/hub/auth.md

@@ -1,8 +1,9 @@
 ---
 description: Learn how to use Ultralytics hub authentication in your projects with examples and guidelines from the Auth page on Ultralytics Docs.
+keywords: Ultralytics, ultralytics hub, api keys, authentication, collab accounts, requests, hub management, monitoring
 ---
 
 # Auth
 ---
 :::ultralytics.hub.auth.Auth
-<br><br>
+<br><br>

docs/reference/hub/session.md

@@ -1,8 +1,9 @@
 ---
 description: Accelerate your AI development with the Ultralytics HUB Training Session. High-performance training of object detection models.
+keywords: YOLOv5, object detection, HUBTrainingSession, custom models, Ultralytics Docs
 ---
 
 # HUBTrainingSession
 ---
 :::ultralytics.hub.session.HUBTrainingSession
-<br><br>
+<br><br>

docs/reference/hub/utils.md

@@ -1,5 +1,6 @@
 ---
 description: Explore Ultralytics events, including 'request_with_credentials' and 'smart_request', to improve your project's performance and efficiency.
+keywords: Ultralytics, Hub Utils, API Documentation, Python, requests_with_progress, Events, classes, usage, examples
 ---
 
 # Events
@@ -20,4 +21,4 @@ description: Explore Ultralytics events, including 'request_with_credentials' an
 # smart_request
 ---
 :::ultralytics.hub.utils.smart_request
-<br><br>
+<br><br>

docs/reference/nn/autobackend.md

@@ -1,5 +1,6 @@
 ---
 description: Ensure class names match filenames for easy imports. Use AutoBackend to automatically rename and refactor model files.
+keywords: AutoBackend, ultralytics, nn, autobackend, check class names, neural network
 ---
 
 # AutoBackend
@@ -10,4 +11,4 @@ description: Ensure class names match filenames for easy imports. Use AutoBacken
 # check_class_names
 ---
 :::ultralytics.nn.autobackend.check_class_names
-<br><br>
+<br><br>

docs/reference/nn/autoshape.md

@@ -1,5 +1,6 @@
 ---
 description: Detect 80+ object categories with bounding box coordinates and class probabilities using AutoShape in Ultralytics YOLO. Explore Detections now.
+keywords: Ultralytics, YOLO, docs, autoshape, detections, object detection, customized shapes, bounding boxes, computer vision
 ---
 
 # AutoShape
@@ -10,4 +11,4 @@ description: Detect 80+ object categories with bounding box coordinates and clas
 # Detections
 ---
 :::ultralytics.nn.autoshape.Detections
-<br><br>
+<br><br>

docs/reference/nn/modules/block.md

@@ -1,5 +1,6 @@
 ---
 description: Explore ultralytics.nn.modules.block to build powerful YOLO object detection models. Master DFL, HGStem, SPP, CSP components and more.
+keywords: Ultralytics, NN Modules, Blocks, DFL, HGStem, SPP, C1, C2f, C3x, C3TR, GhostBottleneck, BottleneckCSP, Computer Vision
 ---
 
 # DFL
@@ -85,4 +86,4 @@ description: Explore ultralytics.nn.modules.block to build powerful YOLO object
 # BottleneckCSP
 ---
 :::ultralytics.nn.modules.block.BottleneckCSP
-<br><br>
+<br><br>

docs/reference/nn/modules/conv.md

@@ -1,5 +1,6 @@
 ---
 description: Explore convolutional neural network modules & techniques such as LightConv, DWConv, ConvTranspose, GhostConv, CBAM & autopad with Ultralytics Docs.
+keywords: Ultralytics, Convolutional Neural Network, Conv2, DWConv, ConvTranspose, GhostConv, ChannelAttention, CBAM, autopad
 ---
 
 # Conv
@@ -70,4 +71,4 @@ description: Explore convolutional neural network modules & techniques such as L
 # autopad
 ---
 :::ultralytics.nn.modules.conv.autopad
-<br><br>
+<br><br>

docs/reference/nn/modules/head.md

@@ -1,5 +1,6 @@
 ---
 description: 'Learn about Ultralytics YOLO modules: Segment, Classify, and RTDETRDecoder. Optimize object detection and classification in your project.'
+keywords: Ultralytics, YOLO, object detection, pose estimation, RTDETRDecoder, modules, classes, documentation
 ---
 
 # Detect
@@ -25,4 +26,4 @@ description: 'Learn about Ultralytics YOLO modules: Segment, Classify, and RTDET
 # RTDETRDecoder
 ---
 :::ultralytics.nn.modules.head.RTDETRDecoder
-<br><br>
+<br><br>

docs/reference/nn/modules/transformer.md

@@ -1,5 +1,6 @@
 ---
 description: Explore the Ultralytics nn modules pages on Transformer and MLP blocks, LayerNorm2d, and Deformable Transformer Decoder Layer.
+keywords: Ultralytics, NN Modules, TransformerEncoderLayer, TransformerLayer, MLPBlock, LayerNorm2d, DeformableTransformerDecoderLayer, examples, code snippets, tutorials
 ---
 
 # TransformerEncoderLayer
@@ -50,4 +51,4 @@ description: Explore the Ultralytics nn modules pages on Transformer and MLP blo
 # DeformableTransformerDecoder
 ---
 :::ultralytics.nn.modules.transformer.DeformableTransformerDecoder
-<br><br>
+<br><br>

docs/reference/nn/modules/utils.md

@@ -1,5 +1,6 @@
 ---
 description: 'Learn about Ultralytics NN modules: get_clones, linear_init_, and multi_scale_deformable_attn_pytorch. Code examples and usage tips.'
+keywords: Ultralytics, NN Utils, Docs, PyTorch, bias initialization, linear initialization, multi-scale deformable attention
 ---
 
 # _get_clones
@@ -25,4 +26,4 @@ description: 'Learn about Ultralytics NN modules: get_clones, linear_init_, and
 # multi_scale_deformable_attn_pytorch
 ---
 :::ultralytics.nn.modules.utils.multi_scale_deformable_attn_pytorch
-<br><br>
+<br><br>

docs/reference/nn/tasks.md

@@ -1,5 +1,6 @@
 ---
 description: Learn how to work with Ultralytics YOLO Detection, Segmentation & Classification Models, load weights and parse models in PyTorch.
+keywords: neural network, deep learning, computer vision, object detection, image segmentation, image classification, model ensemble, PyTorch
 ---
 
 # BaseModel
@@ -70,4 +71,4 @@ description: Learn how to work with Ultralytics YOLO Detection, Segmentation & C
 # guess_model_task
 ---
 :::ultralytics.nn.tasks.guess_model_task
-<br><br>
+<br><br>

docs/reference/tracker/track.md

@@ -1,5 +1,6 @@
 ---
 description: Learn how to register custom event-tracking and track predictions with Ultralytics YOLO via on_predict_start and register_tracker methods.
+keywords: Ultralytics YOLO, tracker registration, on_predict_start, object detection
 ---
 
 # on_predict_start
@@ -15,4 +16,4 @@ description: Learn how to register custom event-tracking and track predictions w
 # register_tracker
 ---
 :::ultralytics.tracker.track.register_tracker
-<br><br>
+<br><br>

docs/reference/tracker/trackers/basetrack.md

@@ -1,5 +1,6 @@
 ---
 description: 'TrackState: A comprehensive guide to Ultralytics tracker''s BaseTrack for monitoring model performance. Improve your tracking capabilities now!'
+keywords: object detection, object tracking, Ultralytics YOLO, TrackState, workflow improvement
 ---
 
 # TrackState
@@ -10,4 +11,4 @@ description: 'TrackState: A comprehensive guide to Ultralytics tracker''s BaseTr
 # BaseTrack
 ---
 :::ultralytics.tracker.trackers.basetrack.BaseTrack
-<br><br>
+<br><br>

docs/reference/tracker/trackers/bot_sort.md

@@ -1,5 +1,6 @@
 ---
 description: '"Optimize tracking with Ultralytics BOTrack. Easily sort and track bots with BOTSORT. Streamline data collection for improved performance."'
+keywords: BOTrack, Ultralytics YOLO Docs, features, usage
 ---
 
 # BOTrack
@@ -10,4 +11,4 @@ description: '"Optimize tracking with Ultralytics BOTrack. Easily sort and track
 # BOTSORT
 ---
 :::ultralytics.tracker.trackers.bot_sort.BOTSORT
-<br><br>
+<br><br>

docs/reference/tracker/trackers/byte_tracker.md

@@ -1,5 +1,6 @@
 ---
 description: Learn how to track ByteAI model sizes and tips for model optimization with STrack, a byte tracking tool from Ultralytics.
+keywords: Byte Tracker, Ultralytics STrack, application monitoring, bytes sent, bytes received, code examples, setup instructions
 ---
 
 # STrack
@@ -10,4 +11,4 @@ description: Learn how to track ByteAI model sizes and tips for model optimizati
 # BYTETracker
 ---
 :::ultralytics.tracker.trackers.byte_tracker.BYTETracker
-<br><br>
+<br><br>

docs/reference/tracker/utils/gmc.md

@@ -1,8 +1,9 @@
 ---
 description: '"Track Google Marketing Campaigns in GMC with Ultralytics Tracker. Learn to set up and use GMC for detailed analytics. Get started now."'
+keywords: Ultralytics, YOLO, object detection, tracker, optimization, models, documentation
 ---
 
 # GMC
 ---
 :::ultralytics.tracker.utils.gmc.GMC
-<br><br>
+<br><br>

docs/reference/tracker/utils/kalman_filter.md

@@ -1,5 +1,6 @@
 ---
 description: Improve object tracking with KalmanFilterXYAH in Ultralytics YOLO - an efficient and accurate algorithm for state estimation.
+keywords: KalmanFilterXYAH, Ultralytics Docs, Kalman filter algorithm, object tracking, computer vision, YOLO
 ---
 
 # KalmanFilterXYAH
@@ -10,4 +11,4 @@ description: Improve object tracking with KalmanFilterXYAH in Ultralytics YOLO -
 # KalmanFilterXYWH
 ---
 :::ultralytics.tracker.utils.kalman_filter.KalmanFilterXYWH
-<br><br>
+<br><br>

docs/reference/tracker/utils/matching.md

@@ -1,5 +1,6 @@
 ---
 description: Learn how to match and fuse object detections for accurate target tracking using Ultralytics' YOLO merge_matches, iou_distance, and embedding_distance.
+keywords: Ultralytics, multi-object tracking, object tracking, detection, recognition, matching, indices, iou distance, gate cost matrix, fuse iou, bbox ious
 ---
 
 # merge_matches
@@ -60,4 +61,4 @@ description: Learn how to match and fuse object detections for accurate target t
 # bbox_ious
 ---
 :::ultralytics.tracker.utils.matching.bbox_ious
-<br><br>
+<br><br>

docs/reference/yolo/data/annotator.md

@@ -1,8 +1,9 @@
 ---
 description: Learn how to use auto_annotate in Ultralytics YOLO to generate annotations automatically for your dataset. Simplify object detection workflows.
+keywords: Ultralytics YOLO, Auto Annotator, AI, image annotation, object detection, labelling, tool
 ---
 
 # auto_annotate
 ---
 :::ultralytics.yolo.data.annotator.auto_annotate
-<br><br>
+<br><br>

docs/reference/yolo/data/augment.md

@@ -1,5 +1,6 @@
 ---
 description: Use Ultralytics YOLO Data Augmentation transforms with Base, MixUp, and Albumentations for object detection and classification.
+keywords: YOLO, data augmentation, transforms, BaseTransform, MixUp, RandomHSV, Albumentations, ToTensor, classify_transforms, classify_albumentations
 ---
 
 # BaseTransform
@@ -95,4 +96,4 @@ description: Use Ultralytics YOLO Data Augmentation transforms with Base, MixUp,
 # classify_albumentations
 ---
 :::ultralytics.yolo.data.augment.classify_albumentations
-<br><br>
+<br><br>

docs/reference/yolo/data/base.md

@@ -1,8 +1,9 @@
 ---
 description: Learn about BaseDataset in Ultralytics YOLO, a flexible dataset class for object detection. Maximize your YOLO performance with custom datasets.
+keywords: BaseDataset, Ultralytics YOLO, object detection, real-world applications, documentation
 ---
 
 # BaseDataset
 ---
 :::ultralytics.yolo.data.base.BaseDataset
-<br><br>
+<br><br>

docs/reference/yolo/data/build.md

@@ -1,5 +1,6 @@
 ---
 description: Maximize YOLO performance with Ultralytics' InfiniteDataLoader, seed_worker, build_dataloader, and load_inference_source functions.
+keywords: Ultralytics, YOLO, object detection, data loading, build dataloader, load inference source
 ---
 
 # InfiniteDataLoader
@@ -35,4 +36,4 @@ description: Maximize YOLO performance with Ultralytics' InfiniteDataLoader, see
 # load_inference_source
 ---
 :::ultralytics.yolo.data.build.load_inference_source
-<br><br>
+<br><br>

docs/reference/yolo/data/converter.md

@@ -1,5 +1,6 @@
 ---
 description: Convert COCO-91 to COCO-80 class, RLE to polygon, and merge multi-segment images with Ultralytics YOLO data converter. Improve your object detection.
+keywords: Ultralytics, YOLO, converter, COCO91, COCO80, rle2polygon, merge_multi_segment, annotations
 ---
 
 # coco91_to_coco80_class
@@ -30,4 +31,4 @@ description: Convert COCO-91 to COCO-80 class, RLE to polygon, and merge multi-s
 # delete_dsstore
 ---
 :::ultralytics.yolo.data.converter.delete_dsstore
-<br><br>
+<br><br>

docs/reference/yolo/data/dataloaders/stream_loaders.md

@@ -1,5 +1,6 @@
 ---
 description: 'Ultralytics YOLO Docs: Learn about stream loaders for image and tensor data, as well as autocasting techniques. Check out SourceTypes and more.'
+keywords: Ultralytics YOLO, data loaders, stream load images, screenshots, tensor data, autocast list, youtube URL retriever
 ---
 
 # SourceTypes
@@ -40,4 +41,4 @@ description: 'Ultralytics YOLO Docs: Learn about stream loaders for image and te
 # get_best_youtube_url
 ---
 :::ultralytics.yolo.data.dataloaders.stream_loaders.get_best_youtube_url
-<br><br>
+<br><br>

docs/reference/yolo/data/dataloaders/v5augmentations.md

@@ -1,5 +1,6 @@
 ---
 description: Enhance image data with Albumentations CenterCrop, normalize, augment_hsv, replicate, random_perspective, cutout, & box_candidates.
+keywords: YOLO, object detection, data loaders, V5 augmentations, CenterCrop, normalize, random_perspective
 ---
 
 # Albumentations
@@ -85,4 +86,4 @@ description: Enhance image data with Albumentations CenterCrop, normalize, augme
 # classify_transforms
 ---
 :::ultralytics.yolo.data.dataloaders.v5augmentations.classify_transforms
-<br><br>
+<br><br>

docs/reference/yolo/data/dataloaders/v5loader.md

@@ -1,5 +1,6 @@
 ---
 description: Efficiently load images and labels to models using Ultralytics YOLO's InfiniteDataLoader, LoadScreenshots, and LoadStreams.
+keywords: YOLO, data loader, image classification, object detection, Ultralytics
 ---
 
 # InfiniteDataLoader
@@ -90,4 +91,4 @@ description: Efficiently load images and labels to models using Ultralytics YOLO
 # create_classification_dataloader
 ---
 :::ultralytics.yolo.data.dataloaders.v5loader.create_classification_dataloader
-<br><br>
+<br><br>

docs/reference/yolo/data/dataset.md

@@ -1,5 +1,6 @@
 ---
 description: Create custom YOLOv5 datasets with Ultralytics YOLODataset and SemanticDataset. Streamline your object detection and segmentation projects.
+keywords: YOLODataset, SemanticDataset, Ultralytics YOLO Docs, Object Detection, Segmentation
 ---
 
 # YOLODataset
@@ -15,4 +16,4 @@ description: Create custom YOLOv5 datasets with Ultralytics YOLODataset and Sema
 # SemanticDataset
 ---
 :::ultralytics.yolo.data.dataset.SemanticDataset
-<br><br>
+<br><br>

docs/reference/yolo/data/dataset_wrappers.md

@@ -1,8 +1,9 @@
 ---
 description: Create a custom dataset of mixed and oriented rectangular objects with Ultralytics YOLO's MixAndRectDataset.
+keywords: Ultralytics YOLO, MixAndRectDataset, dataset wrapper, image-level annotations, object-level annotations, rectangular object detection
 ---
 
 # MixAndRectDataset
 ---
 :::ultralytics.yolo.data.dataset_wrappers.MixAndRectDataset
-<br><br>
+<br><br>

docs/reference/yolo/data/utils.md

@@ -1,5 +1,6 @@
 ---
 description: Efficiently handle data in YOLO with Ultralytics. Utilize HUBDatasetStats and customize dataset with these data utility functions.
+keywords: YOLOv4, Object Detection, Computer Vision, Deep Learning, Convolutional Neural Network, CNN, Ultralytics Docs
 ---
 
 # HUBDatasetStats
@@ -65,4 +66,4 @@ description: Efficiently handle data in YOLO with Ultralytics. Utilize HUBDatase
 # zip_directory
 ---
 :::ultralytics.yolo.data.utils.zip_directory
-<br><br>
+<br><br>

docs/reference/yolo/engine/exporter.md

@@ -1,5 +1,6 @@
 ---
 description: Learn how to export your YOLO model in various formats using Ultralytics' exporter package - iOS, GDC, and more.
+keywords: Ultralytics, YOLO, exporter, iOS detect model, gd_outputs, export
 ---
 
 # Exporter
@@ -30,4 +31,4 @@ description: Learn how to export your YOLO model in various formats using Ultral
 # export
 ---
 :::ultralytics.yolo.engine.exporter.export
-<br><br>
+<br><br>