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

Co-authored-by: Glenn Jocher <[email protected]>
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