diff --git a/.github/ISSUE_TEMPLATE/config.yml b/.github/ISSUE_TEMPLATE/config.yml index 28d4b8f5143..2e31ad2930c 100644 --- a/.github/ISSUE_TEMPLATE/config.yml +++ b/.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 diff --git a/.github/workflows/ci.yaml b/.github/workflows/ci.yaml index 32ceff2c207..85cfb578dde 100644 --- a/.github/workflows/ci.yaml +++ b/.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: diff --git a/.github/workflows/publish.yml b/.github/workflows/publish.yml index c9be02c5bc9..ae759ecd66e 100644 --- a/.github/workflows/publish.yml +++ b/.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: | diff --git a/README.md b/README.md index 5c3c0eabe2d..c6ec78f5da2 100644 --- a/README.md +++ b/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 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! +We hope that the resources here will help you get the most out of YOLOv8. Please browse the YOLOv8 Docs for details, raise an issue on GitHub for support, and join our Discord 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 - + @@ -237,7 +237,7 @@ YOLOv8 is available under two different licenses: ##
Contact
-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!
@@ -259,6 +259,6 @@ For YOLOv8 bug reports and feature requests please visit [GitHub Issues](https:/ - +
diff --git a/README.zh-CN.md b/README.zh-CN.md index e401710f846..6e4ca425a05 100644 --- a/README.zh-CN.md +++ b/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 文档 了解详细信息，在 GitHub 上提交问题以获得支持，并加入我们的 Discord 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 文档 了解详细信息，在 GitHub 上提交问题以获得支持，并加入我们的 Discord 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 @@ - + @@ -236,7 +236,7 @@ YOLOv8 提供两种不同的许可证： ##
联系方式
-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！
@@ -257,6 +257,6 @@ YOLOv8 提供两种不同的许可证： - +
diff --git a/docs/README.md b/docs/README.md index de9d2a1e408..9b6df255d92 100644 --- a/docs/README.md +++ b/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 diff --git a/docs/SECURITY.md b/docs/SECURITY.md index 31eb007d040..a32fb4f7852 100644 --- a/docs/SECURITY.md +++ b/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 diff --git a/docs/datasets/classify/caltech101.md b/docs/datasets/classify/caltech101.md index 02955f8d5da..82335eeacf1 100644 --- a/docs/datasets/classify/caltech101.md +++ b/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 diff --git a/docs/datasets/classify/caltech256.md b/docs/datasets/classify/caltech256.md index ab9d84a3156..6f70f06529a 100644 --- a/docs/datasets/classify/caltech256.md +++ b/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 diff --git a/docs/datasets/classify/cifar10.md b/docs/datasets/classify/cifar10.md index 7a778be716b..adfdb466f83 100644 --- a/docs/datasets/classify/cifar10.md +++ b/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 diff --git a/docs/datasets/classify/cifar100.md b/docs/datasets/classify/cifar100.md index e0d82655685..d20abe9f8e8 100644 --- a/docs/datasets/classify/cifar100.md +++ b/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 diff --git a/docs/datasets/classify/fashion-mnist.md b/docs/datasets/classify/fashion-mnist.md index 4a947a249ae..c05d5f5d531 100644 --- a/docs/datasets/classify/fashion-mnist.md +++ b/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. \ No newline at end of file diff --git a/docs/datasets/classify/imagenet.md b/docs/datasets/classify/imagenet.md index 276b5ea1f05..ca4deec39cd 100644 --- a/docs/datasets/classify/imagenet.md +++ b/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 diff --git a/docs/datasets/classify/imagenet10.md b/docs/datasets/classify/imagenet10.md index 31ca2d1fd1d..069515cfb26 100644 --- a/docs/datasets/classify/imagenet10.md +++ b/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 diff --git a/docs/datasets/classify/imagenette.md b/docs/datasets/classify/imagenette.md index 0f77bfa5a64..371beee55d4 100644 --- a/docs/datasets/classify/imagenette.md +++ b/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 diff --git a/docs/datasets/classify/imagewoof.md b/docs/datasets/classify/imagewoof.md index 63d0f545d5f..17c346f53cd 100644 --- a/docs/datasets/classify/imagewoof.md +++ b/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 diff --git a/docs/datasets/classify/index.md b/docs/datasets/classify/index.md index 0b206671fc4..fd902882dca 100644 --- a/docs/datasets/classify/index.md +++ b/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 diff --git a/docs/datasets/classify/mnist.md b/docs/datasets/classify/mnist.md index 0bde6bca326..054173cf470 100644 --- a/docs/datasets/classify/mnist.md +++ b/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 diff --git a/docs/datasets/detect/argoverse.md b/docs/datasets/detect/argoverse.md index 8a53474dc9d..8d0acbc0499 100644 --- a/docs/datasets/detect/argoverse.md +++ b/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 diff --git a/docs/datasets/detect/coco.md b/docs/datasets/detect/coco.md index ffd4703987f..196977d4ee6 100644 --- a/docs/datasets/detect/coco.md +++ b/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 diff --git a/docs/datasets/detect/coco8.md b/docs/datasets/detect/coco8.md index a307d254313..5fb28234e8f 100644 --- a/docs/datasets/detect/coco8.md +++ b/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 diff --git a/docs/datasets/detect/globalwheat2020.md b/docs/datasets/detect/globalwheat2020.md index 596cbe73e25..66ad45c924e 100644 --- a/docs/datasets/detect/globalwheat2020.md +++ b/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 diff --git a/docs/datasets/detect/index.md b/docs/datasets/detect/index.md index ac31cb6daae..7eec93cd9bd 100644 --- a/docs/datasets/detect/index.md +++ b/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 diff --git a/docs/datasets/detect/objects365.md b/docs/datasets/detect/objects365.md index 8f6df54f622..25a109962c6 100644 --- a/docs/datasets/detect/objects365.md +++ b/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/). \ No newline at end of file diff --git a/docs/datasets/detect/sku-110k.md b/docs/datasets/detect/sku-110k.md index be7b351dd8e..508f82adab6 100644 --- a/docs/datasets/detect/sku-110k.md +++ b/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 diff --git a/docs/datasets/detect/visdrone.md b/docs/datasets/detect/visdrone.md index 3742f500c38..272360135d8 100644 --- a/docs/datasets/detect/visdrone.md +++ b/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 diff --git a/docs/datasets/detect/voc.md b/docs/datasets/detect/voc.md index a04c22ce37b..4e5c546419f 100644 --- a/docs/datasets/detect/voc.md +++ b/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 diff --git a/docs/datasets/detect/xview.md b/docs/datasets/detect/xview.md index a34292040c6..92bac2f0430 100644 --- a/docs/datasets/detect/xview.md +++ b/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/). \ No newline at end of file diff --git a/docs/datasets/index.md b/docs/datasets/index.md index 6a384c5223d..72dbef66bbe 100644 --- a/docs/datasets/index.md +++ b/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 diff --git a/docs/datasets/pose/coco.md b/docs/datasets/pose/coco.md index 772b3273861..217f2b08ef5 100644 --- a/docs/datasets/pose/coco.md +++ b/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 diff --git a/docs/datasets/pose/coco8-pose.md b/docs/datasets/pose/coco8-pose.md index 6540f7d712d..a7a115b62d3 100644 --- a/docs/datasets/pose/coco8-pose.md +++ b/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 diff --git a/docs/datasets/pose/index.md b/docs/datasets/pose/index.md index 6dee62d8d1b..3c35bf72fa5 100644 --- a/docs/datasets/pose/index.md +++ b/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 diff --git a/docs/datasets/segment/coco.md b/docs/datasets/segment/coco.md index 44d2e57d646..bfb72923a83 100644 --- a/docs/datasets/segment/coco.md +++ b/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 diff --git a/docs/datasets/segment/coco8-seg.md b/docs/datasets/segment/coco8-seg.md index fd737fa7653..8b9721c4f66 100644 --- a/docs/datasets/segment/coco8-seg.md +++ b/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 diff --git a/docs/datasets/segment/index.md b/docs/datasets/segment/index.md index 32b66b76441..7d24e4160d7 100644 --- a/docs/datasets/segment/index.md +++ b/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 diff --git a/docs/datasets/track/index.md b/docs/datasets/track/index.md index e16e8f7f2c7..7a6ff5eb02c 100644 --- a/docs/datasets/track/index.md +++ b/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 diff --git a/docs/help/CI.md b/docs/help/CI.md index 45bf77435ec..2fdad908cc2 100644 --- a/docs/help/CI.md +++ b/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! \ No newline at end of file diff --git a/docs/help/CLA.md b/docs/help/CLA.md index e998bb7639e..ffc9371880f 100644 --- a/docs/help/CLA.md +++ b/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 diff --git a/docs/help/FAQ.md b/docs/help/FAQ.md index 0a0e70a3759..13692024be0 100644 --- a/docs/help/FAQ.md +++ b/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) diff --git a/docs/help/code_of_conduct.md b/docs/help/code_of_conduct.md index 29158100056..1cc27e16c8f 100644 --- a/docs/help/code_of_conduct.md +++ b/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 diff --git a/docs/help/contributing.md b/docs/help/contributing.md index b26f6ffba43..4fa8f633832 100644 --- a/docs/help/contributing.md +++ b/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 diff --git a/docs/help/index.md b/docs/help/index.md index 529b6ee298b..9647552b3fc 100644 --- a/docs/help/index.md +++ b/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. diff --git a/docs/help/minimum_reproducible_example.md b/docs/help/minimum_reproducible_example.md index 03335437dba..758287cc0ea 100644 --- a/docs/help/minimum_reproducible_example.md +++ b/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 diff --git a/docs/hub/app/android.md b/docs/hub/app/android.md index bcb95c07ea7..2f2ea9b4f7e 100644 --- a/docs/hub/app/android.md +++ b/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 diff --git a/docs/hub/app/index.md b/docs/hub/app/index.md index ffba7d61a78..76b8ce3ebbb 100644 --- a/docs/hub/app/index.md +++ b/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 diff --git a/docs/hub/app/ios.md b/docs/hub/app/ios.md index 2c134f6616e..ed49f45b0f6 100644 --- a/docs/hub/app/ios.md +++ b/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 diff --git a/docs/hub/datasets.md b/docs/hub/datasets.md index c4f06582dbe..f55e8a76ea4 100644 --- a/docs/hub/datasets.md +++ b/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 diff --git a/docs/hub/index.md b/docs/hub/index.md index 5ff95f1408d..6e25dc2787f 100644 --- a/docs/hub/index.md +++ b/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 Docs for details, raise an issue on GitHub for support, and join our Discord community for questions and discussions! +We hope that the resources here will help you get the most out of HUB. Please browse the HUB Docs for details, raise an issue on GitHub for support, and join our Discord 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. diff --git a/docs/hub/inference_api.md b/docs/hub/inference_api.md index 2244beb1dc4..22851abade9 100644 --- a/docs/hub/inference_api.md +++ b/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 } ] } - ``` + ``` \ No newline at end of file diff --git a/docs/hub/models.md b/docs/hub/models.md index 1d73d4530d9..5ae171f257c 100644 --- a/docs/hub/models.md +++ b/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 diff --git a/docs/hub/projects.md b/docs/hub/projects.md index 19480d27d82..6a006b36591 100644 --- a/docs/hub/projects.md +++ b/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) \ No newline at end of file diff --git a/docs/index.md b/docs/index.md index 1d835df5877..79768a1b002 100644 --- a/docs/index.md +++ b/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 ---
@@ -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. \ No newline at end of file diff --git a/docs/models/index.md b/docs/models/index.md index 051bfb514d7..cce8af13f1d 100644 --- a/docs/models/index.md +++ b/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. \ No newline at end of file diff --git a/docs/models/rtdetr.md b/docs/models/rtdetr.md index a38acbb3ef3..61d156abc59 100644 --- a/docs/models/rtdetr.md +++ b/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 diff --git a/docs/models/sam.md b/docs/models/sam.md index 12dd1159f8d..8dd1e35c24b 100644 --- a/docs/models/sam.md +++ b/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.* \ No newline at end of file diff --git a/docs/models/yolo-nas.md b/docs/models/yolo-nas.md index 9da81756a14..4ce38e85a04 100644 --- a/docs/models/yolo-nas.md +++ b/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 diff --git a/docs/models/yolov3.md b/docs/models/yolov3.md index 0ca49ee7df4..da1415e0606 100644 --- a/docs/models/yolov3.md +++ b/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 diff --git a/docs/models/yolov4.md b/docs/models/yolov4.md new file mode 100644 index 00000000000..36a09ccba90 --- /dev/null +++ b/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. \ No newline at end of file diff --git a/docs/models/yolov5.md b/docs/models/yolov5.md index e163f4272e4..959c06af140 100644 --- a/docs/models/yolov5.md +++ b/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 diff --git a/docs/models/yolov6.md b/docs/models/yolov6.md index b8239c86077..a8a2449f25c 100644 --- a/docs/models/yolov6.md +++ b/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. \ No newline at end of file diff --git a/docs/models/yolov7.md b/docs/models/yolov7.md new file mode 100644 index 00000000000..d8b1ea62745 --- /dev/null +++ b/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. \ No newline at end of file diff --git a/docs/models/yolov8.md b/docs/models/yolov8.md index 6e3adb5078f..8c78d87e77f 100644 --- a/docs/models/yolov8.md +++ b/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 diff --git a/docs/modes/benchmark.md b/docs/modes/benchmark.md index a7600e055e6..c1c159e6bed 100644 --- a/docs/modes/benchmark.md +++ b/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 --- diff --git a/docs/modes/export.md b/docs/modes/export.md index 2352cf4910a..42e45272aae 100644 --- a/docs/modes/export.md +++ b/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 --- diff --git a/docs/modes/index.md b/docs/modes/index.md index c9ae14aabd7..5a00afa5d39 100644 --- a/docs/modes/index.md +++ b/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 diff --git a/docs/modes/predict.md b/docs/modes/predict.md index 3deee7c7564..581d4221d52 100644 --- a/docs/modes/predict.md +++ b/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 --- @@ -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() - ``` + ``` \ No newline at end of file diff --git a/docs/modes/track.md b/docs/modes/track.md index c0d8adb5069..7b9a83a714b 100644 --- a/docs/modes/track.md +++ b/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 --- @@ -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 \ No newline at end of file diff --git a/docs/modes/train.md b/docs/modes/train.md index 1738975c1f2..882c0d1f1eb 100644 --- a/docs/modes/train.md +++ b/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 --- diff --git a/docs/modes/val.md b/docs/modes/val.md index bc294ecbf3e..79fdf6f8e36 100644 --- a/docs/modes/val.md +++ b/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 --- diff --git a/docs/quickstart.md b/docs/quickstart.md index b1fe2afc543..b762e5ab461 100644 --- a/docs/quickstart.md +++ b/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 diff --git a/docs/reference/hub/auth.md b/docs/reference/hub/auth.md index 6c19030704c..a8b7f9dbef7 100644 --- a/docs/reference/hub/auth.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/hub/session.md b/docs/reference/hub/session.md index 2d7033340e6..3b2115ca5af 100644 --- a/docs/reference/hub/session.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/hub/utils.md b/docs/reference/hub/utils.md index 5b710580cf2..bacefc84534 100644 --- a/docs/reference/hub/utils.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/autobackend.md b/docs/reference/nn/autobackend.md index 2166e7c80a6..9feb71b032e 100644 --- a/docs/reference/nn/autobackend.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/autoshape.md b/docs/reference/nn/autoshape.md index 2c5745e33a8..e17976ef8e8 100644 --- a/docs/reference/nn/autoshape.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/modules/block.md b/docs/reference/nn/modules/block.md index 58a23ed63f5..687e3080340 100644 --- a/docs/reference/nn/modules/block.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/modules/conv.md b/docs/reference/nn/modules/conv.md index 7cfaf012cb3..60d0345f4b8 100644 --- a/docs/reference/nn/modules/conv.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/modules/head.md b/docs/reference/nn/modules/head.md index 17488da73fd..b21124e21a2 100644 --- a/docs/reference/nn/modules/head.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/modules/transformer.md b/docs/reference/nn/modules/transformer.md index 654917d8284..8d6429d73aa 100644 --- a/docs/reference/nn/modules/transformer.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/modules/utils.md b/docs/reference/nn/modules/utils.md index 877c52c01b7..f7aa43f0ded 100644 --- a/docs/reference/nn/modules/utils.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/nn/tasks.md b/docs/reference/nn/tasks.md index 502b82d7495..3258e4ff920 100644 --- a/docs/reference/nn/tasks.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/tracker/track.md b/docs/reference/tracker/track.md index 51c48c9f317..156ee0002ab 100644 --- a/docs/reference/tracker/track.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/tracker/trackers/basetrack.md b/docs/reference/tracker/trackers/basetrack.md index d21f29e1cce..9b767eca3ff 100644 --- a/docs/reference/tracker/trackers/basetrack.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/tracker/trackers/bot_sort.md b/docs/reference/tracker/trackers/bot_sort.md index b2d0f9bbcdf..f3f50132ed0 100644 --- a/docs/reference/tracker/trackers/bot_sort.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/tracker/trackers/byte_tracker.md b/docs/reference/tracker/trackers/byte_tracker.md index c96f85a62eb..cbaf90a910d 100644 --- a/docs/reference/tracker/trackers/byte_tracker.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/tracker/utils/gmc.md b/docs/reference/tracker/utils/gmc.md index b208a4a1b3c..461b7fd88b0 100644 --- a/docs/reference/tracker/utils/gmc.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/tracker/utils/kalman_filter.md b/docs/reference/tracker/utils/kalman_filter.md index baa749c03d5..93217151cdb 100644 --- a/docs/reference/tracker/utils/kalman_filter.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/tracker/utils/matching.md b/docs/reference/tracker/utils/matching.md index 5d8474bee0d..4f1725dbbe8 100644 --- a/docs/reference/tracker/utils/matching.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/annotator.md b/docs/reference/yolo/data/annotator.md index 9e97df62775..25ca21aaf8b 100644 --- a/docs/reference/yolo/data/annotator.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/augment.md b/docs/reference/yolo/data/augment.md index 1cb38d06581..1bff44f60d8 100644 --- a/docs/reference/yolo/data/augment.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/base.md b/docs/reference/yolo/data/base.md index 2f14c1dd9ba..eb6defed282 100644 --- a/docs/reference/yolo/data/base.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/build.md b/docs/reference/yolo/data/build.md index b84cefffac6..d1f05084486 100644 --- a/docs/reference/yolo/data/build.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/converter.md b/docs/reference/yolo/data/converter.md index 34856337415..7f8833aa9ee 100644 --- a/docs/reference/yolo/data/converter.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/dataloaders/stream_loaders.md b/docs/reference/yolo/data/dataloaders/stream_loaders.md index afecea7aac6..536aa8d455c 100644 --- a/docs/reference/yolo/data/dataloaders/stream_loaders.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/dataloaders/v5augmentations.md b/docs/reference/yolo/data/dataloaders/v5augmentations.md index c75e57d5c4d..aa5f3f71005 100644 --- a/docs/reference/yolo/data/dataloaders/v5augmentations.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/dataloaders/v5loader.md b/docs/reference/yolo/data/dataloaders/v5loader.md index d8b3110445a..20161f8b7c9 100644 --- a/docs/reference/yolo/data/dataloaders/v5loader.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/dataset.md b/docs/reference/yolo/data/dataset.md index c0d181e3563..f5bd9a10116 100644 --- a/docs/reference/yolo/data/dataset.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/dataset_wrappers.md b/docs/reference/yolo/data/dataset_wrappers.md index 04e2997b99c..49a24af652b 100644 --- a/docs/reference/yolo/data/dataset_wrappers.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/data/utils.md b/docs/reference/yolo/data/utils.md index 3a0e9a4acf7..19bd739a5c2 100644 --- a/docs/reference/yolo/data/utils.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/engine/exporter.md b/docs/reference/yolo/engine/exporter.md index 4b19204243e..aef271b5c65 100644 --- a/docs/reference/yolo/engine/exporter.md +++ b/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 -

+

\ No newline at end of file diff --git a/docs/reference/yolo/engine/model.md b/docs/reference/yolo/engine/model.md index d9b90d766c7..be36339923c 100644 --- a/docs/reference/yolo/engine/model.md +++ b/docs/reference/yolo/engine/model.md @@ -1,8 +1,9 @@ --- description: Discover the YOLO model of Ultralytics engine to simplify your object detection tasks with state-of-the-art models. +keywords: YOLO, object detection, model, architecture, usage, customization, Ultralytics Docs --- # YOLO --- :::ultralytics.yolo.engine.model.YOLO -

+

\ No newline at end of file diff --git a/docs/reference/yolo/engine/predictor.md b/docs/reference/yolo/engine/predictor.md index 52540e738c2..ec17842f51c 100644 --- a/docs/reference/yolo/engine/predictor.md +++ b/docs/reference/yolo/engine/predictor.md @@ -1,8 +1,9 @@ --- description: '"The BasePredictor class in Ultralytics YOLO Engine predicts object detection in images and videos. Learn to implement YOLO with ease."' +keywords: Ultralytics, YOLO, BasePredictor, Object Detection, Computer Vision, Fast Model, Insights --- # BasePredictor --- :::ultralytics.yolo.engine.predictor.BasePredictor -

+

\ No newline at end of file diff --git a/docs/reference/yolo/engine/results.md b/docs/reference/yolo/engine/results.md index e64e603563f..504864cb147 100644 --- a/docs/reference/yolo/engine/results.md +++ b/docs/reference/yolo/engine/results.md @@ -1,5 +1,6 @@ --- description: Learn about BaseTensor & Boxes in Ultralytics YOLO Engine. Check out Ultralytics Docs for quality tutorials and resources on object detection. +keywords: YOLO, Engine, Results, Masks, Probs, Ultralytics --- # BaseTensor @@ -21,3 +22,13 @@ description: Learn about BaseTensor & Boxes in Ultralytics YOLO Engine. Check ou --- :::ultralytics.yolo.engine.results.Masks

+ +# Keypoints +--- +:::ultralytics.yolo.engine.results.Keypoints +

+ +# Probs +--- +:::ultralytics.yolo.engine.results.Probs +

\ No newline at end of file diff --git a/docs/reference/yolo/engine/trainer.md b/docs/reference/yolo/engine/trainer.md index 1892bbd88ca..fc51c24bc83 100644 --- a/docs/reference/yolo/engine/trainer.md +++ b/docs/reference/yolo/engine/trainer.md @@ -1,13 +1,9 @@ --- description: Train faster with mixed precision. Learn how to use BaseTrainer with Advanced Mixed Precision to optimize YOLOv3 and YOLOv4 models. +keywords: Ultralytics YOLO, BaseTrainer, object detection models, training guide --- # BaseTrainer --- :::ultralytics.yolo.engine.trainer.BaseTrainer -

- -# check_amp ---- -:::ultralytics.yolo.engine.trainer.check_amp -

+

\ No newline at end of file diff --git a/docs/reference/yolo/engine/validator.md b/docs/reference/yolo/engine/validator.md index e499fa78b48..d99b062559f 100644 --- a/docs/reference/yolo/engine/validator.md +++ b/docs/reference/yolo/engine/validator.md @@ -1,8 +1,9 @@ --- description: Ensure YOLOv5 models meet constraints and standards with the BaseValidator class. Learn how to use it here. +keywords: Ultralytics, YOLO, BaseValidator, models, validation, object detection --- # BaseValidator --- :::ultralytics.yolo.engine.validator.BaseValidator -

+

\ No newline at end of file diff --git a/docs/reference/yolo/nas/model.md b/docs/reference/yolo/nas/model.md new file mode 100644 index 00000000000..c5fe258469b --- /dev/null +++ b/docs/reference/yolo/nas/model.md @@ -0,0 +1,9 @@ +--- +description: Learn about the Neural Architecture Search (NAS) feature available in Ultralytics YOLO. Find out how NAS can improve object detection models and increase accuracy. Get started today!. +keywords: Ultralytics YOLO, object detection, NAS, Neural Architecture Search, model optimization, accuracy improvement +--- + +# NAS +--- +:::ultralytics.yolo.nas.model.NAS +

\ No newline at end of file diff --git a/docs/reference/yolo/nas/predict.md b/docs/reference/yolo/nas/predict.md new file mode 100644 index 00000000000..0b8a62d1fbe --- /dev/null +++ b/docs/reference/yolo/nas/predict.md @@ -0,0 +1,9 @@ +--- +description: Learn how to use NASPredictor in Ultralytics YOLO for deploying efficient CNN models with search algorithms in neural architecture search. +keywords: Ultralytics YOLO, NASPredictor, neural architecture search, efficient CNN models, search algorithms +--- + +# NASPredictor +--- +:::ultralytics.yolo.nas.predict.NASPredictor +

\ No newline at end of file diff --git a/docs/reference/yolo/nas/val.md b/docs/reference/yolo/nas/val.md new file mode 100644 index 00000000000..6f849a471fb --- /dev/null +++ b/docs/reference/yolo/nas/val.md @@ -0,0 +1,9 @@ +--- +description: Learn about NASValidator in the Ultralytics YOLO Docs. Properly validate YOLO neural architecture search results for optimal performance. +keywords: NASValidator, YOLO, neural architecture search, validation, performance, Ultralytics +--- + +# NASValidator +--- +:::ultralytics.yolo.nas.val.NASValidator +

\ No newline at end of file diff --git a/docs/reference/yolo/utils/autobatch.md b/docs/reference/yolo/utils/autobatch.md index a2bf4a3c690..dc7c0a8b8c1 100644 --- a/docs/reference/yolo/utils/autobatch.md +++ b/docs/reference/yolo/utils/autobatch.md @@ -1,5 +1,6 @@ --- description: Dynamically adjusts input size to optimize GPU memory usage during training. Learn how to use check_train_batch_size with Ultralytics YOLO. +keywords: YOLOv5, batch size, training, Ultralytics Autobatch, object detection, model performance --- # check_train_batch_size @@ -10,4 +11,4 @@ description: Dynamically adjusts input size to optimize GPU memory usage during # autobatch --- :::ultralytics.yolo.utils.autobatch.autobatch -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/benchmarks.md b/docs/reference/yolo/utils/benchmarks.md index e3abcade64a..39112eac232 100644 --- a/docs/reference/yolo/utils/benchmarks.md +++ b/docs/reference/yolo/utils/benchmarks.md @@ -1,5 +1,6 @@ --- description: Improve your YOLO's performance and measure its speed. Benchmark utility for YOLOv5. +keywords: Ultralytics YOLO, ProfileModels, benchmark, model inference, detection --- # ProfileModels @@ -10,4 +11,4 @@ description: Improve your YOLO's performance and measure its speed. Benchmark ut # benchmark --- :::ultralytics.yolo.utils.benchmarks.benchmark -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/base.md b/docs/reference/yolo/utils/callbacks/base.md index a448dac8c66..d82f0fd287e 100644 --- a/docs/reference/yolo/utils/callbacks/base.md +++ b/docs/reference/yolo/utils/callbacks/base.md @@ -1,5 +1,6 @@ --- description: Learn about YOLO's callback functions from on_train_start to add_integration_callbacks. See how these callbacks modify and save models. +keywords: YOLO, Ultralytics, callbacks, object detection, training, inference --- # on_pretrain_routine_start @@ -135,4 +136,4 @@ description: Learn about YOLO's callback functions from on_train_start to add_in # add_integration_callbacks --- :::ultralytics.yolo.utils.callbacks.base.add_integration_callbacks -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/clearml.md b/docs/reference/yolo/utils/callbacks/clearml.md index 8b7bbfce0ea..7dc01a38dfb 100644 --- a/docs/reference/yolo/utils/callbacks/clearml.md +++ b/docs/reference/yolo/utils/callbacks/clearml.md @@ -1,5 +1,6 @@ --- description: Improve your YOLOv5 model training with callbacks from ClearML. Learn about log debug samples, pre-training routines, validation and more. +keywords: Ultralytics YOLO, callbacks, log plots, epoch monitoring, training end events --- # _log_debug_samples @@ -35,4 +36,4 @@ description: Improve your YOLOv5 model training with callbacks from ClearML. Lea # on_train_end --- :::ultralytics.yolo.utils.callbacks.clearml.on_train_end -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/comet.md b/docs/reference/yolo/utils/callbacks/comet.md index 9e81dfc87b0..2e1bffe8344 100644 --- a/docs/reference/yolo/utils/callbacks/comet.md +++ b/docs/reference/yolo/utils/callbacks/comet.md @@ -1,5 +1,6 @@ --- description: Learn about YOLO callbacks using the Comet.ml platform, enhancing object detection training and testing with custom logging and visualizations. +keywords: Ultralytics, YOLO, callbacks, Comet ML, log images, log predictions, log plots, fetch metadata, fetch annotations, create experiment data, format experiment data --- # _get_comet_mode @@ -120,4 +121,4 @@ description: Learn about YOLO callbacks using the Comet.ml platform, enhancing o # on_train_end --- :::ultralytics.yolo.utils.callbacks.comet.on_train_end -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/dvc.md b/docs/reference/yolo/utils/callbacks/dvc.md new file mode 100644 index 00000000000..1ca463697dd --- /dev/null +++ b/docs/reference/yolo/utils/callbacks/dvc.md @@ -0,0 +1,54 @@ +--- +description: Explore Ultralytics YOLO Utils DVC Callbacks such as logging images, plots, confusion matrices, and training progress. +keywords: Ultralytics, YOLO, Utils, DVC, Callbacks, images, plots, confusion matrices, training progress +--- + +# _logger_disabled +--- +:::ultralytics.yolo.utils.callbacks.dvc._logger_disabled +

+ +# _log_images +--- +:::ultralytics.yolo.utils.callbacks.dvc._log_images +

+ +# _log_plots +--- +:::ultralytics.yolo.utils.callbacks.dvc._log_plots +

+ +# _log_confusion_matrix +--- +:::ultralytics.yolo.utils.callbacks.dvc._log_confusion_matrix +

+ +# on_pretrain_routine_start +--- +:::ultralytics.yolo.utils.callbacks.dvc.on_pretrain_routine_start +

+ +# on_pretrain_routine_end +--- +:::ultralytics.yolo.utils.callbacks.dvc.on_pretrain_routine_end +

+ +# on_train_start +--- +:::ultralytics.yolo.utils.callbacks.dvc.on_train_start +

+ +# on_train_epoch_start +--- +:::ultralytics.yolo.utils.callbacks.dvc.on_train_epoch_start +

+ +# on_fit_epoch_end +--- +:::ultralytics.yolo.utils.callbacks.dvc.on_fit_epoch_end +

+ +# on_train_end +--- +:::ultralytics.yolo.utils.callbacks.dvc.on_train_end +

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/hub.md b/docs/reference/yolo/utils/callbacks/hub.md index aa751e3909f..7337c86c527 100644 --- a/docs/reference/yolo/utils/callbacks/hub.md +++ b/docs/reference/yolo/utils/callbacks/hub.md @@ -1,5 +1,6 @@ --- description: Improve YOLOv5 model training with Ultralytics' on-train callbacks. Boost performance on-pretrain-routine-end, model-save, train/predict start. +keywords: Ultralytics, YOLO, callbacks, on_pretrain_routine_end, on_fit_epoch_end, on_train_start, on_val_start, on_predict_start, on_export_start --- # on_pretrain_routine_end @@ -40,4 +41,4 @@ description: Improve YOLOv5 model training with Ultralytics' on-train callbacks. # on_export_start --- :::ultralytics.yolo.utils.callbacks.hub.on_export_start -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/mlflow.md b/docs/reference/yolo/utils/callbacks/mlflow.md index 8c0d717c91a..b6708904981 100644 --- a/docs/reference/yolo/utils/callbacks/mlflow.md +++ b/docs/reference/yolo/utils/callbacks/mlflow.md @@ -1,5 +1,6 @@ --- description: Track model performance and metrics with MLflow in YOLOv5. Use callbacks like on_pretrain_routine_end or on_train_end to log information. +keywords: Ultralytics, YOLO, Utils, MLflow, callbacks, on_pretrain_routine_end, on_train_end, Tracking, Model Management, training --- # on_pretrain_routine_end @@ -15,4 +16,4 @@ description: Track model performance and metrics with MLflow in YOLOv5. Use call # on_train_end --- :::ultralytics.yolo.utils.callbacks.mlflow.on_train_end -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/neptune.md b/docs/reference/yolo/utils/callbacks/neptune.md index 2b4597875e5..195c9beef9c 100644 --- a/docs/reference/yolo/utils/callbacks/neptune.md +++ b/docs/reference/yolo/utils/callbacks/neptune.md @@ -1,5 +1,6 @@ --- description: Improve YOLOv5 training with Neptune, a powerful logging tool. Track metrics like images, plots, and epochs for better model performance. +keywords: Ultralytics, YOLO, Neptune, Callbacks, log scalars, log images, log plots, training, validation --- # _log_scalars @@ -40,4 +41,4 @@ description: Improve YOLOv5 training with Neptune, a powerful logging tool. Trac # on_train_end --- :::ultralytics.yolo.utils.callbacks.neptune.on_train_end -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/raytune.md b/docs/reference/yolo/utils/callbacks/raytune.md index d20f4eada7e..fa1267d356d 100644 --- a/docs/reference/yolo/utils/callbacks/raytune.md +++ b/docs/reference/yolo/utils/callbacks/raytune.md @@ -1,8 +1,9 @@ --- description: '"Improve YOLO model performance with on_fit_epoch_end callback. Learn to integrate with Ray Tune for hyperparameter tuning. Ultralytics YOLO docs."' +keywords: on_fit_epoch_end, Ultralytics YOLO, callback function, training, model tuning --- # on_fit_epoch_end --- :::ultralytics.yolo.utils.callbacks.raytune.on_fit_epoch_end -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/tensorboard.md b/docs/reference/yolo/utils/callbacks/tensorboard.md index 95291dc6ddf..bf0f4c42cba 100644 --- a/docs/reference/yolo/utils/callbacks/tensorboard.md +++ b/docs/reference/yolo/utils/callbacks/tensorboard.md @@ -1,5 +1,6 @@ --- description: Learn how to monitor the training process with Tensorboard using Ultralytics YOLO's "_log_scalars" and "on_batch_end" methods. +keywords: TensorBoard callbacks, YOLO training, ultralytics YOLO --- # _log_scalars @@ -20,4 +21,4 @@ description: Learn how to monitor the training process with Tensorboard using Ul # on_fit_epoch_end --- :::ultralytics.yolo.utils.callbacks.tensorboard.on_fit_epoch_end -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/callbacks/wb.md b/docs/reference/yolo/utils/callbacks/wb.md index 48a6c812c5c..03045e2953b 100644 --- a/docs/reference/yolo/utils/callbacks/wb.md +++ b/docs/reference/yolo/utils/callbacks/wb.md @@ -1,7 +1,13 @@ --- description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain_routine_start` and `on_train_epoch_end` for improved training performance. +keywords: Ultralytics, YOLO, callbacks, weights, biases, training --- +# _log_plots +--- +:::ultralytics.yolo.utils.callbacks.wb._log_plots +

+ # on_pretrain_routine_start --- :::ultralytics.yolo.utils.callbacks.wb.on_pretrain_routine_start @@ -20,4 +26,4 @@ description: Learn how to use Ultralytics YOLO's built-in callbacks `on_pretrain # on_train_end --- :::ultralytics.yolo.utils.callbacks.wb.on_train_end -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/checks.md b/docs/reference/yolo/utils/checks.md index 82b661b8256..4995371a5bc 100644 --- a/docs/reference/yolo/utils/checks.md +++ b/docs/reference/yolo/utils/checks.md @@ -1,5 +1,6 @@ --- description: 'Check functions for YOLO utils: image size, version, font, requirements, filename suffix, YAML file, YOLO, and Git version.' +keywords: YOLO, Ultralytics, Utils, Checks, image sizing, version updates, font compatibility, Python requirements, file suffixes, YAML syntax, image showing, AMP --- # is_ascii @@ -72,6 +73,11 @@ description: 'Check functions for YOLO utils: image size, version, font, require :::ultralytics.yolo.utils.checks.check_yolo

+# check_amp +--- +:::ultralytics.yolo.utils.checks.check_amp +

+ # git_describe --- :::ultralytics.yolo.utils.checks.git_describe @@ -80,4 +86,4 @@ description: 'Check functions for YOLO utils: image size, version, font, require # print_args --- :::ultralytics.yolo.utils.checks.print_args -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/dist.md b/docs/reference/yolo/utils/dist.md index ef70e99c8e6..c5505446eea 100644 --- a/docs/reference/yolo/utils/dist.md +++ b/docs/reference/yolo/utils/dist.md @@ -1,5 +1,6 @@ --- description: Learn how to find free network port and generate DDP (Distributed Data Parallel) command in Ultralytics YOLO with easy examples. +keywords: ultralytics, YOLO, utils, dist, distributed deep learning, DDP file, DDP cleanup --- # find_free_network_port @@ -20,4 +21,4 @@ description: Learn how to find free network port and generate DDP (Distributed D # ddp_cleanup --- :::ultralytics.yolo.utils.dist.ddp_cleanup -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/downloads.md b/docs/reference/yolo/utils/downloads.md index 76580f12ca4..5206e02c9a6 100644 --- a/docs/reference/yolo/utils/downloads.md +++ b/docs/reference/yolo/utils/downloads.md @@ -1,5 +1,6 @@ --- description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs utils.downloads.unzip_file, checks disk space, downloads and attempts assets. +keywords: Ultralytics YOLO, downloads, trained models, datasets, weights, deep learning, computer vision --- # is_url @@ -30,4 +31,4 @@ description: Download and unzip YOLO pretrained models. Ultralytics YOLO docs ut # download --- :::ultralytics.yolo.utils.downloads.download -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/errors.md b/docs/reference/yolo/utils/errors.md index fced2117fbd..a498db258d2 100644 --- a/docs/reference/yolo/utils/errors.md +++ b/docs/reference/yolo/utils/errors.md @@ -1,8 +1,9 @@ --- description: Learn about HUBModelError in Ultralytics YOLO Docs. Resolve the error and get the most out of your YOLO model. +keywords: HUBModelError, Ultralytics YOLO, YOLO Documentation, Object detection errors, YOLO Errors, HUBModelError Solutions --- # HUBModelError --- :::ultralytics.yolo.utils.errors.HUBModelError -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/files.md b/docs/reference/yolo/utils/files.md index 6ba88554bdd..4ca39c721e8 100644 --- a/docs/reference/yolo/utils/files.md +++ b/docs/reference/yolo/utils/files.md @@ -1,5 +1,6 @@ --- description: 'Learn about Ultralytics YOLO files and directory utilities: WorkingDirectory, file_age, file_size, and make_dirs.' +keywords: YOLO, object detection, file utils, file age, file size, working directory, make directories, Ultralytics Docs --- # WorkingDirectory @@ -35,4 +36,4 @@ description: 'Learn about Ultralytics YOLO files and directory utilities: Workin # make_dirs --- :::ultralytics.yolo.utils.files.make_dirs -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/instance.md b/docs/reference/yolo/utils/instance.md index 455669e8d8d..1b32a808827 100644 --- a/docs/reference/yolo/utils/instance.md +++ b/docs/reference/yolo/utils/instance.md @@ -1,5 +1,6 @@ --- description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO for object detection. Improve accuracy and speed with these powerful tools. +keywords: Ultralytics, YOLO, Bboxes, _ntuple, object detection, instance segmentation --- # Bboxes @@ -15,4 +16,4 @@ description: Learn about Bounding Boxes (Bboxes) and _ntuple in Ultralytics YOLO # _ntuple --- :::ultralytics.yolo.utils.instance._ntuple -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/loss.md b/docs/reference/yolo/utils/loss.md index b01e3efd433..ad4aa688210 100644 --- a/docs/reference/yolo/utils/loss.md +++ b/docs/reference/yolo/utils/loss.md @@ -1,5 +1,6 @@ --- description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO for advanced bounding box and pose estimation. Visit our docs for more. +keywords: Ultralytics, YOLO, loss functions, object detection, keypoint detection, segmentation, classification --- # VarifocalLoss @@ -35,4 +36,4 @@ description: Learn about Varifocal Loss and Keypoint Loss in Ultralytics YOLO fo # v8ClassificationLoss --- :::ultralytics.yolo.utils.loss.v8ClassificationLoss -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/metrics.md b/docs/reference/yolo/utils/metrics.md index 10d4728ca2d..4cb1158f121 100644 --- a/docs/reference/yolo/utils/metrics.md +++ b/docs/reference/yolo/utils/metrics.md @@ -1,5 +1,6 @@ --- description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, ClassifyMetrics, and more with Ultralytics Metrics documentation. +keywords: YOLOv5, metrics, losses, confusion matrix, detection metrics, pose metrics, classification metrics, intersection over area, intersection over union, keypoint intersection over union, average precision, per class average precision, Ultralytics Docs --- # FocalLoss @@ -95,4 +96,4 @@ description: Explore Ultralytics YOLO's FocalLoss, DetMetrics, PoseMetrics, Clas # ap_per_class --- :::ultralytics.yolo.utils.metrics.ap_per_class -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/ops.md b/docs/reference/yolo/utils/ops.md index b55c6b88499..0a8aa35650b 100644 --- a/docs/reference/yolo/utils/ops.md +++ b/docs/reference/yolo/utils/ops.md @@ -1,5 +1,6 @@ --- description: Learn about various utility functions in Ultralytics YOLO, including x, y, width, height conversions, non-max suppression, and more. +keywords: Ultralytics, YOLO, Utils Ops, Functions, coco80_to_coco91_class, scale_boxes, non_max_suppression, clip_coords, xyxy2xywh, xywhn2xyxy, xyn2xy, xyxy2ltwh, ltwh2xyxy, resample_segments, process_mask_upsample, process_mask_native, masks2segments, clean_str --- # Profile @@ -135,4 +136,4 @@ description: Learn about various utility functions in Ultralytics YOLO, includin # clean_str --- :::ultralytics.yolo.utils.ops.clean_str -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/plotting.md b/docs/reference/yolo/utils/plotting.md index 801032ed1bd..f402f4814dc 100644 --- a/docs/reference/yolo/utils/plotting.md +++ b/docs/reference/yolo/utils/plotting.md @@ -1,5 +1,6 @@ --- description: 'Discover the power of YOLO''s plotting functions: Colors, Labels and Images. Code examples to output targets and visualize features. Check it now.' +keywords: YOLO, object detection, plotting, visualization, annotator, save one box, plot results, feature visualization, Ultralytics --- # Colors @@ -40,4 +41,4 @@ description: 'Discover the power of YOLO''s plotting functions: Colors, Labels a # feature_visualization --- :::ultralytics.yolo.utils.plotting.feature_visualization -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/tal.md b/docs/reference/yolo/utils/tal.md index b6835073209..1f309322012 100644 --- a/docs/reference/yolo/utils/tal.md +++ b/docs/reference/yolo/utils/tal.md @@ -1,5 +1,6 @@ --- description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, select_highest_overlaps, and dist2bbox utilities. Streamline your workflow today. +keywords: Ultrayltics, YOLO, select_candidates_in_gts, make_anchor, bbox2dist, object detection, tracking --- # TaskAlignedAssigner @@ -30,4 +31,4 @@ description: Improve your YOLO models with Ultralytics' TaskAlignedAssigner, sel # bbox2dist --- :::ultralytics.yolo.utils.tal.bbox2dist -

+

\ No newline at end of file diff --git a/docs/reference/yolo/utils/torch_utils.md b/docs/reference/yolo/utils/torch_utils.md index f8fe445a72a..dbdb78679e4 100644 --- a/docs/reference/yolo/utils/torch_utils.md +++ b/docs/reference/yolo/utils/torch_utils.md @@ -1,5 +1,6 @@ --- description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils functions such as ModelEMA, select_device, and is_parallel. +keywords: Ultralytics YOLO, Torch, Utils, Pytorch, Object Detection --- # ModelEMA @@ -130,4 +131,4 @@ description: Optimize your PyTorch models with Ultralytics YOLO's torch_utils fu # profile --- :::ultralytics.yolo.utils.torch_utils.profile -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/classify/predict.md b/docs/reference/yolo/v8/classify/predict.md index d8c637240f4..a99f833eab0 100644 --- a/docs/reference/yolo/v8/classify/predict.md +++ b/docs/reference/yolo/v8/classify/predict.md @@ -1,5 +1,6 @@ --- description: Learn how to use ClassificationPredictor in Ultralytics YOLOv8 for object classification tasks in a simple and efficient way. +keywords: Ultralytics, YOLO, v8, Classify Predictor, object detection, classification, computer vision --- # ClassificationPredictor @@ -10,4 +11,4 @@ description: Learn how to use ClassificationPredictor in Ultralytics YOLOv8 for # predict --- :::ultralytics.yolo.v8.classify.predict.predict -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/classify/train.md b/docs/reference/yolo/v8/classify/train.md index 33a3967e7e0..076b277493f 100644 --- a/docs/reference/yolo/v8/classify/train.md +++ b/docs/reference/yolo/v8/classify/train.md @@ -1,5 +1,6 @@ --- description: Train a custom image classification model using Ultralytics YOLOv8 with ClassificationTrainer. Boost accuracy and efficiency today. +keywords: Ultralytics, YOLOv8, object detection, classification, training, API --- # ClassificationTrainer @@ -10,4 +11,4 @@ description: Train a custom image classification model using Ultralytics YOLOv8 # train --- :::ultralytics.yolo.v8.classify.train.train -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/classify/val.md b/docs/reference/yolo/v8/classify/val.md index 88157b2dc7a..fda08e21e39 100644 --- a/docs/reference/yolo/v8/classify/val.md +++ b/docs/reference/yolo/v8/classify/val.md @@ -1,5 +1,6 @@ --- description: Ensure model classification accuracy with Ultralytics YOLO's ClassificationValidator. Validate and improve your model with ease. +keywords: ClassificationValidator, Ultralytics YOLO, Validation, Data Science, Deep Learning --- # ClassificationValidator @@ -10,4 +11,4 @@ description: Ensure model classification accuracy with Ultralytics YOLO's Classi # val --- :::ultralytics.yolo.v8.classify.val.val -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/detect/predict.md b/docs/reference/yolo/v8/detect/predict.md index f51fa1351aa..de49aa0f81d 100644 --- a/docs/reference/yolo/v8/detect/predict.md +++ b/docs/reference/yolo/v8/detect/predict.md @@ -1,5 +1,6 @@ --- description: Detect and predict objects in images and videos using the Ultralytics YOLO v8 model with DetectionPredictor. +keywords: detectionpredictor, ultralytics yolo, object detection, neural network, machine learning --- # DetectionPredictor @@ -10,4 +11,4 @@ description: Detect and predict objects in images and videos using the Ultralyti # predict --- :::ultralytics.yolo.v8.detect.predict.predict -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/detect/train.md b/docs/reference/yolo/v8/detect/train.md index 84e47949f20..9ed57ad8752 100644 --- a/docs/reference/yolo/v8/detect/train.md +++ b/docs/reference/yolo/v8/detect/train.md @@ -1,5 +1,6 @@ --- description: Train and optimize custom object detection models with Ultralytics DetectionTrainer and train functions. Get started with YOLO v8 today. +keywords: DetectionTrainer, Ultralytics YOLO, custom object detection, train models, AI applications --- # DetectionTrainer @@ -10,4 +11,4 @@ description: Train and optimize custom object detection models with Ultralytics # train --- :::ultralytics.yolo.v8.detect.train.train -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/detect/val.md b/docs/reference/yolo/v8/detect/val.md index 3d3c8afd597..dec0259dfce 100644 --- a/docs/reference/yolo/v8/detect/val.md +++ b/docs/reference/yolo/v8/detect/val.md @@ -1,5 +1,6 @@ --- description: Validate YOLOv5 detections using this PyTorch module. Ensure model accuracy with NMS IOU threshold tuning and label mapping. +keywords: detection, validator, YOLOv5, object detection, model improvement, Ultralytics Docs --- # DetectionValidator @@ -10,4 +11,4 @@ description: Validate YOLOv5 detections using this PyTorch module. Ensure model # val --- :::ultralytics.yolo.v8.detect.val.val -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/pose/predict.md b/docs/reference/yolo/v8/pose/predict.md index b635a342e2f..6f333cf7554 100644 --- a/docs/reference/yolo/v8/pose/predict.md +++ b/docs/reference/yolo/v8/pose/predict.md @@ -1,5 +1,6 @@ --- description: Predict human pose coordinates and confidence scores using YOLOv5. Use on real-time video streams or static images. +keywords: Ultralytics, YOLO, v8, documentation, PosePredictor, pose prediction, pose estimation, predict method --- # PosePredictor @@ -10,4 +11,4 @@ description: Predict human pose coordinates and confidence scores using YOLOv5. # predict --- :::ultralytics.yolo.v8.pose.predict.predict -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/pose/train.md b/docs/reference/yolo/v8/pose/train.md index 7d3f5863b51..ede822f2208 100644 --- a/docs/reference/yolo/v8/pose/train.md +++ b/docs/reference/yolo/v8/pose/train.md @@ -1,5 +1,6 @@ --- description: Boost posture detection using PoseTrainer and train models using train() API. Learn PoseLoss for ultra-fast and accurate pose detection with Ultralytics YOLO. +keywords: PoseTrainer, human pose models, deep learning, computer vision, Ultralytics YOLO, v8 --- # PoseTrainer @@ -10,4 +11,4 @@ description: Boost posture detection using PoseTrainer and train models using tr # train --- :::ultralytics.yolo.v8.pose.train.train -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/pose/val.md b/docs/reference/yolo/v8/pose/val.md index 8fef7e35afb..af5f5873c91 100644 --- a/docs/reference/yolo/v8/pose/val.md +++ b/docs/reference/yolo/v8/pose/val.md @@ -1,5 +1,6 @@ --- description: Ensure proper human poses in images with YOLOv8 Pose Validation, part of the Ultralytics YOLO v8 suite. +keywords: PoseValidator, Ultralytics YOLO, object detection, pose analysis, validation --- # PoseValidator @@ -10,4 +11,4 @@ description: Ensure proper human poses in images with YOLOv8 Pose Validation, pa # val --- :::ultralytics.yolo.v8.pose.val.val -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/segment/predict.md b/docs/reference/yolo/v8/segment/predict.md index eccd8ec493d..30afdbd0bcf 100644 --- a/docs/reference/yolo/v8/segment/predict.md +++ b/docs/reference/yolo/v8/segment/predict.md @@ -1,5 +1,6 @@ --- description: '"Use SegmentationPredictor in YOLOv8 for efficient object detection and segmentation. Explore Ultralytics YOLO Docs for more information."' +keywords: Ultralytics YOLO, SegmentationPredictor, object detection, segmentation masks, predict --- # SegmentationPredictor @@ -10,4 +11,4 @@ description: '"Use SegmentationPredictor in YOLOv8 for efficient object detectio # predict --- :::ultralytics.yolo.v8.segment.predict.predict -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/segment/train.md b/docs/reference/yolo/v8/segment/train.md index ee25fe232ea..7bf27a0b8f6 100644 --- a/docs/reference/yolo/v8/segment/train.md +++ b/docs/reference/yolo/v8/segment/train.md @@ -1,5 +1,6 @@ --- description: Learn about SegmentationTrainer and Train in Ultralytics YOLO v8 for efficient object detection models. Improve your training with Ultralytics Docs. +keywords: SegmentationTrainer, Ultralytics YOLO, object detection, segmentation, train, tutorial, guide, code examples --- # SegmentationTrainer @@ -10,4 +11,4 @@ description: Learn about SegmentationTrainer and Train in Ultralytics YOLO v8 fo # train --- :::ultralytics.yolo.v8.segment.train.train -

+

\ No newline at end of file diff --git a/docs/reference/yolo/v8/segment/val.md b/docs/reference/yolo/v8/segment/val.md index e9e2c6f732a..382660d2c76 100644 --- a/docs/reference/yolo/v8/segment/val.md +++ b/docs/reference/yolo/v8/segment/val.md @@ -1,5 +1,6 @@ --- description: Ensure segmentation quality on large datasets with SegmentationValidator. Review and visualize results with ease. Learn more at Ultralytics Docs. +keywords: SegmentationValidator, YOLOv8, Ultralytics Docs, segmentation model, validation --- # SegmentationValidator @@ -10,4 +11,4 @@ description: Ensure segmentation quality on large datasets with SegmentationVali # val --- :::ultralytics.yolo.v8.segment.val.val -

+

\ No newline at end of file diff --git a/docs/tasks/classify.md b/docs/tasks/classify.md index 47c6cb750fd..fe0b939b3a9 100644 --- a/docs/tasks/classify.md +++ b/docs/tasks/classify.md @@ -1,6 +1,7 @@ --- comments: true description: Check YOLO class label with only one class for the whole image, using image classification. Get strategies for training and validation models. +keywords: YOLOv8n-cls, image classification, pretrained models --- Image classification is the simplest of the three tasks and involves classifying an entire image into one of a set of @@ -176,4 +177,4 @@ i.e. `yolo predict model=yolov8n-cls.onnx`. Usage examples are shown for your mo | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-cls_web_model/` | ✅ | `imgsz` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-cls_paddle_model/` | ✅ | `imgsz` | -See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. +See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. \ No newline at end of file diff --git a/docs/tasks/detect.md b/docs/tasks/detect.md index 6942060f301..35a3d444c5d 100644 --- a/docs/tasks/detect.md +++ b/docs/tasks/detect.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to use YOLOv8, an object detection model pre-trained with COCO and about the different YOLOv8 models and how to train and export them. +keywords: object detection, YOLOv8 Detect models, COCO dataset, models, train, predict, export --- Object detection is a task that involves identifying the location and class of objects in an image or video stream. @@ -167,4 +168,4 @@ Available YOLOv8 export formats are in the table below. You can predict or valid | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n_web_model/` | ✅ | `imgsz` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n_paddle_model/` | ✅ | `imgsz` | -See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. +See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. \ No newline at end of file diff --git a/docs/tasks/index.md b/docs/tasks/index.md index 982bb6255fa..23e384bd347 100644 --- a/docs/tasks/index.md +++ b/docs/tasks/index.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how Ultralytics YOLOv8 AI framework supports detection, segmentation, classification, and pose/keypoint estimation tasks. +keywords: YOLOv8, computer vision, detection, segmentation, classification, pose, keypoint detection, image segmentation, medical imaging --- # Ultralytics YOLOv8 Tasks diff --git a/docs/tasks/pose.md b/docs/tasks/pose.md index 68ccd1972e2..094f95b8729 100644 --- a/docs/tasks/pose.md +++ b/docs/tasks/pose.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to use YOLOv8 pose estimation models to identify the position of keypoints on objects in an image, and how to train, validate, predict, and export these models for use with various formats such as ONNX or CoreML. +keywords: YOLOv8, Pose Models, Keypoint Detection, COCO dataset, COCO val2017, Amazon EC2 P4d, PyTorch --- Pose estimation is a task that involves identifying the location of specific points in an image, usually referred @@ -181,4 +182,4 @@ i.e. `yolo predict model=yolov8n-pose.onnx`. Usage examples are shown for your m | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-pose_web_model/` | ✅ | `imgsz` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-pose_paddle_model/` | ✅ | `imgsz` | -See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. +See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. \ No newline at end of file diff --git a/docs/tasks/segment.md b/docs/tasks/segment.md index 8eb5db8766e..4f9192ffc0b 100644 --- a/docs/tasks/segment.md +++ b/docs/tasks/segment.md @@ -1,6 +1,7 @@ --- comments: true description: Learn what Instance segmentation is. Get pretrained YOLOv8 segment models, and how to train and export them to segments masks. Check the preformance metrics! +keywords: instance segmentation, YOLOv8, Ultralytics, pretrained models, train, predict, export, datasets --- Instance segmentation goes a step further than object detection and involves identifying individual objects in an image @@ -181,4 +182,4 @@ i.e. `yolo predict model=yolov8n-seg.onnx`. Usage examples are shown for your mo | [TF.js](https://www.tensorflow.org/js) | `tfjs` | `yolov8n-seg_web_model/` | ✅ | `imgsz` | | [PaddlePaddle](https://github.com/PaddlePaddle) | `paddle` | `yolov8n-seg_paddle_model/` | ✅ | `imgsz` | -See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. +See full `export` details in the [Export](https://docs.ultralytics.com/modes/export/) page. \ No newline at end of file diff --git a/docs/usage/callbacks.md b/docs/usage/callbacks.md index 031d64488a2..7968fafdd04 100644 --- a/docs/usage/callbacks.md +++ b/docs/usage/callbacks.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to leverage callbacks in Ultralytics YOLO framework to perform custom tasks in trainer, validator, predictor and exporter modes. +keywords: callbacks, Ultralytics framework, Trainer, Validator, Predictor, Exporter, train, val, export, predict, YOLO, Object Detection --- ## Callbacks diff --git a/docs/usage/cfg.md b/docs/usage/cfg.md index b7ddb496f21..b027da310e6 100644 --- a/docs/usage/cfg.md +++ b/docs/usage/cfg.md @@ -1,6 +1,7 @@ --- comments: true -description: 'Learn about YOLO settings and modes for different tasks like detection, segmentation etc. Train and predict with custom argparse commands.' +description: Learn about YOLO settings and modes for different tasks like detection, segmentation etc. Train and predict with custom argparse commands. +keywords: YOLO settings, hyperparameters, YOLOv8, Ultralytics, YOLO guide, YOLO commands, YOLO tasks, YOLO modes, YOLO training, YOLO detect, YOLO segment, YOLO classify, YOLO pose, YOLO train, YOLO val, YOLO predict, YOLO export, YOLO track, YOLO benchmark --- YOLO settings and hyperparameters play a critical role in the model's performance, speed, and accuracy. These settings diff --git a/docs/usage/cli.md b/docs/usage/cli.md index 1b07b61e187..21879d7e851 100644 --- a/docs/usage/cli.md +++ b/docs/usage/cli.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to use YOLOv8 from the Command Line Interface (CLI) through simple, single-line commands with `yolo` without Python code. +keywords: YOLO, CLI, command line interface, detect, segment, classify, train, validate, predict, export, Ultralytics Docs --- # Command Line Interface Usage diff --git a/docs/usage/engine.md b/docs/usage/engine.md index 852e850bdf9..8f6444390c5 100644 --- a/docs/usage/engine.md +++ b/docs/usage/engine.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to train and customize your models fast with the Ultralytics YOLO 'DetectionTrainer' and 'CustomTrainer'. Read more here! +keywords: Ultralytics, YOLO, DetectionTrainer, BaseTrainer, engine components, trainers, customizing, callbacks, validators, predictors --- Both the Ultralytics YOLO command-line and python interfaces are simply a high-level abstraction on the base engine @@ -83,4 +84,4 @@ To know more about Callback triggering events and entry point, checkout our [Cal ## Other engine components There are other components that can be customized similarly like `Validators` and `Predictors` -See Reference section for more information on these. +See Reference section for more information on these. \ No newline at end of file diff --git a/docs/usage/hyperparameter_tuning.md b/docs/usage/hyperparameter_tuning.md index f3589a77503..06a38414460 100644 --- a/docs/usage/hyperparameter_tuning.md +++ b/docs/usage/hyperparameter_tuning.md @@ -1,6 +1,7 @@ --- comments: true description: Discover how to integrate hyperparameter tuning with Ray Tune and Ultralytics YOLOv8. Speed up the tuning process and optimize your model's performance. +keywords: yolov8, ray tune, hyperparameter tuning, hyperparameter optimization, machine learning, computer vision, deep learning, image recognition --- # Hyperparameter Tuning with Ray Tune and YOLOv8 diff --git a/docs/usage/python.md b/docs/usage/python.md index 04b813b9fe6..2d8bb4c4e6f 100644 --- a/docs/usage/python.md +++ b/docs/usage/python.md @@ -1,6 +1,7 @@ --- comments: true description: Integrate YOLOv8 in Python. Load, use pretrained models, train, and infer images. Export to ONNX. Track objects in videos. +keywords: yolov8, python usage, object detection, segmentation, classification, pretrained models, train models, image predictions --- # Python Usage diff --git a/docs/yolov5/environments/aws_quickstart_tutorial.md b/docs/yolov5/environments/aws_quickstart_tutorial.md index dbcfb3a4aa2..3e3ea83cc59 100644 --- a/docs/yolov5/environments/aws_quickstart_tutorial.md +++ b/docs/yolov5/environments/aws_quickstart_tutorial.md @@ -1,6 +1,7 @@ --- comments: true description: Get started with YOLOv5 on AWS. Our comprehensive guide provides everything you need to know to run YOLOv5 on an Amazon Deep Learning instance. +keywords: YOLOv5, AWS, Deep Learning, Instance, Guide, Quickstart --- # YOLOv5 🚀 on AWS Deep Learning Instance: A Comprehensive Guide diff --git a/docs/yolov5/environments/docker_image_quickstart_tutorial.md b/docs/yolov5/environments/docker_image_quickstart_tutorial.md index 365139d1150..b2b90c9b49d 100644 --- a/docs/yolov5/environments/docker_image_quickstart_tutorial.md +++ b/docs/yolov5/environments/docker_image_quickstart_tutorial.md @@ -1,6 +1,7 @@ --- comments: true description: Get started with YOLOv5 in a Docker container. Learn to set up and run YOLOv5 models and explore other quickstart options. 🚀 +keywords: YOLOv5, Docker, tutorial, setup, training, testing, detection --- # Get Started with YOLOv5 🚀 in Docker diff --git a/docs/yolov5/environments/google_cloud_quickstart_tutorial.md b/docs/yolov5/environments/google_cloud_quickstart_tutorial.md index 47f53b126f3..c834a18a35a 100644 --- a/docs/yolov5/environments/google_cloud_quickstart_tutorial.md +++ b/docs/yolov5/environments/google_cloud_quickstart_tutorial.md @@ -1,6 +1,7 @@ --- comments: true description: Set up YOLOv5 on a Google Cloud Platform (GCP) Deep Learning VM. Train, test, detect, and export YOLOv5 models. Tutorial updated April 2023. +keywords: YOLOv5, GCP, deep learning, tutorial, Google Cloud Platform, virtual machine, VM, setup, free credit, Colab Notebook, AWS, Docker --- # Run YOLOv5 🚀 on Google Cloud Platform (GCP) Deep Learning Virtual Machine (VM) ⭐ diff --git a/docs/yolov5/index.md b/docs/yolov5/index.md index 3f5d6fb406c..8c666a2b9ce 100644 --- a/docs/yolov5/index.md +++ b/docs/yolov5/index.md @@ -1,9 +1,10 @@ --- comments: true -description: Discover the YOLOv5 object detection model designed to deliver fast and accurate real-time results. Let's dive into this documentation to harness its full potential! +description: Explore the extensive functionalities of the YOLOv5 object detection model, renowned for its speed and precision. Dive into our comprehensive guide for installation, architectural insights, use-cases, and more to unlock the full potential of YOLOv5 for your computer vision applications. +keywords: ultralytics, yolov5, object detection, deep learning, pytorch, computer vision, tutorial, architecture, documentation, frameworks, real-time, model training, multicore, multithreading --- -# Ultralytics YOLOv5 +# Comprehensive Guide to Ultralytics YOLOv5

@@ -21,54 +22,48 @@ description: Discover the YOLOv5 object detection model designed to deliver fast

-Welcome to the Ultralytics YOLOv5 🚀 Docs! YOLOv5, or You Only Look Once version 5, is an Ultralytics object detection model designed to deliver fast and accurate real-time results. +Welcome to the Ultralytics' YOLOv5 🚀 Documentation! YOLOv5, the fifth iteration of the revolutionary "You Only Look Once" object detection model, is designed to deliver high-speed, high-accuracy results in real-time.

-This powerful deep learning framework is built on the PyTorch platform and has gained immense popularity due to its ease of use, high performance, and versatility. In this documentation, we will guide you through the installation process, explain the model's architecture, showcase various use-cases, and provide detailed tutorials to help you harness the full potential of YOLOv5 for your computer vision projects. Let's dive in! +Built on PyTorch, this powerful deep learning framework has garnered immense popularity for its versatility, ease of use, and high performance. Our documentation guides you through the installation process, explains the architectural nuances of the model, showcases various use-cases, and provides a series of detailed tutorials. These resources will help you harness the full potential of YOLOv5 for your computer vision projects. Let's get started!

## Tutorials -* [Train Custom Data](tutorials/train_custom_data.md) 🚀 RECOMMENDED -* [Tips for Best Training Results](tutorials/tips_for_best_training_results.md) ☘️ -* [Multi-GPU Training](tutorials/multi_gpu_training.md) -* [PyTorch Hub](tutorials/pytorch_hub_model_loading.md) 🌟 NEW -* [TFLite, ONNX, CoreML, TensorRT Export](tutorials/model_export.md) 🚀 -* [NVIDIA Jetson platform Deployment](tutorials/running_on_jetson_nano.md) 🌟 NEW -* [Test-Time Augmentation (TTA)](tutorials/test_time_augmentation.md) -* [Model Ensembling](tutorials/model_ensembling.md) -* [Model Pruning/Sparsity](tutorials/model_pruning_and_sparsity.md) -* [Hyperparameter Evolution](tutorials/hyperparameter_evolution.md) -* [Transfer Learning with Frozen Layers](tutorials/transfer_learning_with_frozen_layers.md) -* [Architecture Summary](tutorials/architecture_description.md) 🌟 NEW -* [Roboflow for Datasets, Labeling, and Active Learning](tutorials/roboflow_datasets_integration.md) -* [ClearML Logging](tutorials/clearml_logging_integration.md) 🌟 NEW -* [YOLOv5 with Neural Magic's Deepsparse](tutorials/neural_magic_pruning_quantization.md) 🌟 NEW -* [Comet Logging](tutorials/comet_logging_integration.md) 🌟 NEW +Here's a compilation of comprehensive tutorials that will guide you through different aspects of YOLOv5. + +* [Train Custom Data](tutorials/train_custom_data.md) 🚀 RECOMMENDED: Learn how to train the YOLOv5 model on your custom dataset. +* [Tips for Best Training Results](tutorials/tips_for_best_training_results.md) ☘️: Uncover practical tips to optimize your model training process. +* [Multi-GPU Training](tutorials/multi_gpu_training.md): Understand how to leverage multiple GPUs to expedite your training. +* [PyTorch Hub](tutorials/pytorch_hub_model_loading.md) 🌟 NEW: Learn to load pre-trained models via PyTorch Hub. +* [TFLite, ONNX, CoreML, TensorRT Export](tutorials/model_export.md) 🚀: Understand how to export your model to different formats. +* [NVIDIA Jetson platform Deployment](tutorials/running_on_jetson_nano.md) 🌟 NEW: Learn how to deploy your YOLOv5 model on NVIDIA Jetson platform. +* [Test-Time Augmentation (TTA)](tutorials/test_time_augmentation.md): Explore how to use TTA to improve your model's prediction accuracy. +* [Model Ensembling](tutorials/model_ensembling.md): Learn the strategy of combining multiple models for improved performance. +* [Model Pruning/Sparsity](tutorials/model_pruning_and_sparsity.md): Understand pruning and sparsity concepts, and how to create a more efficient model. +* [Hyperparameter Evolution](tutorials/hyperparameter_evolution.md): Discover the process of automated hyperparameter tuning for better model performance. +* [Transfer Learning with Frozen Layers](tutorials/transfer_learning_with_frozen_layers.md): Learn how to implement transfer learning by freezing layers in YOLOv5. +* [Architecture Summary](tutorials/architecture_description.md) 🌟 Delve into the structural details of the YOLOv5 model. +* [Roboflow for Datasets](tutorials/roboflow_datasets_integration.md): Understand how to utilize Roboflow for dataset management, labeling, and active learning. +* [ClearML Logging](tutorials/clearml_logging_integration.md) 🌟 Learn how to integrate ClearML for efficient logging during your model training. +* [YOLOv5 with Neural Magic](tutorials/neural_magic_pruning_quantization.md) Discover how to use Neural Magic's Deepsparse to prune and quantize your YOLOv5 model. +* [Comet Logging](tutorials/comet_logging_integration.md) 🌟 NEW: Explore how to utilize Comet for improved model training logging. ## Environments -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies -including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) -and [PyTorch](https://pytorch.org/) preinstalled): +YOLOv5 is designed to be run in the following up-to-date, verified environments, with all dependencies (including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/), and [PyTorch](https://pytorch.org/)) pre-installed: - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle -- **Google Cloud** Deep Learning VM. - See [GCP Quickstart Guide](environments/google_cloud_quickstart_tutorial.md) +- **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](environments/google_cloud_quickstart_tutorial.md) - **Amazon** Deep Learning AMI. See [AWS Quickstart Guide](environments/aws_quickstart_tutorial.md) -- **Docker Image**. - See [Docker Quickstart Guide](environments/docker_image_quickstart_tutorial.md) Docker Pulls +- **Docker Image**. See [Docker Quickstart Guide](environments/docker_image_quickstart_tutorial.md) Docker Pulls ## Status YOLOv5 CI -If this badge is green, all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous -Integration (CI) tests are currently passing. CI tests verify correct operation of -YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) -and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 -hours and on every commit. +This badge signifies that all [YOLOv5 GitHub Actions](https://github.com/ultralytics/yolov5/actions) Continuous Integration (CI) tests are currently passing. CI tests verify the correct operation of YOLOv5 [training](https://github.com/ultralytics/yolov5/blob/master/train.py), [validation](https://github.com/ultralytics/yolov5/blob/master/val.py), [inference](https://github.com/ultralytics/yolov5/blob/master/detect.py), [export](https://github.com/ultralytics/yolov5/blob/master/export.py) and [benchmarks](https://github.com/ultralytics/yolov5/blob/master/benchmarks.py) on macOS, Windows, and Ubuntu every 24 hours and with every new commit.
@@ -90,6 +85,6 @@ hours and on every commit. - +
\ No newline at end of file diff --git a/docs/yolov5/quickstart_tutorial.md b/docs/yolov5/quickstart_tutorial.md index 055a4ab5242..e46091f8330 100644 --- a/docs/yolov5/quickstart_tutorial.md +++ b/docs/yolov5/quickstart_tutorial.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to quickly start using YOLOv5 including installation, inference, and training on this Ultralytics Docs page. +keywords: YOLOv5, object detection, PyTorch, quickstart, detect.py, training, Ultralytics Docs --- # YOLOv5 Quickstart diff --git a/docs/yolov5/tutorials/architecture_description.md b/docs/yolov5/tutorials/architecture_description.md index 71ef2bbc03a..26a2d911661 100644 --- a/docs/yolov5/tutorials/architecture_description.md +++ b/docs/yolov5/tutorials/architecture_description.md @@ -1,27 +1,34 @@ --- comments: true -description: 'Ultralytics YOLOv5 Docs: Learn model structure, data augmentation & training strategies. Build targets and the losses of object detection.' +description: Explore the details of Ultralytics YOLOv5 architecture, a comprehensive guide to its model structure, data augmentation techniques, training strategies, and various features. Understand the intricacies of object detection algorithms and improve your skills in the machine learning field. +keywords: yolov5 architecture, data augmentation, training strategies, object detection, yolo docs, ultralytics --- +# Ultralytics YOLOv5 Architecture + +YOLOv5 (v6.0/6.1) is a powerful object detection algorithm developed by Ultralytics. This article dives deep into the YOLOv5 architecture, data augmentation strategies, training methodologies, and loss computation techniques. This comprehensive understanding will help improve your practical application of object detection in various fields, including surveillance, autonomous vehicles, and image recognition. + ## 1. Model Structure -YOLOv5 (v6.0/6.1) consists of: +YOLOv5's architecture consists of three main parts: -- **Backbone**: `New CSP-Darknet53` -- **Neck**: `SPPF`, `New CSP-PAN` -- **Head**: `YOLOv3 Head` +- **Backbone**: This is the main body of the network. For YOLOv5, the backbone is designed using the `New CSP-Darknet53` structure, a modification of the Darknet architecture used in previous versions. +- **Neck**: This part connects the backbone and the head. In YOLOv5, `SPPF` and `New CSP-PAN` structures are utilized. +- **Head**: This part is responsible for generating the final output. YOLOv5 uses the `YOLOv3 Head` for this purpose. -Model structure (`yolov5l.yaml`): +The structure of the model is depicted in the image below. The model structure details can be found in `yolov5l.yaml`. ![yolov5](https://user-images.githubusercontent.com/31005897/172404576-c260dcf9-76bb-4bc8-b6a9-f2d987792583.png) -Some minor changes compared to previous versions: +YOLOv5 introduces some minor changes compared to its predecessors: + +1. The `Focus` structure, found in earlier versions, is replaced with a `6x6 Conv2d` structure. This change boosts efficiency [#4825](https://github.com/ultralytics/yolov5/issues/4825). +2. The `SPP` structure is replaced with `SPPF`. This alteration more than doubles the speed of processing. -1. Replace the `Focus` structure with `6x6 Conv2d`(more efficient, refer #4825) -2. Replace the `SPP` structure with `SPPF`(more than double the speed) +To test the speed of `SPP` and `SPPF`, the following code can be used:
-test code +SPP vs SPPF speed profiling example (click to open) ```python import time @@ -67,12 +74,12 @@ def main(): t_start = time.time() for _ in range(100): spp(input_tensor) - print(f"spp time: {time.time() - t_start}") + print(f"SPP time: {time.time() - t_start}") t_start = time.time() for _ in range(100): sppf(input_tensor) - print(f"sppf time: {time.time() - t_start}") + print(f"SPPF time: {time.time() - t_start}") if __name__ == '__main__': @@ -83,63 +90,75 @@ result: ``` True -spp time: 0.5373051166534424 -sppf time: 0.20780706405639648 +SPP time: 0.5373051166534424 +SPPF time: 0.20780706405639648 ```
-## 2. Data Augmentation +## 2. Data Augmentation Techniques + +YOLOv5 employs various data augmentation techniques to improve the model's ability to generalize and reduce overfitting. These techniques include: + +- **Mosaic Augmentation**: An image processing technique that combines four training images into one in ways that encourage object detection models to better handle various object scales and translations. + + ![mosaic](https://user-images.githubusercontent.com/31005897/159109235-c7aad8f2-1d4f-41f9-8d5f-b2fde6f2885e.png) + +- **Copy-Paste Augmentation**: An innovative data augmentation method that copies random patches from an image and pastes them onto another randomly chosen image, effectively generating a new training sample. + + ![copy-paste](https://user-images.githubusercontent.com/31005897/159116277-91b45033-6bec-4f82-afc4-41138866628e.png) + +- **Random Affine Transformations**: This includes random rotation, scaling, translation, and shearing of the images. -- Mosaic - + ![random-affine](https://user-images.githubusercontent.com/31005897/159109326-45cd5acb-14fa-43e7-9235-0f21b0021c7d.png) -- Copy paste - +- **MixUp Augmentation**: A method that creates composite images by taking a linear combination of two images and their associated labels. -- Random affine(Rotation, Scale, Translation and Shear) - + ![mixup](https://user-images.githubusercontent.com/31005897/159109361-3b24333b-f481-478b-ae00-df7838f0b5cd.png) -- MixUp - +- **Albumentations**: A powerful library for image augmenting that supports a wide variety of augmentation techniques. +- **HSV Augmentation**: Random changes to the Hue, Saturation, and Value of the images. -- Albumentations -- Augment HSV(Hue, Saturation, Value) - + ![hsv](https://user-images.githubusercontent.com/31005897/159109407-83d100ba-1aba-4f4b-aa03-4f048f815981.png) -- Random horizontal flip - +- **Random Horizontal Flip**: An augmentation method that randomly flips images horizontally. + + ![horizontal-flip](https://user-images.githubusercontent.com/31005897/159109429-0d44619a-a76a-49eb-bfc0-6709860c043e.png) ## 3. Training Strategies -- Multi-scale training(0.5~1.5x) -- AutoAnchor(For training custom data) -- Warmup and Cosine LR scheduler -- EMA(Exponential Moving Average) -- Mixed precision -- Evolve hyper-parameters +YOLOv5 applies several sophisticated training strategies to enhance the model's performance. They include: + +- **Multiscale Training**: The input images are randomly rescaled within a range of 0.5 to 1.5 times their original size during the training process. +- **AutoAnchor**: This strategy optimizes the prior anchor boxes to match the statistical characteristics of the ground truth boxes in your custom data. +- **Warmup and Cosine LR Scheduler**: A method to adjust the learning rate to enhance model performance. +- **Exponential Moving Average (EMA)**: A strategy that uses the average of parameters over past steps to stabilize the training process and reduce generalization error. +- **Mixed Precision Training**: A method to perform operations in half-precision format, reducing memory usage and enhancing computational speed. +- **Hyperparameter Evolution**: A strategy to automatically tune hyperparameters to achieve optimal performance. -## 4. Others +## 4. Additional Features ### 4.1 Compute Losses -The YOLOv5 loss consists of three parts: +The loss in YOLOv5 is computed as a combination of three individual loss components: + +- **Classes Loss (BCE Loss)**: Binary Cross-Entropy loss, measures the error for the classification task. +- **Objectness Loss (BCE Loss)**: Another Binary Cross-Entropy loss, calculates the error in detecting whether an object is present in a particular grid cell or not. +- **Location Loss (CIoU Loss)**: Complete IoU loss, measures the error in localizing the object within the grid cell. -- Classes loss(BCE loss) -- Objectness loss(BCE loss) -- Location loss(CIoU loss) +The overall loss function is depicted by: ![loss](https://latex.codecogs.com/svg.image?Loss=\lambda_1L_{cls}+\lambda_2L_{obj}+\lambda_3L_{loc}) ### 4.2 Balance Losses -The objectness losses of the three prediction layers(`P3`, `P4`, `P5`) are weighted differently. The balance weights are `[4.0, 1.0, 0.4]` respectively. +The objectness losses of the three prediction layers (`P3`, `P4`, `P5`) are weighted differently. The balance weights are `[4.0, 1.0, 0.4]` respectively. This approach ensures that the predictions at different scales contribute appropriately to the total loss. ![obj_loss](https://latex.codecogs.com/svg.image?L_{obj}=4.0\cdot&space;L_{obj}^{small}+1.0\cdot&space;L_{obj}^{medium}+0.4\cdot&space;L_{obj}^{large}) ### 4.3 Eliminate Grid Sensitivity -In YOLOv2 and YOLOv3, the formula for calculating the predicted target information is: +The YOLOv5 architecture makes some important changes to the box prediction strategy compared to earlier versions of YOLO. In YOLOv2 and YOLOv3, the box coordinates were directly predicted using the activation of the last layer. ![b_x](https://latex.codecogs.com/svg.image?b_x=\sigma(t_x)+c_x) ![b_y](https://latex.codecogs.com/svg.image?b_y=\sigma(t_y)+c_y) @@ -148,9 +167,9 @@ In YOLOv2 and YOLOv3, the formula for calculating the predicted target informati +However, in YOLOv5, the formula for predicting the box coordinates has been updated to reduce grid sensitivity and prevent the model from predicting unbounded box dimensions. - -In YOLOv5, the formula is: +The revised formulas for calculating the predicted bounding box are as follows: ![bx](https://latex.codecogs.com/svg.image?b_x=(2\cdot\sigma(t_x)-0.5)+c_x) ![by](https://latex.codecogs.com/svg.image?b_y=(2\cdot\sigma(t_y)-0.5)+c_y) @@ -168,9 +187,11 @@ Compare the height and width scaling ratio(relative to anchor) before and after ### 4.4 Build Targets -Match positive samples: +The build target process in YOLOv5 is critical for training efficiency and model accuracy. It involves assigning ground truth boxes to the appropriate grid cells in the output map and matching them with the appropriate anchor boxes. + +This process follows these steps: -- Calculate the aspect ratio of GT and Anchor Templates +- Calculate the ratio of the ground truth box dimensions and the dimensions of each anchor template. ![rw](https://latex.codecogs.com/svg.image?r_w=w_{gt}/w_{at}) @@ -186,10 +207,18 @@ Match positive samples: -- Assign the successfully matched Anchor Templates to the corresponding cells +- If the calculated ratio is within the threshold, match the ground truth box with the corresponding anchor. -- Because the center point offset range is adjusted from (0, 1) to (-0.5, 1.5). GT Box can be assigned to more anchors. +- Assign the matched anchor to the appropriate cells, keeping in mind that due to the revised center point offset, a ground truth box can be assigned to more than one anchor. Because the center point offset range is adjusted from (0, 1) to (-0.5, 1.5). GT Box can be assigned to more anchors. + + + +This way, the build targets process ensures that each ground truth object is properly assigned and matched during the training process, allowing YOLOv5 to learn the task of object detection more effectively. + +## Conclusion + +In conclusion, YOLOv5 represents a significant step forward in the development of real-time object detection models. By incorporating various new features, enhancements, and training strategies, it surpasses previous versions of the YOLO family in performance and efficiency. - \ No newline at end of file +The primary enhancements in YOLOv5 include the use of a dynamic architecture, an extensive range of data augmentation techniques, innovative training strategies, as well as important adjustments in computing losses and the process of building targets. All these innovations significantly improve the accuracy and efficiency of object detection while retaining a high degree of speed, which is the trademark of YOLO models. \ No newline at end of file diff --git a/docs/yolov5/tutorials/clearml_logging_integration.md b/docs/yolov5/tutorials/clearml_logging_integration.md index f0843cfb29c..3d8672d09cc 100644 --- a/docs/yolov5/tutorials/clearml_logging_integration.md +++ b/docs/yolov5/tutorials/clearml_logging_integration.md @@ -1,6 +1,7 @@ --- comments: true description: Integrate ClearML with YOLOv5 to track experiments and manage data versions. Optimize hyperparameters and remotely monitor your runs. +keywords: YOLOv5, ClearML, experiment manager, remotely train, monitor, hyperparameter optimization, data versioning tool, HPO, data version management, optimization locally, agent, training progress, custom YOLOv5, AI development, model building --- # ClearML Integration diff --git a/docs/yolov5/tutorials/comet_logging_integration.md b/docs/yolov5/tutorials/comet_logging_integration.md index e1716c957a5..263f1468989 100644 --- a/docs/yolov5/tutorials/comet_logging_integration.md +++ b/docs/yolov5/tutorials/comet_logging_integration.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to use YOLOv5 with Comet, a tool for logging and visualizing machine learning model metrics in real-time. Install, log and analyze seamlessly. +keywords: object detection, YOLOv5, Comet, model metrics, deep learning, image classification, Colab notebook, machine learning, datasets, hyperparameters tracking, training script, checkpoint --- diff --git a/docs/yolov5/tutorials/hyperparameter_evolution.md b/docs/yolov5/tutorials/hyperparameter_evolution.md index eebb554bb3a..8134b36e476 100644 --- a/docs/yolov5/tutorials/hyperparameter_evolution.md +++ b/docs/yolov5/tutorials/hyperparameter_evolution.md @@ -1,6 +1,7 @@ --- comments: true description: Learn to find optimum YOLOv5 hyperparameters via **evolution**. A guide to learn hyperparameter tuning with Genetic Algorithms. +keywords: YOLOv5, Hyperparameter Evolution, Genetic Algorithm, Hyperparameter Optimization, Fitness, Evolve, Visualize --- 📚 This guide explains **hyperparameter evolution** for YOLOv5 🚀. Hyperparameter evolution is a method of [Hyperparameter Optimization](https://en.wikipedia.org/wiki/Hyperparameter_optimization) using a [Genetic Algorithm](https://en.wikipedia.org/wiki/Genetic_algorithm) (GA) for optimization. UPDATED 25 September 2022. @@ -151,7 +152,7 @@ We recommend a minimum of 300 generations of evolution for best results. Note th ## Environments -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): +YOLOv5 is designed to be run in the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) diff --git a/docs/yolov5/tutorials/model_ensembling.md b/docs/yolov5/tutorials/model_ensembling.md index a76996dee09..3e13435048e 100644 --- a/docs/yolov5/tutorials/model_ensembling.md +++ b/docs/yolov5/tutorials/model_ensembling.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to ensemble YOLOv5 models for improved mAP and Recall! Clone the repo, install requirements, and start testing and inference. +keywords: YOLOv5, object detection, ensemble learning, mAP, Recall --- 📚 This guide explains how to use YOLOv5 🚀 **model ensembling** during testing and inference for improved mAP and Recall. @@ -132,7 +133,7 @@ Done. (0.223s) ## Environments -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): +YOLOv5 is designed to be run in the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) diff --git a/docs/yolov5/tutorials/model_export.md b/docs/yolov5/tutorials/model_export.md index 09e72685457..53afd015203 100644 --- a/docs/yolov5/tutorials/model_export.md +++ b/docs/yolov5/tutorials/model_export.md @@ -1,6 +1,7 @@ --- comments: true description: Export YOLOv5 models to TFLite, ONNX, CoreML, and TensorRT formats. Achieve up to 5x GPU speedup using TensorRT. Benchmarks included. +keywords: YOLOv5, object detection, export, ONNX, CoreML, TensorFlow, TensorRT, OpenVINO --- # TFLite, ONNX, CoreML, TensorRT Export @@ -231,7 +232,7 @@ YOLOv5 OpenVINO C++ inference examples: ## Environments -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): +YOLOv5 is designed to be run in the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) diff --git a/docs/yolov5/tutorials/model_pruning_and_sparsity.md b/docs/yolov5/tutorials/model_pruning_and_sparsity.md index 0793f662efc..25e4f8c300a 100644 --- a/docs/yolov5/tutorials/model_pruning_and_sparsity.md +++ b/docs/yolov5/tutorials/model_pruning_and_sparsity.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to apply pruning to your YOLOv5 models. See the before and after performance with an explanation of sparsity and more. +keywords: YOLOv5, ultralytics, pruning, deep learning, computer vision, object detection, AI, tutorial --- 📚 This guide explains how to apply **pruning** to YOLOv5 🚀 models. @@ -95,7 +96,7 @@ In the results we can observe that we have achieved a **sparsity of 30%** in our ## Environments -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): +YOLOv5 is designed to be run in the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) diff --git a/docs/yolov5/tutorials/multi_gpu_training.md b/docs/yolov5/tutorials/multi_gpu_training.md index d002d05c120..24221db783a 100644 --- a/docs/yolov5/tutorials/multi_gpu_training.md +++ b/docs/yolov5/tutorials/multi_gpu_training.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to train your dataset on single or multiple machines using YOLOv5 on multiple GPUs. Use simple commands with DDP mode for faster performance. +keywords: ultralytics, yolo, yolov5, multi-gpu, training, dataset, dataloader, data parallel, distributed data parallel, docker, pytorch --- 📚 This guide explains how to properly use **multiple** GPUs to train a dataset with YOLOv5 🚀 on single or multiple machine(s). @@ -172,7 +173,7 @@ If you went through all the above, feel free to raise an Issue by giving as much ## Environments -YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): +YOLOv5 is designed to be run in the following up-to-date verified environments (with all dependencies including [CUDA](https://developer.nvidia.com/cuda)/[CUDNN](https://developer.nvidia.com/cudnn), [Python](https://www.python.org/) and [PyTorch](https://pytorch.org/) preinstalled): - **Notebooks** with free GPU: Run on Gradient Open In Colab Open In Kaggle - **Google Cloud** Deep Learning VM. See [GCP Quickstart Guide](https://docs.ultralytics.com/yolov5/environments/google_cloud_quickstart_tutorial/) diff --git a/docs/yolov5/tutorials/neural_magic_pruning_quantization.md b/docs/yolov5/tutorials/neural_magic_pruning_quantization.md index 532ced7a7a7..839f7ef9b7e 100644 --- a/docs/yolov5/tutorials/neural_magic_pruning_quantization.md +++ b/docs/yolov5/tutorials/neural_magic_pruning_quantization.md @@ -1,6 +1,7 @@ --- comments: true description: Learn how to deploy YOLOv5 with DeepSparse to achieve exceptional CPU performance close to GPUs, using pruning, and quantization.
+keywords: YOLOv5, DeepSparse, Neural Magic, CPU, Production, Performance, Deployments, APIs, SparseZoo, Ultralytics, Model Sparsity, Inference, Open-source, ONNX, Server ---