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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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48
README.md
View File

@ -1,7 +1,7 @@
<div align="center">
<p>
<a href="https://yolovision.ultralytics.com/" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png" alt="YOLO Vision banner"></a>
</p>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
@ -13,7 +13,7 @@
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
@ -25,21 +25,21 @@ We hope that the resources here will help you get the most out of YOLOv8. Please
To request an Enterprise License please complete the form at [Ultralytics Licensing](https://ultralytics.com/license).
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png" alt="YOLOv8 performance plots"></a>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
</div>
</div>
@ -209,22 +209,22 @@ Our key integrations with leading AI platforms extend the functionality of Ultra
<br>
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
<br>
<br>
<div align="center">
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://cutt.ly/yolov5-readme-clearml">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://bit.ly/yolov8-readme-comet">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://bit.ly/yolov5-neuralmagic">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%"></a>
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
</div>
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
@ -245,7 +245,7 @@ We love your input! YOLOv5 and YOLOv8 would not be possible without help from ou
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
## <div align="center">License</div>
@ -261,16 +261,16 @@ For Ultralytics bug reports and feature requests please visit [GitHub Issues](ht
<br>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
</div>

48
README.zh-CN.md
View File

@ -1,7 +1,7 @@
<div align="center">
<p>
<a href="https://yolovision.ultralytics.com/" target="_blank">
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png"></a>
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/im/banner-yolo-vision-2023.png" alt="YOLO Vision banner"></a>
</p>
[中文](https://docs.ultralytics.com/zh/) | [한국어](https://docs.ultralytics.com/ko/) | [日本語](https://docs.ultralytics.com/ja/) | [Русский](https://docs.ultralytics.com/ru/) | [Deutsch](https://docs.ultralytics.com/de/) | [Français](https://docs.ultralytics.com/fr/) | [Español](https://docs.ultralytics.com/es/) | [Português](https://docs.ultralytics.com/pt/) | [हिन्दी](https://docs.ultralytics.com/hi/) | [العربية](https://docs.ultralytics.com/ar/)
@ -13,7 +13,7 @@
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>
@ -25,21 +25,21 @@
如需申请企业许可，请在 [Ultralytics Licensing](https://ultralytics.com/license) 处填写表格
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png" alt="YOLOv8 performance plots"></a>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="2%" alt="Ultralytics Discord"></a>
</div>
</div>
@ -208,22 +208,22 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
<br>
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
<br>
<br>
<div align="center">
<a href="https://roboflow.com/?ref=ultralytics">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-roboflow.png" width="10%" alt="Roboflow logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://cutt.ly/yolov5-readme-clearml">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-clearml.png" width="10%" alt="ClearML logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://bit.ly/yolov8-readme-comet">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="" />
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-comet.png" width="10%" alt="Comet ML logo"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="15%" height="0" alt="space">
<a href="https://bit.ly/yolov5-neuralmagic">
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/partners/logo-neuralmagic.png" width="10%" alt="NeuralMagic logo"></a>
</div>
| Roboflow | ClearML ⭐ NEW | Comet ⭐ NEW | Neural Magic ⭐ NEW |
@ -244,7 +244,7 @@ success = model.export(format="onnx") # 将模型导出为 ONNX 格式
<!-- SVG image from https://opencollective.com/ultralytics/contributors.svg?width=990 -->
<a href="https://github.com/ultralytics/yolov5/graphs/contributors">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/image-contributors.png" alt="Ultralytics open-source contributors"></a>
## <div align="center">许可证</div>
@ -260,16 +260,16 @@ Ultralytics 提供两种许可证选项以适应各种使用场景：
<br>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
</div>

14
docs/ar/index.md
View File

@ -10,17 +10,17 @@ keywords: Ultralytics، YOLOv8، كشف الكائنات، تجزئة الصور
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
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<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics، YOLOv8، كشف الكائنات، تجزئة الصور
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>

14
docs/de/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, Objekterkennung, Bildsegmentierung, maschinelles
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO Banner"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
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<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, Objekterkennung, Bildsegmentierung, maschinelles
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Zitation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Ziehungen"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Auf Gradient ausführen"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Auf Gradient ausführen"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="In Colab öffnen"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="In Kaggle öffnen"></a>
</div>

16
docs/de/tasks/pose.md
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@ -33,14 +33,14 @@ Hier werden vortrainierte YOLOv8 Pose-Modelle gezeigt. Erkennungs-, Segmentierun
[Modelle](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) werden automatisch aus der neuesten Ultralytics-[Veröffentlichung](https://github.com/ultralytics/assets/releases) bei erstmaliger Verwendung heruntergeladen.
| Modell | Größe<br/><sup>(Pixel) | mAP<sup>pose<br/>50-95 | mAP<sup>pose<br/>50 | Geschwindigkeit<br/><sup>CPU ONNX<br/>(ms) | Geschwindigkeit<br/><sup>A100 TensorRT<br/>(ms) | Parameter<br/><sup>(M) | FLOPs<br/><sup>(B) |
|------------------------------------------------------------------------------------------------------|------------------------|------------------------|---------------------|--------------------------------------------|-------------------------------------------------|------------------------|--------------------|
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 50,4 | 80,1 | 131,8 | 1,18 | 3,3 | 9,2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 60,0 | 86,2 | 233,2 | 1,42 | 11,6 | 30,2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 65,0 | 88,8 | 456,3 | 2,00 | 26,4 | 81,0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67,6 | 90,0 | 784,5 | 2,59 | 44,4 | 168,6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69,2 | 90,2 | 1607,1 | 3,73 | 69,4 | 263,2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71,6 | 91,2 | 4088,7 | 10,04 | 99,1 | 1066,4 |
| Modell | Größe<br><sup>(Pixel) | mAP<sup>pose<br>50-95 | mAP<sup>pose<br>50 | Geschwindigkeit<br><sup>CPU ONNX<br>(ms) | Geschwindigkeit<br><sup>A100 TensorRT<br>(ms) | Parameter<br><sup>(M) | FLOPs<br><sup>(B) |
|------------------------------------------------------------------------------------------------------|-----------------------|-----------------------|--------------------|------------------------------------------|-----------------------------------------------|-----------------------|-------------------|
| [YOLOv8n-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8n-pose.pt) | 640 | 50,4 | 80,1 | 131,8 | 1,18 | 3,3 | 9,2 |
| [YOLOv8s-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8s-pose.pt) | 640 | 60,0 | 86,2 | 233,2 | 1,42 | 11,6 | 30,2 |
| [YOLOv8m-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8m-pose.pt) | 640 | 65,0 | 88,8 | 456,3 | 2,00 | 26,4 | 81,0 |
| [YOLOv8l-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8l-pose.pt) | 640 | 67,6 | 90,0 | 784,5 | 2,59 | 44,4 | 168,6 |
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose.pt) | 640 | 69,2 | 90,2 | 1607,1 | 3,73 | 69,4 | 263,2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v0.0.0/yolov8x-pose-p6.pt) | 1280 | 71,6 | 91,2 | 4088,7 | 10,04 | 99,1 | 1066,4 |
- **mAP<sup>val</sup>** Werte gelten für ein einzelnes Modell mit einfacher Skala auf dem [COCO Keypoints val2017](http://cocodataset.org)-Datensatz.
<br>Zu reproduzieren mit `yolo val pose data=coco-pose.yaml device=0`.

14
docs/en/hub/app/android.md
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@ -11,22 +11,22 @@ keywords: Ultralytics, Android App, real-time object detection, YOLO models, Ten
<br>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
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<br>
<br>
<a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>&nbsp;
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>&nbsp;
</div>
The Ultralytics Android App is a powerful tool that allows you to run YOLO models directly on your Android device for real-time object detection. This app utilizes TensorFlow Lite for model optimization and various hardware delegates for acceleration, enabling fast and efficient object detection.

16
docs/en/hub/app/index.md
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@ -11,24 +11,24 @@ keywords: Ultralytics, HUB App, YOLOv5, YOLOv8, mobile AI, real-time object dete
<br>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
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<br>
<a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a>
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="Apple App store"></a>
<a href="https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app" style="text-decoration:none;">
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="" /></a>&nbsp;
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/google-play.svg" width="15%" alt="Google Play store"></a>&nbsp;
</div>
Welcome to the Ultralytics HUB App! We are excited to introduce this powerful mobile app that allows you to run YOLOv5 and YOLOv8 models directly on your [iOS](https://apps.apple.com/xk/app/ultralytics/id1583935240) and [Android](https://play.google.com/store/apps/details?id=com.ultralytics.ultralytics_app) devices. With the HUB App, you can utilize hardware acceleration features like Apple's Neural Engine (ANE) or Android GPU and Neural Network API (NNAPI) delegates to achieve impressive performance on your mobile device.

14
docs/en/hub/app/ios.md
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@ -11,22 +11,22 @@ keywords: Ultralytics, iOS app, object detection, YOLO models, real time, Apple
<br>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
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<br>
<a href="https://apps.apple.com/xk/app/ultralytics/id1583935240" style="text-decoration:none;">
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="" /></a>
<img src="https://raw.githubusercontent.com/ultralytics/assets/master/app/app-store.svg" width="15%" alt="Apple App store"></a>
</div>
The Ultralytics iOS App is a powerful tool that allows you to run YOLO models directly on your iPhone or iPad for real-time object detection. This app utilizes the Apple Neural Engine and Core ML for model optimization and acceleration, enabling fast and efficient object detection.

2
docs/en/hub/datasets.md
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@ -25,7 +25,7 @@ zip -r coco8.zip coco8
You can download our [COCO8](https://github.com/ultralytics/hub/blob/main/example_datasets/coco8.zip) example dataset and unzip it to see exactly how to structure your dataset.
<p align="center">
<img src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_1.jpg" alt="COCO8 Dataset Structure" width="80%" />
<img src="https://raw.githubusercontent.com/ultralytics/assets/main/docs/hub/datasets/hub_upload_dataset_1.jpg" alt="COCO8 Dataset Structure" width="80%">
</p>
The dataset YAML is the same standard YOLOv5 and YOLOv8 YAML format.

12
docs/en/hub/index.md
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@ -11,17 +11,17 @@ keywords: Ultralytics HUB, YOLOv5, YOLOv8, model training, model deployment, pre
<br>
<div align="center">
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
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<br>
<br>

14
docs/en/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine lea
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
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<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, object detection, image segmentation, machine lea
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>

8
docs/en/integrations/openvino.md
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@ -114,7 +114,7 @@ The Intel® Data Center GPU Flex Series is a versatile and robust solution desig
Benchmarks below run on Intel® Data Center GPU Flex 170 at FP32 precision.
<div align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/253741543-62659bf8-1765-4d0b-b71c-8a4f9885506a.jpg">
<img width="800" src="https://user-images.githubusercontent.com/26833433/253741543-62659bf8-1765-4d0b-b71c-8a4f9885506a.jpg" alt="Flex GPU benchmarks">
</div>
| Model | Format | Status | Size (MB) | mAP50-95(B) | Inference time (ms/im) |
@ -153,7 +153,7 @@ Early reviews have praised the Arc™ series, particularly the integrated A770M
Benchmarks below run on Intel® Arc 770 GPU at FP32 precision.
<div align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/253741545-8530388f-8fd1-44f7-a4ae-f875d59dc282.jpg">
<img width="800" src="https://user-images.githubusercontent.com/26833433/253741545-8530388f-8fd1-44f7-a4ae-f875d59dc282.jpg" alt="Arc GPU benchmarks">
</div>
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
@ -188,7 +188,7 @@ Notably, Xeon® CPUs deliver high compute density and scalability, making them i
Benchmarks below run on 4th Gen Intel® Xeon® Scalable CPU at FP32 precision.
<div align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/253741546-dcd8e52a-fc38-424f-b87e-c8365b6f28dc.jpg">
<img width="800" src="https://user-images.githubusercontent.com/26833433/253741546-dcd8e52a-fc38-424f-b87e-c8365b6f28dc.jpg" alt="Xeon CPU benchmarks">
</div>
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |
@ -221,7 +221,7 @@ The Intel® Core® series is a range of high-performance processors by Intel. Th
Benchmarks below run on 13th Gen Intel® Core® i7-13700H CPU at FP32 precision.
<div align="center">
<img width="800" src="https://user-images.githubusercontent.com/26833433/254559985-727bfa43-93fa-4fec-a417-800f869f3f9e.jpg">
<img width="800" src="https://user-images.githubusercontent.com/26833433/254559985-727bfa43-93fa-4fec-a417-800f869f3f9e.jpg" alt="Core CPU benchmarks">
</div>
| Model | Format | Status | Size (MB) | metrics/mAP50-95(B) | Inference time (ms/im) |

46
docs/en/integrations/roboflow.md
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@ -27,20 +27,20 @@ Roboflow provides two services that can help you collect data for YOLOv8 models:
Universe is an online repository with over 250,000 vision datasets totalling over 100 million images.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_universe.png" alt="Roboflow Universe" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_universe.png" alt="Roboflow Universe" width="800">
</p>
With a [free Roboflow account](https://app.roboflow.com/?ref=ultralytics), you can export any dataset available on Universe. To export a dataset, click the "Download this Dataset" button on any dataset.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_dataset.png" alt="Roboflow Universe dataset export" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_dataset.png" alt="Roboflow Universe dataset export" width="800">
</p>
For YOLOv8, select "YOLOv8" as the export format:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_data_format.png" alt="Roboflow Universe dataset export" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_data_format.png" alt="Roboflow Universe dataset export" width="800">
</p>
Universe also has a page that aggregates all [public fine-tuned YOLOv8 models uploaded to Roboflow](https://universe.roboflow.com/search?q=model:yolov8). You can use this page to explore pre-trained models you can use for testing or [for automated data labeling](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling) or to prototype with [Roboflow inference](https://roboflow.com/inference?ref=ultralytics).
@ -54,13 +54,13 @@ If you want to gather images yourself, try [Collect](https://github.com/roboflow
To label data for a YOLOv8 object detection, instance segmentation, or classification model, first create a project in Roboflow.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_create_project.png" alt="Create a Roboflow project" width="400"/>
<img src="https://media.roboflow.com/ultralytics/rf_create_project.png" alt="Create a Roboflow project" width="400">
</p>
Next, upload your images, and any pre-existing annotations you have from other tools ([using one of the 40+ supported import formats](https://roboflow.com/formats?ref=ultralytics)), into Roboflow.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_upload_data.png" alt="Upload images to Roboflow" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_upload_data.png" alt="Upload images to Roboflow" width="800">
</p>
Select the batch of images you have uploaded on the Annotate page to which you are taken after uploading images. Then, click "Start Annotating" to label images.
@ -68,7 +68,7 @@ Select the batch of images you have uploaded on the Annotate page to which you a
To label with bounding boxes, press the `B` key on your keyboard or click the box icon in the sidebar. Click on a point where you want to start your bounding box, then drag to create the box:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_annotate.png" alt="Annotating an image in Roboflow" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_annotate.png" alt="Annotating an image in Roboflow" width="800">
</p>
A pop-up will appear asking you to select a class for your annotation once you have created an annotation.
@ -80,7 +80,7 @@ Roboflow offers a SAM-based label assistant with which you can label images fast
To use the label assistant, click the cursor icon in the sidebar, SAM will be loaded for use in your project.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_annotate_interactive.png" alt="Annotating an image in Roboflow with SAM-powered label assist" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_annotate_interactive.png" alt="Annotating an image in Roboflow with SAM-powered label assist" width="800">
</p>
Hover over any object in the image and SAM will recommend an annotation. You can hover to find the right place to annotate, then click to create your annotation. To amend your annotation to be more or less specific, you can click inside or outside of the annotation SAM has created on the document.
@ -88,7 +88,7 @@ Hover over any object in the image and SAM will recommend an annotation. You can
You can also add tags to images from the Tags panel in the sidebar. You can apply tags to data from a particular area, taken from a specific camera, and more. You can then use these tags to search through data for images matching a tag and generate versions of a dataset with images that contain a particular tag or set of tags.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_tags.png" alt="Adding tags to an image in Roboflow" width="300"/>
<img src="https://media.roboflow.com/ultralytics/rf_tags.png" alt="Adding tags to an image in Roboflow" width="300">
</p>
Models hosted on Roboflow can be used with Label Assist, an automated annotation tool that uses your YOLOv8 model to recommend annotations. To use Label Assist, first upload a YOLOv8 model to Roboflow (see instructions later in the guide). Then, click the magic wand icon in the left sidebar and select your model for use in Label Assist.
@ -96,13 +96,13 @@ Models hosted on Roboflow can be used with Label Assist, an automated annotation
Choose a model, then click "Continue" to enable Label Assist:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_label_assist.png" alt="Enabling Label Assist" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_label_assist.png" alt="Enabling Label Assist" width="800">
</p>
When you open new images for annotation, Label Assist will trigger and recommend annotations.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_label_assist.png" alt="ALabel Assist recommending an annotation" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_label_assist.png" alt="ALabel Assist recommending an annotation" width="800">
</p>
## Dataset Management for YOLOv8
@ -114,13 +114,13 @@ First, you can use dataset search to find images that meet a semantic text descr
For example, the following text query finds images that contain people in a dataset:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_dataset_management.png" alt="Searching for an image" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_dataset_management.png" alt="Searching for an image" width="800">
</p>
You can narrow your search to images with a particular tag using the "Tags" selector:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_filter_by_tag.png" alt="Filter images by tag" width="350"/>
<img src="https://media.roboflow.com/ultralytics/rf_filter_by_tag.png" alt="Filter images by tag" width="350">
</p>
Before you start training a model with your dataset, we recommend using Roboflow [Health Check](https://docs.roboflow.com/datasets/dataset-health-check), a web tool that provides an insight into your dataset and how you can improve the dataset prior to training a vision model.
@ -128,7 +128,7 @@ Before you start training a model with your dataset, we recommend using Roboflow
To use Health Check, click the "Health Check" sidebar link. A list of statistics will appear that show the average size of images in your dataset, class balance, a heatmap of where annotations are in your images, and more.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_dataset_health_check.png" alt="Roboflow Health Check analysis" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_dataset_health_check.png" alt="Roboflow Health Check analysis" width="800">
</p>
Health Check may recommend changes to help enhance dataset performance. For example, the class balance feature may show that there is an imbalance in labels that, if solved, may boost performance or your model.
@ -138,19 +138,19 @@ Health Check may recommend changes to help enhance dataset performance. For exam
To export your data, you will need a dataset version. A version is a state of your dataset frozen-in-time. To create a version, first click "Versions" in the sidebar. Then, click the "Create New Version" button. On this page, you will be able to choose augmentations and preprocessing steps to apply to your dataset:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_generate_dataset.png" alt="Creating a dataset version on Roboflow" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_generate_dataset.png" alt="Creating a dataset version on Roboflow" width="800">
</p>
For each augmentation you select, a pop-up will appear allowing you to tune the augmentation to your needs. Here is an example of tuning a brightness augmentation within specified parameters:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_augmentations.png" alt="Applying augmentations to a dataset" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_augmentations.png" alt="Applying augmentations to a dataset" width="800">
</p>
When your dataset version has been generated, you can export your data into a range of formats. Click the "Export Dataset" button on your dataset version page to export your data:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_export_data.png" alt="Exporting a dataset" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_export_data.png" alt="Exporting a dataset" width="800">
</p>
You are now ready to train YOLOv8 on a custom dataset. Follow this [written guide](https://blog.roboflow.com/how-to-train-yolov8-on-a-custom-dataset/) and [YouTube video](https://www.youtube.com/watch?v=wuZtUMEiKWY) for step-by-step instructions or refer to the [Ultralytics documentation](https://docs.ultralytics.com/modes/train/).
@ -181,7 +181,7 @@ When you run the code above, you will be asked to authenticate. Then, your model
To test your model and find deployment instructions for supported SDKs, go to the "Deploy" tab in the Roboflow sidebar. At the top of this page, a widget will appear with which you can test your model. You can use your webcam for live testing or upload images or videos.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_test_project.png" alt="Running inference on an example image" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_test_project.png" alt="Running inference on an example image" width="800">
</p>
You can also use your uploaded model as a [labeling assistant](https://docs.roboflow.com/annotate/use-roboflow-annotate/model-assisted-labeling). This feature uses your trained model to recommend annotations on images uploaded to Roboflow.
@ -195,13 +195,13 @@ Once you have uploaded a model to Roboflow, you can access our model evaluation
To access a confusion matrix, go to your model page on the Roboflow dashboard, then click "View Detailed Evaluation":
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_model_eval.png" alt="Start a Roboflow model evaluation" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_model_eval.png" alt="Start a Roboflow model evaluation" width="800">
</p>
A pop-up will appear showing a confusion matrix:
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_confusion_matrix.png" alt="A confusion matrix" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_confusion_matrix.png" alt="A confusion matrix" width="800">
</p>
Hover over a box on the confusion matrix to see the value associated with the box. Click on a box to see images in the respective category. Click on an image to view the model predictions and ground truth data associated with that image.
@ -209,7 +209,7 @@ Hover over a box on the confusion matrix to see the value associated with the bo
For more insights, click Vector Analysis. This will show a scatter plot of the images in your dataset, calculated using CLIP. The closer images are in the plot, the more similar they are, semantically. Each image is represented as a dot with a color between white and red. The more red the dot, the worse the model performed.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_vector_analysis.png" alt="A vector analysis plot" width="800"/>
<img src="https://media.roboflow.com/ultralytics/rf_vector_analysis.png" alt="A vector analysis plot" width="800">
</p>
You can use Vector Analysis to:
@ -233,7 +233,7 @@ Want to learn more about using Roboflow for creating YOLOv8 models? The followin
Below are a few of the many pieces of feedback we have received for using YOLOv8 and Roboflow together to create computer vision models.
<p align="center">
<img src="https://media.roboflow.com/ultralytics/rf_showcase_1.png" alt="Showcase image" width="500"/>
<img src="https://media.roboflow.com/ultralytics/rf_showcase_2.png" alt="Showcase image" width="500"/>
<img src="https://media.roboflow.com/ultralytics/rf_showcase_3.png" alt="Showcase image" width="500"/>
<img src="https://media.roboflow.com/ultralytics/rf_showcase_1.png" alt="Showcase image" width="500">
<img src="https://media.roboflow.com/ultralytics/rf_showcase_2.png" alt="Showcase image" width="500">
<img src="https://media.roboflow.com/ultralytics/rf_showcase_3.png" alt="Showcase image" width="500">
</p>

2
docs/en/yolov5/environments/docker_image_quickstart_tutorial.md
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@ -61,4 +61,4 @@ python detect.py --weights yolov5s.pt --source path/to/images # run inference o
python export.py --weights yolov5s.pt --include onnx coreml tflite # export models to other formats
```
<p align="center"><img width="1000" src="https://user-images.githubusercontent.com/26833433/142224770-6e57caaf-ac01-4719-987f-c37d1b6f401f.png"></p>
<p align="center"><img width="1000" src="https://user-images.githubusercontent.com/26833433/142224770-6e57caaf-ac01-4719-987f-c37d1b6f401f.png" alt="GCP running Docker"></p>

12
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@ -68,16 +68,16 @@ This badge signifies that all [YOLOv5 GitHub Actions](https://github.com/ultraly
<br>
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12
docs/en/yolov5/tutorials/architecture_description.md
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@ -165,7 +165,7 @@ The YOLOv5 architecture makes some important changes to the box prediction strat
![b_w](https://latex.codecogs.com/svg.image?b_w=p_w\cdot&space;e^{t_w})
![b_h](https://latex.codecogs.com/svg.image?b_h=p_h\cdot&space;e^{t_h})
<img src="https://user-images.githubusercontent.com/31005897/158508027-8bf63c28-8290-467b-8a3e-4ad09235001a.png#pic_center" width=40%>
<img src="https://user-images.githubusercontent.com/31005897/158508027-8bf63c28-8290-467b-8a3e-4ad09235001a.png#pic_center" width=40% alt="YOLOv5 grid computation">
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.
@ -178,11 +178,11 @@ The revised formulas for calculating the predicted bounding box are as follows:
Compare the center point offset before and after scaling. The center point offset range is adjusted from (0, 1) to (-0.5, 1.5). Therefore, offset can easily get 0 or 1.
<img src="https://user-images.githubusercontent.com/31005897/158508052-c24bc5e8-05c1-4154-ac97-2e1ec71f582e.png#pic_center" width=40%>
<img src="https://user-images.githubusercontent.com/31005897/158508052-c24bc5e8-05c1-4154-ac97-2e1ec71f582e.png#pic_center" width=40% alt="YOLOv5 grid scaling">
Compare the height and width scaling ratio(relative to anchor) before and after adjustment. The original yolo/darknet box equations have a serious flaw. Width and Height are completely unbounded as they are simply out=exp(in), which is dangerous, as it can lead to runaway gradients, instabilities, NaN losses and ultimately a complete loss of training. [refer this issue](https://github.com/ultralytics/yolov5/issues/471#issuecomment-662009779)
<img src="https://user-images.githubusercontent.com/31005897/158508089-5ac0c7a3-6358-44b7-863e-a6e45babb842.png#pic_center" width=40%>
<img src="https://user-images.githubusercontent.com/31005897/158508089-5ac0c7a3-6358-44b7-863e-a6e45babb842.png#pic_center" width=40% alt="YOLOv5 unbounded scaling">
### 4.4 Build Targets
@ -204,15 +204,15 @@ This process follows these steps:
![match](https://latex.codecogs.com/svg.image?r^{max}<{\rm&space;anchor_t})
<img src="https://user-images.githubusercontent.com/31005897/158508119-fbb2e483-7b8c-4975-8e1f-f510d367f8ff.png#pic_center" width=70%>
<img src="https://user-images.githubusercontent.com/31005897/158508119-fbb2e483-7b8c-4975-8e1f-f510d367f8ff.png#pic_center" width=70% alt="YOLOv5 IoU computation">
- If the calculated ratio is within the threshold, match the ground truth box with the corresponding anchor.
<img src="https://user-images.githubusercontent.com/31005897/158508771-b6e7cab4-8de6-47f9-9abf-cdf14c275dfe.png#pic_center" width=70%>
<img src="https://user-images.githubusercontent.com/31005897/158508771-b6e7cab4-8de6-47f9-9abf-cdf14c275dfe.png#pic_center" width=70% alt="YOLOv5 grid overlap">
- 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.
<img src="https://user-images.githubusercontent.com/31005897/158508139-9db4e8c2-cf96-47e0-bc80-35d11512f296.png#pic_center" width=70%>
<img src="https://user-images.githubusercontent.com/31005897/158508139-9db4e8c2-cf96-47e0-bc80-35d11512f296.png#pic_center" width=70% alt="YOLOv5 anchor selection">
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.

16
docs/en/yolov5/tutorials/clearml_logging_integration.md
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@ -22,15 +22,15 @@ keywords: ClearML, YOLOv5, Ultralytics, AI toolbox, training data, remote traini
🔭 Turn your newly trained <b>YOLOv5 model into an API</b> with just a few commands using ClearML Serving
<br />
<br>
And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline!
<br />
<br />
<br>
<br>
![ClearML scalars dashboard](https://github.com/thepycoder/clearml_screenshots/raw/main/experiment_manager_with_compare.gif)
<br />
<br />
<br>
<br>
## 🦾 Setting Things Up
@ -52,7 +52,7 @@ Either sign up for free to the [ClearML Hosted Service](https://cutt.ly/yolov5-t
That's it! You're done 😎
<br />
<br>
## 🚀 Training YOLOv5 With ClearML
@ -95,7 +95,7 @@ That's a lot right? 🤯 Now, we can visualize all of this information in the Cl
There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works!
<br />
<br>
## 🔗 Dataset Version Management
@ -163,7 +163,7 @@ Now that you have a ClearML dataset, you can very simply use it to train custom
python train.py --img 640 --batch 16 --epochs 3 --data clearml://<your_dataset_id> --weights yolov5s.pt --cache
```
<br />
<br>
## 👀 Hyperparameter Optimization

2
docs/en/yolov5/tutorials/comet_logging_integration.md
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@ -4,7 +4,7 @@ description: Learn how to set up and use Comet to enhance your YOLOv5 model trai
keywords: YOLOv5, Comet, Machine Learning, Ultralytics, Real time metrics tracking, Hyperparameters, Model checkpoints, Model predictions, YOLOv5 training, Comet Credentials
---
<img src="https://cdn.comet.ml/img/notebook_logo.png">
![Comet](https://cdn.comet.ml/img/notebook_logo.png)
# YOLOv5 with Comet

2
docs/en/yolov5/tutorials/model_ensembling.md
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@ -127,7 +127,7 @@ Results saved to runs/detect/exp2
Done. (0.223s)
```
<img src="https://user-images.githubusercontent.com/26833433/124489091-ea4f9a00-ddb0-11eb-8ef1-d6f335c97f6f.jpg" width="500">
<img src="https://user-images.githubusercontent.com/26833433/124489091-ea4f9a00-ddb0-11eb-8ef1-d6f335c97f6f.jpg" width="500" alt="YOLO inference result">
## Environments

4
docs/en/yolov5/tutorials/model_export.md
View File

@ -134,10 +134,10 @@ Visualize: https://netron.app/
```
The 3 exported models will be saved alongside the original PyTorch model:
<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/122827190-57a8f880-d2e4-11eb-860e-dbb7f9fc57fb.png"></p>
<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/122827190-57a8f880-d2e4-11eb-860e-dbb7f9fc57fb.png" alt="YOLO export locations"></p>
[Netron Viewer](https://github.com/lutzroeder/netron) is recommended for visualizing exported models:
<p align="center"><img width="850" src="https://user-images.githubusercontent.com/26833433/191003260-f94011a7-5b2e-4fe3-93c1-e1a935e0a728.png"></p>
<p align="center"><img width="850" src="https://user-images.githubusercontent.com/26833433/191003260-f94011a7-5b2e-4fe3-93c1-e1a935e0a728.png" alt="YOLO model visualization"></p>
## Exported Model Usage Examples

6
docs/en/yolov5/tutorials/neural_magic_pruning_quantization.md
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@ -27,7 +27,7 @@ This guide explains how to deploy YOLOv5 with Neural Magic's DeepSparse.
DeepSparse is an inference runtime with exceptional performance on CPUs. For instance, compared to the ONNX Runtime baseline, DeepSparse offers a 5.8x speed-up for YOLOv5s, running on the same machine!
<p align="center">
<img width="60%" src="https://github.com/neuralmagic/deepsparse/raw/main/examples/ultralytics-yolo/ultralytics-readmes/performance-chart-5.8x.png">
<img width="60%" src="https://github.com/neuralmagic/deepsparse/raw/main/examples/ultralytics-yolo/ultralytics-readmes/performance-chart-5.8x.png" alt="YOLOv5 speed improvement">
</p>
For the first time, your deep learning workloads can meet the performance demands of production without the complexity and costs of hardware accelerators. Put simply, DeepSparse gives you the performance of GPUs and the simplicity of software:
@ -43,7 +43,7 @@ DeepSparse takes advantage of model sparsity to gain its performance speedup.
Sparsification through pruning and quantization is a broadly studied technique, allowing order-of-magnitude reductions in the size and compute needed to execute a network, while maintaining high accuracy. DeepSparse is sparsity-aware, meaning it skips the zeroed out parameters, shrinking amount of compute in a forward pass. Since the sparse computation is now memory bound, DeepSparse executes the network depth-wise, breaking the problem into Tensor Columns, vertical stripes of computation that fit in cache.
<p align="center">
<img width="60%" src="https://github.com/neuralmagic/deepsparse/raw/main/examples/ultralytics-yolo/ultralytics-readmes/tensor-columns.png">
<img width="60%" src="https://github.com/neuralmagic/deepsparse/raw/main/examples/ultralytics-yolo/ultralytics-readmes/tensor-columns.png" alt="YOLO model pruning">
</p>
Sparse networks with compressed computation, executed depth-wise in cache, allows DeepSparse to deliver GPU-class performance on CPUs!
@ -162,7 +162,7 @@ deepsparse.object_detection.annotate --model_filepath zoo:cv/detection/yolov5-s/
Running the above command will create an `annotation-results` folder and save the annotated image inside.
<p align = "center">
<img src="https://github.com/neuralmagic/deepsparse/raw/d31f02596ebff2ec62761d0bc9ca14c4663e8858/src/deepsparse/yolo/sample_images/basilica-annotated.jpg" alt="annotated" width="60%"/>
<img src="https://github.com/neuralmagic/deepsparse/raw/d31f02596ebff2ec62761d0bc9ca14c4663e8858/src/deepsparse/yolo/sample_images/basilica-annotated.jpg" alt="annotated" width="60%">
</p>
## Benchmarking Performance

3
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View File

@ -76,7 +76,8 @@ results.pandas().xyxy[0] # im1 predictions (pandas)
# 3 986.00 304.00 1028.0 420.0 0.286865 27 tie
```
<img src="https://user-images.githubusercontent.com/26833433/124915064-62a49e00-dff1-11eb-86b3-a85b97061afb.jpg" width="500"> <img src="https://user-images.githubusercontent.com/26833433/124915055-60424400-dff1-11eb-9055-24585b375a29.jpg" width="300">
<img src="https://user-images.githubusercontent.com/26833433/124915064-62a49e00-dff1-11eb-86b3-a85b97061afb.jpg" width="500" alt="YOLO inference results on zidane.jpg">
<img src="https://user-images.githubusercontent.com/26833433/124915055-60424400-dff1-11eb-9055-24585b375a29.jpg" width="300" alt="YOLO inference results on bus.jpg">
For all inference options see YOLOv5 `AutoShape()` forward [method](https://github.com/ultralytics/yolov5/blob/30e4c4f09297b67afedf8b2bcd851833ddc9dead/models/common.py#L243-L252).

2
docs/en/yolov5/tutorials/roboflow_datasets_integration.md
View File

@ -49,4 +49,4 @@ We have released a custom training tutorial demonstrating all of the above capab
The real world is messy and your model will invariably encounter situations your dataset didn't anticipate. Using [active learning](https://blog.roboflow.com/what-is-active-learning/) is an important strategy to iteratively improve your dataset and model. With the Roboflow and YOLOv5 integration, you can quickly make improvements on your model deployments by using a battle tested machine learning pipeline.
<p align=""><a href="https://roboflow.com/?ref=ultralytics"><img width="1000" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png"/></a></p>
<p align=""><a href="https://roboflow.com/?ref=ultralytics"><img width="1000" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/615627e5824c9c6195abfda9_computer-vision-cycle.png" alt="Roboflow active learning"></a></p>

4
docs/en/yolov5/tutorials/running_on_jetson_nano.md
View File

@ -216,7 +216,7 @@ uri=file:///opt/nvidia/deepstream/deepstream/samples/streams/sample_1080p_h264.m
deepstream-app -c deepstream_app_config.txt
```
<div align=center><img width=1000 src="https://files.seeedstudio.com/wiki/YOLOV5/FP32-yolov5s.gif"/></div>
<div align=center><img width=1000 src="https://files.seeedstudio.com/wiki/YOLOV5/FP32-yolov5s.gif" alt="YOLOv5 with deepstream FP32"></div>
The above result is running on **Jetson Xavier NX** with **FP32** and **YOLOv5s 640x640**. We can see that the **FPS** is around **30**.
@ -299,7 +299,7 @@ network-mode=1
deepstream-app -c deepstream_app_config.txt
```
<div align=center><img width=1000 src="https://files.seeedstudio.com/wiki/YOLOV5/INT8-yolov5s.gif"/></div>
<div align=center><img width=1000 src="https://files.seeedstudio.com/wiki/YOLOV5/INT8-yolov5s.gif" alt="YOLOv5 with deepstream INT8"></div>
The above result is running on **Jetson Xavier NX** with **INT8** and **YOLOv5s 640x640**. We can see that the **FPS** is around **60**.

2
docs/en/yolov5/tutorials/test_time_augmentation.md
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@ -121,7 +121,7 @@ Results saved to runs/detect/exp
Done. (0.156s)
```
<img src="https://user-images.githubusercontent.com/26833433/124491703-dbb6b200-ddb3-11eb-8b57-ed0d58d0d8b4.jpg" width="500">
<img src="https://user-images.githubusercontent.com/26833433/124491703-dbb6b200-ddb3-11eb-8b57-ed0d58d0d8b4.jpg" width="500" alt="YOLOv5 test time augmentations">
### PyTorch Hub TTA

26
docs/en/yolov5/tutorials/train_custom_data.md
View File

@ -19,7 +19,7 @@ pip install -r requirements.txt # install
## Train On Custom Data
<a href="https://bit.ly/ultralytics_hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/im/integrations-loop.png" alt="Ultralytics active learning"></a>
<br>
<br>
@ -46,7 +46,7 @@ If this is not possible, you can start from [a public dataset](https://universe.
Once you have collected images, you will need to annotate the objects of interest to create a ground truth for your model to learn from.
<p align="center"><a href="https://app.roboflow.com/?model=yolov5&ref=ultralytics" title="Create a Free Roboflow Account"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif" /></a></p>
<p align="center"><a href="https://app.roboflow.com/?model=yolov5&ref=ultralytics" title="Create a Free Roboflow Account"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a275ad4b4ac20cd2e21a_roboflow-annotate.gif" alt="YOLOv5 accuracies"></a></p>
[Roboflow Annotate](https://roboflow.com/annotate?ref=ultralytics) is a simple web-based tool for managing and labeling your images with your team and exporting them in [YOLOv5's annotation format](https://roboflow.com/formats/yolov5-pytorch-txt?ref=ultralytics).
@ -59,18 +59,18 @@ and upload your dataset to a `Public` workspace, label any unannotated images, t
Note: YOLOv5 does online augmentation during training, so we do not recommend applying any augmentation steps in Roboflow for training with YOLOv5. But we recommend applying the following preprocessing steps:
<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273477fccf42a0fd3d6_roboflow-preprocessing.png" title="Recommended Preprocessing Steps" /></p>
<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273477fccf42a0fd3d6_roboflow-preprocessing.png" alt="Recommended Preprocessing Steps"></p>
* **Auto-Orient** - to strip EXIF orientation from your images.
* **Resize (Stretch)** - to the square input size of your model (640x640 is the YOLOv5 default).
Generating a version will give you a point in time snapshot of your dataset so you can always go back and compare your future model training runs against it, even if you add more images or change its configuration later.
<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a2733fd1da943619934e_roboflow-export.png" title="Export in YOLOv5 Format" /></p>
<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a2733fd1da943619934e_roboflow-export.png" alt="Export in YOLOv5 Format"></p>
Export in `YOLOv5 Pytorch` format, then copy the snippet into your training script or notebook to download your dataset.
<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273a92e4f5cb72594df_roboflow-snippet.png" title="Roboflow dataset download snippet" /></p>
<p align="center"><img width="450" src="https://uploads-ssl.webflow.com/5f6bc60e665f54545a1e52a5/6152a273a92e4f5cb72594df_roboflow-snippet.png" alt="Roboflow dataset download snippet"></p>
Now continue with `2. Select a Model`.
</details>
@ -106,14 +106,14 @@ After using an annotation tool to label your images, export your labels to **YOL
- One row per object
- Each row is `class x_center y_center width height` format.
- Box coordinates must be in **normalized xywh** format (from 0 - 1). If your boxes are in pixels, divide `x_center` and `width` by image width, and `y_center` and `height` by image height.
- Box coordinates must be in **normalized xywh** format (from 0 to 1). If your boxes are in pixels, divide `x_center` and `width` by image width, and `y_center` and `height` by image height.
- Class numbers are zero-indexed (start from 0).
<p align="center"><img width="750" src="https://user-images.githubusercontent.com/26833433/91506361-c7965000-e886-11ea-8291-c72b98c25eec.jpg"></p>
<p align="center"><img width="750" src="https://user-images.githubusercontent.com/26833433/91506361-c7965000-e886-11ea-8291-c72b98c25eec.jpg" alt="Roboflow annotations"></p>
The label file corresponding to the above image contains 2 persons (class `0`) and a tie (class `27`):
<p align="center"><img width="428" src="https://user-images.githubusercontent.com/26833433/112467037-d2568c00-8d66-11eb-8796-55402ac0d62f.png"></p>
<p align="center"><img width="428" src="https://user-images.githubusercontent.com/26833433/112467037-d2568c00-8d66-11eb-8796-55402ac0d62f.png" alt="Roboflow dataset preprocessing"></p>
### 1.3 Organize Directories
@ -124,14 +124,14 @@ Organize your train and val images and labels according to the example below. YO
../datasets/coco128/labels/im0.txt # label
```
<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/134436012-65111ad1-9541-4853-81a6-f19a3468b75f.png"></p>
<p align="center"><img width="700" src="https://user-images.githubusercontent.com/26833433/134436012-65111ad1-9541-4853-81a6-f19a3468b75f.png" alt="YOLOv5 dataset structure"></p>
</details>
### 2. Select a Model
Select a pretrained model to start training from. Here we select [YOLOv5s](https://github.com/ultralytics/yolov5/blob/master/models/yolov5s.yaml), the second-smallest and fastest model available. See our README [table](https://github.com/ultralytics/yolov5#pretrained-checkpoints) for a full comparison of all models.
<p align="center"><img width="800" alt="YOLOv5 Models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
<p align="center"><img width="800" alt="YOLOv5 models" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/model_comparison.png"></p>
### 3. Train
@ -168,7 +168,7 @@ python train.py --img 640 --epochs 3 --data coco128.yaml --weights yolov5s.pt #
To learn more about all the supported Comet features for this integration, check out the [Comet Tutorial](https://docs.ultralytics.com/yolov5/tutorials/comet_logging_integration). If you'd like to learn more about Comet, head over to our [documentation](https://bit.ly/yolov5-colab-comet-docs). Get started by trying out the Comet Colab Notebook:
[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1RG0WOQyxlDlo5Km8GogJpIEJlg_5lyYO?usp=sharing)
<img width="1920" alt="yolo-ui" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
<img width="1920" alt="YOLO UI" src="https://user-images.githubusercontent.com/26833433/202851203-164e94e1-2238-46dd-91f8-de020e9d6b41.png">
#### ClearML Logging and Automation 🌟 NEW
@ -182,7 +182,7 @@ You'll get all the great expected features from an experiment manager: live upda
You can use ClearML Data to version your dataset and then pass it to YOLOv5 simply using its unique ID. This will help you keep track of your data without adding extra hassle. Explore the [ClearML Tutorial](https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration) for details!
<a href="https://cutt.ly/yolov5-notebook-clearml">
<img alt="ClearML Experiment Management UI" src="https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg" width="1280"/></a>
<img alt="ClearML Experiment Management UI" src="https://github.com/thepycoder/clearml_screenshots/raw/main/scalars.jpg" width="1280"></a>
#### Local Logging
@ -190,7 +190,7 @@ Training results are automatically logged with [Tensorboard](https://www.tensorf
This directory contains train and val statistics, mosaics, labels, predictions and augmented mosaics, as well as metrics and charts including precision-recall (PR) curves and confusion matrices.
<img alt="Local logging results" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-local_logging.jpg" width="1280"/>
<img alt="Local logging results" src="https://github.com/ultralytics/yolov5/releases/download/v1.0/image-local_logging.jpg" width="1280">
Results file `results.csv` is updated after each epoch, and then plotted as `results.png` (below) after training completes. You can also plot any `results.csv` file manually:

10
docs/en/yolov5/tutorials/transfer_learning_with_frozen_layers.md
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@ -124,19 +124,19 @@ train.py --batch 48 --weights yolov5m.pt --data voc.yaml --epochs 50 --cache --i
The results show that freezing speeds up training, but reduces final accuracy slightly.
![](https://user-images.githubusercontent.com/26833433/98394454-11579f80-205b-11eb-8e57-d8318e1cc2f8.png)
![Freezing training mAP50 results](https://user-images.githubusercontent.com/26833433/98394454-11579f80-205b-11eb-8e57-d8318e1cc2f8.png)
![](https://user-images.githubusercontent.com/26833433/98394459-13216300-205b-11eb-871b-49e20691a423.png)
![Freezing training mAP50-95 results](https://user-images.githubusercontent.com/26833433/98394459-13216300-205b-11eb-871b-49e20691a423.png)
<img width="922" alt="Screenshot 2020-11-06 at 18 08 13" src="https://user-images.githubusercontent.com/26833433/98394485-22081580-205b-11eb-9e37-1f9869fe91d8.png">
<img width="922" alt="Table results" src="https://user-images.githubusercontent.com/26833433/98394485-22081580-205b-11eb-9e37-1f9869fe91d8.png">
### GPU Utilization Comparison
Interestingly, the more modules are frozen the less GPU memory is required to train, and the lower GPU utilization. This indicates that larger models, or models trained at larger --image-size may benefit from freezing in order to train faster.
![](https://user-images.githubusercontent.com/26833433/98394920-c2f6d080-205b-11eb-9611-fd68522b4e0e.png)
![Training GPU memory allocated percent](https://user-images.githubusercontent.com/26833433/98394920-c2f6d080-205b-11eb-9611-fd68522b4e0e.png)
![](https://user-images.githubusercontent.com/26833433/98394918-bf634980-205b-11eb-948d-311036ef9325.png)
![Training GPU memory utilization percent](https://user-images.githubusercontent.com/26833433/98394918-bf634980-205b-11eb-948d-311036ef9325.png)
## Environments

14
docs/es/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, detección de objetos, segmentación de imágenes
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Banner de Ultralytics YOLO"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="GitHub de Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="LinkedIn de Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Twitter de Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="YouTube de Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="TikTok de Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Instagram de Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Discord de Ultralytics"></a>
<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, detección de objetos, segmentación de imágenes
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Cita de YOLOv8"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Descargas de Docker"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Ejecutar en Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Ejecutar en Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir en Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Abrir en Kaggle"></a>
</div>

12
docs/fr/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, détection d'objets, segmentation d'images, appre
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Bannière Ultralytics YOLO"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="GitHub Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="LinkedIn Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Twitter Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="YouTube Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="TikTok Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Instagram Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Discord Ultralytics"></a>
<br>
<br>

14
docs/hi/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, वस्तु पता लगाना, छव
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, वस्तु पता लगाना, छव
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>

14
docs/ja/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, オブジェクト検出, 画像セグメンテ
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLOバナー"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, オブジェクト検出, 画像セグメンテ
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 引用情報"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker プル"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Gradient上で実行"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Gradient上で実行"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Colabで開く"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Kaggleで開く"></a>
</div>

14
docs/ko/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, 객체 탐지, 이미지 분할, 기계 학습,
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO 배너"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, 객체 탐지, 이미지 분할, 기계 학습,
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 인용"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker 당기기"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>

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docs/pt/index.md
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@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, detecção de objetos, segmentação de imagens,
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Banner Ultralytics YOLO"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="GitHub da Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="LinkedIn da Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Twitter da Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="YouTube da Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="TikTok da Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Instagram da Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Discord da Ultralytics"></a>
<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, detecção de objetos, segmentação de imagens,
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Citação do YOLOv8"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Contagem de Pulls no Docker"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Executar no Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Executar no Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Abrir no Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Abrir no Kaggle"></a>
</div>

14
docs/ru/index.md
View File

@ -10,17 +10,17 @@ keywords: Ultralytics, YOLOv8, обнаружение объектов, сегм
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="GitHub Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="LinkedIn Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Twitter Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="YouTube Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="TikTok Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Instagram Ultralytics"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Discord Ultralytics"></a>
<br>
<br>
@ -29,7 +29,7 @@ keywords: Ultralytics, YOLOv8, обнаружение объектов, сегм
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="Цитирование YOLOv8"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Загрузки Docker"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Запустить на Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Запустить на Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Открыть в Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Открыть в Kaggle"></a>
</div>

22
docs/update_translations.py
View File

@ -121,6 +121,27 @@ class MarkdownLinkFixer:
return match.group(0)
@staticmethod
def update_html_tags(content):
"""Updates HTML tags in docs."""
alt_tag = 'MISSING'
# Remove closing slashes from self-closing HTML tags
pattern = re.compile(r'<([^>]+?)\s*/>')
content = re.sub(pattern, r'<\1>', content)
# Find all images without alt tags and add placeholder alt text
pattern = re.compile(r'!\[(.*?)\]\((.*?)\)')
content, num_replacements = re.subn(pattern, lambda match: f'![{match.group(1) or alt_tag}]({match.group(2)})',
content)
# Add missing alt tags to HTML images
pattern = re.compile(r'<img\s+(?!.*?\balt\b)[^>]*src=["\'](.*?)["\'][^>]*>')
content, num_replacements = re.subn(pattern, lambda match: match.group(0).replace('>', f' alt="{alt_tag}">', 1),
content)
return content
def process_markdown_file(self, md_file_path, lang_dir):
"""Process each markdown file in the language directory."""
print(f'Processing file: {md_file_path}')
@ -134,6 +155,7 @@ class MarkdownLinkFixer:
content = self.replace_front_matter(content, lang_dir)
content = self.replace_admonitions(content, lang_dir)
content = self.update_iframe(content)
content = self.update_html_tags(content)
with open(md_file_path, 'w', encoding='utf-8') as file:
file.write(content)

14
docs/zh/index.md
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@ -12,17 +12,17 @@ keywords: Ultralytics, YOLOv8, 目标检测, 图像分割, 机器学习, 深度
<img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
</p>
<a href="https://github.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="Ultralytics GitHub"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.linkedin.com/company/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="Ultralytics LinkedIn"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://twitter.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="Ultralytics Twitter"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://youtube.com/ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="3%" alt="Ultralytics YouTube"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.tiktok.com/@ultralytics"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="3%" alt="Ultralytics TikTok"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://www.instagram.com/ultralytics/"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="3%" alt="Ultralytics Instagram"></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="space">
<a href="https://ultralytics.com/discord"><img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-discord.png" width="3%" alt="Ultralytics Discord"></a>
<br>
<br>
@ -31,7 +31,7 @@ keywords: Ultralytics, YOLOv8, 目标检测, 图像分割, 机器学习, 深度
<a href="https://zenodo.org/badge/latestdoi/264818686"><img src="https://zenodo.org/badge/264818686.svg" alt="YOLOv8 Citation"></a>
<a href="https://hub.docker.com/r/ultralytics/ultralytics"><img src="https://img.shields.io/docker/pulls/ultralytics/ultralytics?logo=docker" alt="Docker Pulls"></a>
<br>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"/></a>
<a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
<a href="https://colab.research.google.com/github/ultralytics/ultralytics/blob/main/examples/tutorial.ipynb"><img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"></a>
<a href="https://www.kaggle.com/ultralytics/yolov8"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
</div>

7
examples/YOLOv8-Region-Counter/readme.md
View File

@ -4,10 +4,9 @@
- Regions can be adjusted to suit the user's preferences and requirements.
<div>
<p align="center">
<img src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/5ab3bbd7-fd12-4849-928e-5f294d6c3fcf" width="45%"/>
<img src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/e7c1aea7-474d-4d78-8d48-b50854ffe1ca" width="45%"/>
&nbsp; &nbsp; &nbsp; &nbsp;
<p align="center">
<img src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/5ab3bbd7-fd12-4849-928e-5f294d6c3fcf" width="45%" alt="YOLOv8 region counting visual 1">
<img src="https://github.com/RizwanMunawar/ultralytics/assets/62513924/e7c1aea7-474d-4d78-8d48-b50854ffe1ca" width="45%" alt="YOLOv8 region counting visual 2">
</p>
</div>

2
examples/YOLOv8-Segmentation-ONNXRuntime-Python/README.md
View File

@ -43,7 +43,7 @@ python main.py --model-path <MODEL_PATH> --source <IMAGE_PATH>
After running the command, you should see segmentation results similar to this:
<img src="https://user-images.githubusercontent.com/51357717/279988626-eb74823f-1563-4d58-a8e4-0494025b7c9a.jpg" alt="Segmentation Demo" width="800"/>
<img src="https://user-images.githubusercontent.com/51357717/279988626-eb74823f-1563-4d58-a8e4-0494025b7c9a.jpg" alt="Segmentation Demo" width="800">
## Advanced Usage

2
ultralytics/trackers/README.md
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@ -1,6 +1,6 @@
# Multi-Object Tracking with Ultralytics YOLO
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png">
<img width="1024" src="https://user-images.githubusercontent.com/26833433/243418637-1d6250fd-1515-4c10-a844-a32818ae6d46.png" alt="YOLOv8 trackers visualization">
Object tracking in the realm of video analytics is a critical task that not only identifies the location and class of objects within the frame but also maintains a unique ID for each detected object as the video progresses. The applications are limitless—ranging from surveillance and security to real-time sports analytics.