docs: split badge groups for better rendering in readme text and badges

README.md

@@ -32,24 +32,25 @@ model-index:
     <a href="https://www.ultralytics.com/blog/ultralytics-yolo26-the-new-standard-for-edge-first-vision-ai" target="_blank">
       <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png" alt="Ultralytics YOLO banner"></a>
   </p>
-  <p style="margin: 5px 0;">
+  <p style="margin: 3px 0;">
     <a href="https://docs.ultralytics.com/zh/">中文</a> | <a href="https://docs.ultralytics.com/ko/">한국어</a> | <a href="https://docs.ultralytics.com/ja/">日本語</a> | <a href="https://docs.ultralytics.com/ru/">Русский</a> | <a href="https://docs.ultralytics.com/de/">Deutsch</a> | <a href="https://docs.ultralytics.com/fr/">Français</a> | <a href="https://docs.ultralytics.com/es">Español</a> | <a href="https://docs.ultralytics.com/pt/">Português</a> | <a href="https://docs.ultralytics.com/tr/">Türkçe</a> | <a href="https://docs.ultralytics.com/vi/">Tiếng Việt</a> | <a href="https://docs.ultralytics.com/ar/">العربية</a>
   </p>
 
-  <div style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: -10px; margin-bottom: -50px">
-   <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
+  <div style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 3px; margin-top: 3px;">
+    <a href="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml"><img src="https://github.com/ultralytics/ultralytics/actions/workflows/ci.yml/badge.svg" alt="Ultralytics CI"></a>
     <a href="https://pepy.tech/projects/ultralytics" style="position: relative; top: -3px;"><img src="https://static.pepy.tech/badge/ultralytics" alt="Ultralytics Downloads"></a>
     <a href="https://zenodo.org/badge/latestdoi/264818686" style="position: relative; top: -3px;"><img src="https://zenodo.org/badge/264818686.svg" alt="Ultralytics YOLO Citation"></a>
     <a href="https://discord.com/invite/ultralytics"><img alt="Ultralytics Discord" src="https://img.shields.io/discord/1089800235347353640?logo=discord&logoColor=white&label=Discord&color=blue"></a>
     <a href="https://community.ultralytics.com/"><img alt="Ultralytics Forums" src="https://img.shields.io/discourse/users?server=https%3A%2F%2Fcommunity.ultralytics.com&logo=discourse&label=Forums&color=blue"></a>
     <a href="https://www.reddit.com/r/ultralytics/"><img alt="Ultralytics Reddit" src="https://img.shields.io/reddit/subreddit-subscribers/ultralytics?style=flat&logo=reddit&logoColor=white&label=Reddit&color=blue"></a>
+  </div>
+  <div style="display: flex; flex-wrap: wrap; justify-content: center; align-items: center; gap: 3px; margin-top: 3px;">
     <a href="https://console.paperspace.com/github/ultralytics/ultralytics"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run Ultralytics 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 Ultralytics In Colab"></a>
     <a href="https://www.kaggle.com/models/ultralytics/yolo26" style="position: relative; top: -3px;"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open Ultralytics In Kaggle"></a>
     <a href="https://mybinder.org/v2/gh/ultralytics/ultralytics/HEAD?labpath=examples%2Ftutorial.ipynb" style="position: relative; top: -3px;"><img src="https://mybinder.org/badge_logo.svg" alt="Open Ultralytics In Binder"></a>
   </div>
 </div>
-<br>
 
 [Ultralytics](https://www.ultralytics.com/) [YOLO26](https://github.com/ultralytics/ultralytics) is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous Ultralytics YOLO versions and introduces new features and improvements to further boost performance and flexibility. Ultralytics YOLO26 is designed to be fast, accurate, and easy to use, making it an excellent choice for a wide range of object detection and tracking, instance segmentation, image classification and pose estimation tasks.