Update docs with YOLOv8 banner (#160)

Co-authored-by: Paula Derrenger <107626595+pderrenger@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>

docs/index.md

@@ -1,17 +1,18 @@
 <div align="center">
-  <a href="https://ultralytics.com/yolov5" target="_blank">
-    <img width="1024" src="https://user-images.githubusercontent.com/26833433/210431393-39c997b8-92a7-4957-864f-1f312004eb54.png"></a>
+  <a href="https://github.com/ultralytics/ultralytics" target="_blank">
+    <img width="1024" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/banner-yolov8.png"></a>
 <br>
   <a href="https://bit.ly/yolov5-paperspace-notebook"><img src="https://assets.paperspace.io/img/gradient-badge.svg" alt="Run on Gradient"></a>
   <a href="https://colab.research.google.com/github/glenn-jocher/glenn-jocher.github.io/blob/main/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/yolov5"><img src="https://kaggle.com/static/images/open-in-kaggle.svg" alt="Open In Kaggle"></a>
 <br>
+<br>
 </div>
 
 # Welcome to Ultralytics YOLOv8
 
-Welcome to the Ultralytics YOLOv8 documentation landing page! Ultralytics YOLOv8 is the latest version of the YOLO (You
-Only Look Once) object detection and image segmentation model developed by Ultralytics. This page serves as the starting
+Welcome to the Ultralytics YOLOv8 documentation landing page! [Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of the YOLO (You
+Only Look Once) object detection and image segmentation model developed by [Ultralytics](https://ultralytics.com). This page serves as the starting
 point for exploring the various resources available to help you get started with YOLOv8 and understand its features and
 capabilities.
 
@@ -20,10 +21,9 @@ object detection and image segmentation tasks. It can be trained on large datase
 variety of hardware platforms, from CPUs to GPUs.
 
 Whether you are a seasoned machine learning practitioner or new to the field, we hope that the resources on this page
-will help you get the most out of YOLOv8. Please feel free to browse the documentation and reach out to us with any
-questions or feedback.
+will help you get the most out of YOLOv8. For any bugs and feature requests please visit [GitHub Issues](https://github.com/ultralytics/ultralytics/issues). For professional support please [Contact Us](https://ultralytics.com/contact).
 
-### A Brief History of YOLO
+## A Brief History of YOLO
 
 YOLO (You Only Look Once) is a popular object detection and image segmentation model developed by Joseph Redmon and Ali
 Farhadi at the University of Washington. The first version of YOLO was released in 2015 and quickly gained popularity
@@ -36,7 +36,7 @@ backbone network, adding a feature pyramid, and making use of focal loss.
 In 2020, YOLOv4 was released which introduced a number of innovations such as the use of Mosaic data augmentation, a new
 anchor-free detection head, and a new loss function.
 
-In 2021, Ultralytics released YOLOv5, which further improved the model's performance and added new features such as
+In 2021, Ultralytics released [YOLOv5](https://github.com/ultralytics/yolov5), which further improved the model's performance and added new features such as
 support for panoptic segmentation and object tracking.
 
 YOLO has been widely used in a variety of applications, including autonomous vehicles, security and surveillance, and
@@ -49,9 +49,9 @@ For more information about the history and development of YOLO, you can refer to
   conference on computer vision and pattern recognition (pp. 779-788).
 - Redmon, J., & Farhadi, A. (2016). YOLO9000: Better, faster, stronger. In Proceedings
 
-### Ultralytics YOLOv8
+## Ultralytics YOLOv8
 
-YOLOv8 is the latest version of the YOLO object detection and image segmentation model developed by
+[Ultralytics YOLOv8](https://github.com/ultralytics/ultralytics) is the latest version of the YOLO object detection and image segmentation model developed by
 Ultralytics. YOLOv8 is a cutting-edge, state-of-the-art (SOTA) model that builds upon the success of previous YOLO
 versions and introduces new features and improvements to further boost performance and flexibility.
 
@@ -66,4 +66,4 @@ detection head, and a new loss function. YOLOv8 is also highly efficient and can
 platforms, from CPUs to GPUs.
 
 Overall, YOLOv8 is a powerful and flexible tool for object detection and image segmentation that offers the best of both
-worlds: the latest SOTA technology and the ability to use and compare all previous YOLO versions.
+worlds: the latest SOTA technology and the ability to use and compare all previous YOLO versions.