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YOLOv8 Docs updates (#19182)

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Signed-off-by: Alex <alexis.barou@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: Muhammad Rizwan Munawar <muhammadrizwanmunawar123@gmail.com>
Co-authored-by: RizwanMunawar <chr043416@gmail.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/models/yolov8.md
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@ -4,11 +4,11 @@ description: Discover YOLOv8, the latest advancement in real-time object detecti
keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer vision, advanced object detection, AI, machine learning, deep learning
---
# Ultralytics YOLOv8
# Explore Ultralytics YOLOv8
## Overview
YOLOv8 was released by Ultralytic on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/glossary/object-detection) tasks in a wide range of applications.
YOLOv8 was released by Ultralytics on January 10th, 2023, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduced new features and optimizations that make it an ideal choice for various [object detection](https://www.ultralytics.com/blog/a-guide-to-deep-dive-into-object-detection-in-2025) tasks in a wide range of applications.
![Ultralytics YOLOv8](https://github.com/ultralytics/docs/releases/download/0/yolov8-comparison-plots.avif)
@ -23,7 +23,7 @@ YOLOv8 was released by Ultralytic on January 10th, 2023, offering cutting-edge p
<strong>Watch:</strong> Ultralytics YOLOv8 Model Overview
</p>
## Key Features
## Key Features of YOLOv8
- **Advanced Backbone and Neck Architectures:** YOLOv8 employs state-of-the-art backbone and neck architectures, resulting in improved [feature extraction](https://www.ultralytics.com/glossary/feature-extraction) and object detection performance.
- **Anchor-free Split Ultralytics Head:** YOLOv8 adopts an anchor-free split Ultralytics head, which contributes to better accuracy and a more efficient detection process compared to anchor-based approaches.
@ -128,7 +128,7 @@ This table provides an overview of the YOLOv8 model variants, highlighting their
| [YOLOv8l-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8l-obb.pt) | 1024 | 80.7 | 1278.42 | 11.83 | 44.5 | 433.8 |
| [YOLOv8x-obb](https://github.com/ultralytics/assets/releases/download/v8.2.0/yolov8x-obb.pt) | 1024 | 81.36 | 1759.10 | 13.23 | 69.5 | 676.7 |
## Usage Examples
## YOLOv8 Usage Examples
This example provides simple YOLOv8 training and inference examples. For full documentation on these and other [modes](../modes/index.md) see the [Predict](../modes/predict.md), [Train](../modes/train.md), [Val](../modes/val.md) and [Export](../modes/export.md) docs pages.
@ -198,7 +198,7 @@ Please note that the DOI is pending and will be added to the citation once it is
### What is YOLOv8 and how does it differ from previous YOLO versions?
YOLOv8 is the latest iteration in the Ultralytics YOLO series, designed to improve real-time object detection performance with advanced features. Unlike earlier versions, YOLOv8 incorporates an **anchor-free split Ultralytics head**, state-of-the-art backbone and neck architectures, and offers optimized [accuracy](https://www.ultralytics.com/glossary/accuracy)-speed tradeoff, making it ideal for diverse applications. For more details, check the [Overview](#overview) and [Key Features](#key-features) sections.
YOLOv8 is the latest iteration in the Ultralytics YOLO series, designed to improve real-time object detection performance with advanced features. Unlike earlier versions, YOLOv8 incorporates an **anchor-free split Ultralytics head**, state-of-the-art backbone and neck architectures, and offers optimized [accuracy](https://www.ultralytics.com/glossary/accuracy)-speed tradeoff, making it ideal for diverse applications. For more details, check the [Overview](#overview) and [Key Features](#key-features-of-yolov8) sections.
### How can I use YOLOv8 for different computer vision tasks?