From aad072a285e529a8310da6f4a5bda7811740ed34 Mon Sep 17 00:00:00 2001 From: Alex Date: Thu, 13 Feb 2025 04:24:38 +0000 Subject: [PATCH] YOLOv8 Docs updates (#19182) Signed-off-by: Alex Co-authored-by: UltralyticsAssistant Co-authored-by: Muhammad Rizwan Munawar Co-authored-by: RizwanMunawar Co-authored-by: Glenn Jocher --- docs/en/models/yolov8.md | 10 +++++----- 1 file changed, 5 insertions(+), 5 deletions(-) diff --git a/docs/en/models/yolov8.md b/docs/en/models/yolov8.md index bf1d0b1b..1ccf81c5 100644 --- a/docs/en/models/yolov8.md +++ b/docs/en/models/yolov8.md @@ -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 Watch: Ultralytics YOLOv8 Model Overview

-## 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?