From 2a1fabcf83df6e44f451065ab46b5b0e6fd3b601 Mon Sep 17 00:00:00 2001 From: Muhammad Rizwan Munawar Date: Sun, 3 Nov 2024 04:54:06 +0500 Subject: [PATCH] Add ultralytics models publication notice in citations section (#17318) Co-authored-by: Glenn Jocher --- docs/en/models/yolo11.md | 8 ++++---- docs/en/models/yolov5.md | 8 ++++---- docs/en/models/yolov8.md | 8 ++++---- 3 files changed, 12 insertions(+), 12 deletions(-) diff --git a/docs/en/models/yolo11.md b/docs/en/models/yolo11.md index fe9115f2ed..dee9344b46 100644 --- a/docs/en/models/yolo11.md +++ b/docs/en/models/yolo11.md @@ -8,10 +8,6 @@ keywords: YOLO11, state-of-the-art object detection, YOLO series, Ultralytics, c ## Overview -!!! tip "Ultralytics YOLO11 Publication" - - Ultralytics has not published a formal research paper for YOLO11 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com). - YOLO11 is the latest iteration in the [Ultralytics](https://www.ultralytics.com/) YOLO series of real-time object detectors, redefining what's possible with cutting-edge [accuracy](https://www.ultralytics.com/glossary/accuracy), speed, and efficiency. Building upon the impressive advancements of previous YOLO versions, YOLO11 introduces significant improvements in architecture and training methods, making it a versatile choice for a wide range of [computer vision](https://www.ultralytics.com/glossary/computer-vision-cv) tasks. ![Ultralytics YOLO11 Comparison Plots](https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png) @@ -132,6 +128,10 @@ Note that the example below is for YOLO11 [Detect](../tasks/detect.md) models fo ## Citations and Acknowledgements +!!! tip "Ultralytics YOLO11 Publication" + + Ultralytics has not published a formal research paper for YOLO11 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com). + If you use YOLO11 or any other software from this repository in your work, please cite it using the following format: !!! quote "" diff --git a/docs/en/models/yolov5.md b/docs/en/models/yolov5.md index 91c562a44e..4d261df5c4 100644 --- a/docs/en/models/yolov5.md +++ b/docs/en/models/yolov5.md @@ -6,10 +6,6 @@ keywords: YOLOv5, YOLOv5u, object detection, Ultralytics, anchor-free, pre-train # Ultralytics YOLOv5 -!!! tip "Ultralytics YOLOv5 Publication" - - Ultralytics has not published a formal research paper for YOLOv5 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com). - ## Overview YOLOv5u represents an advancement in [object detection](https://www.ultralytics.com/glossary/object-detection) methodologies. Originating from the foundational architecture of the [YOLOv5](https://github.com/ultralytics/yolov5) model developed by Ultralytics, YOLOv5u integrates the anchor-free, objectness-free split head, a feature previously introduced in the [YOLOv8](yolov8.md) models. This adaptation refines the model's architecture, leading to an improved accuracy-speed tradeoff in object detection tasks. Given the empirical results and its derived features, YOLOv5u provides an efficient alternative for those seeking robust solutions in both research and practical applications. @@ -96,6 +92,10 @@ This example provides simple YOLOv5 training and inference examples. For full do ## Citations and Acknowledgements +!!! tip "Ultralytics YOLOv5 Publication" + + Ultralytics has not published a formal research paper for YOLOv5 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com). + If you use YOLOv5 or YOLOv5u in your research, please cite the Ultralytics YOLOv5 repository as follows: !!! quote "" diff --git a/docs/en/models/yolov8.md b/docs/en/models/yolov8.md index c8e4397d15..bb4f287a98 100644 --- a/docs/en/models/yolov8.md +++ b/docs/en/models/yolov8.md @@ -6,10 +6,6 @@ keywords: YOLOv8, real-time object detection, YOLO series, Ultralytics, computer # Ultralytics YOLOv8 -!!! tip "Ultralytics YOLOv8 Publication" - - Ultralytics has not published a formal research paper for YOLOv8 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com). - ## Overview YOLOv8 is the latest iteration in the YOLO series of real-time object detectors, offering cutting-edge performance in terms of accuracy and speed. Building upon the advancements of previous YOLO versions, YOLOv8 introduces 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. @@ -169,6 +165,10 @@ Note the below example is for YOLOv8 [Detect](../tasks/detect.md) models for obj ## Citations and Acknowledgements +!!! tip "Ultralytics YOLOv8 Publication" + + Ultralytics has not published a formal research paper for YOLOv8 due to the rapidly evolving nature of the models. We focus on advancing the technology and making it easier to use, rather than producing static documentation. For the most up-to-date information on YOLO architecture, features, and usage, please refer to our [GitHub repository](https://github.com/ultralytics/ultralytics) and [documentation](https://docs.ultralytics.com). + If you use the YOLOv8 model or any other software from this repository in your work, please cite it using the following format: !!! quote ""