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Signed-off-by: Glenn Jocher <glenn.jocher@ultralytics.com>
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docs/en/models/index.md
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@ -12,17 +12,17 @@ Welcome to Ultralytics' model documentation! We offer support for a wide range o
Here are some of the key models supported:
1. **[YOLOv3](./yolov3.md)**: The third iteration of the YOLO model family, originally by Joseph Redmon, known for its efficient real-time object detection capabilities.
2. **[YOLOv4](./yolov4.md)**: A darknet-native update to YOLOv3, released by Alexey Bochkovskiy in 2020.
3. **[YOLOv5](./yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions.
4. **[YOLOv6](./yolov6.md)**: Released by [Meituan](https://about.meituan.com/) in 2022, and in use in many of the company's autonomous delivery robots.
5. **[YOLOv7](./yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4.
6. **[YOLOv8](./yolov8.md)**: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
7. **[Segment Anything Model (SAM)](./sam.md)**: Meta's Segment Anything Model (SAM).
8. **[Mobile Segment Anything Model (MobileSAM)](./mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
9. **[Fast Segment Anything Model (FastSAM)](./fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
10. **[YOLO-NAS](./yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.
11. **[Realtime Detection Transformers (RT-DETR)](./rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
1. **[YOLOv3](yolov3.md)**: The third iteration of the YOLO model family, originally by Joseph Redmon, known for its efficient real-time object detection capabilities.
2. **[YOLOv4](yolov4.md)**: A darknet-native update to YOLOv3, released by Alexey Bochkovskiy in 2020.
3. **[YOLOv5](yolov5.md)**: An improved version of the YOLO architecture by Ultralytics, offering better performance and speed trade-offs compared to previous versions.
4. **[YOLOv6](yolov6.md)**: Released by [Meituan](https://about.meituan.com/) in 2022, and in use in many of the company's autonomous delivery robots.
5. **[YOLOv7](yolov7.md)**: Updated YOLO models released in 2022 by the authors of YOLOv4.
6. **[YOLOv8](yolov8.md)**: The latest version of the YOLO family, featuring enhanced capabilities such as instance segmentation, pose/keypoints estimation, and classification.
7. **[Segment Anything Model (SAM)](sam.md)**: Meta's Segment Anything Model (SAM).
8. **[Mobile Segment Anything Model (MobileSAM)](mobile-sam.md)**: MobileSAM for mobile applications, by Kyung Hee University.
9. **[Fast Segment Anything Model (FastSAM)](fast-sam.md)**: FastSAM by Image & Video Analysis Group, Institute of Automation, Chinese Academy of Sciences.
10. **[YOLO-NAS](yolo-nas.md)**: YOLO Neural Architecture Search (NAS) Models.
11. **[Realtime Detection Transformers (RT-DETR)](rtdetr.md)**: Baidu's PaddlePaddle Realtime Detection Transformer (RT-DETR) models.
<p align="center">
<br>

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docs/en/models/mobile-sam.md
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@ -89,7 +89,7 @@ You can download the model [here](https://github.com/ChaoningZhang/MobileSAM/blo
model.predict('ultralytics/assets/zidane.jpg', bboxes=[439, 437, 524, 709])
```
We have implemented `MobileSAM` and `SAM` using the same API. For more usage information, please see the [SAM page](./sam.md).
We have implemented `MobileSAM` and `SAM` using the same API. For more usage information, please see the [SAM page](sam.md).
## Citations and Acknowledgements

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docs/en/models/yolov4.md
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@ -13,7 +13,7 @@ Welcome to the Ultralytics documentation page for YOLOv4, a state-of-the-art, re
## Introduction
YOLOv4 stands for You Only Look Once version 4. It is a real-time object detection model developed to address the limitations of previous YOLO versions like [YOLOv3](./yolov3.md) and other object detection models. Unlike other convolutional neural network (CNN) based object detectors, YOLOv4 is not only applicable for recommendation systems but also for standalone process management and human input reduction. Its operation on conventional graphics processing units (GPUs) allows for mass usage at an affordable price, and it is designed to work in real-time on a conventional GPU while requiring only one such GPU for training.
YOLOv4 stands for You Only Look Once version 4. It is a real-time object detection model developed to address the limitations of previous YOLO versions like [YOLOv3](yolov3.md) and other object detection models. Unlike other convolutional neural network (CNN) based object detectors, YOLOv4 is not only applicable for recommendation systems but also for standalone process management and human input reduction. Its operation on conventional graphics processing units (GPUs) allows for mass usage at an affordable price, and it is designed to work in real-time on a conventional GPU while requiring only one such GPU for training.
## Architecture

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docs/en/models/yolov5.md
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@ -8,7 +8,7 @@ keywords: YOLOv5u, object detection, pre-trained models, Ultralytics, Inference,
## Overview
YOLOv5u represents an advancement in 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.
YOLOv5u represents an advancement in 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.
![Ultralytics YOLOv5](https://raw.githubusercontent.com/ultralytics/assets/main/yolov5/v70/splash.png)