Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Update HTTP to HTTPS #7548

Merged
merged 3 commits into from
Jan 12, 2024
Merged

Update HTTP to HTTPS #7548

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
3 commits
Select commit Hold shift + click to select a range
ea72485
Update HTTP to HTTPS
glenn-jocher Jan 12, 2024
dd9b45a
Update HTTP to HTTPS
glenn-jocher Jan 12, 2024
8be0bbf
Update HTTP to HTTPS
glenn-jocher Jan 12, 2024
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Jump to
Jump to file
Failed to load files.
Diff view
Diff view
6 changes: 3 additions & 3 deletions README.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -131,7 +131,7 @@ See [Detection Docs](https://docs.ultralytics.com/tasks/detect/) for usage examp
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val detect data=coco.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val detect data=coco.yaml batch=1 device=0|cpu`

</details>
Expand Down Expand Up @@ -165,7 +165,7 @@ See [Segmentation Docs](https://docs.ultralytics.com/tasks/segment/) for usage e
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val segment data=coco-seg.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu`

</details>
Expand All @@ -183,7 +183,7 @@ See [Pose Docs](https://docs.ultralytics.com/tasks/pose/) for usage examples wit
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |

- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](http://cocodataset.org) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **mAP<sup>val</sup>** values are for single-model single-scale on [COCO Keypoints val2017](https://cocodataset.org) dataset. <br>Reproduce by `yolo val pose data=coco-pose.yaml device=0`
- **Speed** averaged over COCO val images using an [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) instance. <br>Reproduce by `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu`

</details>
Expand Down
6 changes: 3 additions & 3 deletions README.zh-CN.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,7 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |

- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val detect data=coco.yaml device=0` 复现
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val detect data=coco.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val detect data=coco.yaml batch=1 device=0|cpu` 复现

</details>
Expand Down Expand Up @@ -167,7 +167,7 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |

- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](http://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val segment data=coco-seg.yaml device=0` 复现
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO val2017](https://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val segment data=coco-seg.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val segment data=coco-seg.yaml batch=1 device=0|cpu` 复现

</details>
Expand All @@ -185,7 +185,7 @@ Ultralytics 提供了 YOLOv8 的交互式笔记本，涵盖训练、验证、跟
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |

- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](http://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
- **mAP<sup>val</sup>** 值是基于单模型单尺度在 [COCO Keypoints val2017](https://cocodataset.org) 数据集上的结果。 <br>通过 `yolo val pose data=coco-pose.yaml device=0` 复现
- **速度** 是使用 [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/) 实例对 COCO val 图像进行平均计算的。 <br>通过 `yolo val pose data=coco-pose.yaml batch=1 device=0|cpu` 复现

</details>
Expand Down
2 changes: 1 addition & 1 deletion docs/ar/tasks/detect.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ Task التعرف على الكائنات هو عبارة عن تعرف على
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |

- قيم mAP<sup>val</sup> تنطبق على مقياس نموذج واحد-مقياس واحد على مجموعة بيانات [COCO val2017](http://cocodataset.org).
- قيم mAP<sup>val</sup> تنطبق على مقياس نموذج واحد-مقياس واحد على مجموعة بيانات [COCO val2017](https://cocodataset.org).
<br>اعيد حسابها بواسطة `yolo val detect data=coco.yaml device=0`
- السرعةتمت متوسطة على صور COCO val باستخدام [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
instance.
Expand Down
2 changes: 1 addition & 1 deletion docs/ar/tasks/pose.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -40,7 +40,7 @@ keywords: Ultralytics، YOLO، YOLOv8، تقدير الوضعية ، كشف نق
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose.pt) | 640 | 69.2 | 90.2 | 1607.1 | 3.73 | 69.4 | 263.2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose-p6.pt) | 1280 | 71.6 | 91.2 | 4088.7 | 10.04 | 99.1 | 1066.4 |

- تعتبر القيم **mAP<sup>val</sup>** لنموذج واحد ومقياس واحد فقط على [COCO Keypoints val2017](http://cocodataset.org)
- تعتبر القيم **mAP<sup>val</sup>** لنموذج واحد ومقياس واحد فقط على [COCO Keypoints val2017](https://cocodataset.org)
مجموعة البيانات.
<br>يمكن إعادة إنتاجه بواسطة `يولو val pose data=coco-pose.yaml device=0`
- يتم حساب **السرعة** من خلال متوسط صور COCO val باستخدام [المروحة الحرارية Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
Expand Down
2 changes: 1 addition & 1 deletion docs/ar/tasks/segment.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ keywords: yolov8 ، فصل الأشكال الفردية ، Ultralytics ، مج
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |

- تُستخدم قيم **mAP<sup>val</sup>** لنموذج واحد وحجم واحد على مجموعة بيانات [COCO val2017](http://cocodataset.org).
- تُستخدم قيم **mAP<sup>val</sup>** لنموذج واحد وحجم واحد على مجموعة بيانات [COCO val2017](https://cocodataset.org).
<br>يمكن إعادة إنتاجها باستخدام `yolo val segment data=coco.yaml device=0`
- **تُحسب السرعة** كمتوسط على صور COCO val باستخدام [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)
instance.
Expand Down
2 changes: 1 addition & 1 deletion docs/de/tasks/detect.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ Hier werden die vortrainierten YOLOv8 Detect Modelle gezeigt. Detect, Segment un
| [YOLOv8l](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l.pt) | 640 | 52.9 | 375.2 | 2.39 | 43.7 | 165.2 |
| [YOLOv8x](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x.pt) | 640 | 53.9 | 479.1 | 3.53 | 68.2 | 257.8 |

- **mAP<sup>val</sup>** Werte sind für Single-Modell Single-Scale auf dem [COCO val2017](http://cocodataset.org) Datensatz.
- **mAP<sup>val</sup>** Werte sind für Single-Modell Single-Scale auf dem [COCO val2017](https://cocodataset.org) Datensatz.
<br>Reproduzieren mit `yolo val detect data=coco.yaml device=0`
- **Geschwindigkeit** gemittelt über COCO Val Bilder mit einer [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)-Instanz.
<br>Reproduzieren mit `yolo val detect data=coco128.yaml batch=1 device=0|cpu`
Expand Down
2 changes: 1 addition & 1 deletion docs/de/tasks/pose.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -42,7 +42,7 @@ Hier werden vortrainierte YOLOv8 Pose-Modelle gezeigt. Erkennungs-, Segmentierun
| [YOLOv8x-pose](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose.pt) | 640 | 69,2 | 90,2 | 1607,1 | 3,73 | 69,4 | 263,2 |
| [YOLOv8x-pose-p6](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-pose-p6.pt) | 1280 | 71,6 | 91,2 | 4088,7 | 10,04 | 99,1 | 1066,4 |

- **mAP<sup>val</sup>** Werte gelten für ein einzelnes Modell mit einfacher Skala auf dem [COCO Keypoints val2017](http://cocodataset.org)-Datensatz.
- **mAP<sup>val</sup>** Werte gelten für ein einzelnes Modell mit einfacher Skala auf dem [COCO Keypoints val2017](https://cocodataset.org)-Datensatz.
<br>Zu reproduzieren mit `yolo val pose data=coco-pose.yaml device=0`.
- **Geschwindigkeit** gemittelt über COCO-Validierungsbilder mit einer [Amazon EC2 P4d](https://aws.amazon.com/de/ec2/instance-types/p4/)-Instanz.
<br>Zu reproduzieren mit `yolo val pose data=coco8-pose.yaml batch=1 device=0|cpu`.
Expand Down
2 changes: 1 addition & 1 deletion docs/de/tasks/segment.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -41,7 +41,7 @@ Hier werden vortrainierte YOLOv8 Segment-Modelle gezeigt. Detect-, Segment- und
| [YOLOv8l-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8l-seg.pt) | 640 | 52.3 | 42.6 | 572.4 | 2.79 | 46.0 | 220.5 |
| [YOLOv8x-seg](https://github.com/ultralytics/assets/releases/download/v8.1.0/yolov8x-seg.pt) | 640 | 53.4 | 43.4 | 712.1 | 4.02 | 71.8 | 344.1 |

- Die **mAP<sup>val</sup>**-Werte sind für ein einzelnes Modell, einzelne Skala auf dem [COCO val2017](http://cocodataset.org)-Datensatz.
- Die **mAP<sup>val</sup>**-Werte sind für ein einzelnes Modell, einzelne Skala auf dem [COCO val2017](https://cocodataset.org)-Datensatz.
<br>Zum Reproduzieren nutzen Sie `yolo val segment data=coco.yaml device=0`
- Die **Geschwindigkeit** ist über die COCO-Validierungsbilder gemittelt und verwendet eine [Amazon EC2 P4d](https://aws.amazon.com/ec2/instance-types/p4/)-Instanz.
<br>Zum Reproduzieren `yolo val segment data=coco128-seg.yaml batch=1 device=0|cpu`
Expand Down
2 changes: 1 addition & 1 deletion docs/en/datasets/classify/imagenet.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -21,7 +21,7 @@ The ImageNet dataset is organized using the WordNet hierarchy. Each node in the

## ImageNet Large Scale Visual Recognition Challenge (ILSVRC)

The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](http://image-net.org/challenges/LSVRC/) has been an important event in the field of computer vision. It has provided a platform for researchers and developers to evaluate their algorithms and models on a large-scale dataset with standardized evaluation metrics. The ILSVRC has led to significant advancements in the development of deep learning models for image classification, object detection, and other computer vision tasks.
The annual [ImageNet Large Scale Visual Recognition Challenge (ILSVRC)](https://image-net.org/challenges/LSVRC/) has been an important event in the field of computer vision. It has provided a platform for researchers and developers to evaluate their algorithms and models on a large-scale dataset with standardized evaluation metrics. The ILSVRC has led to significant advancements in the development of deep learning models for image classification, object detection, and other computer vision tasks.

## Applications

Expand Down
2 changes: 1 addition & 1 deletion docs/en/datasets/classify/imagenette.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ keywords: ImageNette dataset, Ultralytics, YOLO, Image classification, Machine L

# ImageNette Dataset

The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [Imagenet](http://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of Imagenet for software development and education.
The [ImageNette](https://github.com/fastai/imagenette) dataset is a subset of the larger [Imagenet](https://www.image-net.org/) dataset, but it only includes 10 easily distinguishable classes. It was created to provide a quicker, easier-to-use version of Imagenet for software development and education.

## Key Features

Expand Down
4 changes: 2 additions & 2 deletions docs/en/datasets/detect/globalwheat2020.md
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ keywords: Ultralytics, YOLO, Global Wheat Head Dataset, wheat head detection, pl

# Global Wheat Head Dataset

The [Global Wheat Head Dataset](http://www.global-wheat.com/) is a collection of images designed to support the development of accurate wheat head detection models for applications in wheat phenotyping and crop management. Wheat heads, also known as spikes, are the grain-bearing parts of the wheat plant. Accurate estimation of wheat head density and size is essential for assessing crop health, maturity, and yield potential. The dataset, created by a collaboration of nine research institutes from seven countries, covers multiple growing regions to ensure models generalize well across different environments.
The [Global Wheat Head Dataset](https://www.global-wheat.com/) is a collection of images designed to support the development of accurate wheat head detection models for applications in wheat phenotyping and crop management. Wheat heads, also known as spikes, are the grain-bearing parts of the wheat plant. Accurate estimation of wheat head density and size is essential for assessing crop health, maturity, and yield potential. The dataset, created by a collaboration of nine research institutes from seven countries, covers multiple growing regions to ensure models generalize well across different environments.

## Key Features

Expand Down Expand Up @@ -88,4 +88,4 @@ If you use the Global Wheat Head Dataset in your research or development work, p
}
```

We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](http://www.global-wheat.com/).
We would like to acknowledge the researchers and institutions that contributed to the creation and maintenance of the Global Wheat Head Dataset as a valuable resource for the plant phenotyping and crop management research community. For more information about the dataset and its creators, visit the [Global Wheat Head Dataset website](https://www.global-wheat.com/).
22 changes: 11 additions & 11 deletions docs/en/robots.txt
View file
Open in desktop
Original file line number Diff line number Diff line change
@@ -1,12 +1,12 @@
User-agent: *
Sitemap: http://docs.ultralytics.com/sitemap.xml
Sitemap: http://docs.ultralytics.com/ar/sitemap.xml
Sitemap: http://docs.ultralytics.com/de/sitemap.xml
Sitemap: http://docs.ultralytics.com/es/sitemap.xml
Sitemap: http://docs.ultralytics.com/fr/sitemap.xml
Sitemap: http://docs.ultralytics.com/hi/sitemap.xml
Sitemap: http://docs.ultralytics.com/ja/sitemap.xml
Sitemap: http://docs.ultralytics.com/ko/sitemap.xml
Sitemap: http://docs.ultralytics.com/pt/sitemap.xml
Sitemap: http://docs.ultralytics.com/ru/sitemap.xml
Sitemap: http://docs.ultralytics.com/zh/sitemap.xml
Sitemap: https://docs.ultralytics.com/sitemap.xml
Sitemap: https://docs.ultralytics.com/ar/sitemap.xml
Sitemap: https://docs.ultralytics.com/de/sitemap.xml
Sitemap: https://docs.ultralytics.com/es/sitemap.xml
Sitemap: https://docs.ultralytics.com/fr/sitemap.xml
Sitemap: https://docs.ultralytics.com/hi/sitemap.xml
Sitemap: https://docs.ultralytics.com/ja/sitemap.xml
Sitemap: https://docs.ultralytics.com/ko/sitemap.xml
Sitemap: https://docs.ultralytics.com/pt/sitemap.xml
Sitemap: https://docs.ultralytics.com/ru/sitemap.xml
Sitemap: https://docs.ultralytics.com/zh/sitemap.xml