diff --git a/README.md b/README.md index 39fd7bac..c70ed6a4 100644 --- a/README.md +++ b/README.md @@ -26,7 +26,7 @@ We hope that the resources here will help you get the most out of YOLO. Please b To request an Enterprise License please complete the form at [Ultralytics Licensing](https://www.ultralytics.com/license). -YOLO11 performance plots +YOLO11 performance plots
Ultralytics GitHub diff --git a/README.zh-CN.md b/README.zh-CN.md index ac87d1bd..53cb7e05 100644 --- a/README.zh-CN.md +++ b/README.zh-CN.md @@ -26,7 +26,7 @@ 想申请企业许可证，请完成 [Ultralytics Licensing](https://www.ultralytics.com/license) 上的表单。 -YOLO11 performance plots +YOLO11 performance plots
Ultralytics GitHub diff --git a/docs/en/models/index.md b/docs/en/models/index.md index 5e9d07f3..c0f4fd33 100644 --- a/docs/en/models/index.md +++ b/docs/en/models/index.md @@ -8,7 +8,7 @@ keywords: Ultralytics, supported models, YOLOv3, YOLOv4, YOLOv5, YOLOv6, YOLOv7, Welcome to Ultralytics' model documentation! We offer support for a wide range of models, each tailored to specific tasks like [object detection](../tasks/detect.md), [instance segmentation](../tasks/segment.md), [image classification](../tasks/classify.md), [pose estimation](../tasks/pose.md), and [multi-object tracking](../modes/track.md). If you're interested in contributing your model architecture to Ultralytics, check out our [Contributing Guide](../help/contributing.md). -![Ultralytics YOLO11 Comparison Plots](https://github.com/user-attachments/assets/a311a4ed-bbf2-43b5-8012-5f183a28a845) +![Ultralytics YOLO11 Comparison Plots](https://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png) ## Featured Models diff --git a/docs/en/models/yolo11.md b/docs/en/models/yolo11.md index 0c755147..8baf2dd7 100644 --- a/docs/en/models/yolo11.md +++ b/docs/en/models/yolo11.md @@ -10,7 +10,7 @@ keywords: YOLO11, state-of-the-art object detection, YOLO series, Ultralytics, c 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://github.com/user-attachments/assets/a311a4ed-bbf2-43b5-8012-5f183a28a845) +![Ultralytics YOLO11 Comparison Plots](hhttps://raw.githubusercontent.com/ultralytics/assets/refs/heads/main/yolo/performance-comparison.png)