HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

Update YOLO11 comparison plots (#17097)

Browse source 
Signed-off-by: UltralyticsAssistant <web@ultralytics.com>
Co-authored-by: UltralyticsAssistant <web@ultralytics.com>
This commit is contained in:
Glenn Jocher 2024-10-22 19:31:55 +02:00 • committed by GitHub
parent f4d8f7765a
commit 357bd373ba
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
4 changed files with 4 additions and 4 deletions
Download patch file Download diff file Expand all files Collapse all files

2
docs/en/models/index.md
View file

@ -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

2
docs/en/models/yolo11.md
View file

@ -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)
<p align="center">
<br>