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

Update Tasks banner spacing (#17843)

Browse source
This commit is contained in:
Glenn Jocher 2024-11-28 02:05:04 +01:00 • committed by GitHub
parent 386a3b7625
commit 7e2ce71c29
No known key found for this signature in database
GPG key ID: B5690EEEBB952194
2 changed files with 10 additions and 10 deletions
Download patch file Download diff file Expand all files Collapse all files

10
README.md
View file

@ -117,13 +117,13 @@ See YOLO [Python Docs](https://docs.ultralytics.com/usage/python/) for more exam
## <div align="center">Models</div>
YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models.
YOLO11 [Detect](https://docs.ultralytics.com/tasks/detect/), [Segment](https://docs.ultralytics.com/tasks/segment/) and [Pose](https://docs.ultralytics.com/tasks/pose/) models pretrained on the [COCO](https://docs.ultralytics.com/datasets/detect/coco/) dataset are available here, as well as YOLO11 [Classify](https://docs.ultralytics.com/tasks/classify/) models pretrained on the [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) dataset. [Track](https://docs.ultralytics.com/modes/track/) mode is available for all Detect, Segment and Pose models. All [Models](https://docs.ultralytics.com/models/) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
<a href="https://docs.ultralytics.com/tasks/" target="_blank">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
</a>
All [Models](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models) download automatically from the latest Ultralytics [release](https://github.com/ultralytics/assets/releases) on first use.
<br>
<br>
<details open><summary>Detection (COCO)</summary>
@ -214,9 +214,9 @@ See [OBB Docs](https://docs.ultralytics.com/tasks/obb/) for usage examples with
Our key integrations with leading AI platforms extend the functionality of Ultralytics' offerings, enhancing tasks like dataset labeling, training, visualization, and model management. Discover how Ultralytics, in collaboration with [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/), [Comet](https://bit.ly/yolov8-readme-comet), [Roboflow](https://roboflow.com/?ref=ultralytics) and [OpenVINO](https://docs.ultralytics.com/integrations/openvino/), can optimize your AI workflow.
<br>
<a href="https://www.ultralytics.com/hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
</a>
<br>
<br>

10
README.zh-CN.md
View file

@ -117,13 +117,13 @@ path = model.export(format="onnx") # 返回导出模型的路径
## <div align="center">模型</div>
YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/) 和 [姿态](https://docs.ultralytics.com/tasks/pose/) 模型在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上进行预训练，这些模型可在此处获得，此外还有在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的 YOLO11 [分类](https://docs.ultralytics.com/tasks/classify/) 模型。所有检测、分割和姿态模型均支持 [跟踪](https://docs.ultralytics.com/modes/track/) 模式。
YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://docs.ultralytics.com/tasks/segment/) 和 [姿态](https://docs.ultralytics.com/tasks/pose/) 模型在 [COCO](https://docs.ultralytics.com/datasets/detect/coco/) 数据集上进行预训练，这些模型可在此处获得，此外还有在 [ImageNet](https://docs.ultralytics.com/datasets/classify/imagenet/) 数据集上预训练的 YOLO11 [分类](https://docs.ultralytics.com/tasks/classify/) 模型。所有检测、分割和姿态模型均支持 [跟踪](https://docs.ultralytics.com/modes/track/) 模式。所有[模型](https://docs.ultralytics.com/models/)在首次使用时自动从最新的 Ultralytics [发布](https://github.com/ultralytics/assets/releases)下载。
<a href="https://docs.ultralytics.com/tasks/" target="_blank">
<img width="100%" src="https://github.com/ultralytics/docs/releases/download/0/ultralytics-yolov8-tasks-banner.avif" alt="Ultralytics YOLO supported tasks">
</a>
所有[模型](https://github.com/ultralytics/ultralytics/tree/main/ultralytics/cfg/models)在首次使用时自动从最新的 Ultralytics [发布](https://github.com/ultralytics/assets/releases)下载。
<br>
<br>
<details open><summary>检测 (COCO)</summary>
@ -214,9 +214,9 @@ YOLO11 [检测](https://docs.ultralytics.com/tasks/detect/)、[分割](https://d
我们与领先的 AI 平台的关键集成扩展了 Ultralytics 产品的功能，提升了数据集标注、训练、可视化和模型管理等任务。探索 Ultralytics 如何通过与 [W&B](https://docs.wandb.ai/guides/integrations/ultralytics/)、[Comet](https://bit.ly/yolov8-readme-comet)、[Roboflow](https://roboflow.com/?ref=ultralytics) 和 [OpenVINO](https://docs.ultralytics.com/integrations/openvino/) 的合作，优化您的 AI 工作流程。
<br>
<a href="https://www.ultralytics.com/hub" target="_blank">
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations"></a>
<img width="100%" src="https://github.com/ultralytics/assets/raw/main/yolov8/banner-integrations.png" alt="Ultralytics active learning integrations">
</a>
<br>
<br>