diff --git a/docs/en/datasets/pose/hand-keypoints.md b/docs/en/datasets/pose/hand-keypoints.md index dd3c19b1a4..559cdcec65 100644 --- a/docs/en/datasets/pose/hand-keypoints.md +++ b/docs/en/datasets/pose/hand-keypoints.md @@ -10,6 +10,17 @@ keywords: Hand KeyPoints, pose estimation, dataset, keypoints, MediaPipe, YOLO, The hand-keypoints dataset contains 26,768 images of hands annotated with keypoints, making it suitable for training models like Ultralytics YOLO for pose estimation tasks. The annotations were generated using the Google MediaPipe library, ensuring high [accuracy](https://www.ultralytics.com/glossary/accuracy) and consistency, and the dataset is compatible [Ultralytics YOLO11](https://github.com/ultralytics/ultralytics) formats. +

+
+ +
+ Watch: Hand Keypoints Estimation with Ultralytics YOLO11 | Human Hand Pose Estimation Tutorial +

+ ## Hand Landmarks ![Hand Landmarks](https://github.com/ultralytics/docs/releases/download/0/hand_landmarks.jpg) diff --git a/docs/en/usage/simple-utilities.md b/docs/en/usage/simple-utilities.md index 45d3dc66c3..2026e5a216 100644 --- a/docs/en/usage/simple-utilities.md +++ b/docs/en/usage/simple-utilities.md @@ -458,6 +458,17 @@ image_with_obb = ann.result() #### Bounding Boxes Circle Annotation [Circle Label](https://docs.ultralytics.com/reference/utils/plotting/#ultralytics.utils.plotting.Annotator.circle_label) +

+
+ +
+ Watch: In-Depth Guide to Text & Circle Annotations with Python Live Demos | Ultralytics Annotations 🚀 +

+ ```python import cv2