Hand Keypoints Dataset#

Introduction#

The Ultralytics Hand Keypoints dataset contains 26,768 images of hands annotated with 21 keypoints each, generated using the Google MediaPipe library for high accuracy and consistency. It's compatible with Ultralytics YOLO26 formats for training pose estimation models.

Keypoints#

Each hand is annotated with 21 keypoints as follows:

1. Wrist
2. Thumb (4 points)
3. Index finger (4 points)
4. Middle finger (4 points)
5. Ring finger (4 points)
6. Little finger (4 points)

Dataset Structure#

• Total images: 26,768 (18,776 train / 7,992 val).
• Classes: 1 (hand).
• Keypoints: 21 per hand with (x, y, visibility) triplets.
• Download size: ~369 MB.

For a custom gesture vocabulary beyond generic hand landmarks, Ultralytics Platform handles labeling and training your own dataset from the browser.

Applications#

Hand keypoints support several real-world applications:

• Gesture recognition: human-computer interaction and touchless control interfaces.
• AR/VR controls: precise interaction with virtual objects.
• Robotic manipulation: fine-grained control of robotic hands.
• Healthcare: hand movement analysis for medical diagnostics.
• Animation: motion capture for realistic hand movement.
• Biometric authentication: security systems based on hand geometry.

Dataset YAML#

A YAML file is used to define the dataset configuration. It contains information about the dataset's paths, classes, and other relevant information. In the case of the Hand Keypoints dataset, the hand-keypoints.yaml file is maintained at https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml.

# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license

# Hand Keypoints dataset by Ultralytics
# Documentation: https://docs.ultralytics.com/datasets/pose/hand-keypoints
# Example usage: yolo train data=hand-keypoints.yaml
# parent
# ├── ultralytics
# └── datasets
#     └── hand-keypoints ← downloads here (369 MB)

# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: hand-keypoints # dataset root dir
train: images/train # train images (relative to 'path') 18776 images
val: images/val # val images (relative to 'path') 7992 images

# Keypoints
kpt_shape: [21, 3] # number of keypoints, number of dims (2 for x,y or 3 for x,y,visible)
flip_idx: [0, 1, 2, 4, 3, 10, 11, 12, 13, 14, 5, 6, 7, 8, 9, 15, 16, 17, 18, 19, 20]

# Classes
names:
  0: hand

# Keypoint names per class
kpt_names:
  0:
    - wrist
    - thumb_cmc
    - thumb_mcp
    - thumb_ip
    - thumb_tip
    - index_mcp
    - index_pip
    - index_dip
    - index_tip
    - middle_mcp
    - middle_pip
    - middle_dip
    - middle_tip
    - ring_mcp
    - ring_pip
    - ring_dip
    - ring_tip
    - pinky_mcp
    - pinky_pip
    - pinky_dip
    - pinky_tip

# Download script/URL (optional)
download: https://github.com/ultralytics/assets/releases/download/v0.0.0/hand-keypoints.zip

Usage#

To train a YOLO26n-pose model on the Hand Keypoints dataset for 100 epochs with an image size of 640, you can use the following code snippets. For a comprehensive list of available arguments, refer to the model Training page.

from ultralytics import YOLO

# Load a model
model = YOLO("yolo26n-pose.pt")  # load a pretrained model (recommended for training)

# Train the model
results = model.train(data="hand-keypoints.yaml", epochs=100, imgsz=640)

Sample Images and Annotations#

The Hand Keypoints dataset contains a diverse set of images with human hands annotated with keypoints. Here are some examples of images from the dataset, along with their corresponding annotations:

• Mosaiced Image: This image demonstrates a training batch composed of mosaiced dataset images. Mosaicing is a technique used during training that combines multiple images into a single image to increase the variety of objects and scenes within each training batch. This helps improve the model's ability to generalize to different object sizes, aspect ratios, and contexts.

The example showcases the variety and complexity of the images in the Hand Keypoints dataset and the benefits of using mosaicing during the training process.

Citations and Acknowledgments#

If you use the Hand Keypoints dataset in your research or development work, please acknowledge the following sources:

We would also like to acknowledge the creator of this dataset, Rion Dsilva, for his great contribution to Vision AI research.

FAQ#