Hand Keypoints Dataset

Введение

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 and consistency, and the dataset is compatible Ultralytics YOLO11 formats.

Hand Landmarks

KeyPoints

The dataset includes keypoints for hand detection. The keypoints are annotated 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)

Each hand has a total of 21 keypoints.

Основные характеристики

• Large Dataset: 26,768 images with hand keypoint annotations.
• YOLO11 Compatibility: Ready for use with YOLO11 models.
• 21 Keypoints: Detailed hand pose representation.

Структура набора данных

The hand keypoint dataset is split into two subsets:

1. Train: This subset contains 18,776 images from the hand keypoints dataset, annotated for training pose estimation models.
2. Val: This subset contains 7992 images that can be used for validation purposes during model training.

Приложения

Hand keypoints can be used for gesture recognition, AR/VR controls, robotic manipulation, and hand movement analysis in healthcare. They can be also applied in animation for motion capture and biometric authentication systems for security.

Набор данных YAML

A YAML (Yet Another Markup Language) 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 файл хранится по адресу https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml.

# Ultralytics YOLO 🚀, AGPL-3.0 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: ../datasets/hand-keypoints # dataset root dir
train: train # train images (relative to 'path') 18776 images
val: 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

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

Использование

To train a YOLO11n-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("yolo11n-pose.pt")  # load a pretrained model (recommended for training)

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

# Start training from a pretrained *.pt model
yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640

Примеры изображений и аннотаций

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:

• Мозаичное изображение: Это изображение демонстрирует тренировочную партию, состоящую из мозаичных изображений набора данных. Мозаика - это техника, используемая во время обучения, которая объединяет несколько изображений в одно, чтобы увеличить разнообразие объектов и сцен в каждой обучающей партии. Это помогает улучшить способность модели к обобщению на различные размеры объектов, соотношение сторон и контекст.

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.

Цитаты и благодарности

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.

ВОПРОСЫ И ОТВЕТЫ

How do I train a YOLO11 model on the Hand Keypoints dataset?

To train a YOLO11 model on the Hand Keypoints dataset, you can use either Python or the command line interface (CLI). Here's an example for training a YOLO11n-pose model for 100 epochs with an image size of 640:

from ultralytics import YOLO

# Load a model
model = YOLO("yolo11n-pose.pt")  # load a pretrained model (recommended for training)

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

# Start training from a pretrained *.pt model
yolo pose train data=hand-keypoints.yaml model=yolo11n-pose.pt epochs=100 imgsz=640

Полный список доступных аргументов ты найдешь на странице обучения модели.

What are the key features of the Hand Keypoints dataset?

The Hand Keypoints dataset is designed for advanced pose estimation tasks and includes several key features:

• Large Dataset: Contains 26,768 images with hand keypoint annotations.
• YOLO11 Compatibility: Ready for use with YOLO11 models.
• 21 Keypoints: Detailed hand pose representation, including wrist and finger joints.

For more details, you can explore the Hand Keypoints Dataset section.

What applications can benefit from using the Hand Keypoints dataset?

The Hand Keypoints dataset can be applied in various fields, including:

• Gesture Recognition: Enhancing human-computer interaction.
• AR/VR Controls: Improving user experience in augmented and virtual reality.
• Robotic Manipulation: Enabling precise control of robotic hands.
• Healthcare: Analyzing hand movements for medical diagnostics.
• Animation: Capturing motion for realistic animations.
• Biometric Authentication: Enhancing security systems.

For more information, refer to the Applications section.

How is the Hand Keypoints dataset structured?

The Hand Keypoints dataset is divided into two subsets:

1. Train: Contains 18,776 images for training pose estimation models.
2. Val: Contains 7,992 images for validation purposes during model training.

This structure ensures a comprehensive training and validation process. For more details, see the Dataset Structure section.

How do I use the dataset YAML file for training?

The dataset configuration is defined in a YAML file, which includes paths, classes, and other relevant information. The hand-keypoints.yaml file can be found at hand-keypoints.yaml.

To use this YAML file for training, specify it in your training script or CLI command as shown in the training example above. For more details, refer to the Dataset YAML section.

📅 Created 17 days ago ✏️ Updated 8 days ago

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