Hand Keypoints Dataset

导言

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 YOLOv8 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.

主要功能

• Large Dataset: 26,768 images with hand keypoint annotations.
• YOLOv8 Compatibility: Ready for use with YOLOv8 models.
• 21 Keypoints: Detailed hand pose representation.

数据集结构

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.

应用

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.

数据集 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 文件保存在 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') 210 images
val: val # val images (relative to 'path') 53 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

使用方法

To train a YOLOv8n-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("yolov8n-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=yolov8n-pose.pt epochs=100 imgsz=640

图片和注释示例

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:

• 镶嵌图像：该图像展示了由马赛克数据集图像组成的训练批次。马赛克是一种在训练过程中使用的技术，可将多幅图像合并为单幅图像，以增加每个训练批次中物体和场景的多样性。这有助于提高模型对不同物体尺寸、长宽比和环境的泛化能力。

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.

引文和致谢

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.

📅 Created 0 days ago ✏️ Updated 0 days ago

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