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Hand Keypoints Dataset

Giới thiệu

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

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.

Các tính năng chính

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

Cấu trúc tập dữ liệu

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.

Ứng dụng

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.

Tập dữ liệu 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 Tệp được duy trì tại https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml.

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

Sử dụng

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.

Ví dụ về tàu hỏa

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

Hình ảnh mẫu và chú thích

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:

Hình ảnh mẫu tập dữ liệu

  • Hình ảnh khảm: Hình ảnh này thể hiện một lô đào tạo bao gồm các hình ảnh tập dữ liệu được khảm. Khảm là một kỹ thuật được sử dụng trong quá trình đào tạo kết hợp nhiều hình ảnh thành một hình ảnh duy nhất để tăng sự đa dạng của các đối tượng và cảnh trong mỗi đợt đào tạo. Điều này giúp cải thiện khả năng khái quát hóa mô hình cho các kích thước đối tượng, tỷ lệ khung hình và ngữ cảnh khác nhau.

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.

Trích dẫn và xác nhận

If you use the hand-keypoints dataset in your research or development work, please acknowledge the following sources:

We would like to thank the following sources for providing the images used in this dataset:

The images were collected and used under the respective licenses provided by each platform and are distributed under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

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


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