Hand Keypoints Dataset
ΠΠ²Π΅Π΄Π΅Π½ΠΈΠ΅
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:
- Wrist
- Thumb (4 points)
- Index finger (4 points)
- Middle finger (4 points)
- Ring finger (4 points)
- Little finger (4 points)
Each hand has a total of 21 keypoints.
ΠΡΠ½ΠΎΠ²Π½ΡΠ΅ Ρ Π°ΡΠ°ΠΊΡΠ΅ΡΠΈΡΡΠΈΠΊΠΈ
- Large Dataset: 26,768 images with hand keypoint annotations.
- YOLOv8 Compatibility: Ready for use with YOLOv8 models.
- 21 Keypoints: Detailed hand pose representation.
Π‘ΡΡΡΠΊΡΡΡΠ° Π½Π°Π±ΠΎΡΠ° Π΄Π°Π½Π½ΡΡ
The hand keypoint dataset is split into two subsets:
- Train: This subset contains 18,776 images from the hand keypoints dataset, annotated for training pose estimation models.
- Val: This subset contains 7992 images that can be used for validation purposes during model training.
ΠΡΠΈΠ»ΠΎΠΆΠ΅Π½ΠΈΡ
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.
ΠΠ°Π±ΠΎΡ Π΄Π°Π½Π½ΡΡ 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
ΡΠ°ΠΉΠ» Ρ
ΡΠ°Π½ΠΈΡΡΡ ΠΏΠΎ Π°Π΄ΡΠ΅ΡΡ https://github.com/ultralytics/ultralytics/blob/main/ultralytics/cfg/datasets/hand-keypoints.yaml.
ultralytics/cfg/datasets/hand-keypoints.yaml
# Ultralytics YOLO π, 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
# βββ ultralytics
# βββ datasets
# βββ hand-keypoints β 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
ΠΡΠΏΠΎΠ»ΡΠ·ΠΎΠ²Π°Π½ΠΈΠ΅
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.
ΠΡΠΈΠΌΠ΅Ρ ΠΏΠΎΠ΅Π·Π΄Π°
ΠΡΠΈΠΌΠ΅ΡΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ ΠΈ Π°Π½Π½ΠΎΡΠ°ΡΠΈΠΉ
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:
- ΠΠΎΠ·Π°ΠΈΡΠ½ΠΎΠ΅ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅: ΠΡΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠ΅ Π΄Π΅ΠΌΠΎΠ½ΡΡΡΠΈΡΡΠ΅Ρ ΡΡΠ΅Π½ΠΈΡΠΎΠ²ΠΎΡΠ½ΡΡ ΠΏΠ°ΡΡΠΈΡ, ΡΠΎΡΡΠΎΡΡΡΡ ΠΈΠ· ΠΌΠΎΠ·Π°ΠΈΡΠ½ΡΡ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π½Π°Π±ΠΎΡΠ° Π΄Π°Π½Π½ΡΡ . ΠΠΎΠ·Π°ΠΈΠΊΠ° - ΡΡΠΎ ΡΠ΅Ρ Π½ΠΈΠΊΠ°, ΠΈΡΠΏΠΎΠ»ΡΠ·ΡΠ΅ΠΌΠ°Ρ Π²ΠΎ Π²ΡΠ΅ΠΌΡ ΠΎΠ±ΡΡΠ΅Π½ΠΈΡ, ΠΊΠΎΡΠΎΡΠ°Ρ ΠΎΠ±ΡΠ΅Π΄ΠΈΠ½ΡΠ΅Ρ Π½Π΅ΡΠΊΠΎΠ»ΡΠΊΠΎ ΠΈΠ·ΠΎΠ±ΡΠ°ΠΆΠ΅Π½ΠΈΠΉ Π² ΠΎΠ΄Π½ΠΎ, ΡΡΠΎΠ±Ρ ΡΠ²Π΅Π»ΠΈΡΠΈΡΡ ΡΠ°Π·Π½ΠΎΠΎΠ±ΡΠ°Π·ΠΈΠ΅ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ² ΠΈ ΡΡΠ΅Π½ Π² ΠΊΠ°ΠΆΠ΄ΠΎΠΉ ΠΎΠ±ΡΡΠ°ΡΡΠ΅ΠΉ ΠΏΠ°ΡΡΠΈΠΈ. ΠΡΠΎ ΠΏΠΎΠΌΠΎΠ³Π°Π΅Ρ ΡΠ»ΡΡΡΠΈΡΡ ΡΠΏΠΎΡΠΎΠ±Π½ΠΎΡΡΡ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΊ ΠΎΠ±ΠΎΠ±ΡΠ΅Π½ΠΈΡ Π½Π° ΡΠ°Π·Π»ΠΈΡΠ½ΡΠ΅ ΡΠ°Π·ΠΌΠ΅ΡΡ ΠΎΠ±ΡΠ΅ΠΊΡΠΎΠ², ΡΠΎΠΎΡΠ½ΠΎΡΠ΅Π½ΠΈΠ΅ ΡΡΠΎΡΠΎΠ½ ΠΈ ΠΊΠΎΠ½ΡΠ΅ΠΊΡΡ.
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.
Π¦ΠΈΡΠ°ΡΡ ΠΈ Π±Π»Π°Π³ΠΎΠ΄Π°ΡΠ½ΠΎΡΡΠΈ
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.