labelU-Kit
L
Labelu Kit
Overview :
labelU-Kit is an open-source front-end annotation component library that provides annotation capabilities for images, videos, and audio. It supports various annotation methods including 2D boxes, points, lines, polygons, and 3D boxes. Offered in the form of NPM packages, it allows developers to seamlessly integrate it into their own annotation platforms, enhancing the efficiency and flexibility of data annotation.
Target Users :
labelU-Kit is ideal for developers and data scientists who need to perform data annotation, particularly in the fields of machine learning and artificial intelligence. It allows them to quickly create and annotate training datasets.
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Use Cases
Annotation of image data in machine learning projects.
Integration into custom data annotation platforms to enhance annotation efficiency.
Used in the education sector for annotating and analyzing teaching data.
Features
Supports 2D box, point, line (including curves), polygon (including closed curves), and 3D box annotations for images.
Includes video annotation capabilities.
Provides audio annotation functionalities.
Modular design allows for flexible combinations.
Offers React components for easy integration into React projects.
Includes links to an online Playground and demo version for rapid user testing and experience.
How to Use
1. Visit the labelU-Kit GitHub page to learn about the project's basic information and features.
2. Choose the appropriate NPM package for installation based on your needs, for example, @labelu/image for image annotation.
3. Read the documentation to understand how to integrate and use these components in your own project.
4. Utilize the provided online Playground or demo version to test and learn the functionality.
5. Customize the annotation components according to project requirements to achieve a personalized data annotation workflow.
6. Use the provided React components in your React projects to quickly build data annotation interfaces.
7. Participate in the community by contributing code or suggesting improvements to drive the project's development.
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