LabelU
Guide
Schema
Guide
Schema

Product Introduction

Product Overview

LabelU is an open-source data annotation tool that can help users quickly, accurately, and efficiently annotate data, thereby improving the performance and quality of machine learning models. LabelU supports various types of annotations, including label classification, text description, bounding boxes, polygons, points, lines, cuboids, timestamps, segmentations, etc., meeting the annotation tasks of different scenarios and needs. You can experience the product in ways below:

Feature Introduction

LabelU provides a variety of annotation tools and features, supporting image, video, and audio annotations.

  • Image-based: A multifunctional image processing tool that includes various annotation tools such as bounding boxes, point marking, line marking, polygons, and cuboid, assisting in the identification, annotation, and analysis of images.
  • Video-based: Equipped with powerful video processing capabilities, capable of video segmentation, video classification, video timestamping, etc., providing high-quality annotated data for model training.
  • Audio-based: An efficient and precise audio analysis tool capable of audio segmentation, audio classification, audio timestamping, etc., better annotating complex sound information.

Concept Introduction

Concept Explanation
Task A task established for annotating a specific dataset
Label The classification labels that need to be added during annotation, such as cat, dog, pedestrian, vehicle
Annotation The object generated after a round of annotation, like a rectangle box, a point
Attribute Further description of the label, for example, after labeling an object as a vehicle, adding the attribute "vehicle occlusion rate is 20%"
Result Annotation + Label + Attribute, a complete annotation record