LabelU - An open-source multimodal data annotation tool

LabelU is an open-source, multimodal data annotation tool that supports image, video, and audio annotation. It features capabilities such as bounding box annotation, polygon annotation, punctuation, line annotation, classification, and description annotation, meeting the needs of computer vision tasks including object detection, image classification, and instance segmentation. LabelU allows for customizable annotation tasks through flexible tool combinations and supports data export in COCO and MASK formats, making data annotation convenient and efficient. LabelU also supports AI-assisted annotation, allowing one-click loading of pre-annotated data for refinement and adjustment as needed, improving annotation efficiency and accuracy.

LabelU's main functions

• Multifunctional image annotation toolIt provides multiple annotation methods such as 2D bounding boxes, semantic segmentation, polylines, and key points to meet the needs of object detection, scene analysis, image recognition, machine translation, etc.
• Powerful video annotation capabilitiesIt supports functions such as video segmentation, video classification, and video information extraction, and is suitable for tasks such as video retrieval, video summarization, and behavior recognition, helping users process long videos and extract key information.
• Highly efficient audio annotation toolIt has the ability to segment, classify, and extract audio information, visualize complex sound information, and simplify the audio data processing workflow.
• AI-assisted annotationIt supports one-click loading of pre-annotated data, which users can refine and adjust as needed to improve annotation efficiency and accuracy.

How to use LabelU

• Installation and Deployment:CanOnline experienceAlternatively, it can be found inGitHub repositoryDownload the source code and install it according to the provided documentation.
• Create annotation projectAfter installation, create an annotation project. LabelU supports creating different types of annotation tasks, including images, videos, and audio.
• Data importAfter creating the project, import the data that needs to be labeled into LabelU. Currently, LabelU supports importing local data.
• Task ConfigurationAfter importing the data, configure the annotation settings. Depending on the task scenario, select appropriate annotation tools and labels. LabelU offers a rich set of annotation tools, such as bounding boxes, polygons, punctuation marks, lines, categories, and descriptions.
• Start labelingAfter configuration, begin data annotation. LabelU provides a simple and intuitive user interface, supports keyboard shortcuts and visual task management to improve annotation efficiency.
• Export ResultsAfter annotation, the results can be exported in formats such as JSON, COCO, and MASK for convenient subsequent model training and data analysis.
• Local development(If needed): If you need to perform secondary development or integration of LabelU, you can follow the official documentation for local development and environment configuration.

LabelU's project address

• Product Introduction:labelU
• Project address:https://pypi.org/project/labelu/
• Demo experience:https://opendatalab.github.io/labelU-Kit/#/image
• Github repository:https://github.com/opendatalab/labelU

Application scenarios of LabelU

• Data ScientistandMachine Learning EngineerThis requires labeling large amounts of image, video, and audio data to train and optimize AI models. LabelU's tools can meet various needs, from basic object recognition to complex scene analysis.
• ResearchersIn academic research, LabelU supports tasks such as image classification, text description, and object localization, helping researchers to conduct in-depth analysis and research.
• DevelopersandAlgorithm EngineerIt requires labeling of specific datasets and supports the development of custom machine learning projects or algorithms.
• Enterprise usersFor enterprises that need to perform large-scale data annotation, LabelU's local deployment options can ensure data security and privacy, while supporting team collaboration and improving annotation efficiency.
• Independent developersandSmall research teamLabelU's versatility improves annotation efficiency for independent developers and small teams.