opendatalab/labelU

Open-source multimodal data annotation platform with AI auto-annotation support.

What it solves

LabelU addresses the difficulty of preparing high-quality training data for AI models by providing a unified platform for annotating multimodal data. It simplifies the process of labeling images, videos, and audio files, which is traditionally a tedious and manual task.

How it works

LabelU provides a suite of visual annotation tools that allow users to mark data through simple configurations. It supports various modalities:

  • Images: Tools for 2D bounding boxes, semantic segmentation, polylines, and keypoints.
  • Videos: Capabilities for segmentation, classification, and information extraction.
  • Audio: Tools for segmentation, classification, and information extraction via sound visualization.

To speed up the process, it integrates AI-assisted labeling, allowing users to load pre-annotated data or use "AI Auto-Annotation" via model servers (such as Florence-2, GroundingDINO + SAM, or SAM 3) to automatically detect and segment objects.

Who it’s for

It is designed for developers and data scientists who need to create annotated datasets for training machine learning models in fields like object detection, scene analysis, action recognition, and audio analysis.

Highlights

  • Multimodal Support: Handles image, video, and audio data in one platform.
  • AI-Powered Automation: Includes built-in support for auto-annotation using state-of-the-art models like SAM 3.
  • Flexible Export: Supports exporting annotations in common formats such as JSON, COCO, and MASK.
  • Cloud Integration: Allows direct import of data from S3-compatible object storage (e.g., AWS S3, MinIO).

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