LabelU

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What LabelU is

LabelU is a free, open-source multimodal annotation platform for image, video, and audio tasks. Image work can include 2D bounding boxes, semantic segmentation, polylines, and keypoints. For video and audio, the maker describes tools for segmentation, classification, and information extraction. Users can load existing pre-annotations and refine them, or use image AI auto-annotation to detect and segment objects, run batch tasks, and track progress in real time. Reference servers are provided for Florence-2, GroundingDINO with SAM ViT-B, and SAM 3. LabelU can import files from S3-compatible storage, including AWS S3 and MinIO, and exports in formats including JSON, COCO, and MASK. Buyers can use the online instance and browser-based toolkit, or install it locally with pip. The documented local setup uses Python 3.11 and runs at localhost:8000. SQLite is the default database, with optional MySQL support. LabelU is published by the OpenDataLab GitHub organization under the Apache 2.0 license.

Who it suits

LabelU suits teams and individuals labeling image, video, or audio data, especially when image pre-annotations or model-assisted work is useful. It also fits users who want to choose between an online instance and self-hosting.

What it does well

Covers image, video, and audio annotation.
Supports multiple image label types, including boxes and keypoints.
Can import S3-compatible files, including from AWS S3 and MinIO.
Exports include JSON, COCO, and MASK.
Open source under the Apache 2.0 license.

What to know first

Local setup requires Python 3.11.
Reference AI models have stated memory requirements; SAM 3 requires CUDA 12.6 or later.

Limits

The reference AI models list minimum memory requirements of about 4 GB for Florence-2 and GroundingDINO with SAM ViT-B, and about 8 GB for SAM 3. SAM 3 also requires CUDA 12.6 or later.

Getting started

Open the online LabelU instance or browser-based annotation toolkit.
For local installation, use pip with Python 3.11.
Run the local server at localhost:8000.
Choose SQLite by default or configure optional MySQL support.
Import files from S3-compatible storage or load pre-annotated data to refine.

Verdict

Choose LabelU if you need free, open-source annotation across images, video, and audio, with image AI assistance and flexible deployment. Consider the reference models' hardware requirements before relying on them, particularly SAM 3's CUDA requirement.

Questions

Is LabelU free?

Yes. LabelU is free and open source under the Apache 2.0 license.

Which media types can I annotate?

LabelU supports image, video, and audio annotation. Video and audio tools include segmentation, classification, and information extraction.

Can I run LabelU locally?

Yes. The documented installation uses pip and Python 3.11, and runs at localhost:8000.

What export formats does it support?

Listed formats include JSON, COCO, and MASK.

Which AI models are available as reference servers?

The project lists Florence-2, GroundingDINO with SAM ViT-B, and SAM 3. Their stated memory requirements are about 4 GB, 4 GB, and 8 GB respectively; SAM 3 requires CUDA 12.6 or later.

Who publishes LabelU?

The LabelU repository is published under the OpenDataLab GitHub organization.

Facts from LabelU's own pages

AI annotation: Image AI auto-annotation supports batch tasks and real-time progress, with reference servers for Florence-2, GroundingDINO plus SAM ViT-B, and SAM 3. · github.com, 8 Oct 2026
AI auto-annotation: AI services can automatically detect and segment image objects, with batch annotation and real-time progress tracking. · github.com, 9 Oct 2026
AI model options: The project provides reference servers for Florence-2, GroundingDINO with SAM ViT-B, and SAM 3. · github.com, 9 Oct 2026
AI resource limits: The reference AI models list minimum memory requirements of about 4 GB for Florence-2 and GroundingDINO with SAM ViT-B, and about 8 GB for SAM 3; SAM 3 requires CUDA 12.6 or later. · github.com, 9 Oct 2026
Audio annotation: Audio tools support segmentation, classification, and information extraction. · github.com, 9 Oct 2026
Database option: SQLite is the documented default database, and MySQL support is available as an optional installation. · github.com, 9 Oct 2026
Deployment: LabelU can be installed locally with pip and run at localhost:8000; the README specifies Python 3.11. · github.com, 8 Oct 2026
Export formats: The project says it supports exporting annotation data in JSON, COCO, and MASK formats. · github.com, 9 Oct 2026
Image annotation: Image tools include 2D bounding boxes, semantic segmentation, polylines, and keypoints. · github.com, 9 Oct 2026
Image tools: Image annotation includes 2D bounding boxes, semantic segmentation, polylines, and keypoints. · github.com, 8 Oct 2026
License: The repository states that LabelU is released under the Apache 2.0 license. · github.com, 9 Oct 2026
Maker: The LabelU repository is published under the OpenDataLab GitHub organization. · github.com, 9 Oct 2026
Online access: The maker links to an online LabelU instance and a browser based annotation toolkit. · github.com, 8 Oct 2026
Pre-annotations: Users can load pre-annotated data and refine or adjust it. · github.com, 9 Oct 2026
Purpose: LabelU is a multimodal data annotation platform for image, video, and audio tasks. · github.com, 9 Oct 2026
Self-hosting: The README gives local installation steps using Python 3.11, pip, and a local server at localhost:8000, with Windows Anaconda Prompt instructions and a macOS Intel note. · github.com, 9 Oct 2026
Storage integration: LabelU can import data from S3-compatible object storage, including AWS S3 and MinIO. · github.com, 9 Oct 2026
Support channel: The README invites users to join the OpenDataLab official WeChat group. · github.com, 8 Oct 2026
Video and audio: The maker describes video and audio annotation for segmentation, classification, and information extraction. · github.com, 8 Oct 2026
Video annotation: Video features include segmentation, classification, and information extraction. · github.com, 9 Oct 2026

Where LabelU stands on Freedom251

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