AnyLabeling

🌟 AnyLabeling 🌟

Effortless data labeling with AI support from YOLO and Segment Anything!

AnyLabeling = LabelImg + Labelme + Improved UI + Auto-labeling

Auto Labeling with Segment Anything

Youtube Demo: https://www.youtube.com/watch?v=5qVJiYNX5Kk
Documentation: https://anylabeling.nrl.ai

Features:

Image annotation for polygon, rectangle, circle, line and point.
Auto-labeling with YOLOv8 (object detection).
Auto-labeling with Segment Anything family:
- SAM (ViT-B / ViT-L / ViT-H) and MobileSAM
- SAM 2 and SAM 2.1 (Hiera-Tiny / Small / Base+ / Large)
- SAM 3 (ViT-H) — open-vocabulary segmentation with text prompts
Text detection, recognition and KIE (Key Information Extraction) labeling.
Multiple languages availables: English, Vietnamese, Chinese.

Supported Models

Model	Prompt Types	Notes
SAM ViT-B / ViT-L / ViT-H	Point, Rectangle	Original Segment Anything
MobileSAM	Point, Rectangle	Lightweight SAM
SAM 2 Hiera-Tiny / Small / Base+ / Large	Point, Rectangle	Meta SAM 2
SAM 2.1 Hiera-Tiny / Small / Base+ / Large	Point, Rectangle	Improved SAM 2
SAM 3 ViT-H	Text, Point, Rectangle	Open-vocabulary; text drives detection
YOLOv8n / s / m / l / x	—	Object detection & auto-labeling

All models are downloaded automatically on first use from Hugging Face.

Install and Run

1. Download and run executable

Download and run newest version from Releases.
For MacOS:
- Download the folder mode build (AnyLabeling-Folder.zip) from Releases
- See macOS folder mode instructions for details

Install from Pypi

Requirements: Python 3.11+. Recommended: Python 3.12.
Recommended: Miniconda/Anaconda.
Create environment:

conda create -n anylabeling python=3.12
conda activate anylabeling

(For macOS only) Install PyQt6 using Conda:

conda install -c conda-forge pyqt=6

Install anylabeling:

pip install anylabeling # or pip install anylabeling-gpu for GPU support

Start labeling:

anylabeling

Documentation

Website: https://anylabeling.nrl.ai/

Applications

Object Detection	Recognition	Facial Landmark Detection	2D Pose Estimation

2D Lane Detection	OCR	Medical Imaging	Instance Segmentation

Image Tagging	Rotation	And more!
		Your applications here!

Development

Install packages:

pip install -r requirements-dev.txt
# or pip install -r requirements-macos-dev.txt for MacOS

Generate resources:

pyrcc5 -o anylabeling/resources/resources.py anylabeling/resources/resources.qrc

Run app:

python anylabeling/app.py

Build executable

Install PyInstaller:

pip install -r requirements-dev.txt

Build:

bash build_executable.sh

Check the outputs in: dist/.

Contribution

If you want to contribute to AnyLabeling, please read Contribution Guidelines.

Star history

References

Labeling UI built with ideas and components from LabelImg, LabelMe.
Auto-labeling with Segment Anything (SAM, SAM 2, SAM 2.1, SAM 3), MobileSAM.
Auto-labeling with YOLOv8.
Icons from FlatIcon: DinosoftLabs, Freepik, Vectoricons, HideMaru.

Intended Audience
- Developers
- Science/Research
Natural Language
- English
Operating System
- OS Independent
Programming Language

Release files for anylabeling-gpu 0.4.37

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Built distribution (wheel)

Table of built distributions (wheels) for anylabeling-gpu 0.4.37
File	Interpreter	ABI	Platform	Reset
anylabeling_gpu-0.4.37-py3-none-any.whl 688.2 kB Aug 29, 2026	Python 3	none	any	Details

Release files / anylabeling_gpu-0.4.37-py3-none-any.whl

Download URL	anylabeling_gpu-0.4.37-py3-none-any.whl
Size	688.2 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`6fd77c77ad7f6a75170b116b086afe2630907bd1d93ea314ad0349a66fec8fa0`
BLAKE2b-256 checksum How to use checksums	`39097827173f14af156ea211908612333aba96229fbe570d36d1eca92d34864b`
Upload date	Aug 29, 2026
Uploaded using Trusted Publishing? What is trusted publishing?	Yes
Uploaded via	`twine/7.0.0 CPython/3.13.14`

Provenance

Provenance describes where a file came from. On PyPI, provenance is shared via attestations, which provide a verifiable record of the build or publishing details. View details, limitations and caveats.

Identity

Publishing platform: GitHub Actions
Publishing repository: github.com/vietanhdev/anylabeling
Publishing commit: github.com/vietanhdev/anylabeling/tree/0f16a0ad69039672e3a8c8769da942dd43e285f9

Workflow

Publishing configuration: github.com/vietanhdev/anylabeling/blob/0f16a0ad69039672e3a8c8769da942dd43e285f9/.github/workflows/python-publish-gpu.yml
Publishing logs: github.com/vietanhdev/anylabeling/actions/runs/33254636353/attempts/1

Artifact

Subject: anylabeling_gpu-0.4.37-py3-none-any.whl
SHA-256 checksum: 6fd77c77ad7f6a75170b116b086afe2630907bd1d93ea314ad0349a66fec8fa0 Verifying attestations