🌟 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
- Download the folder mode build (
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
For NVIDIA CUDA inference on Linux or Windows, use the GPU distribution in a fresh environment:
pip install anylabeling-gpu
Apple Silicon users can enable both ONNX Runtime CoreML and native CoreML SAM2 models with:
pip install "anylabeling[macos]"
export ANYLABELING_DEVICE=COREML
AnyLabeling automatically selects CUDA for GPU builds and CoreML on macOS,
with CPU fallback for unsupported model operations. Advanced ONNX Runtime
packages can be selected with ANYLABELING_DEVICE; supported values include
CUDA, COREML, DIRECTML, ROCM, MIGRAPHX, OPENVINO, TENSORRT,
CANN, QNN, VITISAI, and WEBGPU. NPU aliases include NPU,
INTEL_NPU, QUALCOMM_NPU, AMD_NPU, and ASCEND_NPU. On Windows
PowerShell, set the override with $env:ANYLABELING_DEVICE = "DIRECTML".
The GPU distribution includes pip-managed CUDA 12 and cuDNN runtime libraries, so a compatible NVIDIA driver is sufficient; a system CUDA toolkit is not required.
NPU execution requires the matching vendor ONNX Runtime package in a fresh,
dedicated environment. For example, Intel Core Ultra systems use
onnxruntime-openvino with ANYLABELING_DEVICE=INTEL_NPU; Qualcomm Snapdragon
Windows ARM64 systems use onnxruntime-qnn with
ANYLABELING_DEVICE=QUALCOMM_NPU. Replace the default onnxruntime package,
because ONNX Runtime requires only one variant in an environment. Qualcomm HTP
models generally need QDQ quantization, and support still depends on the
operator coverage of the selected model.
- 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.
1 maintainer
Release files for anylabeling 0.4.40
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| anylabeling-0.4.40.tar.gz | 645.9 kB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| anylabeling-0.4.40-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 1.3 MB
Release files / anylabeling-0.4.40.tar.gz
| Download URL | anylabeling-0.4.40.tar.gz |
|---|---|
| Size | 645.9 kB |
| Tags | Source |
|
SHA-256 checksum How to use checksums |
43b080313159ce2dbb917b008c50cdd7870bd19845255d6a4030f1abb08d56b2
|
|
BLAKE2b-256 checksum How to use checksums |
a8a6debd30ac84979100717578094abbcf14d5bd144eca0423c9249060767b14
|
| Upload date | |
|
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.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 29, 2026.
Transparency logRelease files / anylabeling-0.4.40-py3-none-any.whl
| Download URL | anylabeling-0.4.40-py3-none-any.whl |
|---|---|
| Size | 703.0 kB |
| Tags | Python 3 |
|
SHA-256 checksum How to use checksums |
bcb29da4d86493077aecde91ecddcdc3694fd27410a79fbed7c1d271c42d75d3
|
|
BLAKE2b-256 checksum How to use checksums |
c609156e1286098cd42a9f18cb551f60ab7f736044cf33434266d84e09aafee5
|
| Upload date | |
|
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.
PyPI Publish Attestation
PyPI verified that this artifact, at this checksum, originated from the publisher listed below.
Signed by GitHub Actions, verified by PyPI on Aug 29, 2026.
Transparency log
