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Anylabeling

AnyLabeling is an open-source Python application for annotating images with geometric shapes and using supported YOLO and Segment Anything models for AI-assisted labeling.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Anylabeling — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use AnyLabeling when preparing or reviewing image annotations and you want manual drawing tools plus documented model-assisted detection or segmentation in the same application.

Who it is for

  • Computer-vision teams preparing image datasets for object detection or segmentation.
  • Annotators who want manual shape tools alongside model-assisted labeling.
  • Developers seeking a Python-based labeling application with CPU and documented accelerated inference options.

Documented uses

  • Draw polygon, rectangle, circle, line or point annotations on images.
  • Use YOLOv8 object detection for auto-labeling.
  • Create prompt-based segmentation annotations with supported Segment Anything models.
  • Label text detection, recognition and key information extraction tasks.
Before you choose

Requirements & limits

The project is open source under GPL-3.0. The README documents downloadable builds for Linux x64, Windows x64 and Apple Silicon macOS, as well as installation from PyPI.

  • The Python package requires Python 3.11 or later; Python 3.12 is recommended.
  • The documented PyPI setup on macOS includes a separate PyQt6 installation via Conda.
  • NVIDIA CUDA and Apple Silicon CoreML have separate documented package or build choices; CPU builds are also available.
  • The application automatically downloads required model weights on first use.
  • SAM-family models differ in supported prompt types; SAM 3 includes text prompts, while the other listed models use point or rectangle prompts.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install AnyLabeling from PyPI

Set up the Python package in a Conda environment.

Before you start

  • Python 3.11 or later; Python 3.12 is recommended.
  • Conda is recommended. On macOS, install PyQt6 through Conda before installing AnyLabeling.

Steps

  1. Create and activate a Conda environment with Python 3.12.

  2. On macOS, install PyQt6 using Conda.

  3. Install the package with pip install anylabeling.

Expected outcome

The AnyLabeling Python package is installed in the environment.

Guide limitations

  • This guide covers installation only, not opening a project or running annotation.
  • For NVIDIA CUDA inference on Linux or Windows, the documentation specifies the separate anylabeling-gpu package in a fresh environment.
  • On macOS, the documented PyPI route requires the separate PyQt6 installation.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
14 October 2026
Original website title
vietanhdev/anylabeling · Official homepage title · retained in its source language
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