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humansignal/labelimg

★ 24,959PythonAudience · developerComplexity · 2/5StaleLicenseSetup · easy

TLDR

Desktop tool for drawing boxes around objects in images and labeling them to train object detection models.

Mindmap

mindmap
  root((repo))
    What it does
      Draw boxes on images
      Label objects
      Create training data
    Output formats
      PASCAL VOC XML
      YOLO text
      CreateML format
    Use cases
      Build custom detectors
      Annotate research datasets
      Label training images
    Tech stack
      Python
      Qt framework
    Audience
      ML practitioners
      Researchers
      Vision engineers

Things people build with this

USE CASE 1

Create labeled training datasets for custom object detection models by drawing boxes around objects in photos.

USE CASE 2

Annotate images for computer vision research projects in standard formats like PASCAL VOC or YOLO.

USE CASE 3

Prepare image datasets for training machine learning models to recognize specific objects or categories.

Tech stack

PythonQt

Getting it running

Difficulty · easy Time to first run · 5min
Open source license allowing free use and modification; check repository for specific license terms.

In plain English

LabelImg is a graphical tool for annotating images to train object detection models. Before a machine learning system can learn to recognize objects in photos, say, cats, cars, or people, it needs thousands of example images where humans have manually drawn boxes around those objects and labeled what they are. LabelImg provides a desktop application that makes this process straightforward: you open a folder of images, draw rectangular boxes around objects, name them, and save the results. It saves annotations in three standard formats: PASCAL VOC (an XML-based format used by the ImageNet dataset), YOLO (a compact text format used by the YOLO family of object detection models), and CreateML (Apple's format). This makes LabelImg compatible with the most popular training pipelines without needing to convert files. You'd use this if you're building a custom image recognition or object detection model and need to create labeled training data from scratch, or if you're a researcher annotating a dataset for academic work. It runs on Linux, macOS, and Windows and installs simply via pip. Note: LabelImg is no longer actively developed, the repository now points users to Label Studio, a broader open source annotation platform by HumanSignal that handles images, text, audio, video, and time-series data. LabelImg still works for its original purpose but receives no new updates. Written in Python using Qt for its graphical interface.

Copy-paste prompts

Prompt 1
How do I install LabelImg and start annotating images for object detection training?
Prompt 2
Show me how to export annotations in YOLO format from LabelImg for training a YOLOv5 model.
Prompt 3
What's the workflow for creating a labeled dataset of 500 images using LabelImg for a custom object detector?
Prompt 4
How do I batch-process multiple images in LabelImg and save them in PASCAL VOC format?
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Generated 2026-05-18 · Model: sonnet-4-6 · Verify against the repo before relying on details.