Every accurate machine learning model starts with good training data, and for computer vision projects that data almost always means thousands of images with the objects in them carefully marked and named. Drawing those boxes by hand sounds simple, but doing it quickly, consistently, and in the right file format is what separates a smooth project from a frustrating one. That is exactly the job Labelimg was built for: it is a free, open-source image annotation tool that lets you draw bounding boxes around objects and save the labels in the formats that popular training frameworks expect.
What Is Labelimg?
LabelImg is a graphical image annotation tool written in Python and built with the Qt interface library. It runs on Windows, macOS, and Linux, and it has become one of the most widely used tools for preparing object detection datasets. Instead of editing complicated configuration files by hand, you open a folder of images, draw rectangles around the objects you care about, type a class name for each one, and save. The tool then writes a small annotation file for every image, recording the position of each box and the label attached to it.
Because the tool is open source and completely free, it is a natural first choice for students, independent researchers, and small teams who need reliable annotations without paying for a commercial labelling platform. It is lightweight, installs in minutes, and works offline, which makes it practical even on modest hardware or in environments without a fast internet connection.
Key Features of the Tool
The feature set is deliberately focused on doing one job well. You can open a single image or an entire directory, move through images with keyboard shortcuts, and create, copy, and edit bounding boxes with the mouse. A label list panel shows every annotation in the current image, and a file list panel makes it easy to jump between pictures in a dataset. Frequently used class names can be preloaded from a simple text file, so you do not have to retype labels such as “car”, “person”, or “dog” hundreds of times.
Other practical touches include automatic saving when you move to the next image, a verification mode for checking finished work, adjustable box colours per class, and the ability to zoom and pan around large images so small objects can be boxed precisely. The interface shows the coordinates of each box as you draw, which helps keep annotations tight and consistent across a whole dataset.
Supported Formats: YOLO and Pascal VOC
One of the strongest reasons for the tool’s popularity is its format support. With a single button you can switch the saving format between Pascal VOC, which stores annotations as XML files, YOLO, which stores them as plain text files with normalised coordinates, and CreateML, which uses JSON. Pascal VOC has been a standard in academic research for years, while the YOLO format is what you need when training YOLO family detectors, among the most popular real-time object detection models available today.
This flexibility matters because it removes a whole conversion step from the workflow. You label your images once, save them directly in the format your training pipeline expects, and move straight on to building the dataset splits and training the model. If you later change frameworks, you can reopen the same images and save the annotations again in a different format.
How to Annotate Images Step by Step
Getting started takes only a few minutes. First, install Python, then install the tool with pip, and launch it from the command line. Next, prepare a text file listing your class names, one per line, and load it as the predefined classes file. Open your image directory, choose your save directory, and select the output format you want before you begin.
To annotate, press the “Create RectBox” button or use the keyboard shortcut, then drag a rectangle tightly around an object. A small dialog appears asking for the class name; pick from your list and confirm. Repeat for every object in the image, save, and move on to the next picture. Working methodically, image by image, a few focused sessions can produce a labelled dataset of several thousand examples, and the built-in shortcuts for saving and advancing make the process steadily faster as you go.
Benefits for Machine Learning Projects
The quality of an object detection model depends heavily on the quality of its labels, and a dedicated annotation tool helps in several ways. Consistent, tightly drawn boxes teach the model exactly where objects begin and end. Standard output formats eliminate conversion errors between labelling and training. Working locally keeps unpublished or sensitive images on your own machine, which matters for medical, industrial, and commercial projects. And because the tool costs nothing, budget can go toward collecting more images rather than paying per-label fees, which usually improves a model far more.
Frequently Asked Questions
Is the tool really free? Yes. It is open-source software released under a free licence, so you can download, use, and even modify it without paying anything, for personal and commercial projects alike.
Which operating systems does it support? It runs on Windows, macOS, and Linux, anywhere Python and the Qt libraries can be installed.
Can it label images for YOLO training? Yes. Switching the format button to YOLO saves each annotation as a text file with the class number and normalised box coordinates that YOLO training requires.
Does it support polygon or segmentation labels? No. The tool is designed for rectangular bounding boxes used in object detection. For pixel-level segmentation masks you would need a different annotation tool.
Conclusion
Preparing training data will never be the glamorous part of machine learning, but it is the part that quietly decides whether a model succeeds. A simple, dependable annotation tool removes friction from that work: open a folder, draw accurate boxes, and save clean labels in the format your framework needs. If you are starting an object detection project and want a proven free option, download the tool from labelimg.org, label a small test batch, and see how quickly a proper dataset comes together.
