Data annotation is one of the most important parts of the machine learning pipeline, where the success of such a pipeline depends on the number of annotated samples and the annotation quality.
With labeled data being the only source of information the machine learning model has about our natural environment, it is no surprise that poor annotations quickly lead those models to perform poorly.
The worst part?
Data annotation is often incredibly tedious and time-consuming. In fact, more and more organizations tend to outsource or crowdsource this process.
As an alternative to costly annotation services and software, open source annotation tools that enable easy and fast annotation are often used by researchers and students. In this article, we will talk about “LabelImg”, a lightweight and popular open source annotation tool often used for annotating image data for computer vision tasks like object detection and recognition.
Here’s what we’ll cover:
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LabelImg is an open-source graphical image annotation tool originally developed by TzuTa Lin and maintained by a community of developers in Label Studio. Currently hosted in a GitHub organization named heartexlabs, LabelImg is written in Python and uses Qt for its graphical interface.
As of now, LabelImg offers annotations only in the form of bounding boxes which can be exported to PASCAL VOC, YOLO, and CreateML formats in the form of XML files.
Check out the demo video here:
If you are looking for a free tool for labeling data for your object detection projects, LabelImg might be just the perfect solution for your needs. It gets the job done.
Although LabelImg makes it possible for users to label data using bounding boxes and to export annotations to multiple forms, like every open-source tool, it comes with several limitations that can slow you down.
Let’s have a look.
In fact, one of the V7ers tried to install LabelImg on her MacOs but…
She encountered several issues and eventually got back to labeling on V7 ;-)
If you end up giving up on LabelImg, too, our team at V7 would be happy to help you label your data hassle-free ;-)
Installing LabelImg requires some technical skills, such as using the command line. Here are a couple of options depending on your operating system. You can find detailed installation instructions in LabelImg Github documentation.
LabelImg has a fairly intuitive user interface for annotating images for object detection. Ready to label some data? Have a look at our quick tutorial.
After drawing this bounding box, the tool will ask you to provide a label. You can either add a new label or provide one from the predefined list of labels that you can find in the drop-down menu.
Below are some of the best practices for labeling your images using LabelImg.
While LabelImg is a great beginner annotation tool, it might not be powerful enough for advanced projects requiring you to label large quantities of data for tasks such as semantic segmentation.
Luckily, LabelImg has various alternatives, including both free and paid options.
Free and open source alternatives to LabelImg include CVAT and LabelMe, which are both easy to use. For researchers and students, V7’s Free Education Plan serves as a great alternative, not only for labeling images for object detection but doing much more with the auto-annotate tool for superfast annotations and the inbuilt training pipeline for training on your annotated data.
For a detailed comparison of paid and free data annotation tools, check out 13 Best Image Annotation Tools
While the process of labeling image data is tedious and often requires manual work, quality image annotation is necessary for machine learning models to perform optimally.
As an open-source tool for image annotation, LabelImg provides an opportunity for researchers and students to have hands-on experience annotating datasets and learn the best practices for data annotation themselves without having to bear the costs that come with annotation softwares and services.
While being easy to use and simple in nature, LabelImg lacks the plethora of tools needed for image annotation software to be sufficient for annotating and maintaining large-scale datasets.
“Collecting user feedback and using human-in-the-loop methods for quality control are crucial for improving Al models over time and ensuring their reliability and safety. Capturing data on the inputs, outputs, user actions, and corrections can help filter and refine the dataset for fine-tuning and developing secure ML solutions.”
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