A Friendly Guide to LabelImg [+Open Datasets, Models, Alternative Tools]

Annotating images with LabelImg

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.

1. Go to View and check if Auto Save mode is enabled. Auto Save mode prevents you from losing your annotations mid-way.

2. Create a directory as our base directory and keep all images for annotation there. Creating a directory enables you to load all images at once and swipe through them as you annotate. Open the directory with LabelImg.

3. LabelImg will then ask you where to save your annotations. Proceed with the corresponding directory.

4. Prior to labeling images, you should check if the export file format is correct. The default format is PASCAL VOC. Clicking on the format in the menu bar on the left allows you to change it to YOLO.
   
   

5. To start annotation, click on the Create RectBox button that changes your mouse cursor to that of a crosshair with which you can draw a rectangular bounding box on the image.

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.

6. Clicking on Create RectBox multiple times allows you to annotate multiple objects in the same image at the same time.

7. Keep clicking on the next image or use the hotkeys “a” and “d” on your keyboard to navigate through images in your directory and keep annotating them. These annotations will be automatically saved in the previously defined annotation folder.

Best practices for labeling with LabelImg

Below are some of the best practices for labeling your images using LabelImg.

1. The bounding boxes annotations should be tight in nature. What this means is that the bounding boxes should not include anything that does not contain the corresponding object, and the boxes should be as small as possible to fit the entire object. Care should be taken, however that the entire object is present in the box and some part of the object is not sticking out of the bounding box.

2. The annotations should be complete in nature. All the objects with their corresponding tags should be annotated so that the machine learning model is not penalized for detecting an unannotated object in the image along with the annotated one.

3. Objects that are partially blocked in the image should be annotated nevertheless. Annotation should proceed as if the occluded object were in full view. Similarly, objects that are partially absent from the image frame should be annotated nevertheless.

4. In case the annotation task is outsourced to an organization or freelancers, make sure to always include specific and crystal clear instructions to prevent ambiguity in annotations.

Have a look at our Introductory Guide to Quality Training Data for Machine Learning

Labelimg alternatives

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.

V7 is also available for commercial use as a paid alternative amongst tools like SuperAnnotate, Labelbox, Dataloop, Roboflow, or Scale AI.

For a detailed comparison of paid and free data annotation tools, check out 13 Best Image Annotation Tools

Key takeaways

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 software 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.