What is LabelImg?

In order to annotate things in images for the training of object detection models, LabelImg is an open software graphical image annotation tool. Python was used in its creation, and Windows, MacOS, and Linux all support it. Users are given an interface to annotate images by drawing bounding boxes around interesting things and giving them class labels. The annotations can be saved in PASCAL VOC XML or other formats for use in training object detection models with common machine learning frameworks such as TensorFlow, PyTorch, and others.

A. BENEFITS OF LabelImg

1. Easy to use: Annotating images with bounding boxes and labels is simple for users because of LabelImg’s user-friendly interface.
2. Cross-platform: It operates on Windows, MacOS, and Linux, making it accessible to a wide range of users.
3. Open-source: It is open-source software, meaning that it is free to use, alter, and distribute.
4. Fast annotation: With LabelImg, users can annotate photos quickly and effectively, producing high-quality datasets for object identification model training.
5. Supports multiple formats: Users can use the annotated data with well-known machine learning frameworks because LabelImg enables saving annotations in a variety of formats, including PASCAL VOC XML.
6. Supports multi-object annotation: It is appropriate for a variety of object detection jobs since it enables users to annotate multiple objects in an image.

B. The steps to use LabelImg to annotate images :

1. Installation: LabelImg is written in Python, so you’ll need to have Python installed on your computer. You can install LabelImg by cloning the Github repository or by downloading the latest release.
2. Open LabelImg: Once you have installed LabelImg, you can launch it by running the labelImg.py script.
3. Load an image: In LabelImg, you can load an image by clicking the “Open Dir” button and selecting the image or folder of images that you want to annotate.
4. Annotate objects: Once you have loaded an image, you can annotate objects in the image by drawing bounding boxes around the objects and labeling them with class names. To draw a bounding box, simply click and drag your mouse over the object. You can also adjust the size and position of the bounding box by clicking and dragging its corners.
5. Save annotations: After you have annotated all the objects in an image, you can save the annotations by clicking the “Save” button. LabelImg supports saving annotations in PASCAL VOC XML format, which is widely used for training object detection models.
6. Repeat for all images: Repeat the above steps for all the images that you want to annotate. When you have finished annotating all the images, you will have created a high-quality dataset that you can use to train object detection models.