Summary

The website provides a comprehensive guide on labeling images for object detection, detailing the use of an open-source tool called LabelImg.

Abstract

The article "How to Label Images for Object Detection, Step by Step" offers a detailed walkthrough for accurately labeling images, a crucial step in object detection model training. It emphasizes the importance of dedicated labeling for model accuracy and outlines the process of downloading and installing the LabelImg tool, either via Git or direct download. The guide includes instructions on defining custom classes, launching the tool, and using it to label images by drawing bounding boxes and saving the annotations. The author also suggests methods for data augmentation to increase the dataset size and variety, which is critical for robust model performance. The article concludes with advice on the importance of a diverse image set for better model accuracy and invites readers to subscribe for more insights on Python and Data Science.

Opinions

The author stresses that meticulous image labeling is directly proportional to the accuracy of the object detection model.
Using an open-source tool like LabelImg is recommended for its ease of use and accessibility.
Collecting a diverse set of images is advised to enhance the model's ability to generalize across various scenarios.
The author encourages readers to engage further by becoming Medium members and subscribing to a newsletter for ongoing learning in Python and Data Science.
Data augmentation is presented as a valuable technique to artificially expand the dataset when the number of images is limited.

How to Label Images for Object Detection, Step by Step

Step by Step Detailed Guide for Labeling Images

Labelling image is the first and most significant part of object detection. Labelling is indeed a very time-consuming process, but the more dedication you will give in labelling images, the more accurate your model can be. In this story, I will be discussing the complete approach of labelling in detail. Finally, with this article, you will have your labelled data ready for your object detection model.

Here you will need a tool that is open source to label your data. To download the tool, follow the step below.

Downloading Labeling Tool

If you have Git installed in your machine in open Command Prompt in your device and type

git clone https://github.com/pranjalAI/labelImg.git

If you don’t have Git installed then go to https://github.com/pranjalAI/labelImg.git and download the zipped directory.
If you have downloaded directly via the link, then unzip your compressed .zip file.

Installing Dependencies

You need to enter into the “labelImg” folder using command prompt.

After that, you need to install a library called “pyqt5”. Type the below command in your command prompt.

Defining Custom Classes

You need to go to the “labelImg\data” folder; there you will find predefined_classes.txt Here, you can define your custom classes.

Activating the “LabelImg” Tool

In your command prompt, type the following command.

After typing the above command, you will see an interactive window, which is an open-source tool.

You will spend a fair amount of time here, As this will help you getting labelled images and make them ready for object detection.

How to Use this tool

Click on “Open Dir” and select the folder where you have saved your images that you need to label.
Then click on “Change Save Dir” here, you need to select the directory to save your label file. This directory should be different from the image directory.
Now you can use “Create Rectbox” to draw boxes over the images.
Click on the Save button. It will generate a file with the box coordinates.

You can draw multiple boxes in an image. It will ask for a class to assign; select the category that you have defined in the above step.

Finally, you will now have a folder that will image label data with the same name as your image. Your data is now ready for object detection. Still, if you feel that you have less image count, then please follow my guide to Generate data for object detection. Here, I have shown different image and label augment techniques. If You want to know more about the next steps of object detection, then please do follow my other article, Custom Object Detection In Python. Here, I have discussed how you can train your model and can deploy it to the localhost.

Some Closing Advice

Labelling data can be a time-consuming process, but if you want to achieve good accuracy in your model, then you must give a reasonable amount of time in this step. Try to collect as much as the variety of images from different resources. The more variety of images you will have, the better your model will be.

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