Speed up the Data labeling Process?

Data Labelling is the process of telling the model about objects. It will help the model to detect objects on its own. It’s normally considered a key part of any object detection task. If labeling is wrong, no object detection model will be able to correct it and will not be able to provide good results on testing data. The data labeling process is a time-consuming task, In this article, I will discuss an auto-labeling technique that can optimize data labeling speed.

• Different Tools for Data Labeling.
• Speed up the Data Labeling Process using Auto-Labeling.

Fig-1.1: Data Labeling YOLO Series

Different Tools for Data Labeling.

The labeling process is very important and necessary for every object detection and object segmentation task. Many online and offline platforms are providing services for labeling custom data quickly and efficiently. The online tools include Roboflow, V7 Labs, etc. The offline tools include labelImg, labelme and labelstudio.

Online and offline tools, everyone has their advantages.

• If you have already an understanding of the Python packages or you don’t want to install packages on your computer or if you are willing to pay some fee for getting labeling services, then online service is the best, as it will be fast and easy.
• If you are a beginner, or you don't want to pay any fee for online labeling tools, or you want to learn the basics of labeling, then offline tools will help you to learn the what packages and why these are needed for data labeling. To set up a data labeling tool on your computer, you can check my article “How to set up and label data using LabelImg?

Speed up the Data Labeling Process?

Imagine, you have 10k images for labeling supposing 10 objects in each image and your task is,

“Label 10k images for object detection with 3 classes {“Person”, “Head”, “Cars”}.”

Let’s say, each image takes 30 seconds on average for labeling objects, which means your (.5 * 10,000) 5k minutes will be utilized only for data labeling, which is approximately 83 hrs. (3.5 days).