Summary

This article discusses an auto-labeling technique that can optimize data labeling speed in object detection tasks.

Abstract

The article titled "Speed up Data labeling Process?" highlights the importance of data labeling in object detection tasks. It explains that incorrect labeling can lead to poor results on testing data. The article then discusses different tools for data labeling, including online tools like Roboflow, V7 Labs, and offline tools like labelImg, labelme, and labelstudio. The article also introduces the concept of auto-labeling, which can save time and reduce the manual effort required for data labeling. The author suggests labeling 25% of the original data with an online or offline labeling tool, training an object detection model on that data, running the detector weights on the remaining 75% of the data, and then checking every auto-labeled image from the detector in the labeling tool to correct any wrong detections. The author estimates that this method can save up to 30% to 35% of the time required for data labeling.

Opinions

Data labeling is a time-consuming task that requires accuracy to ensure good results on testing data.
Online and offline tools for data labeling have their own advantages, and the choice between them depends on the user's preferences and requirements.
Auto-labeling is an effective method to reduce the time consumed in data labeling, and it can save up to 30% to 35% of the time required.
It is essential to add all types of images with different variations from the original data in the initial 25% of the data for labeling to help the detector learn more about the data and return fewer mistakes during auto-labeling.

Speed up Data labeling Process?

Data Labelling is the process to tell 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 will be 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 Data Labeling Process using Auto-Labeling.

Different Tools for Data Labeling.

The labeling process is very important and necessary for every object detection and objects segmentation task. There are many online and offline platforms that are providing services for labeling custom data in a quick and efficient way. The online tools include Roboflow, V7 Labs, etc. The offline tools include labelImg, labelme and labelstudio.

Online and offline tools, everyone has their own advantages.

If you have already 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 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 says, each image is taking 30 seconds on average for labeling objects, that means your (.5 * 10,000) 5k minutes will utilize only for data labeling, which is approximately 83 hrs. (3.5 days).

So here the idea of auto-labeling comes to mind, the main purpose of auto-labeling is to reduce the time consumed in data labeling. The idea of the auto-labeling is explained in mentioned steps below,

Label 25% of the original data with an online or offline labeling tool, then train the object detection model on that data.
After training run the detector weights on the other 75% of the data and save detection results in the labeling format you need.
Check every auto-labeled image from the detector in the labeling tool and correct it if some detections are wrong.”

Note: Make sure, to add all types of images with respect to different variations (light, image quality, etc) from the original data in 25% of the data, on which you want to do labeling at the start. This will help the detector to learn more about the data, which will return fewer mistakes (false positive) during auto-labeling.

~(30% to 35%) of your time will be saved if you will follow auto labeling. There is no exact percentage, but after a lot of experiments with the utilization of the auto-labeling technique, I found this number.

That is all regarding “Speed up the Data labeling Process?”

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About Authors

Muhammad Rizwan Munawar has 2.5 years of experience working in Computer Vision and Software development. Currently, he is working as Software Engineer improving products and services for customers by using retail analytics, standing up big-data analytical tools, creating and maintaining models, and onboarding compelling new data sets. LinkedIn Profile | Consultation with Me | My Services
Muhammad Zahid Hussain has done his Ph.D. in AI focusing on industrial defect detection. Currently a lecturer at the University of Huddersfield in the computer science department. His research was focused on the detection of various faults in particular Micro-cracks forming on the surface of Photovoltaic (PV) cells because of mechanical and thermal stress. LinkedIn Profile

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