Why You Should Update To Automatic Data Labeling.

In today’s fast-paced world, vast industries generate millions of raw data points every minute, all of which require accurate labeling. However, the process of manual data labeling is fraught with potential complications that can prove costly to businesses. In this article, we will explore the reasons why firms should reconsider their reliance on manual data labeling and embrace a more promising, precise, and efficient solution: automatic data labeling. By exploring the pitfalls of manual data labeling and highlighting the advantages of its automated counterpart, we will uncover the compelling case for making the update.

The Pitfalls of Manual Data Labeling:

Subjectivity and Bias:

What would happen if we asked a group of people to label the same data? The data would be prescribed with a different label each time. This is because people’s interpretations differ and accordingly, there will be inconsistencies in the process of data labeling. Furthermore, if an individual were to have an underlying motive for falsifying or labeling data in a biased manner, then this will result in further inaccuracies down the line.

High-Cost and Time-Consuming:

When it comes to labeling large datasets, the traditional method of data labeling will not be sustainable as it is expensive and time-consuming. Think of the number of staff a firm should employ and train to label data aptly, and how long and slow the data labeling process be, causing delays for the training and evaluation steps.

Limited Scalability:

Scaling manual data labeling to handle large datasets or frequent changes can be challenging. Think of it this way. The larger the dataset, the more difficult it is to maintain consistency and quality data, thus bringing forth the hurdle of resources constraints. 

Susceptibility To Error:

When it comes to human labor, error is always a possibility, and for businesses, this translates to costs – and manual data labeling is no exception. Mistakes and errors can occur for numerous inevitable reasons such as that due to human oversight and fatigue. Even with rigorous quality control in place, human errors can prevail, leading up to disruptions and inaccuracies. 

The New Beginning: Automatic Data Labeling

Automatic data labeling is a process in which Machine Learning algorithms are created and used to assign relevant labels to data, automatically. This efficient alternative grants more than speed and accuracy. It offers the ability to scale up and handle much larger datasets, in a cost-effective manner. The algorithm generated also allows for consistent labeling across datasets, eliminating the area for human error.

Case: Automotive Industry

Automatic Data Labeling is experiencing a surge in demand across numerous industries due to the ongoing challenge of labeling large volumes of data. This is highly evident in the automotive industry; at a time where autonomous vehicles are emerging.

How does it work? With built-in sensors, various real-life data will be captured, including those related to the road markings and conditions, pedestrians and traffic signs, in order to ultimately build a visual representation of the real world with the car in it. There are two fundamental sensors: the proprioceptive sensors, and the exteroceptive sensors.

Source: Adobe Stock.

In short, proprioceptive sensors such as Inertial Measurement Units (IMUs), are responsible for capturing data regarding the autonomous car itself and its conditions; while exteroceptive sensors such as cameras and radars, are responsible for capturing data regarding the environment and the changes that occur.

Source: Thomas Tracey.

Data on its own are meaningless, and assigning meaning to data with relevant annotations is highly crucial, especially to build the utmost accurate visual representation of the car and its environment. Think of it this way. A camera on its own will pick up on infinite objects, not knowing what they are or what they intel. A camera only sees, it does not speak. However, incorporate it with artificial intelligence and computer vision softwares, and you will have a model that will read you an entire story. And this begins with automatic data labeling.

Source: HSE University.

Automatic data labeling is vital in the industry of autonomous vehicles due to its sensitive nature – as it deals with people and their wellbeing- in addition to the large volumes of data being obtained concerning the ever changing environmental and vehicle conditions, and hence rapid and accurate data labeling is a must.  

Making The Update: Tuba.ai

Begin your journey with Tuba.ai, a No-Code/Low-Code platform that allows you to build your custom AI Computer Vision Applications effortlessly through its many tools, including the Automatic Image Labeling SDK.  

With this SDK, you will get the power to label an infinite number of data, faster, using fewer resources, and at lower costs. 

Here is a Demo on HuggingFace platform that shows how Automatic Data Labeling Works:

Try the Demo out for yourself!

Sign-up to Tuba.ai now and embark on the journey it paves for you.

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