What is Data Labeling and How to Do It Efficiently

Data is the currency of the future. With technology and AI slowly seeping into our everyday lives, data and its proper use can cause a significant impact in modern society. Accurately annotated data can be used effectively by ML algorithms to detect problems and propose workable solutions, thus making data annotation an integral part of this change. In today’s article we will talk about what is data labeling and how to do it efficiently. 

What is Data Labeling? 

Data labeling refers to the process of adding tags or labels to raw data such as images, videos, text, audio, and 3D Point Cloud.These tags form a representation of what class of objects the data belongs to and helps a machine learning model learn to identify that particular class of objects when encountered in data without a tag. This data is used for training ML algorithms and has been collected to be fed to a machine learning model to help the model learn more about the data.

Training data can be of various forms, including images, voice, text, or features depending on the machine learning model being used and the task at hand to be solved.It can be annotated or unannotated. When training data is annotated, the corresponding label is referred to as ground truth. It is difficult to find a data annotation partner that can perform all data labeling correctly and we wrote about this in a previous blog article

Unlabeled Data vs Labeled Data

The training dataset is completely dependent on the type of machine learning task we want to focus on. Machine/Deep Learning algorithms can be broadly classified on the type of data they require in three classes: 

  • Supervised learning – Supervised learning, the most common type, is a type of machine learning algorithm that requires data and corresponding annotated labels to train. Popular tasks like image classification and image segmentation come under this paradigm.The typical training procedure consists of feeding annotated data to the machine to help the model learn, and testing the learned model on unannotated data.
  • Unsupervised learning – In unsupervised learning, unannotated input data is provided and the model trains without any knowledge of the labels that the input data might have. Common unsupervised algorithms of training include autoencoders that have the outputs the same as the input. Unsupervised learning methods also include clustering algorithms that groups the data into ‘n’ clusters, where ‘n’ is a hyperparameter.
  • Semi-supervised learning – In semi-supervised learning, a combination of both annotated and raw data is used for training the model.While this reduces the cost of data annotation by using both kinds of data, there are generally a lot of severe assumptions of the training data made while training. Use cases of semi-supervised learning include Protein sequence classification and Internet content analysis.

Common Types of Data Labeling 

A data labeling company will provide various types of data labeling services depending on the requirements of the project. Here are some common AI domains and their respective data annotation types: 

  • Image data labeling – This type of image labeling includes things like 2D bounding boxes, polygon annotation, semantic segmentation and  many other types of labeling that make the images readable for machines. 
  • Text data labeling – Text labeling involves data annotators adding particular metadata to certain words or phrases to train the ML algorithms on the meaning and context of each word or phrase. A common use case is sentiment analysis where humans tag texts with a particular sentiment i.e. angry, happy etc. Text labeling is used for content moderation on websites like Facebook and other forums to make sure all user content is within th rules of the platform. 
  • Audio data labeling – This involves classifying components of audio that come from people, animals, the environment, instruments, and so on. Audio labeling is necessary for AI tools that need to identify particular sounds for example, the sounds produced by endangered animals to track their location and monitor population growth.
  • Video data labeling – With video labeling, metadata is added to video datasets. This information can include specifics on people, locations, objects, and more. This type of labeling is used to train ML algorithms to monitor user generated content on platforms like YouTube. 
  • 3D point cloud labeling – 3D Point clouds are produced by LiDARs and create a visual representation of how an AI system sees the physical world. 3D Point Clouds are often used for developing autonomous vehicles to train the ML algorithms to identify all of the objects on the road and allow the vehicle to make intelligent decisions on the road. 

How Does Data Labeling Work? 

Now that we know what data labeling is in AI, let’s now try to understand how it works. Data labeling processes work in the following chronological order: 

  1. Data collection: Raw data is collected which will be used to train the model. This data is cleaned and processed to form a database which will be used as input training data for the model. 
  2. Data tagging: Various data labeling approaches are used to tag the data and associate it with meaningful context that the machine can use as ground truth. Sometimes it is possible to have automated data annotation while other times the annotations will need to be done manually. 
  3. Quality assurance: The quality of data annotations is often determined by how precise the tags are for a particular data point and how accurate the coordinate points are for bounding box or keypoint annotations. QA algorithms like the Consensus algorithm and Cronbach’s alpha test are very useful for determining the average accuracy of these annotations.

Best Practices for Data Labeling

With supervised learning being very popular, data labeling finds itself in almost every workplace that talks about AI. Here are some of the best practices for data labeling for AI to make sure your model isn’t crumbling due to poor data:

  1. Proper dataset collection and cleaning: While talking about ML, one of the primary things we should take care of is the data. The data should be diversified but extremely specific to the problem statement. Diverse data allows us to infer ML models in multiple real-world scenarios while maintaining specificity reduces the chances of errors. Similarly, appropriate bias checks prevent the model from overfitting to a particular scenario.
  2. Proper annotation approach: The next most important thing for data labeling is the assignment of the labeling task. The data to be annotated has to be labeled via in-house labeling, outsourcing, or via crowdsourcing means. The proper choice of data labeling approach undertaken helps keep the budget in check without cutting down the annotation accuracy.
  3. QA checks: Quality Assurance checks are absolutely recommended for data that has been pre-labeled or labeled. QA checks prevent incorrect labels and improperly labeled data from being used as training data for ML algorithms. Improper and imprecise annotation can easily act as noise and lower the quality of an otherwise dependable ML model.


Trust Mindy Support With All of Your Data Labeling Needs

In this article we learned all about data labeling and why is data labeling important. If you are looking for AI data labeling services, you can trust Mindy Support to get the job done. We are a global company for data annotation and business process outsourcing, trusted by several Fortune 500 and GAFAM companies, as well as innovative startups. With 9 years of experience under our belt and offices and representatives in Cyprus, Poland, Romania, The Netherlands, India, and Ukraine, Mindy Support’s team now stands strong with 2000+ professionals helping companies with their most advanced data annotation challenges.


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    September 28th, 2022

    Mindy News Blog