Labeling in ML

What is labeling?

Data labeling is the act of recognizing raw data such as video, text files, and photos and adding additional relevant and useful labels to offer context so that an ML model may learn. Labels could identify whether a photograph has a bird or an automobile, which words were said in an audio recording, or whether an x-ray image contains a tumor. A multitude of use cases, such as computer vision, natural language processing, and speech recognition, need data labeling.

Applications of labeling


To construct your training dataset for natural language processing, you must first manually pick relevant chunks of text or classify the text with particular labels. You could wish to determine the sentiment or intent of a text blurb, categorize proper nouns like locations and people, and recognize text in photos, PDFs, or other files, for example. To do so, put bounding boxes around text in your training dataset and then manually transcribe the content. Sentiment analysis, entity name identification, and optical character recognition are all done using natural language processing models.

Computer vision

To develop your training dataset for a computer vision system, you must first label pictures, pixels, or key spots, or establish a boundary that entirely encloses a digital image, known as a bounding box. For example, you may categorize photographs based on their quality (such as product vs. lifestyle images) or content (what’s really in the image), or you can segment them down to the pixel level. This training data may then be used to create a computer vision model that can automatically categorize pictures, recognize the position of objects, identify important spots in an image, and segment an image.


Audio processing translates all types of sounds into a structured format that may be utilized in machine learning, such as speech, wildlife noises, and building sounds. Audio processing frequently necessitates first transcribing it into written language. By adding tags and classifying the audio, you may have access to more information about it. This audio will be used as your training dataset.

How does it work?

The majority of effective machine learning models today use supervised learning, which uses an algorithm to translate a single input to a single output. A labeled collection of data from which the model may learn to make the right judgments is required for supervised learning to operate. Generally, data labeling begins with people making judgments about an unlabeled piece of data. Labelers may be required to tag all of the photos in a dataset when the question “does it contain cat” is true, for example. The tagging might be as basic as no/yes or as detailed as identifying the individual pixels in an image that correspond to a particular cat.

In a process known as “model training,” the ML model employs human-provided labels to discover the underlying patterns. As a consequence, you’ll have a trained model that you can use to create predictions based on fresh data.

A correctly annotated dataset that you utilize as the objective standard to train and test a particular model is commonly referred to as “ground truth” in machine learning. Because the quality of your trained model is determined by the precision of your ground truth, it’s critical to invest the time and resources necessary to achieve extremely accurate data labeling.


Large amounts of high-quality training data are used to build successful machine learning models. However, obtaining the training data required to develop these models may be costly, time-consuming, and difficult. The bulk of today’s models rely on humans to manually categorize data in such a manner that the model can learn how to make proper conclusions. Labeling may be made more efficient by utilizing a machine learning model to label data automatically to address this difficulty.

An ML model for data labeling is initially trained on a portion of your original data that has been tagged by humans in this process. The labeling model will automatically assign labels to the raw data if it has high confidence in its conclusions based on what it has learned so far. Where the labeling model’s confidence in its conclusions is low, the data will be passed to humans to label. The labeling model is then given the human-generated labels to learn from and enhance its capacity to automatically classify the next collection of raw data. The model can automatically categorize more and more data over time, significantly speeding up the development of training datasets.