Types of Data Annotation: How are they Helping ML Models

In the journey to make machines learn, researchers feed them with datasets. However, training a machine learning model involves complex and clustered tasks. As a way to ease it, data annotation and its significant types make their debut.

Bounding Boxes

This technique is used to identify the location of the object. This method of data annotation uses the x and y-axis coordinates in both the upper-left and lower-right corners of the rectangle. Its primary purpose is to detect the locations and objects. 

Semantic Segmentation

This type of annotation provides a pixel-wise annotation that assigns every pixel of the image to a separate class. Each pixel holds a semantic sense. Semantic segmentation is mostly used for training models for self-driving cars. 

3D cuboids

This type of data annotation is very similar to bounding boxes, except this provides extra information about the depth of the object. 3D cuboids are machine learning technology that can be trained to provide a 3D representation of the object. 

Polygonal Segmentation

Polygonal segmentation is useful for autonomous driving. It allows annotators to define the side of the roads, sidewalks, define objects that are obstructed, and other services. It is used to identify complex polygons to determine the shape and location of the object. 

Entity Annotation

Entity annotation is used for marking unstructured sentences with relevant information, which is understood by a machine. Entity datasets can be trained through a machine learning model to understand the structure and meaning behind a piece of text. 

Text Annotation

As more and more businesses are moving to the digital mode of working, the collection of data in form of text has intensified. Businesses routinely use text annotation for a wide range of annotations like sentiment, intent, and query.

Audio Annotation

The audio annotation takes an in-depth step to transcribe and time-stamp the speech data, including transcription of specific pronunciation and intonation. By using audio annotation, companies gather telephonic audio data, transcribe them to dialogs with speech recognition models, and use NLP to comprehend every conversation.

Image annotation

 Many technologies including computer vision, robotic vision, facial recognition, etc, rely on image annotation to label and interpret image forms. To train the models with image data, metadata must be assigned to the images in form of identifiers, captions, or keywords. 

Lines and splines

This type of data annotation is significant to train the data for autonomous vehicles. Autonomous vehicles need to know the roads and places to take their trip. Therefore, lines and spines help self-driving vehicles detect and recognize lanes to make the journey sophisticated and safe.

Mapping

Mapping means converting a text from one language to another, full text to the summary, the question to answer, raw data to normalize data, etc. It helps easily convert texts that could be fed in a model to train it.