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The essential features of data annotation

Data labels come in various types, based on the items or things that require annotation. 

data annotation

Data is the most crucial part of machine learning and deep learning algorithms to boost artificial intelligence functions. However, computers do not recognize all forms of data. Thus, it is vital to put labels on other data types, such as audio, video, text, and images.

Data labeling, which other people call data annotation, is tedious work that requires skills, expertise, dedication, and attention to detail. 

The task seems easy when looking at data annotation because the annotators only add labels to things and objects. However, the process is not easy, as you will see below. 

A brief look into data annotation

Data annotation adds labels or identifying tags to various data formats, such as videos, images, and text. The labels are placed on the region or area of interest in data annotation, particularly for videos and images.

The labeler adds relevant information and assigns them to a specific class to annotate text data. Data annotation is a vital part of machine learning.

Data annotators use purpose-designed platforms, such as what you will find at https://dataloop.ai/solutions/data-annotation/, to ensure the quality and accuracy of the data labels they process. 

Various types of data labels

Data labels come in various types, based on the items or things that require annotation. 

  • Image annotation means adding labels to images. Labeling images ensures that a machine learning algorithm can recognize the annotated item as a distinct class or object within a specified image. The annotation type can involve the creation of bounding boxes to detect objects, or segmentation masks, for instance, or semantic segmentation to distinguish the objects of different classes. This type of annotation is often used for artificial intelligence systems like drones, skin cancer detection tools, and autonomous cars. 
  • Bounding box draws a rectangle around a specific image or object. The edges of a bounding box must touch a labeled object’s outermost pixel.
  • 3D cuboids are similar to a bounding box but take into account the object’s depth, used for annotating flat planes that need navigations, such as airplanes, things, and cars that need robotic grasping. 
  • Polygons create a more precise bounding box, normally at the pixel level, effectively eliminating the unintentional inclusion of parts of other objects not included in the class. Polygons prevent confusion and misclassification of objects. 
  • Keypoint tools  create several points in the image or video and is very effective when detecting hand gestures, motion tracking, and facial landmarks. Moreover, annotators use them in combination with other tools to create a point map, for example, when defining an object’s pose. 
  • Polyline tools create a sequence of points that are joined by lines. The tool is often used for labeling traffic signs, lane markings, and roads. 

Video annotation

With video annotation, annotators label clips or sections in videos to identify, detect, or classify objects within a frame. 

Text annotation

With this labeling type, the annotator adds relevant information into the language data through metadata or labels, such as a word or sentence describing the class.

When handling a data annotation project, you can have a team of highly-skilled labelers and use a robust annotation platform or outsource the project to professional annotators specializing in the service.

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