Landmark: Landmark annotation comes in handy for facial and emotional recognition, human pose estimation and body detection. Different Types of Data Annotation Image Annotation Image Classification Object Recognition & Object Detection Text Annotation Sentiment Annotation Intent. While polygons just choose some objects of interest, with segmentation, layers of alike objects are labeled until every pixel of the picture is done, which leads to better results of detection. The most common types of annotation are text, image, audio, and video. items is an object array that defines individual annotation items contained in one annotation group. Segmentation: Segmentation is similar to polygons but more complicated. Types of data annotation Depending on what you want your AI to accomplish and what data sources it will need, different types of annotation should be used. It is mainly used to annotate lanes on road images.ģD Cuboids: The 3D Cuboids are utilized to measure the volume of objects which can be vehicles, buildings or furniture. Polyline : Polylines usually serve as a solution to reduce the weakness of bounding boxes, which usually contain unnecessary space. The boundaries drawn around the objects can give an exact idea about the shape and size, which can help the machine make better predictions. Polygon: When it comes to irregular shapes like human bodies, logos or street signs, to have more precise outcome, Polygons should be your choice. The data labelled by using bounding boxes are used in various industries, and most used in automotive vehicle, security and e-Commerce industries. Choosing what type(s) to use mainly depends on what kind of data and annotation tools you are working on.īounding Box : As you can guess, the target object will be framed by a rectangular box.

In our AI training projects, we use different types of annotation.

Needless to say, AI Annotation is of paramount importance to Machine Learning (ML), as ML algorithms need (quality) annotated data to process. Data Annotation is the process of labelling the training data sets, which can be images, videos or audios.

Different types of annotations