Importance of Data Annotation in Supervised ML
Aug 25, 2022
Data Annotation
The process of labeling data is known as data annotation. However, having raw data is not sufficient because it is not enough to simply feed a computer enormous amounts of data and expect it to learn to talk.
Data must be collected and presented in such a way that the machine learning model can spot patterns and draw conclusions from them, like identifying the items in a given image, understanding human speech, and performing a variety of other functions.
Data annotation is everywhere, starting with speech recognition platforms, autonomous vehicles, and translation systems.
They all benefit from annotated data sets, and so will you once you finish reading this article!
Image Annotation
Annotating a text or a photo is simply assigning predetermined categories and tags to documents and images, which is used to improve search relevancy and help in the training of chatbots.
Image annotation is the process of labeling images in order to train an AI or machine learning model to perceive and interpret them in the same way that humans do. We can divide picture annotation into two categories: segmentation and classification.
– Image segmentation facilitates image analysis, it separates the image into several segments known as image objects, and we can divide it into three types:
– Instance segmentation is the process of defining entity attributes like position and number.
– Semantic segmentation is the labeling of related items in an image based on features such as size and location.
The combination of semantic and instance segmentation results in panoptic segmentation.
– Classification of images uses predetermined annotated images to determine what an image represents, whereas detection of objects is a more advanced variant of image classification. It is an accurate depiction of the image’s numbers and places.
