MACHINE LEARNING ANNOTATION

Introduction

Annotations are the labels on your data or records, and annotation is the process of generating them. Machine learning annotation refers to the process of labelling the data either in the form of text, images or even audio. Normally, annotation is done by humans. This way computers can recognize the similar patterns when provided with new data.

For example, when we upload a picture on Facebook, we tag the faces in the picture so when next time we upload a photo, it recognizes the faces and gives tagging suggestions. Similarly, in annotation in machine learning we train the system to learn about different objects on the image such as tree, cat, human etc

Why Annotations For Machine Learning?

Machine learning needs high volumes of data for training, validation, and testing. A machine learning model learns to find patterns in the input. This data input is referred to as training data. As you train your system to form relationships among variables, it’s important to have the right data, structured in the right format.

How Annotated Data Sets Work In Machine Learning?

Many challenging real-world problems can be solved by using deep learning method. Computer vision task makes it happen. With massive training, a deep network can segment and identify the “key points” of every object in the image.

• The more labeled data helps the machine to understand the “key point” easily.