Machine learning is a complex field of artificial intelligence focused on automating analytical model building. The exponential growth of data volume opens up possibilities for advanced data analysis technique research in machine learning model development. Based on allowing models to learn from data, they require minimal human intervention proving their efficiency over human labor.
Image annotation is a process of classifying images and creating labels to describe objects within them. It is a crucial stepping stone in a supervised machine learning project because the quality of the initial data determines the quality of the final model. A mislabeled image could lead to the model getting trained incorrectly, consequently producing undesirable results. To develop a neural network model well, data scientists are collecting vast amounts of data that contains hundreds of images. Therefore, labeling all of them correctly is a tedious, resource-heavy and lengthy process. The more people are working on the same project annotating, the more confusing it can get. Images can get duplicated, mislabeled or not labeled at all. Therefore, having a good management system is a must. To make the image annotation process more efficient programmers have developed numerous data labeling tools that allow for quicker and more precise annotation. One of these powerful tools, called SentiSight.ai is being offered by us.