Blog - SentiSight.ai
January 23, 2022

Top 5 Image Annotation Tools and Their Use Cases

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.
December 6, 2021

Computer Vision Applications in Agriculture

Computer vision is a subfield of artificial intelligence rapidly changing the world around us. Its main goal is to process, analyze and interpret visual data just like the human brain can. 
October 11, 2021

Labeling within the company

users. This article will guide you through the labeling processes within our company, showcasing how the platform can be used to solve real-world problems.
September 1, 2021

Image annotation project management using SentiSight.ai

Image labeling, also commonly known as image annotation, is one of the most important processes when working in the Artificial Intelligence field. It is the stepping […]
August 4, 2021

The use of AI Image Recognition in Medicine

It is hard to imagine the field of modern medicine without the development and harnessing of technology. X-rays, MRIs and other solutions have been widely utilized in various medical fields to improve diagnosis accuracy and efficiency, whilst also minimizing instances of human error. 
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