Explore some of our successfully completed projects
INDUSTRY: FINANCE - The images below show a text classification project, this project was carried out on the Prodigy platform where each document was classified into various classes like header, sub-header, paragraph, table e.t.c., with the bounding box tool.
INDUSTRY: AGRICULTURE - The image below is the annotation of plants for plant detection purposes in the agriculture industry.
INDUSTRY: RETAIL - This is an object detection project. Different shelves in stores where we labelled. This project had 3 classes, when a shelf is out of stock, in stock, or no products available. This was done on the Label Studio platform with the bounding box tool.
INDUSTRY: TRANSPORT - In most cases, annotations in this industry are video annotations. These videos are split into frames for easy annotation. It can be a frame-by-frame object detection project or an object tracking project.
INDUSTRY: SPORTS- This is an object detection project where we annotated poker cards being used for the creation of a poker game application.
INDUSTRY: AGRICULTURE - This project as seen is an object detection project where we annotated potato roots for the purpose of mechanised sorting. This project would make the production process for the client much faster and easier.
INDUSTRY: AGRICULTURE - This was a project that would make the harvesting process for the client easier. We annotated the ripe fruit from the unripe fruit using the colour of the fruits. In this project, there were only 2classes to be annotated. And we were glad to have contributed to the easy harvest for this
INDUSTRY: WASTE MANAGEMENT -This is an object detection project for a waste management company carried out using the VOTT annotation platform. The purpose of this project was mechanically clean up the environment using artificial intelligence. In this project, we annotated different kinds of waste. The project had 52 different classes.
INDUSTRY: AVIATION - This is a sentiment analysis project where we annotated Twitter comments and categorized them into 3 different classes: positive, negative, or neutral comments.
INDUSTRY: AGRICULTURE - The first 2 images on the list were from a project carried out for a client using the LabelMe platform. This project was for the purpose of identifying individual flowers and fruits, and areas of leaves, ground, stem, weed, or other objects. The annotation was collectively done to cover as many image areas as possible
INDUSTRY: AGRICULTURE -The next 2 images were images from a project carried out for rice grain detection purposes and the annotations were exported in CSV and JSON format.
INDUSTRY: FASHION - This is an object tagging project where we tagged a fashion image according to what can be seen in the image for search engine purposes.