Bounding Box Annotation
A simple outline around the object and tagging them with the desired labels can be a feeding data for much hungrier Algorithms which is ready to take as much data as possible to get trained, these imaginary boxes drawn around the object and to classify them with tags for the algorithms to detect them and store them for future reference is one of the key aspects of any Machine Learning Process.
To put this in simple words, bounding boxes are used to describe the object in the simplest form, which in turn helps the program to pick data from the memory and provide the best possible output.
Most of this task is done using images, since images from real-life scenarios are easier to generate and process, images serve as an eye for machine learning, when an input image is fed, the pixel coordinates are used to identify objects. Imagine we have a traffic light system, which needs to detect ambulance alone using the camera and realign signals to clear the road by opening up the signals in the right direction, we need to train the algorithm using object detection for this to work perfectly, too much deep learning in object detecting different ambulances will be required.
i-ConsAI can help drawing bounding boxes based on your requirement for any type of images, output can be delivered in XML, JSON, or TXT format, if there is any specific output type is requested, we can handle that as well.