Hi I am working on a project which requires the You Only Look Once algorithm in order to classify and localise objects within images. I have to prepare my dataset (which has 2 classes, and predicts 6 objects per grid cell, and the 448 * 448 image is split into a 7*7 grid). What would be a viable approach to do that? I found this code, found in this article. However I do not understand why he has done what he has done, e.g why is he specifically checking the 24th element of the “box”, and so what element of the box would I have to check? Is there any tutorial running through that? Would it be possible for someone to explain or even adapt his approach to fit my dataset?
FYI: I am coding the YOLO algorithm from scratch