So far what I understood about YOLO, it expects training image should be divided in to fixed grid, where each grid has Label like P(object present or not), object bounding box, object classes. Similarly it will return the same output for each image prediction.
If it's correct, I'm not able to map those images for both cases training and prediction where some objects are the part of multiple grids. During training we provide bounding box information corresponding to particular(single) grid only, how it clubbed the bounding box info of multiple grids?
Note: Non-max suppression is again confusing, if it is related with it.