In the example above there will be 2 grid cells with non-empty training data and 18 with empty. The grid cells with object centers contained within them have a ground truth bounding box that includes a perimeter around the entire object, not just those parts of the object within the grid cell that contains its center. The grid cells that don’t contain an object’s center will have no information about any objects. During training, incorrect grid cell center and bounding box shape predictions are penalized by the cost function. If training is successful, the network learns to predict objects in the locations and grid cells where they actually are, and to predict nothing in locations where they are not. Sometimes the training goes well but at prediction time mistakes are made. Non-max suppression attempts to filter out, or suppress, lower confidence, ie non-max, false positives.
Does this help?