YOLO object detection: how does the algorithm predict bounding boxes larger than a grid cell?

Question

I am trying to better understand how the YOLO2 & 3 algorithms works. The algorithm processes a series of convolutions until it gets down to a 13x13 grid. Then i

Answer 1

YOLO predicts offsets to anchors. The anchors are initialised such that there are 13x13 sets of anchors. (In Yolov3 each set has k=5 anchors, different yolo versions have different k.) The anchors are spread over the image, to make sure objects in all parts are detected.

The anchors can have an arbitrary size and aspect ratio, unrelated to the grid size. If your dataset has mostly large foreground objects, then you should initialise your anchors to be large. YOLO learns better if it only has to make small adjustments to the anchors.

Each prediction actually uses information from the whole image. Often context from the rest of the image helps the prediction. e.g. black pixels below a vehicle could be either tyres or shadow.

The algorithm doesn't really "know" in which cell the centre of the object is located. But during trainig we have that information from the ground truth, and we can train it to guess. With enough training, it ends up pretty good at guessing. The way that works is that the closest anchor to the ground truth is assigned to the object. Other anchors are assigned to the other objects or to the background. Anchors assigned to the background are supposed to have a low confidence, while anchors assigned to an object are assessed for the IoU of their bounding boxes. So the training reinforces one anchor to give a high confidence and an accurate bounding box, while other anchors give a low confidence. The example in your question doesn't include any predictions with low confidence (probably trying to keep things simple) but actually there will be many many more low confidence predictions than high confidence ones.