What is the benefit of using multiple anchor boxes with the same positions in a single-shot multi-box detector model, like YOLO?
In particular, I notice Google's BlazeFace model does this. If the purpose of an anchor box in a multi-box detector is to provide the model with priors hinting at different possible sizes and positions for a detected object, then what use is providing multiple anchors that provide the same hint?
To consider the BlazeFace example, that model is an SSD-style model but it is trained for detecting multiple faces and facial key points at various size scales. Its authors document it at length and Google ships pretrained weights as part of their MediaPipe product.
Like other SSD-style models, BlazeFace uses anchor boxes which define rects which are used as a starting point for the rects of recognized faces. So what's the value of configuring the network with hundreds of duplicate anchor boxes?
Do the duplicate boxes provide more model capacity simply by representing more weights to be trained, even if they represent training a kernel that applies to exactly the same image?