so I was looking at YOLO and I read several blogs online, but one concept I'm having trouble understanding is why do we want to divide the image into different grids, and then predict the bounding box outputs for all of these cells?
Also, if an object is outside the grid, then how does this grid predict that object? I thought at each grid cell, we basically do 'Convolution Implementation of sliding window' which was explained in Andrew Ng Deep Learning course on coursera, which basically means that network looks at a particular grid cell, and localizes where the image might be. So if image is outside the grid, how is it possible(cause it only looks at things inside the grid cell)? I'm confused about the whole thing... if anyone can explain in simple terms I'd greatly appreciate it!