#023 CNN Object Detection Master Data Science

There's a huge disadvantage of sliding windows detection which is the Computational cost, because we're cropping out so many different square regions in the image and running each of them independently through a \(convnet \). If we use the very course stride, a very big stride, very big step size, then that would reduce the number of windows we need to pass through the \(convnet \), but that coarser granularity may hurt performance, whereas if we use a very fine granularity or a very small stride then the huge number of all these little regions we're passing through the \(convnet \) means that there's a very high computational cost. Before the rise of neural networks people used to use much simpler classifiers, like a simple linear classifier overhand engineer features in order to perform object detection, and in that error because each classifier was relatively cheap to compute it was just a linear function, sliding windows detection ran properly, it was not a bad method, but with \(convnets \) now running a single classification task is much more expensive and sliding windows this way is infeasible slow. Unless we use a very fine granularity or a very small stride we end up not able to localize the objects that accurately within the image as well.