Why use sliding windows with convolutional neural nets in object detection?
<p>I read that CNNs (with both convolution and max-pooling layers) are shift-invariant, but most object detection methods used a sliding window detector with non-maximum suppression. Is it necessary to use sliding windows with CNNs when doing object detection?</p>

<p>Basically, instead of training the network on small 50x50 patches of images containing the desired object, why not train on entire images where the object is present somewhere? All I can think of is practical/performance reasons (doing forward pass on smaller patches instead of whole images), but is there also a theoretical explanation I'm overlooking?</p>