I am trying to classify whether or not a specific object is in panoramic photos. The issue is, a panoramic photo can be any width, so the input to my neural network can't be fixed in that dimension.
I've been using RNNs (QRNNs to be specific, as I am trying to reduce the number of parameters as much as possible), but they always learn where the object usually is in the image, and then have a really hard time classifying an image with the object in a different place.
I'm looking for something similar to CNNs, where it doesn't have a spatial dependance (or in this case, a temporal dependance?), but it can't have a fixed input width.