I'd say you're not making the problem "harder" - rather, requiring that object detection be orietation-agnostic makes the problem exactly as hard as the problem

I'd say you're not making the problem "harder" - rather, requiring that object detection be orietation-agnostic makes the problem exactly as hard as the problem actually is. Allowing the network to train only on images with a known, fixed, (correct) orientation makes the object detection too _easy_, so the network results will likely fail if you feed it any real-world data. i.e. you should be training your image application with skew/stretch/shrink/rotation/color-pallete-shift/contrast-adjust/noise-addition/etc. applied to all training images if you want it to be useful for anything other than getting a high top-N score on the validation set.