Described is a system for automatic digital photo orientation detection. We leverage online public photos with great content variation to extract effective features with layout information. Classification proceeds using an approximate nearest neighbors approach which scales well to massive training sets, hardly compromising efficiency. We have tested the method successfully on the largest data set to date of nearly 30,000 Flickr photos as well as both difficult and typical consumer usage scenarios. Though limited data are available for comparison across different systems, the proposed system significantly outperforms a state of the art system on a common data set.