1. A computer-implemented method, comprising: determining a set of classes corresponding to a type of item; obtaining a set of captured images including representations of items having labels corresponding to those classes and being in real world settings; obtaining a set of catalog images including representations of items against a solid color background and having labels corresponding to those classifications; obtaining a set of background images including representations of at least some of the real world settings; determining binary image masks identifying background regions, of the set of catalog images, having the solid background color, remaining portions of the set of catalog images representing item portions of the set of catalog images, pixels proximate edges of the items represented in the item portions including edge artifacts; processing edge regions of the binary image masks to reduce a number of the pixels corresponding to the edge artifacts included in the item portions; blending selected item portions, selected using the binary image masks, into the background images to create a set of synthesized training images; using the set of synthesized training images and the set of captured images to train a convolutional neural network for the type of item; receiving a query image including a specific representation of the type of item; processing the query image using the convolutional neural network to determine a corresponding classification for the specific representation; and providing information corresponding to the determined classification in response to the query image.