Digital photoelasticity requires demodulating stress fields, wrapped into color fringe patterns. As an alternative to traditional methods, deep convolutional neural networks are trained to recover stress maps from isochromatic images related to different orientations of a polarized camera, reaching high precision in different analytical models.