  We present results from a study of the photometric redshift performance of
the Dark Energy Survey (DES), using the early data from a Science Verification
(SV) period of observations in late 2012 and early 2013 that provided
science-quality images for almost 200 sq.~deg.~at the nominal depth of the
survey. We assess the photometric redshift performance using about 15000
galaxies with spectroscopic redshifts available from other surveys. These
galaxies are used, in different configurations, as a calibration sample, and
photo-$z$'s are obtained and studied using most of the existing photo-$z$
codes. A weighting method in a multi-dimensional color-magnitude space is
applied to the spectroscopic sample in order to evaluate the photo-$z$
performance with sets that mimic the full DES photometric sample, which is on
average significantly deeper than the calibration sample due to the limited
depth of spectroscopic surveys. Empirical photo-$z$ methods using, for
instance, Artificial Neural Networks or Random Forests, yield the best
performance in the tests, achieving core photo-$z$ resolutions $\sigma_{68}
\sim 0.08$. Moreover, the results from most of the codes, including template
fitting methods, comfortably meet the DES requirements on photo-$z$
performance, therefore, providing an excellent precedent for future DES data
sets.
