  We present a new training set for estimating empirical photometric redshifts
of galaxies, which was created as part of the 2dFLenS project. This training
set is located in a 700 sq deg area of the KiDS South field and is randomly
selected and nearly complete at r<19.5. We investigate the photometric redshift
performance obtained with ugriz photometry from VST-ATLAS and W1/W2 from WISE,
based on several empirical and template methods. The best redshift errors are
obtained with kernel-density estimation, as are the lowest biases, which are
consistent with zero within statistical noise. The 68th percentiles of the
redshift scatter for magnitude-limited samples at r<(15.5, 17.5, 19.5) are
(0.014, 0.017, 0.028). In this magnitude range, there are no known ambiguities
in the colour-redshift map, consistent with a small rate of redshift outliers.
In the fainter regime, the KDE method produces p(z) estimates per galaxy that
represent unbiased and accurate redshift frequency expectations. The p(z) sum
over any subsample is consistent with the true redshift frequency plus Poisson
noise. Further improvements in redshift precision at r<20 would mostly be
expected from filter sets with narrower passbands to increase the sensitivity
of colours to small changes in redshift.
