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 \sim 700 deg ^ { 2 } 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 .