  We present an updated version of MegaZ-LRG (Collister et al.,(2007)) with
photometric redshifts derived with the neural network method, ANNz as well as
five other publicly available photo-z codes (HyperZ, SDSS, Le PHARE, BPZ and
ZEBRA) for ~1.5 million Luminous Red Galaxies (LRGs) in SDSS DR6. This allows
us to identify how reliable codes are relative to each other if used as
described in their public release. We compare and contrast the relative merits
of each code using ~13000 spectroscopic redshifts from the 2SLAQ sample. We
find that the performance of each code depends on the figure of merit used to
assess it. As expected, the availability of a complete training set means that
the training method performs best in the intermediate redshift bins where there
are plenty of training objects. Codes such as Le PHARE, which use new observed
templates perform best in the lower redshift bins. All codes produce reasonable
photometric redshifts, the 1-sigma scatters ranging from 0.057 to 0.097 if
averaged over the entire redshift range. We also perform tests to check whether
a training set from a small region of the sky such as 2SLAQ produces biases if
used to train over a larger area of the sky. We conclude that this is not
likely to be a problem for future wide-field surveys. The complete photometric
redshift catalogue including redshift estimates and errors on these from all
six methods can be found at www.star.ucl.ac.uk/~mbanerji/MegaZLRGDR6/megaz.html
