  We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17)
and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the final
data release of the MaNGA survey, which is part of the SDSS Data Release 17
(DR17). The MPP-VAC-DR17 provides photometric parameters from S\`ersic and
S\`ersic+Exponential fits to the 2D surface brightness profiles of the MaNGA
DR17 galaxy sample in the $g$, $r$, and $i$ bands (e.g. total fluxes, half
light radii, bulge-disk fractions, ellipticities, position angles, etc.). The
MDLM-VAC-DR17 provides Deep Learning-based morphological classifications for
the same galaxies. The MDLM-VAC-DR17 includes a number of morphological
properties: e.g., a T-Type, a finer separation between elliptical and S0, as
well as the identification of edge-on and barred galaxies. While the
MPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in the
SDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies which were added to
make the final DR17, the MDLM-VAC-DR17 implements some changes and improvements
compared to the previous release (MDLM-VAC-DR15): namely, the low-end of the
T-Types are better recovered in this new version. The catalogue also includes a
separation between Early- or Late-type Galaxies (ETG, LTG), which classifies
the two populations in a complementary way to the T-Type, especially at the
intermediate types (-1 < T-Type < 2), where the T-Type values show a large
scatter. In addition, $k-$fold based uncertainties on the classifications are
also provided. To ensure robustness and reliability, we have also visually
inspected all the images. We describe the content of the catalogues and show
some interesting ways in which they can be combined.
