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We present the MaNGA PyMorph photometric Value Added Catalogue (MPP-VAC-DR17)and the MaNGA Deep Learning Morphological VAC (MDLM-VAC-DR17) for the finaldata 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 andS\`ersic+Exponential fits to the 2D surface brightness profiles of the MaNGADR17 galaxy sample in the $g$, $r$, and $i$ bands (e.g. total fluxes, halflight radii, bulge-disk fractions, ellipticities, position angles, etc.). TheMDLM-VAC-DR17 provides Deep Learning-based morphological classifications forthe same galaxies. The MDLM-VAC-DR17 includes a number of morphologicalproperties: e.g., a T-Type, a finer separation between elliptical and S0, aswell as the identification of edge-on and barred galaxies. While theMPP-VAC-DR17 simply extends the MaNGA PyMorph photometric VAC published in theSDSS Data Release 15 (MPP-VAC-DR15) to now include galaxies which were added tomake the final DR17, the MDLM-VAC-DR17 implements some changes and improvementscompared to the previous release (MDLM-VAC-DR15): namely, the low-end of theT-Types are better recovered in this new version. The catalogue also includes aseparation between Early- or Late-type Galaxies (ETG, LTG), which classifiesthe two populations in a complementary way to the T-Type, especially at theintermediate types (-1 < T-Type < 2), where the T-Type values show a largescatter. In addition, $k-$fold based uncertainties on the classifications arealso provided. To ensure robustness and reliability, we have also visuallyinspected all the images. We describe the content of the catalogues and showsome interesting ways in which they can be combined.

SDSS-IV DR17: Final Release of MaNGA PyMorph Photometric and Deep Learning Morphological Catalogs