  Galaxy morphology is a key parameter in galaxy evolution studies. The
enormous number of galaxies which current and future surveys will observe
demand of automated methods for morphological classification. Supervised
learning techniques have been successfully used for the morphological
classification of galaxies from different datasets, including Sloan Digital Sky
Survey (SDSS), Mapping Galaxies with Apache Point Observatory (MaNGA) or Dark
Energy Survey (DES). With these proceedings, we release the morphological
catalogue for a sample of 670,000 SDSS galaxies based on the deep learning
models trained on SDSS RGB images with morphological labels from human-based
classification catalogues. The released catalogue includes binary
classifications (early-type versus late-type, elliptical versus lenticular,
identification of edge-on and barred galaxies) plus a T-Type. The
classifications also include k-fold based uncertainties. This is, as of today,
the largest catalogue including a T-Type classification. As an example of the
scientific potential of this classification, we show how the location of the
galaxies in the star formation - stellar mass plane (SFR-M$^{*}$) depends on
morphology. This is the first time the SFR-M$^{*}$ relation is combined with
T-Type information for such a large sample of galaxies.
