  The morphological diversity of galaxies is a relevant probe of galaxy
evolution and cosmological structure formation. However, in large sky surveys,
even the morphological classification of galaxies into two classes, like
late-type (LT) and early-type (ET), still represents a significant challenge.
In this work we present a Deep Learning (DL) based morphological catalog built
from images obtained by the Southern Photometric Local Universe Survey (S-PLUS)
Data Release 3 (DR3). Our DL method achieves an precision rate of 98.5$\%$ in
accurately distinguishing between spiral, as part of the larger category of
late type (LT) galaxies, and elliptical, belonging to early type (ET) galaxies.
Additionally, we have implemented a secondary classifier that evaluates the
quality of each galaxy stamp, which allows to select only high-quality images
when studying properties of galaxies on the basis of their DL morphology. From
our LT/ET catalog of galaxies, we recover the expected color--magnitude diagram
in which LT galaxies display bluer colors than ET ones. Furthermore, we also
investigate the clustering of galaxies based on their morphology, along with
their relationship to the surrounding environment. As a result, we deliver a
full morphological catalog with $164314$ objects complete up to $r_{petro}<18$,
covering $\sim 1800$ deg$^2$, including a significant area of the Southern
hemisphere that was not covered by previous morphology catalogues.
