  Structured beams carrying topological defects, namely phase and Stokes
singularities, have gained extensive interest in numerous areas of optics. The
non-separable spin and orbital angular momentum states of hybridly polarized
Stokes singular beams provide additional freedom for manipulating optical
fields. However, the characterization of hybridly polarized Stokes vortex beams
remains challenging owing to the degeneracy associated with the complex
polarization structures of these beams. In addition, experimental noise factors
such as relative phase, amplitude, and polarization difference together with
beam fluctuations add to the perplexity in the identification process. Here, we
present a generalized diffraction-based Stokes polarimetry approach assisted
with deep learning for efficient identification of Stokes singular beams. A
total of 15 classes of beams are considered based on the type of Stokes
singularity and their associated mode indices. The resultant total and
polarization component intensities of Stokes singular beams after diffraction
through a triangular aperture are exploited by the deep neural network to
recognize these beams. Our approach presents a classification accuracy of
98.67% for 15 types of Stokes singular beams that comprise several degenerate
cases. The present study illustrates the potential of diffraction of the Stokes
singular beam with polarization transformation, modeling of experimental noise
factors, and a deep learning framework for characterizing hybridly polarized
beams
