Abstract: Digital in-line holography is a versatile and reliable imaging technique for characterizing the size and spatial distribution of particles in flows. It maps 3D particle snapshots into a 2D image -- a hologram. The analysis bottleneck of in-line holographic imaging is the immense cost of computation and manual effort that must be invested. Here we propose a learning-based approach to extract the size and 3D spatial distribution of objects from holograms. We outperform the standard propagation based method in terms of detection rate, precision and processing time on a small sensor aperture and at $1/4$ of the original resolution. Our method performs with an F1 score of 0.91 in suspensions with more than 60 particles$/$\si{\centi m}$^3$, which is an $18$ percent performance boost in comparison to propagation-based software commonly used in practice. At the same time, our method is also several orders of magnitude faster, eliminating the computational bottleneck.