  We examine five machine learning-based architectures to estimate the droplet
size distributions obtained using digital inline holography. The architectures,
namely, U-Net, R2 U-Net, Attention U-Net, V-Net, and Residual U-Net are trained
using synthetic holographic images. Our assessment focuses on evaluating the
training, validation, and prediction performance of these architectures. We
found that U-Net and R2 U-Net to be the most proficient, displaying consistent
performance trends and achieving the highest Intersection Over Union (IOU)
scores compared to the other three architectures. We employ additional training
using experimental holographic images for the two top-performing architectures
to validate their efficacy further. Subsequently, they are employed to segment
an experimental dataset illustrating the bag breakup phenomenon, facilitating
the extraction of size distribution. The extracted size distribution from U-Net
and R2 U-Net segmentation is then compared with the analytical model proposed
by \cite{jackiw2022prediction} by employing the gamma and log-normal
distributions. Our findings indicate that the gamma distribution provides a
more accurate prediction of the multi-modal size distribution than the
log-normal distribution owing to its long exponential tail. The present study
offers valuable insights into the effectiveness of machine learning
architectures in estimating particle/droplet sizes, highlighting their
practical application in real-world experimental scenarios.
