  In Digital Holography (DH), it is crucial to extract the object distance from
a hologram in order to reconstruct its amplitude and phase. This step is called
auto-focusing and it is conventionally solved by first reconstructing a stack
of images and then by sharpening each reconstructed image using a focus metric
such as entropy or variance. The distance corresponding to the sharpest image
is considered the focal position. This approach, while effective, is
computationally demanding and time-consuming. In this paper, the determination
of the distance is performed by Deep Learning (DL). Two deep learning (DL)
architectures are compared: Convolutional Neural Network (CNN) and Vision
Transformer (ViT). ViT and CNN are used to cope with the problem of
auto-focusing as a classification problem. Compared to a first attempt [11] in
which the distance between two consecutive classes was 100$\mu$m, our proposal
allows us to drastically reduce this distance to 1$\mu$m. Moreover, ViT reaches
similar accuracy and is more robust than CNN.
