This paper reports on a convolutional neural network (CNN) – based regression model, called FocusNET, to predict the accurate reconstruction distance of raw holograms in Digital Lensless Holographic Microscopy (DLHM). This proposal provides a physical-mathematical formulation to extend its use to different DLHM setups than the optical and geometrical conditions utilized for recording the training dataset; this unique feature is tested by applying the proposal to holograms of diverse samples recorded with different DLHM setups. Additionally, a comparison between FocusNET and conventional autofocusing methods in terms of processing times and accuracy is provided. Although the proposed method predicts reconstruction distances with approximately 54µm standard deviation, accurate information about the samples in the validation dataset is still retrieved, reducing processing times of conventional stack reconstruction methods by 600 times for single hologram processing and around 1200 times for hologram batches. The training and validation datasets, and the code implementations, are hosted on a public GitHub repository that can be freely accessed using the following link: https://github.com/mmonto95/focusnet [1].