Abstract In this paper we demonstrate the application of object detection networks for the classification and localization of cells in fluorescence microscopy. We benchmark two leading object detection algorithms across multiple challenging 2-D microscopy datasets as well as develop and demonstrate an algorithm which can localize and image cells in 3-D, in real-time. Furthermore, we exploit the fast processing of these algorithms and develop a simple and effective Augmented Reality (AR) system for fluorescence microscopy systems. Object detection networks are well-known high performance networks famously applied to the task of identifying and localizing objects in photography images. Here we show their application and efficiency for localizing cells in fluorescence microscopy images. Object detection algorithms are typically trained on many thousands of images, which can be prohibitive within the biological sciences due to the cost of imaging and annotating large amounts of data. Through taking different cell types and assays as an example, we show that with some careful considerations it is possible to achieve very high performance with datasets with as few as 26 images present. Using our approach, it is possible for relatively non-skilled users to automate detection of cell classes with a variety of appearances and enable new avenues for automation of conventionally manual fluorescence microscopy acquisition pipelines.