Paired image-to-image translation of 2D images. Label-free Prediction (fnet) is a neural network used to infer the features of cellular structures from brightfield or EM images without coloured labels. The network is trained using paired training images from the same field of view, imaged in a label-free (e.g. brightfield) and labelled condition (e.g. fluorescent protein). When trained, this allows the user to identify certain structures from brightfield images alone. The performance of fnet may depend significantly on the structure at hand. Note - visit the ZeroCostDL4Mic wiki to check the original publications this network is based on and make sure you cite these.