UNet is a deep-learning method for pixel classification. It can be trained to segment different kinds of objects in images. The first half of the U-Net architecture is a downsampling convolutional neural network which acts as a feature extractor from input images. The other half upsamples these results and restores an image by combining results from downsampling with the upsampled images. Use dl4mic to train and apply the UNet.