U-Net is a computer vision model developed for biomedical image segmentation at the University of Freiburg. It was created by Olaf Ronneberger, Philipp Fischer and Thomas Brox in 2015 and reported in their paper “U-Net: Convolutional Networks for Biomedical Image Segmentation”. U-Net is based on the fully convolutional network proposed by Long, Shelhamer, and Darrell (2014). It was originally developed for medical images but has since been adapted for other computer vision tasks. The model is based on a fully convolutional neural network and uses a “u-shaped” architecture, hence its name. U-Net has been shown to be effective at segmenting images with high accuracy and has become a popular choice for many computer vision applications.  

U-Net’s architecture allows it to use both low-level and high-level features, making it suitable for a variety of machine learning tasks. The architecture has been modified to work with fewer training images while yielding more precise segmentations; it can take less than a second to segment an 512×512 image using modern GPUs. It is also capable of handling complex image data such as medical images with its robust feature extraction capabilities. An important modification made in U-Net involves having many feature channels which allow context information to be propagated up into higher resolution layers – this creates a u-shaped architecture without any fully connected layers. U-Net is an important component in computer vision research and development. Its powerful performance makes it a great choice for many computer vision projects. 

As high-resolution data and images becomes more available in fields such medical imaging, the ability to process large enough images to capture the contextual information present in these images becomes increasingly important. Currently however, the maximum image sizes that can be used for training are often limited by the memory available on the training system, which in turn limits the insights that can be gained from such images. The Cerebras CS-2 system is uniquely built to handle the high-memory and high-computational demands that will allow researchers to more fully extract the information contained in these high-resolution images.