UNet is a deep learning architecture primarily used for semantic segmentation tasks in computer vision. It is widely adopted in medical imaging, where pixel-level accuracy is crucial for identifying structures like organs or tumors. UNet's architecture consists of a contracting path to capture context and a symmetric expanding path for precise localization. The model utilizes skip connections, which help retain spatial information during the encoding-decoding process. This architecture is particularly effective in situations with limited labeled data due to its ability to learn both local and global features.