Segmentation Dataset

Segmentation datasets are collections of images paired with detailed annotations, typically pixel-level masks, defining the boundaries of objects or regions of interest. Research focuses on creating larger, more diverse datasets encompassing various domains (medical imaging, agriculture, construction) and addressing challenges like annotation inconsistencies and limited data availability, often employing deep learning models such as U-Net, transformers, and adaptations of the Segment Anything Model (SAM). These datasets are crucial for training and evaluating robust image segmentation algorithms, impacting diverse fields by enabling automated object detection and analysis in applications ranging from medical diagnosis to autonomous systems.