DETR Model

DETR (DEtection TRansformer) models represent a significant advancement in object detection, aiming to replace traditional methods with a more efficient and conceptually elegant approach based on transformer networks. Current research focuses on improving DETR's performance in challenging scenarios, such as detecting small objects, handling low-light conditions, and achieving accurate predictions for irregular shapes like polygons and text. These improvements often involve architectural modifications, such as adaptive feature fusion, enhanced query designs, and incorporating segmentation information, leading to more robust and accurate object detection across diverse applications. The resulting advancements have broad implications for various fields, including autonomous driving, medical image analysis, and document processing.