DEIM is an advanced training framework for DETR models that enhances the matching mechanism between predictions and ground truth during training. It aims to achieve faster convergence and improved accuracy compared to standard DETR approaches. The project serves as a research foundation for object detection using transformer-based architectures and has been published at CVPR 2025.