DETR Training

DETR (DEtection TRansformer) training focuses on improving the efficiency and accuracy of this end-to-end object detection approach, which uses transformers instead of traditional convolutional neural networks. Current research addresses challenges like slow convergence, attention collapse, and the effective design of object queries, exploring solutions such as hybrid matching schemes, query refinement networks, and improved attention mechanisms within various DETR architectures (e.g., Deformable DETR, DINO). These advancements aim to enhance the performance of DETR-based detectors across diverse applications, including image and video object detection, 3D object detection from point clouds, and even specialized tasks like tiny object detection and prohibited item detection in X-ray images.