  One-to-one set matching is a key design for DETR to establish its end-to-end
capability, so that object detection does not require a hand-crafted NMS
(non-maximum suppression) to remove duplicate detections. This end-to-end
signature is important for the versatility of DETR, and it has been generalized
to broader vision tasks. However, we note that there are few queries assigned
as positive samples and the one-to-one set matching significantly reduces the
training efficacy of positive samples. We propose a simple yet effective method
based on a hybrid matching scheme that combines the original one-to-one
matching branch with an auxiliary one-to-many matching branch during training.
Our hybrid strategy has been shown to significantly improve accuracy. In
inference, only the original one-to-one match branch is used, thus maintaining
the end-to-end merit and the same inference efficiency of DETR. The method is
named H-DETR, and it shows that a wide range of representative DETR methods can
be consistently improved across a wide range of visual tasks, including
DeformableDETR, PETRv2, PETR, and TransTrack, among others. The code is
available at: https://github.com/HDETR
