  DETR has set up a simple end-to-end pipeline for object detection by
formulating this task as a set prediction problem, showing promising potential.
However, despite the significant progress in improving DETR, this paper
identifies a problem of misalignment in the output distribution, which prevents
the best-regressed samples from being assigned with high confidence, hindering
the model's accuracy. We propose a metric, recall of best-regressed samples, to
quantitively evaluate the misalignment problem. Observing its importance, we
propose a novel Align-DETR that incorporates a localization precision-aware
classification loss in optimization. The proposed loss, IA-BCE, guides the
training of DETR to build a strong correlation between classification score and
localization precision. We also adopt the mixed-matching strategy, to
facilitate DETR-based detectors with faster training convergence while keeping
an end-to-end scheme. Moreover, to overcome the dramatic decrease in sample
quality induced by the sparsity of queries, we introduce a prime sample
weighting mechanism to suppress the interference of unimportant samples.
Extensive experiments are conducted with very competitive results reported. In
particular, it delivers a 46 (+3.8)% AP on the DAB-DETR baseline with the
ResNet-50 backbone and reaches a new SOTA performance of 50.2% AP in the 1x
setting on the COCO validation set when employing the strong baseline DINO. Our
code is available at https://github.com/FelixCaae/AlignDETR.
