  DETR has been recently proposed to eliminate the need for many hand-designed
components in object detection while demonstrating good performance. However,
it suffers from slow convergence and limited feature spatial resolution, due to
the limitation of Transformer attention modules in processing image feature
maps. To mitigate these issues, we proposed Deformable DETR, whose attention
modules only attend to a small set of key sampling points around a reference.
Deformable DETR can achieve better performance than DETR (especially on small
objects) with 10 times less training epochs. Extensive experiments on the COCO
benchmark demonstrate the effectiveness of our approach. Code is released at
https://github.com/fundamentalvision/Deformable-DETR.
