DEFORMABLE DETR Implementation #9
Description
Implement this paper
https://openreview.net/pdf?id=gZ9hCDWe6ke
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× less training epochs. Extensive experiments on the COCO benchmark
demonstrate the effectiveness of our approach. Code shall be released.