  We present a new model named Stacked-DETR(SDETR), which inherits the main
ideas in canonical DETR. We improve DETR in two directions: simplifying the
cost of training and introducing the stacked architecture to enhance the
performance. To the former, we focus on the inside of the Attention block and
propose the QKVA grid, a new perspective to describe the process of attention.
By this, we can step further on how Attention works for image problems and the
effect of multi-head. These two ideas contribute the design of single-head
encoder-layer. To the latter, SDETR reaches better performance(+0.6AP, +2.7APs)
to DETR. Especially to the performance on small objects, SDETR achieves better
results to the optimized Faster R-CNN baseline, which was a shortcoming in
DETR. Our changes are based on the code of DETR. Training code and pretrained
models are available at https://github.com/shengwenyuan/sdetr.
