  Recent DEtection TRansformer-based (DETR) models have obtained remarkable
performance. Its success cannot be achieved without the re-introduction of
multi-scale feature fusion in the encoder. However, the excessively increased
tokens in multi-scale features, especially for about 75\% of low-level
features, are quite computationally inefficient, which hinders real
applications of DETR models. In this paper, we present Lite DETR, a simple yet
efficient end-to-end object detection framework that can effectively reduce the
GFLOPs of the detection head by 60\% while keeping 99\% of the original
performance. Specifically, we design an efficient encoder block to update
high-level features (corresponding to small-resolution feature maps) and
low-level features (corresponding to large-resolution feature maps) in an
interleaved way. In addition, to better fuse cross-scale features, we develop a
key-aware deformable attention to predict more reliable attention weights.
Comprehensive experiments validate the effectiveness and efficiency of the
proposed Lite DETR, and the efficient encoder strategy can generalize well
across existing DETR-based models. The code will be available in
\url{https://github.com/IDEA-Research/Lite-DETR}.
