  In this paper, we present a light-weight detection transformer, LW-DETR,
which outperforms YOLOs for real-time object detection. The architecture is a
simple stack of a ViT encoder, a projector, and a shallow DETR decoder. Our
approach leverages recent advanced techniques, such as training-effective
techniques, e.g., improved loss and pretraining, and interleaved window and
global attentions for reducing the ViT encoder complexity. We improve the ViT
encoder by aggregating multi-level feature maps, and the intermediate and final
feature maps in the ViT encoder, forming richer feature maps, and introduce
window-major feature map organization for improving the efficiency of
interleaved attention computation. Experimental results demonstrate that the
proposed approach is superior over existing real-time detectors, e.g., YOLO and
its variants, on COCO and other benchmark datasets. Code and models are
available at (https://github.com/Atten4Vis/LW-DETR).
