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 (this https URL).

本文介绍了一种轻量级检测变换器 (LW-DETR)，它在实时目标检测方面优于 YOLO，架构简单，由 ViT 编码器、投影器和浅层 DETR 解码器组成，结合训练有效技术和插入式窗口和全局注意力等最新技术，改进了 ViT 编码器的复杂性，实验证明所提出的方法在 COCO 和其他基准数据集上优于现有的实时检测器。