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LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

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arxiv 2406.03459 v1 pith:FIUJJU4D submitted 2024-06-05 cs.CV

LW-DETR: A Transformer Replacement to YOLO for Real-Time Detection

Qiang Chen , Xiangbo Su , Xinyu Zhang , Jian Wang , Jiahui Chen , Yunpeng Shen , Chuchu Han , Ziliang Chen
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Weixiang Xu Fanrong Li Shan Zhang Kun Yao Errui Ding Gang Zhang Jingdong Wang
This is my paper
classification cs.CV
keywords encoderfeaturedetectionlw-detrmapsreal-timeapproachinterleaved
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved
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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).

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