Open-vocabulary object detection focusing on detecting novel categories guided by natural language. In this report, we propose Open-Vocabulary Light-Weighted Detection Transformer (OVLW-DETR), a deployment friendly open-vocabulary detector with strong performance and low latency. Building upon OVLW-DETR, we provide an end-to-end training recipe that transferring knowledge from vision-language model (VLM) to object detector with simple alignment. We align detector with the text encoder from VLM by replacing the fixed classification layer weights in detector with the class-name embeddings extracted from the text encoder. Without additional fusing module, OVLW-DETR is flexible and deployment friendly, making it easier to implement and modulate. improving the efficiency of interleaved attention computation. Experimental results demonstrate that the proposed approach is superior over existing real-time open-vocabulary detectors on standard Zero-Shot LVIS benchmark. Source code and pre-trained models are available at [https://github.com/Atten4Vis/LW-DETR].

提出了OVLW-DETR，一种部署友好型的开放词汇检测器，它具有出色的性能和低延迟，通过从视觉-语言模型（VLM）提取词类名嵌入来对齐其检测器，无需额外的融合模块，OVLW-DETR具有灵活性、部署友好性，其在标准零距离LVIS基准测试上优于现有的实时开放词汇检测器。

OVLW-DETR: 开放词汇轻量级检测变换器