Revisiting DETR Pre-training for Object Detection

• URL: http://arxiv.org/abs/2308.01300v2
• Date: Fri, 1 Dec 2023 18:25:19 GMT
• Title: Revisiting DETR Pre-training for Object Detection
• Authors: Yan Ma, Weicong Liang, Bohan Chen, Yiduo Hao, Bojian Hou, Xiangyu Yue, Chao Zhang, Yuhui Yuan
• Abstract summary: We investigate the shortcomings of DETReg in enhancing the performance of robust DETR-based models under full data conditions. We employ an optimized approach named Simple Self-training which leads to marked enhancements through the combination of an improved box predictor and the Objects$365$ benchmark. The culmination of these endeavors results in a remarkable AP score of $59.3%$ on the COCO val set, outperforming $mathcalH$-Deformable-DETR + Swin-L without pre-training by $1.4%$.
• Score: 24.372444866927538
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Motivated by the remarkable achievements of DETR-based approaches on COCO object detection and segmentation benchmarks, recent endeavors have been directed towards elevating their performance through self-supervised pre-training of Transformers while preserving a frozen backbone. Noteworthy advancements in accuracy have been documented in certain studies. Our investigation delved deeply into a representative approach, DETReg, and its performance assessment in the context of emerging models like $\mathcal{H}$-Deformable-DETR. Regrettably, DETReg proves inadequate in enhancing the performance of robust DETR-based models under full data conditions. To dissect the underlying causes, we conduct extensive experiments on COCO and PASCAL VOC probing elements such as the selection of pre-training datasets and strategies for pre-training target generation. By contrast, we employ an optimized approach named Simple Self-training which leads to marked enhancements through the combination of an improved box predictor and the Objects$365$ benchmark. The culmination of these endeavors results in a remarkable AP score of $59.3\%$ on the COCO val set, outperforming $\mathcal{H}$-Deformable-DETR + Swin-L without pre-training by $1.4\%$. Moreover, a series of synthetic pre-training datasets, generated by merging contemporary image-to-text(LLaVA) and text-to-image (SDXL) models, significantly amplifies object detection capabilities.

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