Revisiting DETR Pre-training for Object Detection
Authors: name: Yan Ma, name: Weicong Liang, name: Yiduo Hao, name: Bohan Chen, name: Xiangyu Yue, name: Chao Zhang, name: Yuhui Yuan

Motivated by that DETR-based approaches have established new records on COCO
detection and segmentation benchmarks, many recent endeavors show increasing
interest in how to further improve DETR-based approaches by pre-training the
Transformer in a self-supervised manner while keeping the backbone frozen. Some
studies already claimed significant improvements in accuracy. In this paper, we
take a closer look at their experimental methodology and check if their
approaches are still effective on the very recent state-of-the-art such as
H-Deformable-DETR. We conduct thorough experiments on COCO object
detection tasks to study the influence of the choice of pre-training datasets,
localization, and classification target generation schemes. Unfortunately, we
find the previous representative self-supervised approach such as DETReg, fails
to boost the performance of the strong DETR-based approaches on full data
regimes. We further analyze the reasons and find that simply combining a more
accurate box predictor and Objects365 benchmark can significantly improve the
results in follow-up experiments. We demonstrate the effectiveness of our
approach by achieving strong object detection results of AP=59.3% on COCO
val set, which surpasses H-Deformable-DETR + Swin-L by +1.4%.
Last, we generate a series of synthetic pre-training datasets by combining the
very recent image-to-text captioning models (LLaVA) and text-to-image
generative models (SDXL). Notably, pre-training on these synthetic datasets
leads to notable improvements in object detection performance. Looking ahead,
we anticipate substantial advantages through the future expansion of the
synthetic pre-training dataset.