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Data is the cornerstone of deep learning. This paper reveals that therecently developed Diffusion Model is a scalable data engine for objectdetection. Existing methods for scaling up detection-oriented data oftenrequire manual collection or generative models to obtain target images,followed by data augmentation and labeling to produce training pairs, which arecostly, complex, or lacking diversity. To address these issues, wepresentDiffusionEngine (DE), a data scaling-up engine that provideshigh-quality detection-oriented training pairs in a single stage. DE consistsof a pre-trained diffusion model and an effective Detection-Adapter,contributing to generating scalable, diverse and generalizable detection datain a plug-and-play manner. Detection-Adapter is learned to align the implicitsemantic and location knowledge in off-the-shelf diffusion models withdetection-aware signals to make better bounding-box predictions. Additionally,we contribute two datasets, i.e., COCO-DE and VOC-DE, to scale up existingdetection benchmarks for facilitating follow-up research. Extensive experimentsdemonstrate that data scaling-up via DE can achieve significant improvements indiverse scenarios, such as various detection algorithms, self-supervisedpre-training, data-sparse, label-scarce, cross-domain, and semi-supervisedlearning. For example, when using DE with a DINO-based adapter to scale updata, mAP is improved by 3.1% on COCO, 7.6% on VOC, and 11.5% on Clipart.

DiffusionEngine: Diffusion Model is Scalable Data Engine for Object Detection