  We propose DiffusionDet, a new framework that formulates object detection as
a denoising diffusion process from noisy boxes to object boxes. During the
training stage, object boxes diffuse from ground-truth boxes to random
distribution, and the model learns to reverse this noising process. In
inference, the model refines a set of randomly generated boxes to the output
results in a progressive way. Our work possesses an appealing property of
flexibility, which enables the dynamic number of boxes and iterative
evaluation. The extensive experiments on the standard benchmarks show that
DiffusionDet achieves favorable performance compared to previous
well-established detectors. For example, DiffusionDet achieves 5.3 AP and 4.8
AP gains when evaluated with more boxes and iteration steps, under a zero-shot
transfer setting from COCO to CrowdHuman. Our code is available at
https://github.com/ShoufaChen/DiffusionDet.
