  This work presents Focal-Stable-DINO, a strong and reproducible object
detection model which achieves 64.6 AP on COCO val2017 and 64.8 AP on COCO
test-dev using only 700M parameters without any test time augmentation. It
explores the combination of the powerful FocalNet-Huge backbone with the
effective Stable-DINO detector. Different from existing SOTA models that
utilize an extensive number of parameters and complex training techniques on
large-scale private data or merged data, our model is exclusively trained on
the publicly available dataset Objects365, which ensures the reproducibility of
our approach.
