  We propose Scale-aware AutoAug to learn data augmentation policies for object
detection. We define a new scale-aware search space, where both image- and
box-level augmentations are designed for maintaining scale invariance. Upon
this search space, we propose a new search metric, termed Pareto Scale Balance,
to facilitate search with high efficiency. In experiments, Scale-aware AutoAug
yields significant and consistent improvement on various object detectors
(e.g., RetinaNet, Faster R-CNN, Mask R-CNN, and FCOS), even compared with
strong multi-scale training baselines. Our searched augmentation policies are
transferable to other datasets and box-level tasks beyond object detection
(e.g., instance segmentation and keypoint estimation) to improve performance.
The search cost is much less than previous automated augmentation approaches
for object detection. It is notable that our searched policies have meaningful
patterns, which intuitively provide valuable insight for human data
augmentation design. Code and models will be available at
https://github.com/Jia-Research-Lab/SA-AutoAug.
