- TL;DR: We propose CR-NAS to reallocate engaged computation resources in different resolution and spatial position.
- Abstract: The allocation of computation resources across different feature resolutions in the backbone is a crucial issue in object detection. However, classification allocation pattern is usually adopted directly to object detection, which is proved to be sub-optimal. In order to reallocate the engaged computation resources in a more efficient way, we present CR-NAS (Computation Reallocation Neural Architecture Search) that can learn computation reallocation strategies on the target detection dataset. A two-level reallocation space is proposed for both stage and spatial reallocation. A novel hierarchical search procedure is adopted to cope with the complex search space. We apply CR-NAS to multiple backbones and achieve consistent improvements. Our CR-ResNet50 and CR-MobileNetV2 outperforms the baseline by 1.9\% and 1.7\% COCO AP respectively without any additional computation budget. The models discovered by CR-NAS can be easily transfered to other dataset, e.g. PASCAL VOC, and other vision tasks, e.g. instance segmentation. Our CR-NAS can be used as a plugin to improve the performance of various networks, which is demanding.
- Keywords: Neural Architecture Search, Object Detection