  Designing an efficient model within the limited computational cost is
challenging. We argue the accuracy of a lightweight model has been further
limited by the design convention: a stage-wise configuration of the channel
dimensions, which looks like a piecewise linear function of the network stage.
In this paper, we study an effective channel dimension configuration towards
better performance than the convention. To this end, we empirically study how
to design a single layer properly by analyzing the rank of the output feature.
We then investigate the channel configuration of a model by searching network
architectures concerning the channel configuration under the computational cost
restriction. Based on the investigation, we propose a simple yet effective
channel configuration that can be parameterized by the layer index. As a
result, our proposed model following the channel parameterization achieves
remarkable performance on ImageNet classification and transfer learning tasks
including COCO object detection, COCO instance segmentation, and fine-grained
classifications. Code and ImageNet pretrained models are available at
https://github.com/clovaai/rexnet.
