Rethinking Channel Dimensions for Efficient Model Design
This work addresses the challenge of improving accuracy in lightweight models for computer vision applications, though it appears incremental as it builds on existing channel configuration methods.
The paper tackles the problem of designing efficient models by challenging the conventional stage-wise channel dimension configuration, proposing a parameterized channel configuration that achieves remarkable performance on ImageNet classification and transfer learning tasks like COCO object detection and instance segmentation.
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.