Efficient Scale-Permuted Backbone with Learned Resource Distribution

• URL: http://arxiv.org/abs/2010.11426v1
• Date: Thu, 22 Oct 2020 03:59:51 GMT
• Title: Efficient Scale-Permuted Backbone with Learned Resource Distribution
• Authors: Xianzhi Du, Tsung-Yi Lin, Pengchong Jin, Yin Cui, Mingxing Tan, Quoc Le, and Xiaodan Song
• Abstract summary: SpineNet has demonstrated promising results on object detection and image classification over ResNet model. We propose a technique to combine efficient operations and compound scaling with a previously learned scale-permuted architecture. The resulting efficient scale-permuted models outperform state-of-the-art EfficientNet-based models on object detection and achieve competitive performance on image classification and semantic segmentation.
• Score: 41.45085444609275
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Recently, SpineNet has demonstrated promising results on object detection and image classification over ResNet model. However, it is unclear if the improvement adds up when combining scale-permuted backbone with advanced efficient operations and compound scaling. Furthermore, SpineNet is built with a uniform resource distribution over operations. While this strategy seems to be prevalent for scale-decreased models, it may not be an optimal design for scale-permuted models. In this work, we propose a simple technique to combine efficient operations and compound scaling with a previously learned scale-permuted architecture. We demonstrate the efficiency of scale-permuted model can be further improved by learning a resource distribution over the entire network. The resulting efficient scale-permuted models outperform state-of-the-art EfficientNet-based models on object detection and achieve competitive performance on image classification and semantic segmentation. Code and models will be open-sourced soon.

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