From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search

TL;DR

This paper introduces NEXcepTion, a modified Xception architecture enhanced through new design choices and Neural Architecture Search, achieving higher accuracy and throughput on ImageNet compared to the original model.

Contribution

The paper presents a novel architecture, NEXcepTion, with improved performance and efficiency, achieved by combining design modifications, training procedures, and NAS.

Findings

NEXcepTion achieves 81.5% top-1 accuracy on ImageNet.

NEXcepTion-TP reaches 81.8% accuracy with higher throughput.

The models outperform the original Xception architecture.

Abstract

In this paper, we present a modified Xception architecture, the NEXcepTion network. Our network has significantly better performance than the original Xception, achieving top-1 accuracy of 81.5% on the ImageNet validation dataset (an improvement of 2.5%) as well as a 28% higher throughput. Another variant of our model, NEXcepTion-TP, reaches 81.8% top-1 accuracy, similar to ConvNeXt (82.1%), while having a 27% higher throughput. Our model is the result of applying improved training procedures and new design decisions combined with an application of Neural Architecture Search (NAS) on a smaller dataset. These findings call for revisiting older architectures and reassessing their potential when combined with the latest enhancements.