Authors:
Hadar Shavit
;
Filip Jatelnicki
;
Pol Mor-Puigventós
and
Wojtek Kowalczyk
Affiliation:
Leiden Institute of Advanced Computer Science (LIACS), Leiden University, Niels Bohrweg 1, 2333CA, The Netherlands
Keyword(s):
Deep Learning, ConvNeXt, Xception, Image Classification, ImageNet, Computer Vision.
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. Our code is available at https://github.com/hadarshavit/NEXcepTion.