Papers › From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search
From Xception to NEXcepTion: New Design Decisions and Neural Architecture Search
Hadar Shavit, Filip Jatelnicki, Pol Mor-Puigventós, Wojtek Kowalczyk
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.
Code
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Tasks
Results from the paper archive 2025-07-28
| Task | Dataset | Model | Metric | Value | Rank at snapshot | Leaderboard | Report |
|---|---|---|---|---|---|---|---|
| Image Classification | ImageNet | NEXcepTion-S | Top 1 Accuracy | 82% | #581 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | NEXcepTion-TP | Top 1 Accuracy | 81.8% | #607 of 1060 | Archive leaderboard | report |
| Image Classification | ImageNet | NEXcepTion-T | Top 1 Accuracy | 81.5% | #631 of 1060 | Archive leaderboard | report |
Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.
Methods
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