ReXNet series
December 7, 2021 · View on GitHub
Catalogue
Overview
ReXNet is proposed by NAVER AI Lab, which is based on new network design principles. Aiming at the problem of representative bottleneck in the existing network, a set of design principles are proposed. The author believes that the conventional design produce representational bottlenecks, which would affect model performance. To investigate the representational bottleneck, the author study the matrix rank of the features generated by ten thousand random networks. Besides, entire layer’s channel configuration is also studied to design more accurate network architectures. In the end, the author proposes a set of simple and effective design principles to mitigate the representational bottleneck. paper
Accuracy, FLOPs and Parameters
| Models | Top1 | Top5 | Reference top1 | FLOPs (G) | Params (M) |
|---|---|---|---|---|---|
| ReXNet_1_0 | 77.46 | 93.70 | 77.9 | 0.415 | 4.838 |
| ReXNet_1_3 | 79.13 | 94.64 | 79.5 | 0.683 | 7.611 |
| ReXNet_1_5 | 80.06 | 95.12 | 80.3 | 0.900 | 9.791 |
| ReXNet_2_0 | 81.22 | 95.36 | 81.6 | 1.561 | 16.449 |
| ReXNet_3_0 | 82.09 | 96.12 | 82.8 | 3.445 | 34.833 |
Inference speed and other information are coming soon.