# ReXNet series
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## Catalogue

* [1. Overview](#1)
* [2. Accuracy, FLOPs and Parameters](#2)

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## 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](https://arxiv.org/pdf/2007.00992.pdf)

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## Accuracy, FLOPs and Parameters

|   Models   | Top1  | Top5  | Reference<br>top1 | FLOPs<br/>(G) | Params<br/>(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.
