| 网络网络 | 输入尺寸 | 图片数/GPU | 学习率策略 | mAPval 0.5:0.95 |
mAPval 0.5 |
Params(M) | FLOPs(G) | 下载链接 | 配置文件 |
|---|---|---|---|---|---|---|---|---|---|
| PP-YOLOE-ConvNeXt-tiny | 640 | 16 | 36e | 44.6 | 63.3 | 33.04 | 13.87 | 下载链接 | 配置文件 |
| YOLOX-ConvNeXt-s | 640 | 8 | 36e | 44.6 | 65.3 | 36.20 | 27.52 | 下载链接 | 配置文件 |
| YOLOv5-s ConvNeXt | 640 | 8 | 36e | 42.4 | 65.3 | 34.54 | 17.96 | 下载链接 | 配置文件 |
@Article{liu2022convnet,
author = {Zhuang Liu and Hanzi Mao and Chao-Yuan Wu and Christoph Feichtenhofer and Trevor Darrell and Saining Xie},
title = {A ConvNet for the 2020s},
journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
year = {2022},
}