yangjun dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 il y a 1 an
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README.md dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 il y a 1 an
ppyoloe_convnext_tiny_36e_coco.yml dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 il y a 1 an
yolox_convnext_s_36e_coco.yml dfa27afb39 提交PaddleDetection develop 分支 d56cf3f7c294a7138013dac21f87da4ea6bee829 il y a 1 an

README.md

ConvNeXt (A ConvNet for the 2020s)

模型库

ConvNeXt on COCO

Citations

@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},
}
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