DDRNet.pytorch:这是用于道路场景的实时和精确语义分割的深度双分辨率网络的非官方代码。 无需使用推断加速和额外数据,即可在城市景观和camvid上实现精度与速度之间的最新权衡-源码

上传者: 42138780 | 上传时间: 2021-08-27 14:39:26 | 文件大小: 1.19MB | 文件类型: ZIP
深度双分辨率网络,可实时,准确地对道路场景进行语义分割 介绍 这是的非官方代码。 在单个2080Ti GPU上,DDRNet-23-slim在109 FPS的Cityscapes测试装置上可产生77.4%的mIoU,从而在城市景观和camvid的精度和速度之间实现了最先进的权衡,而无需使用推理加速和额外的数据!在CamVid测试装置上以230 FPS时有74.4%的mIoU。 该代码主要来自和,感谢他们的工作。 要求 在这里列出了实验中使用的软件和硬件 pytorch == 1.7.0 3080 * 2 cuda == 11.1 快速开始 0.数据准备 您需要下载数据集。 并重命名cityscapes文件夹,然后将数据放在data文件夹下。 └── data ├── cityscapes └── list 1.预训练模型 从imagenet上下载预训练模型或从那里下载分割模

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