Road_extraction:注意Unet和Deep Unet实现,用于道路提取多gpu张量流-源码

上传者: 42172972 | 上传时间: 2021-02-19 17:07:34 | 文件大小: 16.51MB | 文件类型: ZIP
Road_extraction 使用多GPU模型张量流的Attention Unet和Deep Unet实现道路提取 Deep U-Net的多种变体已经过额外的层和额外的卷积测试。 尽管如此,优于所有人的模型是Attention U-Net:学习在哪里寻找胰腺。 我添加了一个额外的调整来提高性能,将卷积块切换为残差块 TensorFlow分割 TF细分模型,U-Net,Attention Unet,Deep U-Net(U-Net的所有变体) 使用神经网络(NN)进行图像分割,旨在从遥感影像中提取道路网络,它可用于其他应用中,标记图像中的每个像素(语义分割) 可以在以下论文中找到详细信息: 注意U-Net附加模块 要求 Python 3.6 CUDA 10.0 TensorFlow 1.9 Keras 2.0 模组 utils.py和helper.py函数用于预处理数据并保存。

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