Road-Semantic-Segmentation:使用FCN对图像（或视频）进行语义分割，以将道路，汽车，自行车和骑手从图像中分割出来-源码
语义分割
介绍
该项目旨在使用完全卷积网络（FCN）标记图像的像素（也称为语义分割）。 它使用VGG-16实现转移学习并提取layer7的输出，然后使用1x1卷积，随后是几个转置的卷积层，并与跳过连接相结合以进行升采样。 网络架构如下：
设置
显卡
请确保您已启用Tensorflow GPU。 如果您的系统上没有GPU，则可以使用AWS或其他云计算平台。 该项目使用 。
框架和包装
确保已安装以下设备：
数据集
从下载。 将数据集提取到data文件夹中。 这将创建文件夹data_semantics其中包含所有培训的测试图像。
标签格式
名称ID类别'未标记'，0，'无效' '自我车辆'，1，'无效' '校正边界'，2，'无效' '超出投资回报率'，3，'无效' '静态'，4，'无效' '动态'，5，'无效' 'ground'，6，'void' '道路'，7，'地面' 人行道，8，地面'停车
文件下载
资源详情
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