# Residual Conv-Deconv Grid Network for Semantic Segmentation Source: [Residual Conv-Deconv Grid Network for Semantic Segmentation]() Collection index: [llms.txt]() Data updated: 2025-10-25T20:42:16.909Z Authors: Damien Fourure, R\\'emi Emonet, Elisa Fromont, Damien Muselet, Alain Tremeau, Christian Wolf Published: 2017-07-25T12:49:11.000Z ## Abstract This paper presents GridNet, a new Convolutional Neural Network (CNN) architecture for semantic image segmentation (full scene labelling). Classical neural networks are implemented as one stream from the input to the output with subsampling operators applied in the stream in order to reduce the feature maps size and to increase the receptive field for the final prediction. However, for semantic image segmentation, where the task consists in providing a semantic class to each pixel of an image, feature maps reduction is harmful because it leads to a resolution loss in the output prediction. To tackle this problem, our GridNet follows a grid pattern allowing multiple interconnected streams to work at different resolutions. We show that our network generalizes many well known networks such as conv-deconv, residual or U-Net networks. GridNet is trained from scratch and achieves competitive results on the Cityscapes dataset. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Semantic Segmentation on Cityscapes (test)](): Semantic segmentation; Cityscapes (test); mIoU: 69.5. ## Original sources and implementation [Original paper]() [PDF]()