Title: RMNet: Equivalently Removing Residual Connection from Networks. Abstract: Although residual connection enables training very deep neural networks, it is not friendly for online inference due to its multi-branch topology. This encourages many researchers to work on designing DNNs without residual connections at inference. For example, RepVGG re-parameterizes multi-branch topology to a VGG-like (single-branch) model when deploying, showing great performance when the network is relatively shallow. However, RepVGG can not transform ResNet to VGG equivalently because re-parameterizing methods can only be applied to linear Blocks and the non-linear layers (ReLU) have to be put outside of the residual connection which results in limited representation ability, especially for deeper networks. In this paper, we aim to remedy this problem and propose to remove the residual connection in a vanilla ResNet equivalently by a reserving and merging (RM) operation on ResBlock. Specifically, RM operation allows input feature maps to pass through the block while reserving their information and merges all the information at the end of each block, which can remove residual connection without changing original output. RMNet basically has two advantages: 1) it achieves a better accuracy-speed trade-off compared with ResNet and RepVGG; 2) its implementation makes it naturally friendly for high ratio network pruning. Extensive experiments are performed to verify the effectiveness of RMNet. We believe the ideology of RMNet can inspire many insights on model design for the community in the future.