  Segment Anything Model (SAM) has shown impressive zero-shot transfer
performance for various computer vision tasks recently. However, its heavy
computation costs remain daunting for practical applications. MobileSAM
proposes to replace the heavyweight image encoder in SAM with TinyViT by
employing distillation, which results in a significant reduction in
computational requirements. However, its deployment on resource-constrained
mobile devices still encounters challenges due to the substantial memory and
computational overhead caused by self-attention mechanisms. Recently, RepViT
achieves the state-of-the-art performance and latency trade-off on mobile
devices by incorporating efficient architectural designs of ViTs into CNNs.
Here, to achieve real-time segmenting anything on mobile devices, following
MobileSAM, we replace the heavyweight image encoder in SAM with RepViT model,
ending up with the RepViT-SAM model. Extensive experiments show that RepViT-SAM
can enjoy significantly better zero-shot transfer capability than MobileSAM,
along with nearly $10\times$ faster inference speed. The code and models are
available at \url{https://github.com/THU-MIG/RepViT}.
