  Mamba, an architecture with RNN-like token mixer of state space model (SSM),
was recently introduced to address the quadratic complexity of the attention
mechanism and subsequently applied to vision tasks. Nevertheless, the
performance of Mamba for vision is often underwhelming when compared with
convolutional and attention-based models. In this paper, we delve into the
essence of Mamba, and conceptually conclude that Mamba is ideally suited for
tasks with long-sequence and autoregressive characteristics. For vision tasks,
as image classification does not align with either characteristic, we
hypothesize that Mamba is not necessary for this task; Detection and
segmentation tasks are also not autoregressive, yet they adhere to the
long-sequence characteristic, so we believe it is still worthwhile to explore
Mamba's potential for these tasks. To empirically verify our hypotheses, we
construct a series of models named MambaOut through stacking Mamba blocks while
removing their core token mixer, SSM. Experimental results strongly support our
hypotheses. Specifically, our MambaOut model surpasses all visual Mamba models
on ImageNet image classification, indicating that Mamba is indeed unnecessary
for this task. As for detection and segmentation, MambaOut cannot match the
performance of state-of-the-art visual Mamba models, demonstrating the
potential of Mamba for long-sequence visual tasks. The code is available at
https://github.com/yuweihao/MambaOut
