# A Survey on Visual Mamba
## 3 Mamba for Vision
### Comparison of Mamba Models and Other State-of-the-Art Models
#### 3.4.2 Analysis and Comparison in Object Detection and Instance Segmentation Tasks
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Model** & **Backbone** & **Image Size** & **Params (M)** & **FLOPs (G)** & **Top-1 ACC (\%)** \\ \hline \multirow{8}{*}{**CNN**} & ResNet-50 [98] & 224\({}^{2}\) & 25.5 & 4.1 & 76.50 \\  & ResNet-50-D [99] & 224\({}^{2}\) & 25.0 & 4.3 & 77.16 \\  & ResNet-101 [98] & 224\({}^{2}\) & 44.6 & 7.8 & 77.4 \\  & ResNet-152 [98] & 224\({}^{2}\) & 60.2 & 11.6 & 78.3 \\  & ResNeXt-50-32 \(\times\) 4d [100] & 224\({}^{2}\) & 25 & 4.1 & 77.8 \\  & ResNeXt-101-32 \(\times\) 4d [100] & 224\({}^{2}\) & 44 & 7.8 & 78.8 \\  & RegNet\(\uparrow\)-AG [94] & 224\({}^{2}\) & 21 & 4.0 & 80.0 \\  & RegNet\(\uparrow\)-SG [94] & 224\({}^{2}\) & 39 & 8.0 & 81.7 \\ \hline \multirow{8}{*}{**Transformer**} & ViT-B [169] & 384\({}^{2}\) & 86 & 55.4 & 77.9 \\  & ViT-L-1 [95] & 384\({}^{2}\) & 307 & 190.7 & 76.5 \\  & DeiT-S [101] & 224\({}^{2}\) & 22 & 4.6 & 79.8 \\  & DeiT-B [101] & 224\({}^{2}\) & 86 & 17.6 & 81.8 \\  & DeiT-B [101] & 384\({}^{2}\) & 86 & 55.4 & 83.1 \\  & Swin-T [91] & 224\({}^{2}\) & 29 & 4.5 & 81.3 \\  & Swin-S [91] & 224\({}^{2}\) & 50 & 8.7 & 83.0 \\  & Swin-B [91] & 224\({}^{2}\) & 88 & 15.4 & 83.5 \\  & Swin-B [91] & 384\({}^{2}\) & 88 & 47.0 & 84.5 \\  & ViT-Small-APE [96] & 224\({}^{2}\) & 24.6 & 4.9 & 82.0 \\  & ViT-Small-RPB [96] & 224\({}^{2}\) & 24.6 & 4.9 & 82.4 \\  & ViT-Medium-APE [96] & 224\({}^{2}\) & 39.7 & 8.7 & 83.3 \\  & ViT-Medium-RPB [96] & 224\({}^{2}\) & 39.7 & 8.7 & 83.5 \\  & ViT-Base-APE [96] & 224\({}^{2}\) & 55.7 & 13.4 & 83.2 \\  & ViT-Base-RPB [96] & 224\({}^{2}\) & 55.7 & 13.4 & 83.7 \\  & Focal-Tiny [97] & 224\({}^{2}\) & 29.1 & 4.9 & 82.2 \\  & Focal-Small [97] & 224\({}^{2}\) & 51.1 & 9.1 & 83.5 \\  & Focal-Base [97] & 224\({}^{2}\) & 89.8 & 16.0 & 83.8 \\ \hline \multirow{8}{*}{**Mamba**} & ViT-B [26] & 224\({}^{2}\) & 7 & - & 76.1 \\  & ViT-S [26] & 224\({}^{2}\) & 26 & - & 80.5 \\  & ViT-Match-T [27] & 224\({}^{2}\) & 22 & 4.5 & 82.2 \\  & ViT-Match-S [27] & 224\({}^{2}\) & 44 & 9.1 & 83.5 \\  & ViT-Match-B [27] & 224\({}^{2}\) & 75 & 15.2 & 83.2 \\  & PlainMamba-L1 [28] & 224\({}^{2}\) & 7 & 3.0 & 77.9 \\  & PlainMamba-L2 [28] & 224\({}^{2}\) & 25 & 8.1 & 81.6 \\  & PlainMamba-L3 [28] & 224\({}^{2}\) & 50 & 14.4 & 82.3 \\  & LocalVim-T [29] & 224\({}^{2}\) & 8 & 1.5 & 76.2 \\  & LocalVim-S [29] & 224\({}^{2}\) & 28 & 4.8 & 81.2 \\  & LocalV Mamba-T [29] & 224\({}^{2}\) & 26 & 5.7 & 82.7 \\  & LocalV Mamba-S [29] & 224\({}^{2}\) & 50 & 11.4 & 83.7 \\  & EfficientV Mamba-T [30] & 224\({}^{2}\) & 6 & 0.8 & 76.5 \\  & EfficientV Mamba-S [30] & 224\({}^{2}\) & 11 & 1.3 & 78.7 \\  & EfficientV Mamba-S [30] & 224\({}^{2}\) & 33 & 4.0 & 81.8 \\  & Mamba-2D-S [38] & 224\({}^{2}\) & 24 & - & 81.7 \\  & Mamba-2D-B [38] & 224\({}^{2}\) & 92 & - & 83.0 \\  & SiMBA-S (Monarch) [39] & 224\({}^{2}\) & 18.5 & 3.6 & 81.1 \\  & SiMBA-S (EnFiT) [39] & 224\({}^{2}\) & 15.3 & 2.4 & 81.7 \\  & SiMBA-S (MLP) [39] & 224\({}^{2}\) & 26.5 & 5.0 & 84.0 \\  & SiMBA-B (Monarch) [39] & 224\({}^{2}\) & 26.9 & 5.5 & 82.6 \\  & SiMBA-B (EnFiT) [39] & 224\({}^{2}\) & 22.8 & 4.2 & 83.0 \\  & SiMBA-B (MLP) [39] & 224\({}^{2}\) & 40.0 & 9.0 & 84.7 \\  & SiMBA-L (Monarch) [39] & 224\({}^{2}\) & 42 & 8.7 & 83.8 \\  & SiMBA-L (EnFiT) [39] & 224\({}^{2}\) & 36.6 & 7.6 & 83.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Comparison of Mamba models and other state-of-the-art models on t ImageNet-1K dataset. performed excellently but with relatively high parameters. Transformer models offer good performance but generally have higher parameters and FLOPs. On the other hand, Mamba models, while maintaining lower parameters and computational complexity, can deliver performance very close to or even surpassing some Transformer models, particularly with the LocalVAMaba [29] and VMamba [27] backbones.