# 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}{l c c c c c c c c c} \hline \hline
**Model** & **Backbone** & \(\mathbf{AP}^{b}\) & \(\mathbf{AP}^{b}_{50}\) & \(\mathbf{AP}^{b}_{75}\) & \(\mathbf{AP}^{n}\) & \(\mathbf{AP}^{n}_{50}\) & \(\mathbf{AP}^{n}_{75}\) & **Params (M)** & **FLOPs (G)** \\ \hline \multirow{5}{*}{**CNN**} & ResNet-50 [98] & 38.2 & 58.8 & 41.4 & 34.7 & 55.7 & 37.2 & 44 & 260 \\  & ResNet-101 [98] & 38.2 & 58.8 & 41.4 & 34.7 & 55.7 & 37.2 & 63 & 336 \\  & ResNet101-32 \(\times\) 4d [100] & 41.9 & - & - & 37.5 & - & - & 63 & 340 \\  & ResNet101-64 \(\times\) 4d [100] & 42.8 & - & - & 38.4 & - & - & 102 & 493 \\ \hline \multirow{5}{*}{**Transformer**} & ViT-Adapter-T [102] & 41.1 & 62.5 & 44.3 & 37.5 & 59.7 & 39.9 & 28.1 & - \\  & ViT-Adapter-S [102] & 44.7 & 65.8 & 48.3 & 39.9 & 62.5 & 42.8 & 47.8 & - \\  & ViT-Adapter-B [102] & 47.0 & 68.2 & 51.4 & 41.8 & 65.1 & 44.9 & 120.2 & - \\  & Swin-Tiny [91] & 42.2 & - & - & 39.1 & - & - & 48 & 264 \\  & Swin-Small [91] & 44.8 & - & - & 40.9 & - & - & 69 & 354 \\  & PVT-Tiny [103] & 36.7 & 59.2 & 39.3 & 35.1 & 56.7 & 37.3 & 32.9 & - \\  & PVT-Small [103] & 40.4 & 62.9 & 43.8 & 37.8 & 60.1 & 40.3 & 44.1 & - \\  & PVT-Medium [103] & 42.0 & 64.4 & 45.6 & 39.0 & 61.6 & 42.1 & 63.9 & - \\  & PVT-Large [103] & 42.9 & 65.0 & 46.6 & 39.5 & 61.9 & 42.5 & 81.0 & - \\ \hline \multirow{5}{*}{**Transformer**} & VMamba-T [27] & 46.5 & 68.5 & 50.7 & 42.1 & 65.5 & 45.3 & 42 & 262 \\  & VMamba-S [27] & 48.2 & 69.7 & 52.5 & 43.0 & 66.6 & 46.4 & 64 & 357 \\  & VMamba-B [27] & 48.5 & 69.6 & 53.0 & 43.1 & 67.0 & 46.4 & 96 & 482 \\  & PlainMamba-Adapter-L1 [28] & 44.1 & 64.8 & 47.9 & 39.1 & 61.6 & 41.9 & 31 & 388 \\  & PlainMamba-Adapter-L2 [28] & 46.0 & 66.9 & 50.1 & 40.6 & 63.8 & 43.6 & 53 & 542 \\  & PlainMamba-Adapter-L3 [28] & 46.8 & 68.0 & 51.1 & 41.2 & 64.7 & 43.9 & 79 & 696 \\  & EfficientVAMba-T [30] & 35.6 & 57.7 & 38.0 & 33.2 & 54.4 & 35.1 & 11 & 60 \\  & EfficientVAMba-S [30] & 39.3 & 61.8 & 42.6 & 36.7 & 58.9 & 39.2 & 31 & 197 \\  & EfficientVAMba-B [30] & 43.7 & 66.2 & 47.9 & 40.2 & 63.3 & 42.9 & 53 & 252 \\  & LocalVAMba-T [29] & 46.7 & 68.7 & 50.8 & 42.2 & 65.7 & 45.5 & 45 & 291 \\  & LocalVAMba-S [29] & 48.4 & 69.9 & 52.7 & 43.2 & 66.7 & 46.5 & 69 & 414 \\  & SiMBA-S [39] & 46.9 & 68.6 & 51.7 & 42.6 & 65.9 & 45.8 & 60 & 382 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of Mamba Models and other state-of-the-art models on COCO dataset (Mask R-CNN 1\(\times\) schedule). \begin{table}
\begin{tabular}{l c c c c c c c c} \hline \hline
**Model** & **Backbone** & \(\mathbf{AP}^{b}\) & \(\mathbf{AP}^{b}_{50}\) & \(\mathbf{AP}^{b}_{75}\) & \(\mathbf{AP}^{n}\) & \(\mathbf{AP}^{n}_{50}\) & \(\mathbf{AP}^{n}_{75}\) & **Params (M)** & **FLOPs (G)** \\ \hline
**CNN** & ConvNeXt-T [104] & 46.2 & 67.9 & 50.8 & 41.7 & 65.0 & 44.9 & 48 & 262 \\ \hline \multirow{5}{*}{**Transformer**} & Swin-T [91] & 50.5 & 69.3 & 54.9 & 43.7 & 66.6 & 47.1 & 86 & 745 \\  & Swin-S [91] & 51.8 & 70.4 & 56.3 & 44.7 & 67.9 & 48.5 & 107 & 838 \\  & Swin-B [91] & 51.9 & 70.9 & 56.5 & 45.0 & 68.4 & 48.7 & 145 & 982 \\  & ViT-Adapter-T [102] & 46.0 & 67.6 & 50.4 & 41.0 & 64.4 & 44.1 & 28.1 & - \\  & ViT-Adapter-S [102] & 48.2 & 69.7 & 52.5 & 42.8 & 66.4 & 45.9 & 47.8 & - \\  & ViT-Adapter-B [102] & 49.6 & 70.6 & 54.0 & 43.6 & 67.7 & 46.9 & 120.2 & - \\  & PVT-Tiny [103] & 39.8 & 62.2 & 43.0 & 37.4 & 59.3 & 39.9 & 32.9 & - \\  & PVT-Small [103] & 43.0 & 65.3 & 46.9 & 39.9 & 62.5 & 42.8 & 44.1 & - \\  & PVT-Medium [104] & 44.2 & 66.0 & 48.2 & 40.5 & 63.1 & 43.5 & 63.9 & - \\  & PVT-Large [103] & 44.5 & 66.0 & 48.3 & 40.7 & 63.4 & 43.7 & 81.0 & - \\  & ViT-Triny-RPB [96] & 44.2 & 66.4 & 48.2 & 40.6 & 63.2 & 44.0 & 26.9 & 199 \\  & ViT-Small-RPB [96] & 47.1 & 68.7 & 51.5 & 42.7 & 65.9 & 46.2 & 45.0 & 277 \\  & ViT-Medium-RPFrom Table 5, it is evident that the characteristics of the three models become more pronounced under the Mask R-CNN \(3\times MS\) schedule. In object detection and instance segmentation tasks, CNN models, while suitable for resource-constrained environments, are somewhat less accurate.