# SMS-Net: Bridging the Gap Between High Accuracy and Low Computational Cost in Pose Estimation
## 4 Experimental Results
### Results
#### 4.4.1 MPII
\begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Params** & **GFLOPs** & **AP** & **AP50** & **AP75** & **APM** & **APL** & **AR** \\ \hline \multicolumn{7}{c}{Large models} \\ \hline HRNet [3] & 25.1 M & 7.10 & 73.4 & 89.5 & 80.7 & 70.2 & 80.1 & 78.9 \\
8-stage hourglass & 28.5 M & 14.3 & 66.9 & - & - & - & - \\ Network [2] & 27.0 M & 6.20 & 68.6 & - & - & - & - \\ Simple Baselines [11] & 34.0 M & 8.90 & 70.4 & 88.6 & 78.3 & 67.1 & 77.2 & 76.3 \\ TransPose-H-A6 [4] & 17.5 M & 21.8 & 75.8 & - & - & - & 80.8 \\ ViTPose-B [5] & 14.0 M & 17.1 & 75.8 & 90.7 & - & - & - & 81.8 \\ \hline \multicolumn{7}{c}{Small models} \\ \hline MobileNetV2 [22] & 9.60 M & 1.48 & 64.6 & 87.4 & 72.3 & 61.1 & 71.2 & 70.7 \\ ShuffleNetV2 [18] & 7.60 M & 1.28 & 59.9 & 85.4 & 66.3 & 56.6 & 66.2 & 66.4 \\ Lite-HRNet-18 [17] & 1.10 M & 0.20 & 64.8 & 86.7 & 73.0 & 62.1 & 70.5 & 71.2 \\ Lite-HRNet-30 [17] & 1.80 M & 0.31 & 67.2 & 88.0 & 75.0 & 64.3 & 73.1 & 73.3 \\ Dite-HRNet-18 [17] & 1.10 M & 0.20 & 65.9 & 87.3 & 74.0 & 63.2 & 71.6 & 72.1 \\ Dite-HRNet-30 [24] & 1.80 M & 0.32 & 68.3 & 88.2 & 76.2 & 65.5 & 74.1 & 74.2 \\ Greit-HRNet-18 [25] & 1.10 M & 0.20 & 65.8 & 87.7 & 73.8 & 63.2 & 71.3 & 71.8 \\ Greit-HRNet-30 [25] & 1.80 M & 0.40 & 68.2 & 88.1 & 76.0 & 65.4 & 73.8 & 74.1 \\ EL-HRNet-W32 [26] & 5.00 M & 2.00 & 67.1 & 86.4 & 74.2 & 65.3 & 72.0 & 74.9 \\ LMFormer-T [27] & 0.50 M & 0.20 & 56.5 & 82.8 & 62.5 & - & - & 63.4 \\ LMFormer-B [27] & 1.90 M & 0.70 & 65.8 & 87.0 & 73.8 & - & - & 71.9 \\ LMFormer-L [27] & 4.10 M & 1.40 & 68.9 & 88.3 & 76.4 & - & - & 74.7 \\ SMS-Net-Tiny & 0.26 M & 0.16 & 54.5 & 83.1 & 58.9 & 52.4 & 57.8 & 58.5 \\ SMS-Net-Small & 0.38 M & 0.22 & 61.1 & 86.4 & 67.4 & 59.2 & 64.5 & 64.8 \\ SMS-Net-Large & 0.63 M & 0.35 & 66.0 & 89.4 & 73.0 & 63.9 & 69.4 & 69.4 \\ SMS-Net-Huge & 1.14 M & 0.59 & 69.6 & 90.6 & 77.3 & 67.4 & 73.6 & 72.9 \\ \hline \end{tabular}
\end{table} 
Table 2: Pose estimation accuracy results for the COCO validation dataset (256 \(\times\) 192). \begin{table}
\begin{tabular}{c c c c c c c} \hline
**Model** & **Params** & **GFLOPs** & **AP** & **AP50** & **AP75** & **APM** & **APL** & **AR** \\ \hline \multicolumn{7}{c}{Large models} \\ \hline HRNet [3] & 25.1 M & 7.10 & 73.4 & 89.5 & 80.7 & 70.2 & 80.1 & 78.9 \\
8-stage hourglass & 28.5 M & 14.3 & 66.9 & - & - & - & - & - \\ Network [2] & 27.0 M & 6.20 & 68.6 & - & - & - & - & - \\ Simple Baselines [11] & 34.0 M & 8.90 & 70.4 & 88.6 & 78.3 & 67.1 & 77.2 & 76.3 \\ TransPose-H-A6 [4] & 17.5 M & 21.8 & 75.8 & - & - & - & 80.8 \\ ViTPose-B [5] & 14.0 M & 17.1 & 75.8 & 90.7 & - & - & - & 81.8 \\ \hline \multicolumn{7}{c}{Small models} \\ \hline MobileNetV2 [22] & 9.60 M & 1.48 & 64.6 & 87.4 & 72.3 & 61.1 & 71.2 & 70.7 \\ ShuffleNetV2 [18] & 7.60 M & 1.28 & 59.9 & 85.4 & 66.3 & 56.6 & 66.2 & 66.4 \\ Lite-HRNet-18 [17] & 1.10 M & 0.20 & 64.8 & 86.7 & 73.0 & 62.1 & 70.5 & 71.2 \\ Lite-HRNet-30 [17] & 1.80 M & 0.31 & 67.2 & 88.0 & 75.0 & 64.3 & 73.1 & 73.3 \\ Dite-HRNet-18 [17] & 1.10 M & 0.20 & 65.9 & 87.3 & 74.0 & 63.2 & 71.6 & 72.1 \\ Dite-HRNet-30 [24] & 1.80 M & 0.32 & 68.3 & 88.2 & 76.2 & 65.5 & 74.1 & 74.2 \\ Greit-HRNet-18 [25] & 1.10 M & 0.20 & 65.8 & 87.7 & 73.8 & 63.2 & 71.3 & 71.8 \\ Greit-HRNet-30 [25] & 1.80 M & 0.40 & 68.2 & 88.1 & 76.0 & 65.4 & 73.8 & 74.1 \\ EL-HRNet-W32 [26] & 5.00 M & 2.00 & 67.1 & 86.4 & 74.2 & 65.3 & 72.0 & 74.9 \\ LMFormer-T [27] & 0.50 M & 0.20 & 56.5 & 82.8 & 62.5 & - & - & 63.4 \\ LMFormer-B [27] & 1.90 M & 0.70 & 65.8 & 87.0 & 73.8 & - & - & 71.9 \\ LMFormer-L [27] & 4.10 M & 1.40 & 68.9 & 88.3 & 76.4 & - & - & 74.7 \\ SMS-Net-Tiny & 0.26 M & 0.16 & 54.5 & 83.1 & 58.9 & 52.4 & 57.8 & 58.5 \\ SMS-Net-Small & 0.38 M & 0.22 & 61.1 & 86.4 & 67.4 & 59.2 & 64.5 & 64.8 \\ SMS-Net-Large & 0.63 M & 0.35 & 66.0 & 89.4 & 73.0 & 63.9 & 69.4 & 69.4 \\ SMS-Net-Huge & 1.14 M & 0.59 & 69.6 & 90.6 & 77.3 & 67.4 & 73.6 & 72.9 \\ \hline \end{tabular}
\end{table} 
Table 1: _Cont._