# Benchmarking Deep Learning Models for Instance Segmentation
## 5 Experiments
### Quantitative Results
#### 5.1.5 GPU GFLOPS versus Inference Speed  
Additionally, we investigated the GFLOPS of the basic model of the workstation GPU in Table 4; Figure 18 compares the GFLOPS and speed of each method. We confirmed that the speed is proportional to the GFLOPS, but the increase is not as significant.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Method** & **Tested GPU** & **Tested FPS** &
\begin{tabular}{c} **Relative Performance (\%)** \\ **RTX 3090/GTX 1080 Ti** \\ \end{tabular} \\ \hline \multirow{2}{*}{YolactEdge} & RTX 3090 & 53.5/177.3 * & \multirow{2}{*}{164/217 *} \\ \cline{2-3}  & GTX 1080 Ti & 32.6/81.6 * & \\ \hline \multirow{2}{*}{Blendmask} & RTX 3090 & 52.2 & \multirow{2}{*}{138} \\ \cline{2-3}  & GTX 1080 Ti & 37.8 & & \\ \hline \multirow{2}{*}{Centermask} & RTX 3090 & 45.2 & \multirow{2}{*}{147} \\ \cline{2-3}  & GTX 1080 Ti & 30.8 & & \\ \hline \multirow{2}{*}{YOLACT} & RTX 3090 & 41 & \multirow{2}{*}{191} \\ \cline{2-3}  & GTX 1080 Ti & 21.5 & \\ \hline \multirow{2}{*}{SOLOv2} & RTX 3090 & 37 & \multirow{2}{*}{139} \\ \cline{2-3}  & GTX 1080 Ti & 26.6 & \\ \hline \multirow{2}{*}{Mask R-CNN} & RTX 3090 & 23.2 & \multirow{2}{*}{198} \\ \cline{2-3}  & GTX 1080 Ti & 11.7 & \\ \hline \multirow{2}{*}{SOTR} & RTX 3090 & 8.1 & \multirow{2}{*}{198} \\ \cline{2-3}  & GTX 1080 Ti & 4.1 & \\ \hline \multirow{2}{*}{Dual-Swin-L} & RTX 3090 & 1.9 & \multirow{2}{*}{238} \\ \cline{2-3}  & GTX 1080 Ti & 0.8 & \\ \hline \hline \end{tabular}
\end{table}
Table 3: MS COCO results on the RTX 3090 and GTX 1080 Ti. The relative performance on the RTX 3090 was quantified with respect to the FPS of the GTX 1080 Ti for each model (“*” indicates the FPS results with NVIDIA TensorRT [22] optimization).  
Figure 18: Comparison of GFLOPS according to speed for each inference GPU.  
Figure 19 shows that, the accuracy-focused models segmented most of the objects on the central dining table. The speed-focused models segmented fewer objects on the dining table; however, CenterMask is the only model that recognized the knife on the dining table, and it obtained segmentation results that are similar to those of the accuracy-focused models. Among the speed-focused models, YOLACT recognized only one object on the dining table and YolactEdge did not recognize any.  
Figure 21: _Cont_.  
Figure 20: Visualization of instance segmentation results (COCO-val2017-000000380913 [10]). (**a**) Dual-Swin-L; (**b**) SOTR; (**c**) Mask R-CNN; (**d**) BlendMask; (**e**) CenterMask; (**f**) SOLOv2; (**g**) YOLACT; (**h**) YolactEdge.  
Figure 20 shows that, Dual-Swin-L and CenterMask segmented the inner side of the subway train. All models except for BlendMask segmented at least one cell phone object held by people. It can be confirmed that the mask areas of different objects overlap in the Dual-Swin-L, CenterMask, and YOLACT results.  
Figure 21 shows that, Mask R-CNN, CenterMask, and SOLOv2 segmented the necktie object from a human that was wearing one. Again, the mask regions of different objects overlap in the results of Mask R-CNN, CenterMask, YOLACT, and YolactEdge. In the results of CenterMask, YOLACT, and YolactEdge, there are empty spaces inside the mask regions and several disconnected mask areas.