# Containment Control-Guided Boundary Information for Semantic Segmentation
## 4 Experiment
### Quantitative Analysis  
**Cityscapes**  
Our quantitative analysis focuses on evaluating the performance of CcNet against state-of-the-art semantic segmentation models using the Cityscape dataset. Table 1 provides a comprehensive overview of the performance of various models, including CcNet, on different metrics. CcNet exhibits very competitive performance, with mIOUs of 78.5% and 81.2% for the small variant (CcNet-S) and the large variant (CcNet-L). In comparison with AMKBANet-T, CcNet-L attains superior performance while featuring a smaller parameter size. These results demonstrate the efficacy of the proposed models in achieving high segmentation accuracy. CcNet-S with 83.4 FPS and CcNet-L with 30.2 FPS belong to the real-time semantic segmentation models. CcNet shows good results in different sizes of models. CcNet performs well in models of different sizes and always maintains a very competitive performance. In conclusion, our quantitative analysis emphasizes the strong performance  
Figure 5: Illustration of FEU module, consisting of a split stage and restructure stage, with the objective of enhancing the crucial features. of CcNet in semantic segmentation tasks. This is strong evidence that containment control can have a positive impact on the results of semantic segmentation tasks. **CamVid**  
Our quantitative evaluation is centered on assessing the efficacy of CcNet in comparison to state-of-the-art semantic segmentation models, employing the CamVid dataset as depicted in Table 2. In terms of mIOU, CcNet emerges as the superior performer, achieving an impressive score of 80.6%. While CcNet shares a similar scale with PIDNet-S, its performance does not surpass that of PIDNet-S-Wider; however, CcNet demonstrates faster inference. Additionally, CcNet-s attains an impressive rate of 182 frames per second on a RTX 4090. This underscores the suitability of the overall CcNet model for GPU computations, with the inference speed exhibiting a notable dependence on the scale of the input image. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Model** & **GPU** & **Resolution** & **mIOU (\%)** & **\#FPS** & **GFLOPs** & **Params (M)** \\ \hline CABiNet [30] & GTX 2080 Ti & 2048 \(\times\) 1024 & 76.6 & 76.5 & 12.0 & 2.64 \\ \hline AMKBANet-T [19] & A100 & 2048 \(\times\) 1024 & 81.1 & - & 884 & 64 \\ \hline BiSeNetV2 [5] & GTX 1080 Ti & 1536 \(\times\) 768 & 74.8 & 65.5 & 55.3 & 49 \\ BiSeNetV2-L [5] & GTX 1080 Ti & 1024 \(\times\) 512 & 75.8 & 47.3 & 118.5 & - \\ \hline STDC1-Seg75 [6] & RTX 3090 & 1536 \(\times\) 768 & 74.5 & 74.8 & - & - \\ STDC2-Seg75 [6] & RTX 3090 & 1536 \(\times\) 768 & 77.0 & 58.2 & - & - \\ \hline PP-LiteSeg-T2 [14] & RTX 3090 & 1536 \(\times\) 768 & 76.0 & 96.0 & - & - \\ PP-LiteSeg-B2 [14] & RTX 3090 & 1536 \(\times\) 768 & 78.2 & 68.2 & - & - \\ \hline HyperSeg-M [31] & RTX 3090 & 1024 \(\times\) 512 & 76.2 & 59.1 & 7.5 & 10.1 \\ HyperSeg-S [31] & RTX 3090 & 1536 \(\times\) 768 & 78.2 & 45.7 & 17.0 & 10.2 \\ \hline DDRNet-23-S [15] & RTX 3090 & 2048 \(\times\) 1024 & 77.8 & 108.1 & 36.3 & 5.7 \\ DDRNet-23 [15] & RTX 3090 & 2048 \(\times\) 1024 & 79.5 & 51.4 & 143.1 & 20.1 \\ MRFNet-S [32] & GTX 1080 Ti & 512 \(\times\) 1024 & 79.3 & 144.5 & - & - \\ MRFNet-L [32] & GTX 1080 Ti & 1024 \(\times\) 1024 & 79.9 & 73.63 & - & - \\ \hline PIDNet-S-Simple [24] & RTX 3090 & 2048 \(\times\) 1024 & 78.8 & 100.8 & 46.3 & 7.6 \\ PIDNet-S [24] & RTX 4090 & 2048 \(\times\) 1024 & 78.8 & 93.2 & 47.6 & 7.6 \\ PIDNet-M [24] & RTX 3090 & 2048 \(\times\) 1024 & 80.1 & 39.8 & 197.4 & 34.4 \\ PIDNet-L [24] & RTX 4090 & 2048 \(\times\) 1024 & 80.9 & 31.1 & 275.8 & 36.9 \\ \hline CcNet-S & RTX 4090 & 2048 \(\times\) 1024 & 78.5 & 83.4 & 55.0 & 7.85 \\ CcNet-L & RTX 4090 & 2048 \(\times\) 1024 & 81.2 & 30.2 & 290.2 & 37.4 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of accuracy and speed on Cityscapes.