# Detail Guided Multilateral Segmentation Network for Real-Time Semantic Segmentation
## 4 Experimental Results
### Comparison to State-of-the-Art Methods
#### 4.4.1 Comparative Experiments in Cityscapes
\begin{table}
\begin{tabular}{l l l l l l l l l l l l} \hline \hline
**Method** & **Retrain** & **Backbone** & **Resolution** & **Device** & **Params (M)** & **GFLOPs** & **FPS** & **mIoU** \\ \hline SegNet [4] & ImageNet & VGG16 & \(360\times 640\) & - & 29.5 & 286.0 & 14.6 & 57.0 \\ FCN-85 [12] & ImageNet & VGG16 & \(512\times 1024\) & - & 136.2 & 2.0 & 65.3 \\ DeepLabv2 [15] & ImageNet & ResNet101 & \(512\times 1024\) & Titan X & 44 & 457.8 & \textless{}1 & 70.4 \\ SA-FFNet [25] & ImageNet & ResNet101 & \(768\times 768\) & Titan X & 57.5 & - & - & 73.1 \\ RefineNet [26] & ImageNet & ResNet101 & \(512\times 1024\) & Titan X & 118.1 & 428.3 & 9 & 73.6 \\ PSPNet [13] & ImageNet & ResNet101 & \(713\times 713\) & Titan X & 250.8 & 412.2 & 0.78 & 78.4 \\ FLANet [28] & ImageNet & HRNet2-WA84 & \(768\times 768\) & & 436 & 19.37 & - & 78.9 \\ AttaNet [27] & ImageNet & DF2 & \(512\times 1024\) & GTX 1080 Ti & - & - & 71 & 79.9 \\ DeepLabV3 [17] & ImageNet & ResNet101 & \(769\times 769\) & Tesla K80 & - & - & 1.3 & 81.3 \\ Lawin [38] & ImageNet & Swin-L & \(1024\times 1024\) & Tesla V100 & - & 1797 & - & 84.4 \\ ViT-Adapter-L [39] & ImageNet & ViT-Adapter-L & \(896\times 896\) & Tesla V100 & 571 & - & - & 85.2 \\ \hline \hline ENet [18] & No & No & \(630\times 630\) & Titan X & 0.4 & 4.4 & 76.9 & 58.3 \\ ESPNet [29] & No & ESPNet & \(512\times 1024\) & Titan X & 0.36 & 4.0 & 112 & 60.2 \\ ERFNet [30] & ImageNet & VGG16 & \(512\times 1024\) & Titan X & 2.1 & 26.8 & 49 & 68.0 \\ ICNet [5] & ImageNet & PSPNet50 & \(1024\times 2048\) & Titan X & 26.5 & 29.8 & 30.3 & 69.5 \\ AGLNet [31] & No & No & \(512\times 1024\) & GTX 1080 Ti & 1.12 & 13.9 & 52 & 70.1 \\ DABNet [32] & No & No & \(1024\times 2048\) & GTX 1080 Ti & 0.76 & 10.5 & 27.7 & 70.1 \\ LEDNet [33] & No & SSNRet & \(512\times 1024\) & GTX 1080 Ti & 0.94 & 11.5 & 71 & 70.6 \\ TinyMseg [40] & No & No & \(768\times 1536\) & GTX 1080 Ti & 0.7 & 3.0 & 172.4 & 71.4 \\ DSANet [34] & No & FDDSNet & \(512\times 1024\) & GTX 1080 Ti & 3.47 & 37.4 & 34 & 71.4 \\ FDDWNet [35] & No & No & \(512\times 1024\) & GTX 2080 Ti & 0.8 & 8.5 & 60 & 71.5 \\ BiSeNet [27] & No & No & \(512\times 1024\) & GTX 1080 Ti & - & 11.5 & 156 & 72.6 \\ STDC-Seg [8] & No & STDC & \(512\times 1024\) & GTX 1080 Ti & - & - & 188.6 & 73.4 \\ LMFFNet [36] & No & LMFFNet & \(512\times 1024\) & GTX 3090 & - & 16.7 & 118.9 & 75.1 \\ \hline DGMNet & No & MobileNet v3-small & \(512\times 1024\) & GTX 2080 Ti & 2.4 & 13.4 & 141.5 & 76.9 \\ DGMNet\_2 & No & ResNet 101 & \(512\times 1024\) & GTX 2080 Ti & 97.2 & 264.6 & 14.9 & 81.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison of our method and other state-of-the-art methods on the Cityscapes test dataset. \begin{table}
\begin{tabular}{l l l l l l l l l l l l l l l l l l l l} \hline \hline
**Method** & **Roa** & **Sid** & **Bui** & **Wal** & **Fen** & **Pol** & **Lig** & **Sig** & **Veg** & **Ter** & **Sky** & **Tra** & **Tru** & **Bus** & **Car** & **Mot** & **Bic** & **Per** & **Rid** & **mIoU(s)** \\ \hline SegNet [4] & 96.4 & 73.2 & 84.0 & 28.4 & 29.0 & 35.7 & 39.8 & 45.1 & 87.0 & 63.8 & 91.8 & 44.1 & 38.1 & 43.1 & 89.3 & 35.8 & 51.9 & 62.8 & 42.8 & 57.0 \\ FCN [12] & 97.4 & 78.4 & 89.2 & 34.9 & 44.2 & 47.4 & 60.1 & 65.0 & 91.4 & 69.3 & 93.9 & 46.5 & 35.3 & 48.6 & 92.6 & 51.6 & 66.8 & 77.1 & 51.4 & 65.3 \\ DeepLabv2 [15] & 97.9 & 81.3 & 90.3 & 48.8 & 47.4 & 49.6 & 57.9 & 67.3 & 91.9 & 66.4 & 94.2 & 57.2 & 56.5 & 67.5 & 93.7 & 57.7 & 68.8 & 79.8 & 59.8 ary, which further verifies the effectiveness of our proposed method for the segmentation of thin and small objects.