# Efficient Semantic Segmentation Using Multi-Path Decoder
## 4 Results
### Ade20k  
Unlike Cityscapes dataset, images in ADE20K dataset have different sizes. To prove the robustness of our model, we evaluate our model on the ADE20K validation set. ADE20K segmentation dataset contains 150 foreground object categories and is divided into 20,210, 2000, 3000 images for training, validation and testing. To evaluate our model, we conduct experiments with several settings. The cropsize is set to 480 for ADE20K. We set maximum iteration number to 125K. If inputting single-scale images during evaluation, our model yields a result of 43.14/80.91 on ADE20K val set with only 246.9 GFLOPs@1 Mpx. If inputting multi-scale images during evaluation, the performance of our model can be promoted to 44.01/81.44. Experimental results are listed in Table 5. Table 6 shows the comparison with other models. Several examples are shown in Figure 3. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c c c c} \hline \hline  & **Road** & **Swalk** & **Build.** & **Wall** & **Fence** & **Pole** & **Tlight** & **Sign** & **veg.** & **Terrain** & **Sky** & **Person** & **Rider** & **Car** & **Truck** & **Bus** & **Train** & **mbike** & **Bike** & **mIoU** \\ \hline BL & 98.3 & 84.8 & 92.2 & 50.6 & 52.6 & 63.0 & 68.6 & 74.1 & 92.2 & 71.8 & 94.3 & 84.4 & 66.2 & 95.1 & 60.4 & 72.4 & 59.5 & 61.8 & 72.4 & 74.5 \\ +RB & 98.3 & 84.2 & 92.0 & 53.4 & 53.5 & 62.6 & 68.1 & 73.0 & 92.4 & 71.8 & 94.6 & 83.1 & 65.6 & 94.7 & 65.0 & 77.5 & 69.0 & 59.8 & 71.2 & 75.2 \\ +ED & 98.4 & 84.2 & 92.1 & 57.0 & 55.6 & 60.0 & 60.5 & 72.2 & 92.8 & 69.6 & 95.2 & 83.4 & 65.4 & 95.1 & 70.7 & 84.7 & 82.0 & 58.3 & 71.5 & 76.3 \\ +MPD & 98.5 & 85.4 & 92.3 & 51.9 & 56.1 & 61.2 & 67.6 & 74.4 & 92.9 & 71.2 & 95.2 & 83.9 & 66.7 & 95.4 & 72.7 & 83.0 & 76.0 & 62.8 & 72.6 & 76.8 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Comparison to state-of-the-art on Cityscapes dataset. “-” means that they are not reported in these papers. The column ‘Resolution’ shows the image resolution on which the inference were performed. The column ’Val mIoU’ shows results on Cityscapes validation set. The column ’Test mIoU’ shows results on Cityscapes test set. The column ’FLOPs’ shows the number of floating point operations. The column ’FPS’ shows the number of processed frames per second. \begin{table}
\begin{tabular}{c c c c c c c c c c c c c c c c c} \hline \hline
**Encoder** & **Resolution** & **RB** & **Edge Decoder** & **Multi-Path Decoder** & **MS** & **mIoU(\%)** & **FLOPs** \\ \hline ResNet-50 & 768 \(\times\) 1536 & & & & & & & & & 74.45 & 174.59G \\ ResNet-50 & 768 \(\times\) 1536 & \(\checkmark\) & & & & & & & 75.11 & 174.59G \\ ResNet-50 & 768 \(\times\) 1536 & \(\checkmark\) & & & & & & & 76.25 & 116.1G \\ ResNet-50 & 768 \(\times\) 1536 & \(\checkmark\) & \(\checkmark\) & & & & & 76.84 & 118.84G \\ ResNet-50 & 1024 \(\times\) 2048 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & & 78.06 & 211.27G \\ ResNet-101 & 1024 \(\times\) 2048 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & & 78.68 & 366.33G \\ ResNet-101 & 1024 \(\times\) 2048 & \(\checkmark\) & \(\checkmark\) & \(\checkmark\) & & \(\checkmark\) & & 79.99 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Investigation of MPDNet with different settings on the validation set of Cityscapes. ’RB’ denotes the lateral connection used in our model. The column ’Resolution’ shows the image resolution on which the inference were performed. ’MS’ denotes multi-scale testing. The column ’FLOPs’ shows the number of floating point operations.