# JANNet: A U-Shape Network with Jump Attention for Semantic Segmentation of Road Scenes
## 3 Results
### Experimental Evaluation
#### 3.5.3 Cityscape Dataset Results  
The variation of loss function for the training process of Cityscape dataset is shown in Figure 12.  
In the Cityscape dataset, the loss function of JAUNet is slightly higher than the other networks, which may be due to the higher complexity of the network making the convergence slower after the learning rate decreases. Similar to the training of the CamVid dataset, there were some fluctuations in the loss function during the training process, but they all basically leveled off after 100 epochs in the end. The objective evaluation metrics of the four models on the Cityscape dataset are shown in Table 6.  
The JAUNet proposed in this paper achieves 69.1% mIoU with 33.91 M parameters on the Cityscapes test set. Owing to more training images and higher resolution input images, better segmentation results could be achieved than on the CamVid dataset but accompanied by a decrease in inference speed. Compared to other neural networks, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Network** & **Pretrain** & **Speed/fps** & **Params/M** & **MIoU** & **FWIoU** & **GFLOPs** \\ \hline FCN-ResNet34 & No & 157.10 & 21.34 & 49.1 & 62.1 & 55.89 \\ ENet & No & 41.70 & 0.36 & 58.3 & 65.3 & 4.35 \\ SegNet & No & 35.13 & 29.45 & 56.1 & 65.1 & 104.54 \\ FastSCNN & No & 198.41 & 1.10 & 62.8 & 67.8 & 1.76 \\ ContextNet [38] & No & 176.60 & 0.88 & 65.5 & 71.2 & 1.78 \\ PSPNet-ResNet18 & No & 18.73 & 46.64 & 63.4 & 69.6 & 123.69 \\ GAU & No & 35.23 & 7.94 & 66.5 & 73.2 & 81.64 \\ CGNet [39] & No & 44.70 & 0.50 & 65.6 & 71.1 & 7.00 \\ EDANet & No & 105.50 & 0.68 & 67.3 & 73.2 & 9.00 \\ ICNet & No & 30.3 & 26.5 & 69.0 & 76.3 & 28.3 \\ DFANet A\({}^{\prime}\) & No & 100 & 7.8 & 70.3 & 77.1 & 3.4 \\ DFANet B & No & 120 & 4.8 & 67.1 & 71.9 & 2.1 \\ BiSeNetV1\_X & ImageNet & 105.80 & 5.80 & 68.4 & 75.8 & 14.90 \\ BiSeNetV1\_R & ImageNet & 65.50 & 49.0 & 74.7 & 81.3 & 55.3 \\ UNet & No & 36.73 & 7.76 & 64.3 & 70.5 & 119.03 \\ ConvLSTM\_UNet & No & 19.23 & 26.58 & 64.9 & 70.1 & 137.53 \\ JAUNet & No & 28.51 & 33.91 & 69.1 & 76.3 & 137.76 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Evaluation metrics for each model on the Cityscape dataset. Figure 12: Cityscape train loss. JAUNet can obtain better segmentation accuracy. Like the test results on the CamVid dataset, JAUNet's segmentation accuracy is higher than that of lightweight networks such as ENet and ContextNet, although the inference speed is slower. Compared with other encoder-decoder network structures, some inference speed is lost, but higher segmentation accuracy is obtained. The segmentation performance of JAUNet is still better than that of GAU, which also adds an attention mechanism, which means that the JAM module is better at extracting channel attention. The segmentation accuracy of JAUNet is similar to that of the Xception39-based network but inferior to that of the ResNet18-based network when compared to the BiSeNetV1 network pre-trained on ImageNet, which is already very good without pre-training on a large dataset. We also give results on the Cityscape dataset using ConvLSTM and UNet alone. Similar to CamVid, using the ConvLSTM module alone does not show much improvement for UNet, and we speculate that the slow convergence may be caused by too many parameters. The simultaneous use of the ConvLSTM and JAM modules also has a better performance on the Cityscape dataset. Figure 13 shows the variation of the validation set mIoU with the number of training rounds on the Cityscape dataset. On this dataset, SegNet's mIoU differs more from other UNet-based networks due to its use of only pooled index-assisted upsampling. JAUNet, on the other hand, has a higher segmentation accuracy than the UNet network after training 110 epochs, although its upsampling speed is slower. This is the same as the results obtained from testing on the CamVid dataset, which further illustrates the effectiveness of the JAM attention module.