# Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation
## 4 Results and Discussion
### Experiment on the MS COCO Dataset
We report the performance of the proposed APN on the COCO dataset for comparison. As shown in Table 2, the APN applied to Mask R-CNN trained and tested on single-scale images already outperforms the FPN applied to Mask R-CNN, and both use ResNet-50 as their backbone network. All instantiations of our proposed model outperform the base variants of the latter model by nearly 2.0 points on average. \begin{table}
\begin{tabular}{c c c c c c c} \hline \hline  & \(\mathbf{AP^{M}}\) & \(\mathbf{AP^{M}_{0.5}}\) & \(\mathbf{AP^{M}_{0.75}}\) & \(\mathbf{AP^{M}_{S}}\) & \(\mathbf{AP^{M}_{M}}\) & \(\mathbf{AP^{M}_{L}}\) \\ \hline Mask R-CNN + FPN & 46.5 & 72.4 & 51.0 & 29.2 & 50.6 & 59.7 \\ Mask R-CNN + APN & 48.3 & 74.7 & 53.1 & 31.2 & 52.4 & 61.4 \\ \hline  & +1.8 & +2.3 & +2.1 & +2.0 & +2.4 & 1.7 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: The value of instance segmentation results for the COCO dataset. All entries are single-model results. Figure 5: Selected examples from the MS COCO 2017 test dataset using our method and other methods using the FPN model. Moreover, Figure 6 shows the partial examples of the segmentation results using the APN. In Figure 6, we can see that different classes of objects can be accurately segmented. In Table 3, we compare two different designs of our fusion block. Our simple channel-wise addition fusion design results in a severe decrease in bounding box loss _AP_ (2.4 points). Compared with the simple channel-wise addition fusion strategy, the convolutional fusion strategy takes all pixels from the feature maps into the calculation. This suggests that the more the spatial information is considered in the fusion block, the better are the results the fusion block gains. The result was tested on the MS COCO val-2017 set, and the backbone network was ResNet-50. Thus, adding a convolutional layer to fuse the reweighted feature map obtains greater gains over simple channel-wise fusion. We further tested the efficiency of the APN for the task of object detection on the COCO set test-dev. The comparison in Table 4 reveals that, with much fewer parameters, the APN compared with the FPN when the backbone is ResNet-101 can still achieve better performance. Based on the same backbone of ResNet-50, the proposed APN can achieve much better detection results. As shown in Table 4, the APN based on the ResNet-50 is 2.8 points better than the FPN based on the ResNet-101 with nearly 20 million fewer parameters and the smaller backbone of ResNet-50. As the performance of ResNeXt-101 is better than ResNet-50, and with a larger backbone model, the performance of the APN can achieve better results. To test the efficiency of our design, we carried out more ablation studies of the individual module of the APN. Figure 2 shows that the feature attention fusion block we proposed is composed of the adaptive transformation module and feature attention block and fusion block. Next, we tested the detection performance of the small module in the feature attention fusion block when it is assembled separately and multiplied. In Table 4, we can see that the third row is the case where the APN only contains the adaptive transformation module, and the fourth row the simultaneous existence of the adaptive transformation module and the feature attention block. The fusion block used in these two cases is channel-wise addition fusion. The fusion block used by the APN in the last row of the table is convolutional fusion, which means that this APN used a complete feature attention fusion block structure. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **AP** & **AP\({}_{\textbf{00.5}}\)** & **AP\({}_{\textbf{00.75}}\)** & **AP\({}_{\textbf{S}}\)** & **AP\({}_{\textbf{M}}\)** & **AP\({}_{\textbf{L}}\)** \\ \hline Mask R-CNN + APN(channel-wise addition fusion) & 53.0 & 76.2 & 59.9 & 35.9 & 58.0 & 64.1 \\ Mask R-CNN + APN(convolutional fusion) & 55.4 & 78.5 & 63.2 & 38.7 & 60.6 & 66.4 \\ \hline  & +2.4 & +2.3 & +3.3 & +2.8 & +1.4 & +2.3 \\ \hline \hline \end{tabular}
\end{table}