# Lightweight Attention Pyramid Network for Object Detection and Instance Segmentation
## 4 Results and Discussion
### Experiment on the 2018 WAD Dataset  
We followed the standard evaluation metrics: the mAP, i.e., AP, AP\({}_[EMAIL_ADDRESS]}\), AP\({}_[EMAIL_ADDRESS]}\), AP\({}_{S}\), AP\({}_{M}\), and AP\({}_{L}\). Specifically, our average precision was calculated based on the top 100 proposals. We also used two images in one image batch for the training. Because the dataset is full of images with high resolutions, we chose the minimum and maximum resolutions of the resized images to be 800 and 1024. We adopted stochastic gradient descent training on two NVidia TitanX GPUs with a batch size of one. The initial learning rate was set to 0.001, and it was decreased by a factor of 0.1 after 100 k and 120 k iterations and finally terminated at 140 k iterations. We did not adopt any augmentation for the training, which we found made it harder to converge. Owing to the small size of the mini-batch, we chose to freeze the batch normalization layer. The learning rate was different from the Mask R-CNN and the FPN because of the different platforms. Other implementation details were the same as [PERSON] et al. (2018), and models were trained on the training subset and used ResNet-101 [PERSON] et al. (2018). Moreover, Figure 7 shows part of the results of object detection. Table 5 shows the object detection results for the comparison of our model to the baseline FPN. We present the results with ResNet-50 pre-trained on the COCO dataset, which was also trained and tested on single-scale images. According to the results, our model outperforms the baseline by a large margin over all of the compared evaluation metrics. This represents an improvement of 1.7 points for _AP_ compared with the original module. To specify the effectiveness of our module, we divided the test-dev into separate classes. Table 5 presents the results for different separated classes in the test-dev dataset. Note that the results of the APN show better results across the detection results of all sizes, and achieves nearly two points over all classes and all sizes on average. The performance of \begin{table}
\begin{tabular}{c c c c} \hline  & **AP** & **Million Parameters** & **Backbone** \\ \hline Mask R-CNN + FPN & 37.9 & 84.3 & ResNet-50 \\ Mask R-CNN + FPN & 38.2 & 184.7 & ResNet-101 \\ Mask R-CNN + APN(only adaptive transformation) & 38.0(+0.1) & 90.1(+5.8) & ResNet-50 \\ Mask R-CNN + APN(adaptive transformation+attention block) & 40.3(+2.4) & 136.7(+52.4) & ResNet-50 \\ Mask R-CNN + APN & 41.0(+3.1) & 158.9(+74.6) & ResNet-50 \\ \hline \end{tabular}
\end{table} 
Table 4: Ablation experiments: We trained on trainval35k, tested on val-2017 or test-dev, and report the value of the AP. the separated classes also improves significantly, which indicates the benefit of using a fusion module with contextual information. Therefore, the APN proposed by us is better than the FPN in target recognition of different categories or the whole. Similar to the object detection results on the Mask R-CNN, we also report the instance segmentation performance of our proposed module on the WAD test-dev for comparison. As shown in Table 6, compared with the baseline, our APN consistently improves the performance across different evaluation metrics. The result of the segmentation on the evaluation metrics shows a significant improvement of 1.3 points for AP compared with the original FPN. In Figure 7, we can see the results of instance segmentation.