# Object Detection Algorithm Based on Multiheaded Attention
## 4 Experiments
### Results on Pascal VOC 2007  
MANet was developed with PyTorch v0.4.1. We conducted experiments on a machine with 2 NVIDIA 1080 Ti graphics processing units (GPUs), CUDA8.0 and cuDNN 6.0.21. The input sizes were set to 300 and 320. Owing to memory limitations, we set the batch size to 32 to train the model. When the input size is 512, the batch size is set to 12 to train the model. Similar to the original SSD [2] model, we used a weight attenuation of 0.0005 and a momentum of 0.9. First, a stochastic gradient descent (SGD) [27] optimizer with an initial learning rate of 0.001) was used, but the training process was not so stable owing to the abrupt loss fluctuations. To solve this problem, we used a warm-up strategy by training the model at a learning rate of 0.001 in the first two epochs (an epoch refers to the process of completing a forward calculation and backpropagation in all data feeds into the network). After the warm-up phase, the process returned to the original learning rate plan, which was reduced by a factor of 10 at the 150 th and 200 th epochs. Training was executed up to the 250 th epoch before the process was terminated.  
Table 2 shows the results of our tests on the PASCAL VOC 2007 test set. We set the batch size to 1, take the sum of the CNN time and NMS time of 4000 images, and divide by 4000 to get the inference time of a single image. For fair comparison, we reproduce and test the speed of SSD300-VGG16 on our device. By replacing the VGG [24] with ResNet-101 [25], the performance becomes lower than the original SSD (input resolution size is 320 and 300). This may be related to the capability of the feature layer. The original SSD only uses the feature maps at the different depths for prediction and does not fuse the low-level and high-level features. We used the original SSD as the baseline of our experiment. We know from the table results that we have improved the detection rate on the basis of the original SSD512 [2] (the backbone network is VGG16) by 2.9%. At the same time, our MANet320 can achieve  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**VGG** & **Conv4\_3** & **Conv7** & **Conv8\_2** & **Conv9\_2** & **Conv10\_2** & **Conv11\_2** \\ \hline Resolution & 38 \(\times\)38 & 19 \(\times\)19 & 10 \(\times\)10 & 5 \(\times\)5 & 3 \(\times\)3 & 1 \\ Depth & 13 & 20 & 22 & 24 & 26 & 27 \\ \hline
**ResNet-101** & **Conv3\_x** & **Conv5\_x** & **Conv6\_x** & **Conv7\_x** & **Conv8\_x** & **Conv9\_x** \\ \hline Resolution & 40 \(\times\)40 & 20 \(\times\)20 & 10 \(\times\)10 & 5 \(\times\)5 & 3 \(\times\)3 & 1 \\ Depth & 23 & 101 & 104 & 107 & 110 & 113 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Selected feature layers in VGG and ResNet–101.  
a mAP of 80.2%, which is improved by 1.6% compared with the DSSD321. In addition, when our image input size is 512, MANet leads to some improvement compared with other multiscale fusion methods (such as FSSD [18] and ESSD [19]). This proves the effectiveness of MANet. To compare the performance of SSD and MANet in an intuitive manner, as shown in Figure 4, we randomly sampled a certain number of images from the PASCAL VOC test set and compared the outcomes.