# Efficient Shot Detector: Lightweight Network Based on Deep Learning Using Feature Pyramid
## 4 Experiment Results
### Comparison to Other Networks  
The comparison with the latest detectors is performed to evaluate the performance of ESDet, as shown in Table 4. The proposed network showed competitive performance with 81.9% mAP in the PASCAL VOC 2007 test set. Moreover, it showed similar performance and faster detection speed compared with the latest two-stage detector, which has large input resolution and excellent performance. High-resolution images improve the detection success rate because they provide more features for small objects. Conversely, the detection accuracy of cases of the one-stage detector tends to be relatively low because the detection is performed with a small resolution. The proposed network has a faster detection speed than the latest detector because it has fewer parameters. In addition, the proposed network has a detection speed that is three times faster than that of the RefineDet512+ with the same input size. Most detectors use VGG-16 and ResNet-101 as backbones, and YOLO series uses Darknet as backbones. The aforementioned backbone networks have more parameters than EfficientNet because of the larger number of feature channels. In this study, we used EfficientNet with relatively few parameters. Results showed a good balance between accuracy and detection speed when compared with other conventional detectors. In addition, training in this experiment was also performed with MS COCO2017, and the detection results is shown in Table 5. Experimental settings were set similarly as PASCAL VOC. However, all detectors showed lower mAP results than PASCAL VOC since there are 80 classes to classify in MS COCO2017. \begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Detector** & **Backbone** & **Input Resolution** & **Proposals** & **FPS** & **mAP (\%)** \\ \hline
**Two-stage** & & & & & \\ Fast R-CNN [20] & VGG-16 & \(\sim\)1000 \(\times\) 600 & 2000 & 0.5 & 70.0 \\ Faster R-CNN [21] & VGG-16 & \(\sim\)1000 \(\times\) 600 & 300 & 7 & 73.2 \\ OHER [22] & VGG-16 & \(\sim\)1000 \(\times\) 600 & 300 & 7 & 74.6 \\ HyperNet [23] & VGG-16 & \(\sim\)1000 \(\times\) 600 & 100 & 0.88 & 76.3 \\ Faster R-CNN [21] & ResNet-101 & \(\sim\)1000 \(\times\) 600 & 300 & 2.4 & 76.4 \\ ION [24] & VGG-16 & \(\sim\)1000 \(\times\) 600 & 4000 & 1.25 & 76.5 \\ MR-CNN [25] & VGG-16 & \(\sim\)1000 \(\times\) 600 & 250 & 0.03 & 78.2 \\ R-FCN [26] & ResNet-101 & \(\sim\)1000 \(\times\) 600 & 300 & 9 & 80.5 \\ CoupleNet [27] & ResNet-101 & \(\sim\)1000 \(\times\) 600 & 300 & 8.2 & 82.7 \\ \hline
**One-stage** & & & & & \\ YOLO [28] & GoogleNet & \(448\times 448\) & 98 & 45 & 63.4 \\ RON [29] & VGG-16 & \(384\times 384\) & 30,600 & 15 & 75.4 \\ SSD321 [30] & Resnet-101 & \(321\times 321\) & 17,080 & 11.2 & 77.1 \\ SSD300 [12] & VGG-16 & \(300\times 300\) & 8732 & 46 & 77.2 \\ YOLOv2 [31] & Darknet-19 & \(544\times 544\) & 845 & 40 & 78.6 \\ DSSD321 [30] & Resnet-101 & \(321\times 321\) & 17,080 & 9.5 & 78.6 \\ SSD512 [12] & VGG-16 & \(512\times 512\) & 24,564 & 19 & 79.8 \\ SSD512 [30] & Resnet-101 & \(513\times 513\) & 43,688 & 6.8 & 80.6 \\ DSSD13 [30] & Resnet-101 & \(513\times 513\) & 43,688 & 5.5 & 81.5 \\ HSD512 [32] & VGG-16 & \(512\times 512\) & - & - & 83.0 \\ YOLOv3 + mixup [33] & Darknet-53 & \(416\times 416\) & 10,647 & 34.5 & 83.6 \\ RefineDet512+ [34] & VGG-16 & \(512\times 512\) & 16,320 & 24.1 & 83.8 \\ ESDet-baseline & EfficientNet-B0 & \(512\times 512\) & 16,368 & 99 & 81.9 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection results on PASCAL VOC 07 test data set. All networks were trained using VOC 07 and VOC 12 train sets. The AP evaluation method of MS COCO is different from PASCAL VOC. The AP in Table 5 uses only a value with an intersection of union (IoU) ratio between 50% and 95% between the predicted and groundtruth boxes. AP50 is the same evaluation method as PASCAL VOC, and AP75 is evaluated to predict only items with an IoU ratio of 75% or more. A high AP was achieved with fewer parameters compared with EfficientDet-50 using the same backbone.