# LRP-DS: Lightweight RepPoints with Decoupled Sampling Point Set
## 6 Conclusions  
This paper proposes a lightweight RepPoints with decoupled sampling point set (LRP-DS). The LRP-DS employs MobileNetV2 and FPN as the backbone network, in order to create a lightweight network and pursue fast detection speed. Considering the differences between classification and localization tasks, two classification free sampling methods, LRP-DS-V1 and LRP-DS-V2, are proposed for decoupling the sampling points of classification and localization. In order to split the responsibility of classification confidence, a localization score is introduced to describe the localization accuracy independently. The final architecture of this paper can achieve 73.3% mAP on the PASCAL VOC07 test dataset, which is better than RepPoints, Libra RCNN and other methods. The experimental results also verify the effectiveness of the proposed method LRP-DS.  
**Author Contributions:** Conceptualization, [PERSON], L.W. and F.G.; methodology, [PERSON]; validation, [PERSON]; formal analysis, [PERSON]; investigation, [PERSON]; resources, [PERSON]; data curation, [PERSON]; writing--original draft  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Detector** & **mAP** & **Aero** & **Bike** & **Bird** & **Boat** & **Bottle** & **Bus** & **Car** & **Cat** \\ \hline Faster R-CNN [3] & 70.3 & 70.6 & 79.5 & 74.0 & 56.1 & 46.2 & 77.5 & 77.2 & 85.4 \\ Libra R-CNN [14] & 72.5 & 72.9 & 81.7 & 74.4 & 60.4 & 51.4 & 75.4 & 81.6 & 85.2 \\ Double Head [24] & 72.9 & 69.6 & 82.4 & 71.7 & 61.1 & 54.0 & 80.1 & 82.9 & 85.4 \\ Retinanet [17] & 70.0 & 73.4 & 77.5 & 72.7 & 60.3 & 50.3 & 76.5 & 82.3 & 82.3 \\ FCOS [23] & 69.9 & 74.7 & 73.9 & 74.2 & 63.0 & 52.1 & 77.9 & 82.0 & 83.4 \\ RepPoints [6] & 71.4 & 74.0 & 80.3 & 73.1 & 62.7 & 53.9 & 79.3 & 82.9 & 85.9 \\ LRP-DS-V1 (ours) & 72.6 & 75.9 & 80.9 & 74.1 & 63.3 & 56.2 & 82.3 & 83.3 & 85.5 \\ LRP-DS-V2 (ours) & 73.3 & 76.4 & 79.2 & 74.6 & 64.3 & 58.2 & 81.6 & 83.4 & 86.4 \\ \hline \hline \end{tabular}  
* Due to GPU memory limitation, the batch size of the Double Head is set to 2.  
\end{table}
Table 4: The precision comparison with other detectors on PASCAL VOC07 test set.  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline
**Detector** & **mAP** & **MACC (G)** & **Params (M)** & **GPU Memory (G)** & **Time (ms)** & **FPS** \\ \hline Faster R-CNN [3] & 70.3 & 9.16 & 13.90 & 1.14 & 49.55 & 20.2 \\ Libra R-CNN [14] & 72.5 & 9.18 & 13.94 & 1.14 & 51.28 & 19.5 \\ Double Head [24] & 72.9 & 26.53 & 14.69 & 1.32 & 73.08 & 13.7 \\ Retinanet [17] & 70.0 & 8.54 & 12.34 & 1.09 & 45.59 & 21.9 \\ FCOS [23] & 69.9 & 8.54 & 12.34 & 1.09 & 53.74 & 18.6 \\ RepPoints [6] & 71.4 & 8.75 & 12.41 & 1.14 & 52.56 & 19.0 \\ LRP-DS-V1(ours) & 72.6 & 9.02 & 12.50 & 1.14 & 51.08 & 19.6 \\ LRP-DS-V2(ours) & 73.3 & 9.02 & 12.50 & 1.22 & 52.46 & 19.1 \\ \hline \hline \end{tabular}  
* Due to GPU memory limitation, the batch size of the Double Head is set to 2.  
\end{table}
Table 5: The Performance comparison with other detectors on PASCAL VOC07 test set.  
preparation, J.W.; writing--review and editing, F.G.; visualization, J.W.; supervision, L.W.; funding acquisition, F.G. All authors have read and agreed to the published version of the manuscript.  
This research is being supported by the National Key Research and Development Project of China under Grant No. 2020 AAA0104001, the Zhejiang Lab. under Grant No. 2019 KD0 AD011005, the Zhejiang Provincial Science and Technology Planning Key Project of China under Grant No. 2021C03129 and Scientific Research Fund of Zhejiang Provincial Education Department of China under Grant No. Y201941873.  
Not applicable.  
The authors declare no conflict of interest.