# A Filter Pruning Method of CNN Models Based on Feature Maps Clustering
## 6 Conclusions  
This paper studies the filter pruning method in a convolutional neural network and proposes a filter selection method based on feature maps clustering. Through experiment, we verify the feasibility of this method. It is effective to select pruned filters based on feature maps clustering, and its precision is higher than that of a random selection of pruned filters. Among the two methods adopted in this thesis, the HCA method is superior to the K-Means method.  
The silhouette coefficient method is a feasible method to find the best clustering number. In general, when the number of layers is pruned to the same number as that of clusters, the detection precision of the model will not decrease much. The precision in the HCA method is higher and more robust. This work sets the number of initial centers in the K-Means method to 112 and the threshold in the HCA method to 0.049.  
The detection speed of the pruned model improves greatly. The improvement of computing speed in CPU and GPU is mainly dependent on the structure of the model. Models that require more parallel operations can save more computational time in GPU after pruning. In contrast, models that require more serial operations can save more time on the CPU.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Pruned** & **Pruning** & **GPU** & **CPU** & **Precision** \\
**Network** & **Ratio (\%)** & **Speed Up (\%)** & **Speed Up (\%)** & **Drop (\%)** \\ \hline FPN & 29.3 & 1.1 & 43.5 & 0.97 \\ FPN & 37.6 & 6.8 & 54.2 & 3.23 \\ FPN & 40.2 & 9.2 & 56.1 & 6.16 \\ FPN & 41.1 & 10.8 & 56.9 & 9.02 \\ FPN & 41.4 & 12.8 & 57.2 & 11.21 \\ Resnet18 & 59.9 & 15.8 & 10.9 & 10.04 \\ Resnet18 & 62.5 & 20.4 & 13.7 & 12.02 \\ Resnet18 & 63.7 & 21.3 & 18.7 & 17.42 \\ Both & 90.7 & 17.9 & 79.3 & 8.93 \\ Both & 95.7 & 30.6 & 78.7 & 12.93 \\ Both & 96.3 & 35.6 & 82.6 & 25.42 \\ \hline \hline \end{tabular}
\end{table}
Table 2: The statistics for the pruned network.  
\begin{table}
\begin{tabular}{c c c} \hline \hline
**FPN Layers** & **Precision (\%)** & **Speed(s)** \\ \hline
256 & 71.75 & 0.388 \\
128 & 70.78 & 0.219 \\
64 & 68.52 & 0.178 \\
32 & 65.59 & 0.170 \\ SSD & 60.73 & 0.195 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Comparison between SSD and Pruned Network.  
This paper also compared the pruned network with the existing lightweight network, SSD. By pruning, Retinanet exceeds the SSD in the precision and speed of the wide face dataset detection as shown in Table 3.  
In addition to the above research results, there are also many areas that need to be improved. There are a lot of map clustering methods, and it is better if the method is more robust and easy to determine the best clustering number. It also needs a more complete theory to clarify the relationship among feature maps.  
Conceptualization, Z.W. and F.L.; methodology, F.L. and Y.Z.; software, C.Z.; validation, K.L., M.W. and C.Z.; formal analysis, K.L.; investigation, M.W.; resources, Y.Z.; data curation, K.L.; writing--original draft preparation, F.L.; writing--review and editing, F.L.; visualization, F.L.; supervision, Z.W.; project administration, Y.Z.; funding acquisition, M.W. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Nanchang Automotive Institute of Intelligence & New Energy. Tongii University (NAIT) under the project the Perspective Study Funding of Nanchang Automotive Institute of Intelligence and New Energy. Tongji University (TPD-TC202110-13).  
Not applicable.  
The authors declare no conflict of interest.