# Accelerated Inference of Face Detection under Edge-Cloud Collaboration
## 4 Evaluation
### Deconvolution Pruning Results  
The CenterNet network is an end-to-end detection model based on center point regression. This algorithm uses the resnet residual network as the backbone network to extract the features of the input data and tests its performance on the WIDER FACE dataset. For the deconvolution layer, this algorithm tries to use the pruning strategy to remove redundant features and makes a comparison with the traditional pruning convolution  
\begin{table}
\begin{tabular}{c c c c} \hline \hline \multirow{2}{*}{**Component**} & **Cloud** & **NVIDIA** & \multirow{2}{*}{**PC**} \\  & **Server** & **JETSON NANO** & \\ \hline \multirow{2}{*}{GPU} & Two NVIDIA & NVIDIA & NVIDIA \\  & GeForce RTX 3090 & Maxwell w/128 & \multirow{2}{*}{GeForce GTX 1060} \\ \hline \multirow{2}{*}{CPU} & Intel(R) Xeon(R) & quad-core ARM & Intel(R) Core (TM) \\  & silver 4210 CPU @ 2.20 GHz & Cortex-A57 64-bit & i7-8750H CPU @ 2.20 GHz \\ \hline Memory & 128 GB DDR4 & 4 GB LPDDR4 & 16 GB DDR4 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Details of hardware resource configuration.  
layer, which is represented as backbone pruning in the table (see Table 2 for details). On the one hand, the AP values of three different indexes (easy, medium, hard) for different models in the WIDER FACE dataset are kept within a 2% accuracy loss compared to the model with a compression rate of 1. For the pruning model with a compression rate of 0.8, compared with the baseline, whether it is backbone pruning or deconvolution pruning, the AP value improved, and the hard class has the most obvious improvement. On the other hand, the AP value, the amount of calculation, and the number of parameters have different degrees of improvement under the same compression rate for backbone pruning and deconvolution pruning. In the experiments, the input resolution size is set to \(3\times 512\times 512\) when calculating FLOPs. For deconvolution pruning with a compression ratio of 0.6, compared with the baseline, not only is the loss of AP value kept within a reasonable range, but the amount of computation is also reduced by about 59.33%, and the number of parameters is reduced by 63.47%. The experimental results show that the deconvolution pruning algorithm can fully reduce the calculation amount and parameter amount of the CenterNet model, and has a good acceleration inference effect.  
In addition, this algorithm compares other outstanding face detection models on the WIDER FACE dataset, Pyramidbox [1], S3 FD [33], and SSH [34] (see Table 3 for details). For the sake of fairness, the validation datasets are all the validation datasets in the WIDER FACE dataset and the same input resolution is set to \(3\times 640\times 480\) when calculating FLOPs. Compared with the advanced face detection model, although this algorithm sacrifices the accuracy of the model, it greatly reduces the calculation amount and parameter amount of the model. Compared with the calculation amount of Pyramidbox, this algorithm is 8% of it, and the parameter amount is 10.1% of it. Through the experimental results in Tables 2 and 3, deconvolution pruning can achieve a good balance between accuracy and model size.