## 5 Experimental Results
### Hardware Results
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Class** & **Prune30\%** & **Prune50\%** & **Prune80\%** & **Shortcut30\%** & **Shortcut50\%** & **Shortcut80\%** & **Slim30\%** & **Slim50\%** & **Slim80\%** \\ \hline Chopsticks-two & 0.869 & 0.789 & 0.545 & 0.872 & 0.706 & 0.012 & 0.748 & 0.135 & 0.148 \\ Cup & 0.987 & 0.981 & 0.982 & 0.986 & 0.984 & 0.549 & 0.985 & 0.159 & 0.138 \\ Water-cup & 0.976 & 0.983 & 0.963 & 0.984 & 0.974 & 0.255 & 0.987 & 0.717 & 0.427 \\ Tea-cup & 0.997 & 0.983 & 0.98 & 0.982 & 0.987 & 0.37 & 0.972 & 0.573 & 0.173 \\ Water-paper & 0.891 & 0.792 & 0.78 & 0.845 & 0.803 & 0.098 & 0.684 & 0373, & 0.217 \\ Chopsticks-one & 0.831 & 0.697 & 0.68 & 0.814 & 0.689 & 0.002 & 0.736 & 0.035 & 0.027 \\ Wine-cup & 1 & 1 & 1 & 1 & 1 & 1.74 & 1 & 0.732 & 0.316 \\ Fish-dish & 0.819 & 0.814 & 0.779 & 0.825 & 0.811 & 0.239 & 0.786 & 0.25 & 0.096 \\ Spoon & 0.847 & 0.808 & 0.682 & 0.851 & 0.741 & 0.06 & 0.748 & 0.041 & 0.041 \\ Sup-bowl & 0.961 & 0.95 & 0.841 & 0.979 & 0.972 & 0.368 & 0.97 & 0.586 & 0.754 \\ Square-bowl & 0.906 & 0.874 & 0.902 & 0.942 & 0.903 & 0.086 & 0.908 & 0.52 & 0.277 \\ Rice-bowl & 0.979 & 0.984 & 0.789 & 0.996 & 0.987 & 0.041 & 0.968 & 0.511 & 0.742 \\ Towel & 0.949 & 0.879 & 0.765 & 0.881 & 0.821 & 0.176 & 0.672 & 0.5 & 0.134 \\ Chopsticks-cover & 0.895 & 0.793 & 0.532 & 0.889 & 0.851 & 0.005 & 0.772 & 0.065 & 0.028 \\ Towel-dish & 0.917 & 0.543 & 0.568 & 0.81 & 0.713 & 0.053 & 0.747 & 0.344 & 0.019 \\ Paper & 0.931 & 0.827 & 0.357 & 0.618 & 0.884 & 0.453 & 0.726 & 0.667 & 0.237 \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Detection accuracy using sparse training Method A on FFGA. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Class** & **Origin** & **Sparse Method A** & **Sparse Method B** \\ \hline Chopsticks-two & 0.958 & 0.958 & 0.936 \\ Cup & 0.985 & 0.987 & 0.985 \\ Water-cup & 0.98 & 0.986 & 0.97 \\ Tea-cup & 0.999 & 0.989 & 0.999 \\ Water-paper & 0.992 & 0.955 & 0.965 \\ Chopsticks-one & 0.949 & 0.92 & 0.912 \\ Wine-cup & 1 & 1 & 1 \\ Fish-dish & 0.979 & 0.878 & 0.948 \\ Spoon & 0.94 & 0.932 & 0.921 \\ Soup-bowl & 0.969 & 0.953 & 0.969 \\ Square-bowl & 0.974 & 0.972 & 0.943 \\ Rice-bowl & 0.998 & 0.995 & 0.99 \\ Towel & 0.97 & 0.953 & 0.971 \\ Chopsticks-cover & 0.947 & 0.895 & 0.99 \\ Towel-dish & 0.966 & 0.997 & 0.936 \\ Paper & 0.97 & 0.96 & 0.964 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Detection accuracy of model before pruning on FPGA. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline
**Class** & **Prune50\%** & **Prune50\%** & **Prune80\%** & **Shortcut50\%** & **Shortcut50\%** & **Shortcut80\%** & **Slim30\%** & **Slim50\%** & **Slim80\%** \\ \hline mAP & 0.922 & 0.856 & 0.759 & 0.892 & 0.864 & 0.18 & 0.838 & 0.388 & 0.236 \\ Prune channels & 4012 & 6688 & 10,700 & 4618 & 7692 & 16,250 & 7777 & 12,877 & 20,633 \\ Prune ratio & 0.153 & 0.254 & 0.407 & 0.176 & 0.292 & 0.618 & 0.296 & 0.49 & 0.784 \\ Parameters & 40,257,085 & 26,227,273 & 9,942,629 & 37,017,469 & 21,481,070 & 6,684,490 & 21,124,184 & 6,788,468 & 6,246,82 \\ Size & 42 MB & 28 MB & 12 MB & 39 MB & 23 MB & 8936 KB & 23 MB & 9044 KB & 8460 KB \\ Time & 10.948 s & 8.780 s & 6.686 s & 10.430 s & 8.260 s & 6.487 s & 8.128 s & 7,853 s & 8.438 s \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Performance using sparse training Method A on FPGA. In this research, we mainly evaluate the results on FPGA, which are shown from Tables 2-7. Among these tables, Tables 2, 4 and 6 indicate detection accuracy of 16 different kinds of dishes, respectively. Tables 3, 5 and 7 indicate performance of testing all 68 testing images on FPGA. Many metrics are used for comparison of performance: mAP, prune channels, prune ratio, parameters, size, and time. Our aim is to achieve acceleration of the model as much as possible, while maintaining the detection accuracy, so it is ideal to obtain the minimum of time, while maintaining the maximum of mAP.