## 6 Analysis and Discussion
### Analysis on Sparsity Training  
By making comparisons on the results in general, the performance of sparse training Method B is better than sparse training Method A. In terms of the mAP for both methods:  
* If we use sparse training Method A, then no matter which pruning method is used, the final mAP will drop significantly as the pruning ratio rises--especially when the Slim pruning method is used, only when the pruning ratio is set to 30%, it can achieve mAP of more than 80%, and when the pruning ratio is 50% or 80%, the mAP cannot even reach 40%, indicating that the model accuracy has been significantly affected. \begin{table}
\begin{tabular}{c c c c c c c c c c} \hline \hline
**Class** & **Prune30\%** & **Prune50\%** & **Prune80\%** & **Shortcut30\%** & **Shortcut50\%** & **Shortcut80\%** & **Slim30\%** & **Slim50\%** & **Slim80\%** \\ \hline Chopsticks-two & 0.882 & 0.887 & 0.867 & 0.906 & 0.907 & / & 0.944 & 0.906 & 0.602 \\ Cup & 0.983 & 0.977 & 0.948 & 0.99 & 0.988 & / & 0.969 & 0.833 & 0.816 \\ Water-cup & 0.978 & 0.975 & 0.975 & 0.963 & 0.979 & / & 0.972 & 0.978 & 0.977 \\ Tea-cup & 1 & 0.988 & 0.985 & 0.999 & 0.999 & / & 1 & 0.988 & 0.906 \\ Waster-paper & 0.923 & 0.961 & 0.945 & 0.974 & 0.989 & / & 0.992 & 0.971 & 0.872 \\ Chopsticks-one & 0.943 & 0.924 & 0.918 & 0.951 & 0.922 & / & 0.879 & 0.878 & 0.718 \\ Wine-cup & 1 & 0.998 & 1 & 1 & 0.998 & / & 0.998 & 0.991 & 0.998 \\ Fish-dish & 0.886 & 0.8 & 0.837 & 0.859 & 0.889 & / & 0.86 & 0.434 & 0.882 \\ Spoon & 0.917 & 0.914 & 0.882 & 0.899 & 0.898 & / & 0.928 & 0.915 & 0.815 \\ Soup-bowl & 0.969 & 0.963 & 0.967 & 0.971 & 0.969 & / & 0.967 & 0.971 & 0.957 \\ Square-bowl & 0.966 & 0.941 & 0.931 & 0.953 & 0.94 & / & 0.944 & 0.869 & 0.651 \\ Rice-bowl & 0.986 & 0.997 & 0.989 & 0.987 & 0.986 & / & 0.986 & 0.994 & 0.818 \\ Towel & 0.963 & 0.913 & 0.968 & 0.945 & 0.938 & / & 0.965 & 0.982 & 0.962 \\ Chopsticks-cover & 0.942 & 0.919 & 0.972 & 0.892 & 0.925 & / & 0.892 & 0.895 & 0.839 \\ Towel-dish & 0.998 & 0.999 & 0.983 & 0.999 & 0.994 & / & 0.992 & 0.962 & 0.912 \\ Paper & 0.925 & 0.931 & 0.891 & 0.916 & 0.951 & / & 0.929 & 0.974 & 0.901 \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Detection accuracy using sparse training Method B on FPGA. \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 mAP & 0.954 & 0.943 & 0.941 & 0.95 & 0.955 & / & 0.951 & 0.909 & 0.852 \\ Prune channels & 4012 & 6688 & 10,700 & 5643 & 9555 & 19,725 & 777 & 12,957 & 20,698 \\ Prune ratio & 0.153 & 0.254 & 0.407 & 0.215 & 0.363 & 0.75 & 0.296 & 0.493 & 0.787 \\ Parameters & 40,534,434 & 26,372,586 & 9,900,539 & 35,527,894 & 18,907,404 & 2,095,122 & 23,217,395 & 9,604,954 & 5,709,836 \\ Size & 42 MB & 28 MB & 12 MB & 37 MB & 20 MB & 3481 KB & 25 MB & 12 MB & 7793 KB \\ Time & 10,687 s & 8.892 s & 6.828 s & 9.614 s & 7.208 s & / & 8.440 s & 6.692 s & 5.925 s \\ \hline \hline \end{tabular}
\end{table} 
Table 7: Performance using sparse training Method B on FPGA. * If we use sparse training Method B, we can find that the decrease of mAP is not obvious; even when the Slim method is used and the pruning ratio is set to 80%, which has the largest decrease of accuracy, its mAP only decreases about 11% and is much better compared with sparse training Method A. It can be seen that sparse training is very critical, and different parameter setting during sparse training affects the effect of pruning greatly. In fact, sparse training means the balance of accuracy and sparsity, and it is worth studying how to find a good strategy to make the sparse model achieve high accuracy as well as high sparsity. According to the experience, for the coefficient \(s\) multiplied on the scaling factor \(\gamma\), the large \(s\) generally sparse trains the model faster but the accuracy drops fast, and the small \(s\) generally sparsely trains the model slower, but the accuracy drops slowly.