# Block-Wisely Supervised Network Pruning with Knowledge Distillation and Markov Chain Monte Carlo
## 4 Experiments
### Results
#### 4.2.3 Results on ImageNet  
To further examine the effectiveness of BNP in pruning networks on large datasets, we applied it to ResNet-50 on ImageNet dataset. The performance comparison results of the SOTA methods are shown in Table 5. It is observed that our method is able to compress more FLOPs with less model accuracy loss when \(\alpha=0.1\). For instance, with a FLOP reduction rate of 51%, BNP shows similar Top-5 accuracy loss as that of the autopruner (AP) [32] (0.73% vs. 0.72%), while achieving less than Top-1 accuracy loss (1.27% vs. 1.39%). Compared with DSA [29], BNP eliminates more FLOPs as well as better maintains the model capability in both Top-1 and Top-5 accuracy. Our method also trims more FLOPs at less loss of accuracy when compared with channel pruning (CP) [1], FPGM [27], SCP [33], and the artificial bee colony algorithm (ABC) [34]. With the same FLOP reduction of 55%, BNP yields much better results than GAL [6] in maintaining both Top-1 and Top-5 accuracies. In addition, at a higher FLOP reduction rate of 63%, BNP still performs acceptably well in rebuilding the model capability, with only 1.93% of Top-1 and 1.09% of Top-5 accuracy loss, while removing 59.91% of parameters. By comparison, HRank [15] achieves 4.17% of Top-1 and 1.86% of Top-5 loss with 62.10% of FLOPs and 46.00% of parameters reduced. The results suggest BNP is effective in pruning networks even on more complex datasets.  
\begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Method** & **Paras\_\_\%** & **FLOPs\_\_\%** & **Baseline Acc** & **Acc\_\_\%** \\ \hline SFP [31] & - & 52.60 & 71.40 & 2.61 \\ FPGM [27] & - & 52.60 & 71.41 & 1.75 \\ LFPC [28] & - & 51.60 & 71.41 & 0.58 \\ \hline BNP (\(\alpha=0.1\)) & 58.08 & 53.78 & 70.53 & 0.55 \\ BNP (\(\alpha=0.1\)) & 51.94 & 61.34 & 70.53 & 0.97 \\ BNP (\(\alpha=0.2\)) & 59.73 & 62.94 & 70.53 & 1.80 \\ \hline \hline \end{tabular}
\end{table}
Table 4: Network pruning results of ResNet-56 on CIFAR-100.  
\begin{table}
\begin{tabular}{c c c c c c} \hline \hline
**Method** & **Paras\_\_\%** & **FLOPs\_\_\%** & **Top-1 Acc\_\%** & **Top-5 Acc\_\_\%** & **Top-1 Acc\_\_\%** & **Top-5 Acc\_\_\%** \\ \hline CP [1] & - & 50.00 & - & 92.20 & - & 1.40 \\ AP [32] & - & 51.20 & 76.15 & 92.87 & 1.39 & 0.72 \\ DSA [29] & - & 50.00 & 76.02 & 92.86 & 1.33 & 0.80 \\ FPGM [27] & - & 53.50 & 76.15 & 92.87 & 2.02 & 0.93 \\ GAL [6] & - & 55.00 & 76.15 & 92.87 & 4.35 & 2.05 \\ SCP [33] & - & 54.30 & 75.89 & 92.98 & 1.69 & 0.98 \\ ABC [34] & - & 54.00 & 76.01 & 92.96 & 2.15 & 1.27 \\ HRank [15] & 46.00 & 62.10 & 76.15 & 92.87 & 4.17 & 1.86 \\ \hline BNP (\(\alpha=0.1\)) & 47.73 & 51.31 & 77.73 & 93.83 & 1.27 & 0.73 \\ BNP (\(\alpha=0.1\)) & 41.89 & 55.00 & 77.73 & 93.83 & 1.50 & 0.91 \\ BNP (\(\alpha=0.1\)) & 59.91 & 63.04 & 77.73 & 93.83 & 1.93 & 1.09 \\ BNP (\(\alpha=0.2\)) & 61.05 & 70.63 & 77.73 & 93.83 & 3.10 & 2.37 \\ \hline \hline \end{tabular}
\end{table}
Table 5: Network pruning results of ResNet-50 on ImageNet. Where Top-1/Top-5 Acc denotes Top-1/Top-5 accuracy, Top-1 Acc\_/Top-5 Acc\_1 means accuracy loss.