## 4 Experimental Results
### ResNet on ImageNet  
We evaluate our pruning method with ResNet-18, ResNet-34, and ResNet-50 models on the ILSVRC-2012 (ImageNet) dataset. Each model is evaluated with two different FLOPs reduction rates to show the robustness of the proposed pruning method. The ResNet-18 and ResNet-34 model is evaluated with 41% and 45% FLOPs reduction rate whereas ResNet-50 is evaluated with 42% and 53% FLOPs reduction rate. For all experiments on the ImageNet dataset, we used combination \(l_{1}\)-norm and euclidean similarity based filter selection criteria with partition ratio \(w_{mag}=0.5\) in Algorithm 1. The choice of those criteria and partition rate is motivated by the results in Table 1 and Table 3 for VGGNet and ResNet models pruning on the CIFAR-10 dataset. Table 5 demonstrate the superiority of our method with several other state-of-the-art pruning methods such as SFP [13], FPGM [15], MetaPruning [51], MFP [16], LFC [52], FuPruner [53], GFI-AP [25] etc. when pruning ResNet on ImageNet dataset. The SFP [13], FPGM [15], and MFP [16] have competitive results with our method but use time-consuming soft filter pruning. Also, an extra step is required to get slimmed pruned model after final finetune as filters are not removed permanently during pruning. In contrast, our method is simple, and the same training routine as the original model training can be used for retraining the pruned model. From Table 5, we can see that, with similar pruning rates, for the ResNet models with depths 18, 34, and 50, our method consistently outperforms other listed methods in the table. Notably, for the ResNet-50 model, although our baseline accuracy is the highest among others, pruning with 42.4% FLOPs reduction rate, there is only a 0.15% top-1 accuracy drop. Similarly, in the case of the ResNet-34 model, when pruning with 41.1% FLOPs reduction rate, only our method exceeds 73% top-1 accuracy. Again, in the case of the ResNet-50 model, with 42.4% FLOPs reduction rate, our method is the first to exceed 76% top-1 accuracy. We also notice that as depth increases accuracy drop w.r.t. baseline model accuracy decreases. For example, with 41% FLOPs reduction rate, for the ResNet-18 model, top-1 accuracy is dropped by 2.16%, whereas for the ResNet-34 model, the top-1 accuracy drop is only 0.85%. Moreover, for the ResNet-50 model, with only a 1.11% top-1 accuracy drop, we can achieve almost 54% FLOPs reduction rate. \begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline \multirow{2}{*}{**Depth**} & \multirow{2}{*}{**Method**} & **Baseline** & **Pruned** & **Top-1 Acc.** & **Baseline** & **Pruned** & **Top-5 Acc.** & **FLOPs** \\  & & **Top-1 Acc.** & **Top-1 Acc.** & **(\(\downarrow\))** & **Top-5 Acc.** & **Top-5 Acc.** & **Top-5 Acc.** & (\(\downarrow\)) & (\(\downarrow\)) \\  & & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** & **(\%)** \\ \hline \multirow{8}{*}{18} & MIL [47] & 69.98 & 66.33 & 3.65 & 89.24 & 86.94 & 2.30 & 34.6 \\  & SFP [13] & 70.28 & 67.10 & 3.18 & 89.63 & 87.78 & 1.85 & 41.8 \\  & FPCM [15] & 70.28 & 68.41 & 1.87 & 89.63 & **88.48** & 1.15 & 41.8 \\  & FuPruner [53] & 69.76 & 68.24 & **1.52** & 89.08 & 88.21 & **0.87** & 41.8 \\  & MFP [16] & 70.28 & 68.31 & 1.97 & 89.63 & 88.28 & 1.35 & 41.8 \\  & MSVFP(ours) & **70.65** & **68.49** & 2.16 & **89.73** & 88.44 & 1.29 & 41.4 \\  & MSVFP(ours) & **70.65** & 68.43 & 2.22 & **89.73** & 88.40 & 1.33 & **45.4** \\ \hline \multirow{8}{*}{34} & PFEC [24] & 73.23 & 72.17 & 1.06 & - & - & - & 24.2 \\  & SFP [13] & 73.92 & 71.83 & 2.09 & 91.62 & 90.33 & 1.29 & 41.1 \\ \cline{1-1}  & FPCM [15] & 73.92 & 72.63 & 1.29 & 91.62 & 91.08 & 0.54 & 41.1 \\ \cline{1-1}  & FuPruner [53] & 73.32 & 72.14 & 1.18 & 91.42 & 90.66 & 0.75 & 41.1 \\ \cline{1-1}  & MSVFP(ours) & **74.04** & **73.20** & **0.84** & **91.70** & 91.14 & 0.56 & 41.1 \\ \cline{1-1}  & MSVFP(ours) & **74.04** & 73.07 & 0.97 & **91.70** & **91.21** & **0.49** & **45.3** \\ \hline \hline \end{tabular}
\end{table} 
Table 5: Comparison of pruned ResNet models on ImageNet dataset.