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Table 5 Results of pruning ResNet-50 on ILSVRC-12

From: An efficient pruning scheme of deep neural networks for Internet of Things applications

Method Top-1 Top-1 Top-5 Top-5 FLOPs Parameters
  baseline (%) Accuracy (%) baseline (%) Accuracy (%) pruned (%) pruned (%)
CP[31] - - 92.20 1.40 50.0 -
ThiNet[20] 72.88 1.87 91.14 1.12 55.8 51.6
SFP[37] 76.15 1.54 92.87 0.81 41.8 -
CFP[32] - - 92.20 0.80 49.6 -
DCP[30] 76.01 1.06 92.93 0.61 55.7 51.5
FPGM[45] 76.15 1.21 92.87 0.48 42.2 -
PFS[38] 77.20 1.60 - - 51.2 57.2
GAL[35] 76.15 4.20 92.87 1.93 43.0 16.9
ASS[39] 76.01 2.49 92.96 1.45 56.6 56.0
Ours 76.13 2.38 92.86 1.09 49.5 66.3