RegX-1.6G

Reported on 3 benchmarks across 1 task · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Methodology3 results

Network PruningonImageNet
Accuracy· 2021-08-02
77.97
best: 78.79 (ResNet50-2.3 GFLOPs)
Group Fisher Pruning for Practical Network Compression arXiv:2108.00708
Network PruningonImageNet
GFLOPs· 2021-08-02
1.588
best: 2.5 (ResNet50 2.5 GFLOPS)
Group Fisher Pruning for Practical Network Compression arXiv:2108.00708
Network PruningonImageNet
MParams· 2021-08-02
9.3
best: 14.811 (ResNet50-2.3 GFLOPs)
Group Fisher Pruning for Practical Network Compression arXiv:2108.00708