Accueil Actualités Articles de recherche récents Tutoriels Ensembles de données Wiki SOTA Modèles LLM Classement GPU Événements

Français

Network Pruning On Imagenet

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
MobileNetV1-50% FLOPs	70.7	EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	-
ResNet50-2.3 GFLOPs	78.79	Pruning Filters for Efficient ConvNets	-
ResNet50-1G FLOPs	74.2	EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	-
ResNet50-1G FLOPs	74.2	EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	-
ResNet50-1.5 GFLOPs	78.07	Pruning Filters for Efficient ConvNets	-
TAS-pruned ResNet-50	76.20	Network Pruning via Transformable Architecture Search	-
ResNet50	73.14	AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks	-
ResNet50-2G FLOPs	76.4	EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	-
ResNet50-1G FLOPs	76.376	Pruning Filters for Efficient ConvNets	-
SqueezeNet (6-bit Deep Compression)	57.5%	SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size	-
RegX-1.6G	77.97	Group Fisher Pruning for Practical Network Compression	-
ResNet50-3G FLOPs	77.1	EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning	-
ResNet50 2.0 GFLOPS	77.70	Knapsack Pruning with Inner Distillation	-
ResNet50 2.5 GFLOPS	78.0	Knapsack Pruning with Inner Distillation	-
ResNet50	75.59	Network Pruning That Matters: A Case Study on Retraining Variants	-
MobileNetV2	73.42	Group Fisher Pruning for Practical Network Compression	-

0 of 16 row(s) selected.

Network Pruning On Imagenet | SOTA | HyperAI