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Network Pruning On Imagenet

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Model Name
Accuracy
Paper TitleRepository
MobileNetV1-50% FLOPs70.7EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning-
ResNet50-2.3 GFLOPs78.79Pruning Filters for Efficient ConvNets-
ResNet50-1G FLOPs74.2EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning-
ResNet50-1G FLOPs74.2EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning-
ResNet50-1.5 GFLOPs78.07Pruning Filters for Efficient ConvNets-
TAS-pruned ResNet-5076.20Network Pruning via Transformable Architecture Search-
ResNet5073.14AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks-
ResNet50-2G FLOPs76.4EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning-
ResNet50-1G FLOPs76.376Pruning 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-1.6G77.97Group Fisher Pruning for Practical Network Compression-
ResNet50-3G FLOPs77.1EagleEye: Fast Sub-net Evaluation for Efficient Neural Network Pruning-
ResNet50 2.0 GFLOPS77.70Knapsack Pruning with Inner Distillation-
ResNet50 2.5 GFLOPS78.0Knapsack Pruning with Inner Distillation-
ResNet5075.59Network Pruning That Matters: A Case Study on Retraining Variants-
MobileNetV273.42Group Fisher Pruning for Practical Network Compression-
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Network Pruning On Imagenet | SOTA | HyperAI