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Codes of "SPANet: Spatial Pyramid Attention Network for Enhanced Image Recognition"

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Results on CIFAR100

The table prvoides the models and results of various models on CIFAR100. Learning rate =0.1 and will be divided by 10 every 70 epochs. Total 300 epochs. Using SGD optimizer, momentum=0.9, weight_decay=5e-4. Loss is CrossEntropyLoss. Batch-size=512.

Model Parameters Flops CIFAR-100
PreActResNet18 - - 74.91%
PreActResNet50 - - 77.39%
PreActResNet101 - - 77.74%
SEResNet18 - - 75.19%
SEResNet50 - - 77.91%
SEResNet101 - - 78.03%
PSEResNet18 - - 74.97%
PSEResNet50 - - 77.45%
PSEResNet101 - - 77.88%
CPSEResNet18 - - 75.25%
CPSEResNet50 - - 77.43%
CPSEResNet101 - - 77.61%
SPPSEResNet18 - - 75.41%
SPPSEResNet50 - 78.21%
SPPSEResNet101 - - 78.11
PSPPSEResNet18 - - 75.01%
PSPPSEResNet50 - - 78.11%
PSPPSEResNet101 - - 78.35%
CPSPPSEResNet18 - - 75.56%
CPSPPSEResNet50 - - 77.95%
CPSPPSEResNet101 - - 79.17%

For a better understanding, we reschedule the table as follows:

Model 18-Layer 50-Layer 101-Layer
PreActResNet 74.91% 77.39% 77.74%
SEResNet 75.19% 77.91% 78.03%
PSEResNet 74.97% 77.45% 77.88%
CPSEResNet 75.25% 77.43% 77.61%
SPPSEResNet 75.41% 78.21% 78.11%
PSPPSEResNet 75.01% 78.11% 78.35%
CPSPPSEResNet 75.56% 77.95% 79.17%

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Codes of "SPANet: Spatial Pyramid Attention Network for Enhanced Image Recognition"

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