ResNet-RS is a family of ResNet architectures that are 1.7x faster than EfficientNets on TPUs, while achieving similar accuracies on ImageNet. The authors propose two new scaling strategies: (1) scale model depth in regimes where overfitting can occur (width scaling is preferable otherwise); (2) increase image resolution more slowly than previously recommended.
Additional improvements include the use of a cosine learning rate schedule, label smoothing, stochastic depth, RandAugment, decreased weight decay, squeeze-and-excitation and the use of the ResNet-D architecture.
Source: Revisiting ResNets: Improved Training and Scaling StrategiesPaper | Code | Results | Date | Stars |
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Task | Papers | Share |
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Action Classification | 2 | 33.33% |
Video Recognition | 1 | 16.67% |
Document Image Classification | 1 | 16.67% |
Image Classification | 1 | 16.67% |
Video Classification | 1 | 16.67% |