ResNet-RS
ResNet-RS
**ResNet-RS** is a family of [ResNet](https://paperswithcode.com/method/resnet) architectures that are 1.7x faster than [EfficientNets](https://paperswithcode.com/method/efficientnet) 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](https://paperswithcode.com/method/cosine-annealing), [label smoothing](https://paperswithcode.com/method/label-smoothing), [stochastic depth](https://paperswithcode.com/method/stochastic-depth), [RandAugment](https://paperswithcode.com/method/randaugment), decreased [weight decay](https://paperswithcode.com/method/weight-decay), [squeeze-and-excitation](https://paperswithcode.com/method/squeeze-and-excitation-block) and the use of the [ResNet-D](https://paperswithcode.com/method/resnet-d) architecture.
Revisiting ResNets: Improved Training and Scaling Strategies
https://paperswithcode.com/paper/revisiting-resnets-improved-training-and
Revisiting ResNets: Improved Training and Scaling Strategies