Methods › Computer Vision › Convolutional Neural Networks › ResNet-RS

ResNet-RS

2 papers tagged archive 2025-07-28

Introduced by Irwan Bello et al. in Revisiting ResNets: Improved Training and Scaling Strategies

archive 2025-07-28 Description, source and code snippet are the archive's method entry.

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.

PaperSource

Papers archive 2025-07-28

2 shown of 2, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.

Tasks archive 2025-07-28

7 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.

TaskPapers
Action Classification2
Contrastive Learning1
Document Image Classification1
Image Classification1
Semantic Object Interaction Classification1
Video Classification1
Video Recognition1

Usage over time archive 2025-07-28

Papers per year tagged with ResNet-RS: 2021 to 2021, peak 2 2 0 2021: 2 papers 2021
Papers per year the archive tags with this method, by the paper's archive date (2 dated). Bars are counts, not a trend claim.

Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).

Categories archive 2025-07-28

Convolutional Neural Networks

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