pytorch-image-models/docs/models/.templates/models/regnety.md

RegNetY

RegNetY is a convolutional network design space with simple, regular models with parameters: depth d, initial width w\_{0} > 0, and slope w\_{a} > 0, and generates a different block width u\_{j} for each block j < d. The key restriction for the RegNet types of model is that there is a linear parameterisation of block widths (the design space only contains models with this linear structure):

 u\_{j} = w\_{0} + w\_{a}\cdot{j} 

For RegNetX authors have additional restrictions: we set b = 1 (the bottleneck ratio), 12 \leq d \leq 28, and w\_{m} \geq 2 (the width multiplier).

For RegNetY authors make one change, which is to include Squeeze-and-Excitation blocks.

{% include 'code_snippets.md' %}

How do I train this model?

You can follow the timm recipe scripts for training a new model afresh.

Citation

@misc{radosavovic2020designing,
      title={Designing Network Design Spaces}, 
      author={Ilija Radosavovic and Raj Prateek Kosaraju and Ross Girshick and Kaiming He and Piotr Dollár},
      year={2020},
      eprint={2003.13678},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}