pytorch-image-models/docs/models/.templates/models/resnet-d.md

ResNet-D

ResNet-D is a modification on the ResNet architecture that utilises an average pooling tweak for downsampling. The motivation is that in the unmodified ResNet, the 1×1 convolution for the downsampling block ignores 3/4 of input feature maps, so this is modified so no information will be ignored

{% 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{he2018bag,
      title={Bag of Tricks for Image Classification with Convolutional Neural Networks}, 
      author={Tong He and Zhi Zhang and Hang Zhang and Zhongyue Zhang and Junyuan Xie and Mu Li},
      year={2018},
      eprint={1812.01187},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}