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pytorch-image-models/modelindex/.templates/models/fbnet.md

2.1 KiB

Summary

FBNet is a type of convolutional neural architectures discovered through DNAS neural architecture search. It utilises a basic type of image model block inspired by MobileNetv2 that utilises depthwise convolutions and an inverted residual structure (see components).

The principal building block is the FBNet Block.

{% 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{wu2019fbnet,
      title={FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Search}, 
      author={Bichen Wu and Xiaoliang Dai and Peizhao Zhang and Yanghan Wang and Fei Sun and Yiming Wu and Yuandong Tian and Peter Vajda and Yangqing Jia and Kurt Keutzer},
      year={2019},
      eprint={1812.03443},
      archivePrefix={arXiv},
      primaryClass={cs.CV}
}