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pytorch-image-models/docs/models/.templates/models/res2net.md

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Res2Net

Res2Net is an image model that employs a variation on bottleneck residual blocks, Res2Net Blocks. The motivation is to be able to represent features at multiple scales. This is achieved through a novel building block for CNNs that constructs hierarchical residual-like connections within one single residual block. This represents multi-scale features at a granular level and increases the range of receptive fields for each network layer.

{% include 'code_snippets.md' %}

How do I train this model?

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

Citation

@article{Gao_2021,
   title={Res2Net: A New Multi-Scale Backbone Architecture},
   volume={43},
   ISSN={1939-3539},
   url={http://dx.doi.org/10.1109/TPAMI.2019.2938758},
   DOI={10.1109/tpami.2019.2938758},
   number={2},
   journal={IEEE Transactions on Pattern Analysis and Machine Intelligence},
   publisher={Institute of Electrical and Electronics Engineers (IEEE)},
   author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
   year={2021},
   month={Feb},
   pages={652662}
}