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2.2 KiB
2.2 KiB
Res2NeXt
Res2NeXt is an image model that employs a variation on ResNeXt bottleneck residual 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={652–662}
}