31 lines
1.4 KiB
Markdown
31 lines
1.4 KiB
Markdown
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# Res2Net: A New Multi-scale Backbone Architecture
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<!-- {Res2Net} -->
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## Introduction
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<!-- [ALGORITHM] -->
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```latex
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@article{gao2019res2net,
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title={Res2Net: A New Multi-scale Backbone Architecture},
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author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip},
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journal={IEEE TPAMI},
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year={2021},
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doi={10.1109/TPAMI.2019.2938758},
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}
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```
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## Pretrain model
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The pre-trained models are converted from [official repo](https://github.com/Res2Net/Res2Net-PretrainedModels).
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### ImageNet 1k
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| Model | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Download |
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|:---------------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:--------:|
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| Res2Net-50-14w-8s\* | 224x224 | 25.06 | 4.22 | 78.14 | 93.85 | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w14-s8_3rdparty_8xb32_in1k_20210927-bc967bf1.pth)|
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| Res2Net-50-26w-8s\* | 224x224 | 48.40 | 8.39 | 79.20 | 94.36 | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net50-w26-s8_3rdparty_8xb32_in1k_20210927-f547a94b.pth)|
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| Res2Net-101-26w-4s\* | 224x224 | 45.21 | 8.12 | 79.19 | 94.44 | [model](https://download.openmmlab.com/mmclassification/v0/res2net/res2net101-w26-s4_3rdparty_8xb32_in1k_20210927-870b6c36.pth)|
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*Models with \* are converted from other repos.*
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