# Res2Net: A New Multi-scale Backbone Architecture ## Introduction ```latex @article{gao2019res2net, title={Res2Net: A New Multi-scale Backbone Architecture}, author={Gao, Shang-Hua and Cheng, Ming-Ming and Zhao, Kai and Zhang, Xin-Yu and Yang, Ming-Hsuan and Torr, Philip}, journal={IEEE TPAMI}, year={2021}, doi={10.1109/TPAMI.2019.2938758}, } ``` ## Pretrain model The pre-trained models are converted from [official repo](https://github.com/Res2Net/Res2Net-PretrainedModels). ### ImageNet 1k | Model | resolution | Params(M) | Flops(G) | Top-1 (%) | Top-5 (%) | Download | |:---------------------:|:-----------:|:---------:|:---------:|:---------:|:---------:|:--------:| | 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)| | 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)| | 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)| *Models with \* are converted from other repos.*