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The official pytorch implemention of the paper "Res2Net: A New Multi-scale Backbone Architecture"
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The official pytorch implemention of the paper "Res2Net: A New Multi-scale Backbone Architecture"


We propose a novel building block for CNNs, namely Res2Net, by constructing hierarchical residual-like connections within one single residual block. The Res2Net represents multi-scale features at a granular level and increases the range of receptive fields for each network layer. The proposed Res2Net block can be plugged into the state-of-the-art backbone CNN models, e.g. , ResNet, ResNeXt, BigLittleNet, and DLA. We evaluate the Res2Net block on all these models and demonstrate consistent performance gains over baseline models.


Res2Net module



PyTorch>=0.4.1 (PyTorch1.0 and 1.1 are supported.)


git clone

from res2net import res2net50
model = res2net50(pretrained=True)

Input image should be normalized as follows:

normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                  std=[0.229, 0.224, 0.225])

(By default, the model will be downloaded automatically. If the default download link is not available, please refer to the Download Link listed on Pretrained models.)

Pretrained models

model #Params GFLOPs top-1 error top-5 error Link
Res2Net-50-48w-2s 25.29M 4.2 22.68 6.47 OneDrive
Res2Net-50-26w-4s 25.70M 4.2 22.01 6.15 OneDrive
Res2Net-50-14w-8s 25.06M 4.2 21.86 6.14 OneDrive
Res2Net-50-26w-6s 37.05M 6.3 21.42 5.87 OneDrive
Res2Net-50-26w-8s 48.40M 8.3 20.80 5.63 OneDrive
Res2Net-101-26w-4s 45.21M 8.1 20.81 5.57 OneDrive
Res2NeXt-50 24.67M 4.2 21.76 6.09 OneDrive
Res2Net-DLA-60 21.15M 4.2 21.53 5.80 OneDrive
Res2NeXt-DLA-60 17.33M 3.6 21.55 5.86 OneDrive

The download link from Baidu Disk is now available. (Baidu Disk password: vbix)



If you find this work or code is helpful in your research, please cite:

  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={arXiv preprint arXiv:1904.01169},


If you have any questions, feel free to E-mail me via: shgao(at)

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