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(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"

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Res2Net

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

Our paper is accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI).

Update

Introduction

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.

Sample

Res2Net module

Useage

Requirement

PyTorch>=0.4.1

Examples

git clone https://github.com/gasvn/Res2Net.git

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 MACCs 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
Res2Net-v1b-50 25.72M 4.5 19.73 4.96 Link
Res2Net-v1b-101 45.23M 8.3 18.77 4.64 Link
Res2Net-v1d-200-SSLD 76.21M 15.7 14.87 2.58 PaddlePaddleLink

News

  • Res2Net_v1b is now available.
  • You can load the pretrained model by using pretrained = True.

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

Applications

Other applications such as Classification, Instance segmentation, Object detection, Semantic segmentation, Salient object detection, Class activation map,Tumor segmentation on CT scans can be found on https://mmcheng.net/res2net/ .

Citation

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

@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}, 
}

Contact

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

License

The code is released under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International Public License for Noncommercial use only. Any commercial use should get formal permission first.

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(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"

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