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

• 2020.10.20 PaddlePaddle version Res2Net achieves 85.13% top-1 acc. on ImageNet: PaddlePaddle Res2Net.
• 2020.8.21 Online demo for detection and segmentation using Res2Net is released: http://mc.nankai.edu.cn/res2net-det
• 2020.7.29 The training code of Res2Net on ImageNet is released https://github.com/Res2Net/Res2Net-ImageNet-Training (non-commercial use only)
• 2020.6.1 Res2Net is now in the official model zoo of the new deep learning framework Jittor.
• 2020.5.21 Res2Net is now one of the basic bonebones in MMDetection v2 framework https://github.com/open-mmlab/mmdetection. Using MMDetection v2 with Res2Net achieves better performance with less computational cost.
• 2020.5.11 Res2Net achieves about 2% performance gain on Panoptic Segmentation based on detectron2 with no trick. We have released our code on: https://github.com/Res2Net/Res2Net-detectron2.
• 2020.2.24 Our Res2Net_v1b achieves a considerable performance gain on mmdetection compared with existing backbone models. We have released our code on: https://github.com/Res2Net/mmdetection. Detailed comparision between our method and HRNet, which previously generates best results, could be found at: https://github.com/Res2Net/mmdetection/tree/master/configs/res2net
• 2020.2.21: Pretrained models of Res2Net_v1b with more than 2% improvement on ImageNet top1 acc. compared with original version of Res2Net are released! Res2Net_v1b achieves much better performance when transfer to other tasks such as object detection and semantic segmentation.

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.

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.