The official 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).
Please go to the project page http://mmcheng.net/res2net/ and the github page https://github.com/res2net to get the source code of applications using Res2Net such as Classification, Instance segmentation, Object detection, Semantic segmentation, Weakly supervised segmentation, Video classification, Salient object detection, Class activation map,Tumor segmentation on CT scans.
- 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.
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
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.)
| 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 |
| 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_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)
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/ .
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={2020},
doi={10.1109/TPAMI.2019.2938758},
}
If you have any questions, feel free to E-mail me via: shgao(at)live.com