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(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
| Date | Stars |
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| 2026-07-24 | 1115 |
| 2026-07-25 | 1115 |
| 2026-07-28 | 1115 |
| 2026-07-30 | 1115 |
| 2026-08-06 | 1115 |
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# Res2Net
The official pytorch implemention of the paper ["Res2Net: A New Multi-scale Backbone Architecture"](https://arxiv.org/pdf/1904.01169.pdf)
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](https://github.com/PaddlePaddle/PaddleClas/blob/master/docs/en/advanced_tutorials/distillation/distillation_en.md).
- 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**](https://github.com/Jittor/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.
<p align="center">
<img src="https://mftp.mmcheng.net/imgs800/19Res2Net.jpg" alt="Sample" width="500">
<p align="center">
<em>Res2Net module</em>
</p>
</p>
## 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](https://1drv.ms/u/s!AkxDDnOtroRPbo7RnRUz-7ejhLg?e=gU2EZG)
| Res2Net-50-26w-4s | 25.70M | 4.2 | 22.01 | 6.15 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPbMavn7eawKhvCPY?e=TBHOuT)
| Res2Net-50-14w-8s | 25.06M | 4.2 | 21.86 | 6.14 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPdOTqhF8ne_aakDI?e=EVb8Ri)
| Res2Net-50-26w-6s | 37.05M | 6.3 | 21.42 | 5.87 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPc2mqy1h8324sxxI?e=Go4p7I)
| Res2Net-50-26w-8s | 48.40M | 8.3 | 20.80 | 5.63 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPdTrAd_Afzc26Z7Q?e=slYqsR)
| Res2Net-101-26w-4s | 45.21M | 8.1 | 20.81 | 5.57 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPExcerpt of 6,025 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:dc87a0e1c4b63184, topic:pytorch, readme:deep learning framework
