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https://github.com/Res2Net/Res2Net-PretrainedModels
(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
https://github.com/Res2Net/Res2Net-PretrainedModels
backbone jittor multi-scale pytorch res2net
Last synced: about 2 months ago
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(ImageNet pretrained models) The official pytorch implemention of the TPAMI paper "Res2Net: A New Multi-scale Backbone Architecture"
- Host: GitHub
- URL: https://github.com/Res2Net/Res2Net-PretrainedModels
- Owner: Res2Net
- Created: 2019-07-04T03:35:43.000Z (about 5 years ago)
- Default Branch: master
- Last Pushed: 2022-12-08T12:54:57.000Z (almost 2 years ago)
- Last Synced: 2024-04-23T00:23:22.406Z (5 months ago)
- Topics: backbone, jittor, multi-scale, pytorch, res2net
- Language: Python
- Homepage: https://mmcheng.net/res2net/
- Size: 76.2 KB
- Stars: 1,040
- Watchers: 27
- Forks: 210
- Open Issues: 10
-
Metadata Files:
- Readme: README.md
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- awesome-speaker-embedding - Res2Net
README
# 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.
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](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!AkxDDnOtroRPcJRgTLkahL0cFYw?e=nwbnic)
| Res2NeXt-50 | 24.67M | 4.2 | 21.76 | 6.09 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPcWlWLXBuKxma7DQ?e=mt4dQf)
| Res2Net-DLA-60 | 21.15M | 4.2 | 21.53 | 5.80 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPbWAqdcatece24vs?e=t3shXH)
| Res2NeXt-DLA-60 | 17.33M | 3.6 | 21.55 | 5.86 |[OneDrive](https://1drv.ms/u/s!AkxDDnOtroRPcjxCM0kAYHEaEd0?e=9WrBpj)
| **Res2Net-v1b-50** | 25.72M | 4.5 | 19.73 | 4.96 |[Link](https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net50_v1b_26w_4s-3cf99910.pth)
| **Res2Net-v1b-101**| 45.23M | 8.3 | 18.77 | 4.64 |[Link](https://shanghuagao.oss-cn-beijing.aliyuncs.com/res2net/res2net101_v1b_26w_4s-0812c246.pth)
| **Res2Net-v1d-200-SSLD**| 76.21M | 15.7 | 14.87 | 2.58 |[PaddlePaddleLink](https://paddle-imagenet-models-name.bj.bcebos.com/Res2Net200_vd_26w_4s_ssld_pretrained.tar)
#### 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](https://pan.baidu.com/s/1BP7X222ZPqOndbojwOPjkw) 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.