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ResNeSt: Split-Attention Network

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Split-Attention Network, A New ResNet Variant. It significantly boosts the performance of downstream models such as Mask R-CNN, Cascade R-CNN and DeepLabV3.

Table of Contents

  1. Pretrained Models
  2. Transfer Learning Models
  3. Verify Backbone Models
  4. How to Train
  5. Reference

Pypi / GitHub Install

  1. Install this package repo, note that you only need to choose one of the options
# using github url
pip install git+

# using pypi
pip install resnest --pre

Pretrained Models

crop size PyTorch Gluon
ResNeSt-50 224 81.03 81.04
ResNeSt-101 256 82.83 82.81
ResNeSt-200 320 83.84 83.88
ResNeSt-269 416 84.54 84.53

PyTorch Models

  • Load using Torch Hub
import torch
# get list of models
torch.hub.list('zhanghang1989/ResNeSt', force_reload=True)

# load pretrained models, using ResNeSt-50 as an example
net = torch.hub.load('zhanghang1989/ResNeSt', 'resnest50', pretrained=True)
  • Load using python package
# using ResNeSt-50 as an example
from resnest.torch import resnest50
net = resnest50(pretrained=True)

Gluon Models

  • Load pretrained model:
# using ResNeSt-50 as an example
from resnest.gluon import resnest50
net = resnest50(pretrained=True)

Transfer Learning Models

Detectron Models

Training code and pretrained models are coming soon.

  • Object Detection on MS-COCO validation set
Method Backbone mAP%
Faster R-CNN ResNet-50 39.25
ResNet-101 41.37
ResNeSt-50 (ours) 42.33
ResNeSt-101 (ours) 44.72
Cascade R-CNN ResNet-50 42.52
ResNet-101 44.03
ResNeSt-50 (ours) 45.41
ResNeSt-101 (ours) 47.50
ResNeSt-200 (ours) 49.03
  • Instance Segmentation
Method Backbone bbox mask
Mask R-CNN ResNet-50 39.97 36.05
ResNet-101 41.78 37.51
ResNeSt-50 (ours) 42.81 38.14
ResNeSt-101 (ours) 45.75 40.65
Cascade R-CNN ResNet-50 43.06 37.19
ResNet-101 44.79 38.52
ResNeSt-50 (ours) 46.19 39.55
ResNeSt-101 (ours) 48.30 41.56

Semantic Segmentation

  • Results on ADE20K
Method Backbone pixAcc% mIoU%
ResNet-50 80.39 42.1
ResNet-101 81.11 44.14
ResNeSt-50 (ours) 81.17 45.12
ResNeSt-101 (ours) 82.07 46.91
  • PyTorch Models: Please visit PyTorch Encoding Toolkit.
  • Training with Gluon: Please visit GluonCV Toolkit.

Verify Backbone Models:

Note: the inference speed reported in the paper are tested using Gluon implementation with RecordIO data.

Prepare ImageNet dataset:

Here we use raw image data format for simplicity, please follow GluonCV tutorial if you would like to use RecordIO format.

cd scripts/dataset/
# assuming you have downloaded the dataset in the current folder
python --download-dir ./

Torch Model

# use resnest50 as an example
cd scripts/torch/
python --model resnest50 --crop-size 224

Gluon Model

# use resnest50 as an example
cd scripts/gluon/
python --model resnest50 --crop-size 224

How to Train

  • Training with Gluon: Please visit GluonCV Toolkit.
  • Training with PyTorch: Please visit PyTorch Encoding Toolkit (slightly worse than Gluon implementation).


ResNeSt: Split-Attention Networks [arXiv]

Hang Zhang, Chongruo Wu, Zhongyue Zhang, Yi Zhu, Zhi Zhang, Haibin Lin, Yue Sun, Tong He, Jonas Muller, R. Manmatha, Mu Li and Alex Smola

title={ResNeSt: Split-Attention Networks},
author={Zhang, Hang and Wu, Chongruo and Zhang, Zhongyue and Zhu, Yi and Zhang, Zhi and Lin, Haibin and Sun, Yue and He, Tong and Muller, Jonas and Manmatha, R. and Li, Mu and Smola, Alexander},
journal={arXiv preprint},

Major Contributors


ResNeSt: Split-Attention Network

License:Apache License 2.0


Language:Python 100.0%