Pre-release

This release is a pre-release and may not be stable for production use.

ResNeSt

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.

Pretrained Models
Transfer Learning Models
Verify ImageNet Results
How to Train
Reference

Pypi / GitHub Install

Install this package repo, note that you only need to choose one of the options

# using github url
pip install git+https://github.com/zhanghang1989/ResNeSt

# 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

3rd party implementations are available: Tensorflow, Caffe, JAX.
Extra ablation study models are available in link

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

Detectron2

We provide a wrapper for training Detectron2 models with ResNeSt backbone at d2. Training configs and pretrained models are released. See details in d2.

MMDetection

The ResNeSt backbone has been adopted by MMDetection.

Semantic Segmentation

PyTorch models and training: Please visit PyTorch Encoding Toolkit.
Gluon models and training: Please visit GluonCV Toolkit.

Verify ImageNet Results:

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 prepare_imagenet.py --download-dir ./

Torch Model

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

Gluon Model

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

How to Train

ImageNet Models

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

Detectron Models

For object detection and instance segmentation models, please visit our detectron2-ResNeSt fork.

Semantic Segmentation

Training with PyTorch: Encoding Toolkit.
Training with MXNet: GluonCV Toolkit.

Reference

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

@article{zhang2020resnest,
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 arXiv:2004.08955},
year={2020}
}

Major Contributors

ResNeSt Backbone (Hang Zhang)
Detectron Models (Chongruo Wu, Zhongyue Zhang)
Semantic Segmentation (Yi Zhu)
Distributed Training (Haibin Lin)

Metadata

Release files for resnest 0.0.6b20220725

For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.

Source distribution (sdist)

Source distribution for resnest 0.0.6b20220725
File	Size	Uploaded
resnest-0.0.6b20220725.tar.gz	36.1 kB	Jul 25, 2022	Details

Built distribution (wheel)

Table of built distributions (wheels) for resnest 0.0.6b20220725
File	Interpreter	ABI	Platform	Reset
resnest-0.0.6b20220725-py3-none-any.whl 49.0 kB Jul 25, 2022	Python 3	none	any	Details

Total release size: 85.1 kB

Release files / resnest-0.0.6b20220725.tar.gz

Download URL	resnest-0.0.6b20220725.tar.gz
Size	36.1 kB
Tags	Source
SHA-256 checksum How to use checksums	`bbb8537c8eeea10f39bc935b147d6d9d7f0b1ac570ffce05dadccd5468838a3b`
BLAKE2b-256 checksum How to use checksums	`ca94e877c7b71453ad34e373adc020da03a0f6291ea1e3cb8f6c83704bfcecbc`
Upload date	Jul 25, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.7.13`

Release files / resnest-0.0.6b20220725-py3-none-any.whl

Download URL	resnest-0.0.6b20220725-py3-none-any.whl
Size	49.0 kB
Tags	Python 3
SHA-256 checksum How to use checksums	`f478f6820035ade28d879362e6ef270ffd635759cbc8f5daeef9e7ed115c0de6`
BLAKE2b-256 checksum How to use checksums	`63e082a4c9a08fbe473efb2edc8dd70154253d16c639fb70c81919d97d65ad3f`
Upload date	Jul 25, 2022
Uploaded using Trusted Publishing? What is trusted publishing?	No
Uploaded via	`twine/4.0.1 CPython/3.7.13`