OpenMixup
📘Documentation |
🛠️Installation |
🚀Model Zoo |
👀Awesome Mixup |
🔍Awesome MIM |
🆕News
Introduction
The main branch works with PyTorch 1.8 (required by some self-supervised methods) or higher (we recommend PyTorch 1.12). You can still use PyTorch 1.6 for supervised classification methods.
OpenMixup is an open-source toolbox for supervised, self-, and semi-supervised visual representation learning with mixup based on PyTorch, especially for mixup-related methods. Recently, OpenMixup is on updating to adopt new features and code structures of OpenMMLab 2.0 (#42).
Major Features
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Modular Design.
OpenMixup follows a similar code architecture of OpenMMLab projects, which decompose the framework into various components, and users can easily build a customized model by combining different modules. OpenMixup is also transplantable to OpenMMLab projects (e.g., MMPreTrain).
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All in One.
OpenMixup provides popular backbones, mixup methods, semi-supervised, and self-supervised algorithms. Users can perform image classification (CNN & Transformer) and self-supervised pre-training (contrastive and autoregressive) under the same framework.
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Standard Benchmarks.
OpenMixup supports standard benchmarks of image classification, mixup classification, self-supervised evaluation, and provides smooth evaluation on downstream tasks with open-source projects (e.g., object detection and segmentation on Detectron2 and MMSegmentation).
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State-of-the-art Methods.
Openmixup provides awesome lists of popular mixup and self-supervised methods. OpenMixup is updating to support more state-of-the-art image classification and self-supervised methods.
Table of Contents
- Introduction
- News and Updates
- Installation
- Getting Started
- Overview of Model Zoo
- Change Log
- License
- Acknowledgement
- Contributors
- Contributors and Contact
News and Updates
[2025-03-19] OpenMixup v0.2.10 is released, supporting PyTorch >= 2.0 and more mixup augmentations and networks.
Installation
OpenMixup is compatible with Python 3.6/3.7/3.8/3.9 and PyTorch >= 1.6. Here are quick installations for installation in the development mode:
conda create -n openmixup python=3.8 pytorch=1.12 cudatoolkit=11.3 torchvision -c pytorch -y
conda activate openmixup
pip install openmim
mim install mmcv-full
git clone https://github.com/Westlake-AI/openmixup.git
cd openmixup
python setup.py develop
Installation with PyTorch 2.x requiring different processes.
conda create -n openmixup python=3.9
conda activate openmixup
pip install torch==2.1.0 torchvision==0.16.0 torchaudio==2.1.0 --index-url https://download.pytorch.org/whl/cu118
pip install https://download.openmmlab.com/mmcv/dist/cu118/torch2.1.0/mmcv_full-1.7.2-cp39-cp39-manylinux1_x86_64.whl
git clone https://github.com/Westlake-AI/openmixup.git
cd openmixup
pip install -r requirements/runtime.txt
python setup.py develop
Fore more detailed installation and dataset preparation, please refer to install.md.
Getting Started
OpenMixup supports Linux and macOS. It enables easy implementation and extensions of mixup data augmentation methods in existing supervised, self-, and semi-supervised visual recognition models. Please see get_started.md for the basic usage of OpenMixup.
Training and Evaluation Scripts
Here, we provide scripts for starting a quick end-to-end training with multiple GPUs and the specified CONFIG_FILE.
bash tools/dist_train.sh ${CONFIG_FILE} ${GPUS} [optional arguments]
For example, you can run the script below to train a ResNet-50 classifier on ImageNet with 4 GPUs:
CUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash tools/dist_train.sh configs/classification/imagenet/resnet/resnet50_4xb64_cos_ep100.py 4
After training, you can test the trained models with the corresponding evaluation script:
bash tools/dist_test.sh ${CONFIG_FILE} ${GPUS} ${PATH_TO_MODEL} [optional arguments]
Development
Please see Tutorials for more developing examples and tech details:
Downetream Tasks for Self-supervised Learning
Useful Tools
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Overview of Model Zoo
Please run experiments or find results on each config page. Refer to Mixup Benchmarks for benchmarking results of mixup methods. View Model Zoos Sup and Model Zoos SSL for a comprehensive collection of mainstream backbones and self-supervised algorithms. We also provide the paper lists of Awesome Mixups and Awesome MIM for your reference. Please view config files and links to models at the following config pages. Checkpoints and training logs are on updating!
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Supported Backbone Architectures
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Mixup Data Augmentations
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Self-supervised Learning Algorithms
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Supported Datasets
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Change Log
Please refer to changelog.md for more details and release history.
License
This project is released under the Apache 2.0 license. See LICENSE for more information.
Acknowledgement
- OpenMixup is an open-source project for mixup methods and visual representation learning created by researchers in CAIRI AI Lab. We encourage researchers interested in backbone architectures, mixup augmentations, and self-supervised learning methods to contribute to OpenMixup!
- This project borrows the architecture design and part of the code from MMPreTrain and the official implementations of supported algorisms.
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Citation
If you find this project useful in your research, please consider star OpenMixup or cite our tech report:
@article{li2022openmixup,
title = {OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification},
author = {Siyuan Li and Zedong Wang and Zicheng Liu and Di Wu and Cheng Tan and Stan Z. Li},
journal = {ArXiv},
year = {2022},
volume = {abs/2209.04851}
}
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For help, new features, or reporting bugs associated with OpenMixup, please open a GitHub issue and pull request with the tag “help wanted” or “enhancement”. For now, the direct contributors include: Siyuan Li (@Lupin1998), Zedong Wang (@Jacky1128), and Zicheng Liu (@pone7). We thank all public contributors and contributors from MMPreTrain (MMSelfSup and MMClassification)!
This repo is currently maintained by:
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