# OpenMixup
[](https://github.com/Westlake-AI/openmixup/releases)
[](https://pypi.org/project/openmixup)
[](https://openmixup.readthedocs.io/en/latest/)
[](https://github.com/Westlake-AI/openmixup/blob/main/LICENSE)
[](https://github.com/Westlake-AI/openmixup/issues)
[](https://github.com/Westlake-AI/openmixup/issues)
[📘Documentation](https://openmixup.readthedocs.io/en/latest/) |
[🛠️Installation](https://openmixup.readthedocs.io/en/latest/install.html) |
[🚀Model Zoo](https://github.com/Westlake-AI/openmixup/tree/main/docs/en/model_zoos) |
[👀Awesome Mixup](https://openmixup.readthedocs.io/en/latest/awesome_mixups/Mixup_SL.html) |
[🔍Awesome MIM](https://openmixup.readthedocs.io/en/latest/awesome_selfsup/MIM.html) |
[🆕News](https://openmixup.readthedocs.io/en/latest/changelog.html)
## 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](https://github.com/Westlake-AI/openmixup/issues/42)).*
<div align="center">
<img src="https://user-images.githubusercontent.com/44519745/179018883-a166f0fa-4d51-4ef1-aed1-d0d4643bcffd.jpg" width="100%"/>
</div>
<details open>
<summary>Major Features</summary>
- **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](https://github.com/open-mmlab/mmpretrain)).
- **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.
- **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](https://github.com/facebookresearch/maskrcnn-benchmark) and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation)).
- **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.
</details>
<details>
<summary>Table of Contents</summary>
<ol>
<li><a href="#introduction">Introduction</a></li>
<li><a href="#news-and-updates">News and Updates</a></li>
<li><a href="#installation">Installation</a></li>
<li><a href="#getting-started">Getting Started</a></li>
<li><a href="#overview-of-model-zoo">Overview of Model Zoo</a></li>
<li><a href="#change-log">Change Log</a></li>
<li><a href="#license">License</a></li>
<li><a href="#acknowledgement">Acknowledgement</a></li>
<li><a href="#contributors">Contributors</a></li>
<li><a href="#contributors-and-contact">Contributors and Contact</a></li>
</ol>
</details>
## News and Updates
[2023-12-23] `OpenMixup` v0.2.9 is released, updating more features in mixup augmentations, self-supervised learning, and optimizers.
## Installation
OpenMixup is compatible with **Python 3.6/3.7/3.8/3.9** and **PyTorch >= 1.6**. Here are quick installation steps for development:
```shell
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
```
Please refer to [install.md](docs/en/install.md) for more detailed installation and dataset preparation.
## 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](docs/en/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`.
```shell
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:
```shell
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:
```shell
bash tools/dist_test.sh ${CONFIG_FILE} ${GPUS} ${PATH_TO_MODEL} [optional arguments]
```
### Development
Please see [Tutorials](docs/en/tutorials) for more developing examples and tech details:
- [config files](docs/en/tutorials/0_config.md)
- [add new dataset](docs/en/tutorials/1_new_dataset.md)
- [data pipeline](docs/en/tutorials/2_data_pipeline.md)
- [add new modules](docs/en/tutorials/3_new_module.md)
- [customize schedules](docs/en/tutorials/4_schedule.md)
- [customize runtime](docs/en/tutorials/5_runtime.md)
Downetream Tasks for Self-supervised Learning
- [Classification](docs/en/tutorials/ssl_classification.md)
- [Detection](docs/en/tutorials/ssl_detection.md)
- [Segmentation](docs/en/tutorials/ssl_segmentation.md)
Useful Tools
- [Analysis](docs/en/tutorials/analysis.md)
- [Visualization](docs/en/tutorials/visualization.md)
- [pytorch2onnx](docs/en/tutorials/pytorch2onnx.md)
- [pytorch2torchscript](docs/en/tutorials/pytorch2torchscript.md)
<p align="right">(<a href="#top">back to top</a>)</p>
## Overview of Model Zoo
Please run experiments or find results on each config page. Refer to [Mixup Benchmarks](docs/en/mixup_benchmarks) for benchmarking results of mixup methods. View [Model Zoos Sup](docs/en/model_zoos/Model_Zoo_sup.md) and [Model Zoos SSL](docs/en/model_zoos/Model_Zoo_selfsup.md) for a comprehensive collection of mainstream backbones and self-supervised algorithms. We also provide the paper lists of [Awesome Mixups](docs/en/awesome_mixups) and [Awesome MIM](docs/en/awesome_selfsup/MIM.md) for your reference. Please view config files and links to models at the following config pages. Checkpoints and training logs are on updating!
* Backbone architectures for supervised image classification on ImageNet.
<details open>
<summary>Currently supported backbones</summary>
- [x] [AlexNet](https://dl.acm.org/doi/10.1145/3065386) (NIPS'2012) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/alexnet/)]
- [x] [VGG](https://arxiv.org/abs/1409.1556) (ICLR'2015) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/vgg/)]
- [x] [InceptionV3](https://arxiv.org/abs/1512.00567) (CVPR'2016) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/inception_v3/)]
- [x] [ResNet](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html) (CVPR'2016) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]
- [x] [ResNeXt](https://arxiv.org/abs/1611.05431) (CVPR'2017) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]
- [x] [SE-ResNet](https://arxiv.org/abs/1709.01507) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]
- [x] [SE-ResNeXt](https://arxiv.org/abs/1709.01507) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]
- [x] [ShuffleNetV1](https://arxiv.org/abs/1807.11164) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/shufflenet_v1/)]
- [x] [ShuffleNetV2](https://arxiv.org/abs/1807.11164) (ECCV'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/shufflenet_v2/)]
- [x] [MobileNetV2](https://arxiv.org/abs/1801.04381) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobilenet_v2/)]
- [x] [MobileNetV3](https://arxiv.org/abs/1905.02244) (ICCV'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobilenet_v3/)]
- [x] [EfficientNet](https://arxiv.org/abs/1905.11946) (ICML'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/efficientnet/)]
- [x] [EfficientNetV2](https://arxiv.org/abs/2104.00298) (ICML'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/efficientnet_v2/)]
- [x] [HRNet](https://arxiv.org/abs/1908.07919) (TPAMI'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/hrnet/)]
- [x] [Res2Net](https://arxiv.org/abs/1904.01169) (ArXiv'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/res2net/)]
- [x] [CSPNet](https://arxiv.org/abs/1911.11929) (CVPRW'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/cspnet/)]
- [x] [RegNet](https://arxiv.org/abs/2003.13678) (CVPR'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/regnet/)]
- [x] [Vision-Transformer](https://arxiv.org/abs/2010.11929) (ICLR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/vision_transformer/)]
- [x] [Swin-Transformer](https://arxiv.org/abs/2103.14030) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/swin_transformer/)]
- [x] [PVT](https://arxiv.org/abs/2102.12122) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/pvt/)]
- [x] [T2T-ViT](https://arxiv.org/abs/2101.11986) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/t2t_vit/)]
- [x] [LeViT](https://arxiv.org/abs/2104.01136) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/levit/)]
- [x] [RepVGG](https://arxiv.org/abs/2101.03697) (CVPR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/repvgg/)]
- [x] [DeiT](https://arxiv.org/abs/2012.12877) (ICML'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/)]
- [x] [MLP-Mixer](https://arxiv.org/abs/2105.01601) (NIPS'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mlp_mixer/)]
- [x] [Twins](https://proceedings.neurips.cc/paper/2021/hash/4e0928de075538c593fbdabb0c5ef2c3-Abstract.html) (NIPS'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/twins/)]
- [x] [ConvMixer](https://arxiv.org/abs/2201.09792) (Openreview'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convmixer/)]
- [x] [BEiT](https://arxiv.org/abs/2106.08254) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/beit/)]
- [x] [UniFormer](https://arxiv.org/abs/2201.09450) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/uniformer/)]
- [x] [MobileViT](http://arxiv.org/abs/2110.02178) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobilevit/)]
- [x] [PoolFormer](https://arxiv.org/abs/2111.11418) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/poolformer/)]
- [x] [ConvNeXt](https://arxiv.org/abs/2201.03545) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext/)]
- [x] [MViTV2](https://arxiv.org/abs/2112.01526) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mvit/)]
- [x] [RepMLP](https://arxiv.org/abs/2105.01883) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/repmlp/)]
- [x] [VAN](https://arxiv.org/abs/2202.09741) (CVMJ'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/van/)]
- [x] [DeiT-3](https://arxiv.org/abs/2204.07118) (ECCV'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit3/)]
- [x] [LITv2](https://arxiv.org/abs/2205.13213) (NIPS'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/)]
- [x] [HorNet](https://arxiv.org/abs/2207.14284) (NIPS'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/hornet/)]
- [x] [DaViT](https://arxiv.org/abs/2204.03645) (ECCV'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/davit/)]
- [x] [EdgeNeXt](https://arxiv.org/abs/2206.10589) (ECCVW'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/edgenext/)]
- [x] [EfficientFormer](https://arxiv.org/abs/2206.01191) (ArXiv'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/efficientformer/)]
- [x] [MogaNet](https://arxiv.org/abs/2211.03295) (ICLR'2024) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/moganet/)]
- [x] [MetaFormer](http://arxiv.org/abs/2210.13452) (ArXiv'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/metaformer/)]
- [x] [ConvNeXtV2](http://arxiv.org/abs/2301.00808) (ArXiv'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext_v2/)]
- [x] [CoC](https://arxiv.org/abs/2303.01494) (ICLR'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/context_cluster/)]
- [x] [MobileOne](http://arxiv.org/abs/2206.04040) (CVPR'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobileone/)]
- [x] [VanillaNet](http://arxiv.org/abs/2305.12972) (ArXiv'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/vanillanet/)]
- [x] [RWKV](https://arxiv.org/abs/2305.13048) (ArXiv'2023) [[config](IP51/openmixup/configs/classification/imagenet/rwkv/)]
</details>
* Mixup methods for supervised image classification.
<details open>
<summary>Currently supported mixup methods</summary>
- [x] [Mixup](https://arxiv.org/abs/1710.09412) (ICLR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [CutMix](https://arxiv.org/abs/1905.04899) (ICCV'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [ManifoldMix](https://arxiv.org/abs/1806.05236) (ICML'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [FMix](https://arxiv.org/abs/2002.12047) (ArXiv'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [AttentiveMix](https://arxiv.org/abs/2003.13048) (ICASSP'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [SmoothMix](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w45/Lee_SmoothMix_A_Simple_Yet_Effective_Data_Augmentation_to_Train_Robust_CVPRW_2020_paper.pdf) (CVPRW'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [SaliencyMix](https://arxiv.org/abs/1710.09412) (ICLR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [PuzzleMix](https://arxiv.org/abs/2009.06962) (ICML'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [SnapMix](https://arxiv.org/abs/2012.04846) (AAAI'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cifar100/mixups/)]
- [x] [GridMix](https://www.sciencedirect.com/science/article/pii/S0031320320303976) (Pattern Recognition'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [ResizeMix](https://arxiv.org/abs/2012.11101) (CVMJ'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [AlignMix](https://arxiv.org/abs/2103.15375) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [TransMix](https://arxiv.org/abs/2111.09833) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [AutoMix](https://arxiv.org/abs/2103.13027) (ECCV'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/automix)]
- [x] [SAMix](https://arxiv.org/abs/2111.15454) (ArXiv'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/samix)]
- [x] [DecoupleMix](https://arxiv.org/abs/2203.10761) (NeurIPS'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/decouple)]
- [ ] [SMMix](https://arxiv.org/abs/2212.12977) (ICCV'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [AdAutoMix](https://arxiv.org/abs/2312.11954) (ICLR'2024) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/adautomix)]
</details>
<details open>
<summary>Currently supported datasets for mixups</summary>
- [x] [ImageNet](https://arxiv.org/abs/1409.0575) [[download (1K)](http://www.image-net.org/challenges/LSVRC/2012/)] [[download (21K)](https://image-net.org/data/imagenet21k_resized.tar.gz)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]
- [x] [CIFAR-10](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) [[download](https://www.cs.toronto.edu/~kriz/cifar.html)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cifar10/)]
- [x] [CIFAR-100](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) [[download](https://www.cs.toronto.edu/~kriz/cifar.html)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cifar100/)]
- [x] [Tiny-ImageNet](https://arxiv.org/abs/1707.08819) [[download](http://cs231n.stanford.edu/tiny-imagenet-200.zip)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/tiny_imagenet/)]
- [x] [FashionMNIST](https://arxiv.org/abs/1708.07747) [[download](https://github.com/zalandoresearch/fashion-mnist)]
- [x] [STL-10](http://proceedings.mlr.press/v15/coates11a/coates11a.pdf) [[download](https://cs.stanford.edu/~acoates/stl10/)]
- [x] [CUB-200-2011](https://resolver.caltech.edu/CaltechAUTHORS:20111026-120541847) [[download](http://www.vision.caltech.edu/datasets/cub_200_2011/)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cub200/)]
- [x] [FGVC-Aircraft](https://arxiv.org/abs/1306.5151) [[download](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/aircrafts/)]
- [x] [Stanford-Cars](http://ai.stanford.edu/~jkrause/papers/3drr13.pdf) [[download](http://ai.stanford.edu/~jkrause/cars/car_dataset.html)]
- [x] [Places205](http://places2.csail.mit.edu/index.html) [[download](http://places.csail.mit.edu/downloadData.html)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/place205/)]
- [x] [iNaturalist-2017](https://arxiv.org/abs/1707.06642) [[download](https://github.com/visipedia/inat_comp/tree/master/2017)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/inaturalist2017/)]
- [x] [iNaturalist-2018](https://arxiv.org/abs/1707.06642) [[download](https://github.com/visipedia/inat_comp/tree/master/2018)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/inaturalist2018/)]
- [x] [AgeDB](https://ieeexplore.ieee.org/document/8014984) [[download](https://ibug.doc.ic.ac.uk/resources/agedb/)] [[download (baidu)](https://pan.baidu.com/s/1XdibVxiGoWf46HLOHKiIyw?pwd=0n6p)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/regression/agedb)]
- [x] [IMDB-WIKI](https://link.springer.com/article/10.1007/s11263-016-0940-3) [[download (imdb)](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/imdb_crop.tar)] [[download (wiki)](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/wiki_crop.tar)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/regression/imdb_wiki)]
- [x] [RCFMNIST](https://arxiv.org/abs/2210.05775) [[download](https://github.com/zalandoresearch/fashion-mnist)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/regression/rcfmnist)]
</details>
* Self-supervised algorithms for visual representation learning.
<details open>
<summary>Currently supported self-supervised algorithms</summary>
- [x] [Relative Location](https://arxiv.org/abs/1505.05192) (ICCV'2015) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/relative_loc/)]
- [x] [Rotation Prediction](https://arxiv.org/abs/1803.07728) (ICLR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/rotation_pred/)]
- [x] [DeepCluster](https://arxiv.org/abs/1807.05520) (ECCV'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/deepcluster/)]
- [x] [NPID](https://arxiv.org/abs/1805.01978) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/npid/)]
- [x] [ODC](https://arxiv.org/abs/2006.10645) (CVPR'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/odc/)]
- [x] [MoCov1](https://arxiv.org/abs/1911.05722) (CVPR'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mocov1/)]
- [x] [SimCLR](https://arxiv.org/abs/2002.05709) (ICML'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/simclr/)]
- [x] [MoCoV2](https://arxiv.org/abs/2003.04297) (ArXiv'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mocov2/)]
- [x] [BYOL](https://arxiv.org/abs/2006.07733) (NIPS'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/byol/)]
- [x] [SwAV](https://arxiv.org/abs/2006.09882) (NIPS'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/swav/)]
- [x] [DenseCL](https://arxiv.org/abs/2011.09157) (CVPR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/densecl/)]
- [x] [SimSiam](https://arxiv.org/abs/2011.10566) (CVPR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/simsiam/)]
- [x] [Barlow Twins](https://arxiv.org/abs/2103.03230) (ICML'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/barlowtwins/)]
- [x] [MoCoV3](https://arxiv.org/abs/2104.02057) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mocov3/)]
- [x] [BEiT](https://arxiv.org/abs/2106.08254) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/beit/)
- [x] [MAE](https://arxiv.org/abs/2111.06377) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mae/)]
- [x] [SimMIM](https://arxiv.org/abs/2111.09886) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/simmim/)]
- [x] [MaskFeat](https://arxiv.org/abs/2112.09133) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/maskfeat/)]
- [x] [CAE](https://arxiv.org/abs/2202.03026) (ArXiv'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/cae/)]
- [x] [A2MIM](https://arxiv.org/abs/2205.13943) (ICML'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/a2mim/)]
</details>
<p align="right">(<a href="#top">back to top</a>)</p>
## Change Log
Please refer to [changelog.md](docs/en/changelog.md) for more details and release history.
## License
This project is released under the [Apache 2.0 license](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](https://github.com/open-mmlab/mmpretrain) and the official implementations of supported algorisms.
<p align="right">(<a href="#top">back to top</a>)</p>
## Citation
If you find this project useful in your research, please consider star `OpenMixup` or cite our [tech report](https://arxiv.org/abs/2209.04851):
```BibTeX
@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}
}
```
<p align="right">(<a href="#top">back to top</a>)</p>
## Contributors and Contact
For help, new features, or reporting bugs associated with OpenMixup, please open a [GitHub issue](https://github.com/Westlake-AI/openmixup/issues) and [pull request](https://github.com/Westlake-AI/openmixup/pulls) with the tag "help wanted" or "enhancement". For now, the direct contributors include: Siyuan Li ([@Lupin1998](https://github.com/Lupin1998)), Zedong Wang ([@Jacky1128](https://github.com/Jacky1128)), and Zicheng Liu ([@pone7](https://github.com/pone7)). We thank all public contributors and contributors from MMPreTrain (MMSelfSup and MMClassification)!
This repo is currently maintained by:
- Siyuan Li (lisiyuan@westlake.edu.cn), Westlake University
- Zedong Wang (wangzedong@westlake.edu.cn), Westlake University
- Zicheng Liu (liuzicheng@westlake.edu.cn), Westlake University
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Raw data
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"keywords": "computer vision,image classification,mixup classification,semi- and self-supervised learning",
"author": "CAIRI Westlake University Contributors",
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"description": "# OpenMixup\n[](https://github.com/Westlake-AI/openmixup/releases)\n[](https://pypi.org/project/openmixup)\n[](https://openmixup.readthedocs.io/en/latest/)\n[](https://github.com/Westlake-AI/openmixup/blob/main/LICENSE)\n[](https://github.com/Westlake-AI/openmixup/issues)\n[](https://github.com/Westlake-AI/openmixup/issues)\n\n[\ud83d\udcd8Documentation](https://openmixup.readthedocs.io/en/latest/) |\n[\ud83d\udee0\ufe0fInstallation](https://openmixup.readthedocs.io/en/latest/install.html) |\n[\ud83d\ude80Model Zoo](https://github.com/Westlake-AI/openmixup/tree/main/docs/en/model_zoos) |\n[\ud83d\udc40Awesome Mixup](https://openmixup.readthedocs.io/en/latest/awesome_mixups/Mixup_SL.html) |\n[\ud83d\udd0dAwesome MIM](https://openmixup.readthedocs.io/en/latest/awesome_selfsup/MIM.html) |\n[\ud83c\udd95News](https://openmixup.readthedocs.io/en/latest/changelog.html)\n\n## Introduction\n\nThe 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.\n\n`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](https://github.com/Westlake-AI/openmixup/issues/42)).*\n\n<div align=\"center\">\n <img src=\"https://user-images.githubusercontent.com/44519745/179018883-a166f0fa-4d51-4ef1-aed1-d0d4643bcffd.jpg\" width=\"100%\"/>\n</div>\n\n<details open>\n<summary>Major Features</summary>\n\n- **Modular Design.**\n 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](https://github.com/open-mmlab/mmpretrain)).\n\n- **All in One.**\n 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.\n\n- **Standard Benchmarks.**\n 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](https://github.com/facebookresearch/maskrcnn-benchmark) and [MMSegmentation](https://github.com/open-mmlab/mmsegmentation)).\n\n- **State-of-the-art Methods.**\n 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.\n\n</details>\n\n<details>\n <summary>Table of Contents</summary>\n <ol>\n <li><a href=\"#introduction\">Introduction</a></li>\n <li><a href=\"#news-and-updates\">News and Updates</a></li>\n <li><a href=\"#installation\">Installation</a></li>\n <li><a href=\"#getting-started\">Getting Started</a></li>\n <li><a href=\"#overview-of-model-zoo\">Overview of Model Zoo</a></li>\n <li><a href=\"#change-log\">Change Log</a></li>\n <li><a href=\"#license\">License</a></li>\n <li><a href=\"#acknowledgement\">Acknowledgement</a></li>\n <li><a href=\"#contributors\">Contributors</a></li>\n <li><a href=\"#contributors-and-contact\">Contributors and Contact</a></li>\n </ol>\n</details>\n\n## News and Updates\n\n[2023-12-23] `OpenMixup` v0.2.9 is released, updating more features in mixup augmentations, self-supervised learning, and optimizers.\n\n## Installation\n\nOpenMixup is compatible with **Python 3.6/3.7/3.8/3.9** and **PyTorch >= 1.6**. Here are quick installation steps for development:\n\n```shell\nconda create -n openmixup python=3.8 pytorch=1.12 cudatoolkit=11.3 torchvision -c pytorch -y\nconda activate openmixup\npip install openmim\nmim install mmcv-full\ngit clone https://github.com/Westlake-AI/openmixup.git\ncd openmixup\npython setup.py develop\n```\n\nPlease refer to [install.md](docs/en/install.md) for more detailed installation and dataset preparation.\n\n## Getting Started\n\nOpenMixup 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](docs/en/get_started.md) for the basic usage of OpenMixup.\n\n### Training and Evaluation Scripts\n\nHere, we provide scripts for starting a quick end-to-end training with multiple `GPUs` and the specified `CONFIG_FILE`. \n```shell\nbash tools/dist_train.sh ${CONFIG_FILE} ${GPUS} [optional arguments]\n```\nFor example, you can run the script below to train a ResNet-50 classifier on ImageNet with 4 GPUs:\n```shell\nCUDA_VISIBLE_DEVICES=0,1,2,3 PORT=29500 bash tools/dist_train.sh configs/classification/imagenet/resnet/resnet50_4xb64_cos_ep100.py 4\n```\nAfter training, you can test the trained models with the corresponding evaluation script:\n```shell\nbash tools/dist_test.sh ${CONFIG_FILE} ${GPUS} ${PATH_TO_MODEL} [optional arguments]\n```\n\n### Development\n\nPlease see [Tutorials](docs/en/tutorials) for more developing examples and tech details:\n\n- [config files](docs/en/tutorials/0_config.md)\n- [add new dataset](docs/en/tutorials/1_new_dataset.md)\n- [data pipeline](docs/en/tutorials/2_data_pipeline.md)\n- [add new modules](docs/en/tutorials/3_new_module.md)\n- [customize schedules](docs/en/tutorials/4_schedule.md)\n- [customize runtime](docs/en/tutorials/5_runtime.md)\n\nDownetream Tasks for Self-supervised Learning\n\n- [Classification](docs/en/tutorials/ssl_classification.md)\n- [Detection](docs/en/tutorials/ssl_detection.md)\n- [Segmentation](docs/en/tutorials/ssl_segmentation.md)\n\nUseful Tools\n\n- [Analysis](docs/en/tutorials/analysis.md)\n- [Visualization](docs/en/tutorials/visualization.md)\n- [pytorch2onnx](docs/en/tutorials/pytorch2onnx.md)\n- [pytorch2torchscript](docs/en/tutorials/pytorch2torchscript.md)\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n## Overview of Model Zoo\n\nPlease run experiments or find results on each config page. Refer to [Mixup Benchmarks](docs/en/mixup_benchmarks) for benchmarking results of mixup methods. View [Model Zoos Sup](docs/en/model_zoos/Model_Zoo_sup.md) and [Model Zoos SSL](docs/en/model_zoos/Model_Zoo_selfsup.md) for a comprehensive collection of mainstream backbones and self-supervised algorithms. We also provide the paper lists of [Awesome Mixups](docs/en/awesome_mixups) and [Awesome MIM](docs/en/awesome_selfsup/MIM.md) for your reference. Please view config files and links to models at the following config pages. Checkpoints and training logs are on updating!\n\n* Backbone architectures for supervised image classification on ImageNet.\n\n <details open>\n <summary>Currently supported backbones</summary>\n\n - [x] [AlexNet](https://dl.acm.org/doi/10.1145/3065386) (NIPS'2012) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/alexnet/)]\n - [x] [VGG](https://arxiv.org/abs/1409.1556) (ICLR'2015) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/vgg/)]\n - [x] [InceptionV3](https://arxiv.org/abs/1512.00567) (CVPR'2016) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/inception_v3/)]\n - [x] [ResNet](https://openaccess.thecvf.com/content_cvpr_2016/html/He_Deep_Residual_Learning_CVPR_2016_paper.html) (CVPR'2016) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]\n - [x] [ResNeXt](https://arxiv.org/abs/1611.05431) (CVPR'2017) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]\n - [x] [SE-ResNet](https://arxiv.org/abs/1709.01507) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]\n - [x] [SE-ResNeXt](https://arxiv.org/abs/1709.01507) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/resnet/)]\n - [x] [ShuffleNetV1](https://arxiv.org/abs/1807.11164) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/shufflenet_v1/)]\n - [x] [ShuffleNetV2](https://arxiv.org/abs/1807.11164) (ECCV'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/shufflenet_v2/)]\n - [x] [MobileNetV2](https://arxiv.org/abs/1801.04381) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobilenet_v2/)]\n - [x] [MobileNetV3](https://arxiv.org/abs/1905.02244) (ICCV'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobilenet_v3/)]\n - [x] [EfficientNet](https://arxiv.org/abs/1905.11946) (ICML'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/efficientnet/)]\n - [x] [EfficientNetV2](https://arxiv.org/abs/2104.00298) (ICML'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/efficientnet_v2/)]\n - [x] [HRNet](https://arxiv.org/abs/1908.07919) (TPAMI'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/hrnet/)]\n - [x] [Res2Net](https://arxiv.org/abs/1904.01169) (ArXiv'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/res2net/)]\n - [x] [CSPNet](https://arxiv.org/abs/1911.11929) (CVPRW'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/cspnet/)]\n - [x] [RegNet](https://arxiv.org/abs/2003.13678) (CVPR'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/regnet/)]\n - [x] [Vision-Transformer](https://arxiv.org/abs/2010.11929) (ICLR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/vision_transformer/)]\n - [x] [Swin-Transformer](https://arxiv.org/abs/2103.14030) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/swin_transformer/)]\n - [x] [PVT](https://arxiv.org/abs/2102.12122) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/pvt/)]\n - [x] [T2T-ViT](https://arxiv.org/abs/2101.11986) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/t2t_vit/)]\n - [x] [LeViT](https://arxiv.org/abs/2104.01136) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/levit/)]\n - [x] [RepVGG](https://arxiv.org/abs/2101.03697) (CVPR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/repvgg/)]\n - [x] [DeiT](https://arxiv.org/abs/2012.12877) (ICML'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit/)]\n - [x] [MLP-Mixer](https://arxiv.org/abs/2105.01601) (NIPS'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mlp_mixer/)]\n - [x] [Twins](https://proceedings.neurips.cc/paper/2021/hash/4e0928de075538c593fbdabb0c5ef2c3-Abstract.html) (NIPS'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/twins/)]\n - [x] [ConvMixer](https://arxiv.org/abs/2201.09792) (Openreview'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convmixer/)]\n - [x] [BEiT](https://arxiv.org/abs/2106.08254) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/beit/)]\n - [x] [UniFormer](https://arxiv.org/abs/2201.09450) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/uniformer/)]\n - [x] [MobileViT](http://arxiv.org/abs/2110.02178) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobilevit/)]\n - [x] [PoolFormer](https://arxiv.org/abs/2111.11418) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/poolformer/)]\n - [x] [ConvNeXt](https://arxiv.org/abs/2201.03545) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext/)]\n - [x] [MViTV2](https://arxiv.org/abs/2112.01526) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mvit/)]\n - [x] [RepMLP](https://arxiv.org/abs/2105.01883) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/repmlp/)]\n - [x] [VAN](https://arxiv.org/abs/2202.09741) (CVMJ'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/van/)]\n - [x] [DeiT-3](https://arxiv.org/abs/2204.07118) (ECCV'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/deit3/)]\n - [x] [LITv2](https://arxiv.org/abs/2205.13213) (NIPS'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/lit_v2/)]\n - [x] [HorNet](https://arxiv.org/abs/2207.14284) (NIPS'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/hornet/)]\n - [x] [DaViT](https://arxiv.org/abs/2204.03645) (ECCV'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/davit/)]\n - [x] [EdgeNeXt](https://arxiv.org/abs/2206.10589) (ECCVW'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/edgenext/)]\n - [x] [EfficientFormer](https://arxiv.org/abs/2206.01191) (ArXiv'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/efficientformer/)]\n - [x] [MogaNet](https://arxiv.org/abs/2211.03295) (ICLR'2024) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/moganet/)]\n - [x] [MetaFormer](http://arxiv.org/abs/2210.13452) (ArXiv'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/metaformer/)]\n - [x] [ConvNeXtV2](http://arxiv.org/abs/2301.00808) (ArXiv'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/convnext_v2/)]\n - [x] [CoC](https://arxiv.org/abs/2303.01494) (ICLR'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/context_cluster/)]\n - [x] [MobileOne](http://arxiv.org/abs/2206.04040) (CVPR'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mobileone/)]\n - [x] [VanillaNet](http://arxiv.org/abs/2305.12972) (ArXiv'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/vanillanet/)]\n - [x] [RWKV](https://arxiv.org/abs/2305.13048) (ArXiv'2023) [[config](IP51/openmixup/configs/classification/imagenet/rwkv/)]\n </details>\n\n* Mixup methods for supervised image classification.\n\n <details open>\n <summary>Currently supported mixup methods</summary>\n\n - [x] [Mixup](https://arxiv.org/abs/1710.09412) (ICLR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [CutMix](https://arxiv.org/abs/1905.04899) (ICCV'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [ManifoldMix](https://arxiv.org/abs/1806.05236) (ICML'2019) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [FMix](https://arxiv.org/abs/2002.12047) (ArXiv'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [AttentiveMix](https://arxiv.org/abs/2003.13048) (ICASSP'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [SmoothMix](https://openaccess.thecvf.com/content_CVPRW_2020/papers/w45/Lee_SmoothMix_A_Simple_Yet_Effective_Data_Augmentation_to_Train_Robust_CVPRW_2020_paper.pdf) (CVPRW'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [SaliencyMix](https://arxiv.org/abs/1710.09412) (ICLR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [PuzzleMix](https://arxiv.org/abs/2009.06962) (ICML'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [SnapMix](https://arxiv.org/abs/2012.04846) (AAAI'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cifar100/mixups/)]\n - [x] [GridMix](https://www.sciencedirect.com/science/article/pii/S0031320320303976) (Pattern Recognition'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [ResizeMix](https://arxiv.org/abs/2012.11101) (CVMJ'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [AlignMix](https://arxiv.org/abs/2103.15375) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [TransMix](https://arxiv.org/abs/2111.09833) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [AutoMix](https://arxiv.org/abs/2103.13027) (ECCV'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/automix)]\n - [x] [SAMix](https://arxiv.org/abs/2111.15454) (ArXiv'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/samix)]\n - [x] [DecoupleMix](https://arxiv.org/abs/2203.10761) (NeurIPS'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/decouple)]\n - [ ] [SMMix](https://arxiv.org/abs/2212.12977) (ICCV'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [AdAutoMix](https://arxiv.org/abs/2312.11954) (ICLR'2024) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/adautomix)]\n </details>\n\n <details open>\n <summary>Currently supported datasets for mixups</summary>\n\n - [x] [ImageNet](https://arxiv.org/abs/1409.0575) [[download (1K)](http://www.image-net.org/challenges/LSVRC/2012/)] [[download (21K)](https://image-net.org/data/imagenet21k_resized.tar.gz)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/imagenet/mixups/)]\n - [x] [CIFAR-10](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) [[download](https://www.cs.toronto.edu/~kriz/cifar.html)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cifar10/)]\n - [x] [CIFAR-100](https://www.cs.toronto.edu/~kriz/learning-features-2009-TR.pdf) [[download](https://www.cs.toronto.edu/~kriz/cifar.html)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cifar100/)]\n - [x] [Tiny-ImageNet](https://arxiv.org/abs/1707.08819) [[download](http://cs231n.stanford.edu/tiny-imagenet-200.zip)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/tiny_imagenet/)]\n - [x] [FashionMNIST](https://arxiv.org/abs/1708.07747) [[download](https://github.com/zalandoresearch/fashion-mnist)]\n - [x] [STL-10](http://proceedings.mlr.press/v15/coates11a/coates11a.pdf) [[download](https://cs.stanford.edu/~acoates/stl10/)]\n - [x] [CUB-200-2011](https://resolver.caltech.edu/CaltechAUTHORS:20111026-120541847) [[download](http://www.vision.caltech.edu/datasets/cub_200_2011/)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/cub200/)]\n - [x] [FGVC-Aircraft](https://arxiv.org/abs/1306.5151) [[download](https://www.robots.ox.ac.uk/~vgg/data/fgvc-aircraft/)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/aircrafts/)]\n - [x] [Stanford-Cars](http://ai.stanford.edu/~jkrause/papers/3drr13.pdf) [[download](http://ai.stanford.edu/~jkrause/cars/car_dataset.html)]\n - [x] [Places205](http://places2.csail.mit.edu/index.html) [[download](http://places.csail.mit.edu/downloadData.html)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/place205/)]\n - [x] [iNaturalist-2017](https://arxiv.org/abs/1707.06642) [[download](https://github.com/visipedia/inat_comp/tree/master/2017)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/inaturalist2017/)]\n - [x] [iNaturalist-2018](https://arxiv.org/abs/1707.06642) [[download](https://github.com/visipedia/inat_comp/tree/master/2018)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/classification/inaturalist2018/)]\n - [x] [AgeDB](https://ieeexplore.ieee.org/document/8014984) [[download](https://ibug.doc.ic.ac.uk/resources/agedb/)] [[download (baidu)](https://pan.baidu.com/s/1XdibVxiGoWf46HLOHKiIyw?pwd=0n6p)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/regression/agedb)]\n - [x] [IMDB-WIKI](https://link.springer.com/article/10.1007/s11263-016-0940-3) [[download (imdb)](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/imdb_crop.tar)] [[download (wiki)](https://data.vision.ee.ethz.ch/cvl/rrothe/imdb-wiki/static/wiki_crop.tar)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/regression/imdb_wiki)]\n - [x] [RCFMNIST](https://arxiv.org/abs/2210.05775) [[download](https://github.com/zalandoresearch/fashion-mnist)] [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/regression/rcfmnist)]\n </details>\n\n* Self-supervised algorithms for visual representation learning.\n\n <details open>\n <summary>Currently supported self-supervised algorithms</summary>\n\n - [x] [Relative Location](https://arxiv.org/abs/1505.05192) (ICCV'2015) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/relative_loc/)]\n - [x] [Rotation Prediction](https://arxiv.org/abs/1803.07728) (ICLR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/rotation_pred/)]\n - [x] [DeepCluster](https://arxiv.org/abs/1807.05520) (ECCV'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/deepcluster/)]\n - [x] [NPID](https://arxiv.org/abs/1805.01978) (CVPR'2018) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/npid/)]\n - [x] [ODC](https://arxiv.org/abs/2006.10645) (CVPR'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/odc/)]\n - [x] [MoCov1](https://arxiv.org/abs/1911.05722) (CVPR'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mocov1/)]\n - [x] [SimCLR](https://arxiv.org/abs/2002.05709) (ICML'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/simclr/)]\n - [x] [MoCoV2](https://arxiv.org/abs/2003.04297) (ArXiv'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mocov2/)]\n - [x] [BYOL](https://arxiv.org/abs/2006.07733) (NIPS'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/byol/)]\n - [x] [SwAV](https://arxiv.org/abs/2006.09882) (NIPS'2020) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/swav/)]\n - [x] [DenseCL](https://arxiv.org/abs/2011.09157) (CVPR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/densecl/)]\n - [x] [SimSiam](https://arxiv.org/abs/2011.10566) (CVPR'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/simsiam/)]\n - [x] [Barlow Twins](https://arxiv.org/abs/2103.03230) (ICML'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/barlowtwins/)]\n - [x] [MoCoV3](https://arxiv.org/abs/2104.02057) (ICCV'2021) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mocov3/)]\n - [x] [BEiT](https://arxiv.org/abs/2106.08254) (ICLR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/beit/)\n - [x] [MAE](https://arxiv.org/abs/2111.06377) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/mae/)]\n - [x] [SimMIM](https://arxiv.org/abs/2111.09886) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/simmim/)]\n - [x] [MaskFeat](https://arxiv.org/abs/2112.09133) (CVPR'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/maskfeat/)]\n - [x] [CAE](https://arxiv.org/abs/2202.03026) (ArXiv'2022) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/cae/)]\n - [x] [A2MIM](https://arxiv.org/abs/2205.13943) (ICML'2023) [[config](https://github.com/Westlake-AI/openmixup/tree/main/configs/selfsup/a2mim/)]\n </details>\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n## Change Log\n\nPlease refer to [changelog.md](docs/en/changelog.md) for more details and release history.\n\n## License\n\nThis project is released under the [Apache 2.0 license](LICENSE). See `LICENSE` for more information.\n\n## Acknowledgement\n\n- 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!\n- This project borrows the architecture design and part of the code from [MMPreTrain](https://github.com/open-mmlab/mmpretrain) and the official implementations of supported algorisms.\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n## Citation\n\nIf you find this project useful in your research, please consider star `OpenMixup` or cite our [tech report](https://arxiv.org/abs/2209.04851):\n\n```BibTeX\n@article{li2022openmixup,\n title = {OpenMixup: A Comprehensive Mixup Benchmark for Visual Classification},\n author = {Siyuan Li and Zedong Wang and Zicheng Liu and Di Wu and Cheng Tan and Stan Z. Li},\n journal = {ArXiv},\n year = {2022},\n volume = {abs/2209.04851}\n}\n```\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n\n## Contributors and Contact\n\nFor help, new features, or reporting bugs associated with OpenMixup, please open a [GitHub issue](https://github.com/Westlake-AI/openmixup/issues) and [pull request](https://github.com/Westlake-AI/openmixup/pulls) with the tag \"help wanted\" or \"enhancement\". For now, the direct contributors include: Siyuan Li ([@Lupin1998](https://github.com/Lupin1998)), Zedong Wang ([@Jacky1128](https://github.com/Jacky1128)), and Zicheng Liu ([@pone7](https://github.com/pone7)). We thank all public contributors and contributors from MMPreTrain (MMSelfSup and MMClassification)!\n\nThis repo is currently maintained by:\n\n- Siyuan Li (lisiyuan@westlake.edu.cn), Westlake University\n- Zedong Wang (wangzedong@westlake.edu.cn), Westlake University\n- Zicheng Liu (liuzicheng@westlake.edu.cn), Westlake University\n\n<p align=\"right\">(<a href=\"#top\">back to top</a>)</p>\n",
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