With the remarkable progress of deep neural networks in computer vision, data
mixing augmentation techniques are widely studied to alleviate problems of
degraded generalization when the amount of training data is limited. However,
mixup strategies have not been well assembled in current vision toolboxes. In
this paper, we propose \texttt{OpenMixup}, an open-source all-in-one toolbox
for supervised, semi-, and self-supervised visual representation learning with
mixup. It offers an integrated model design and training platform, comprising a
rich set of prevailing network architectures and modules, a collection of data
mixing augmentation methods as well as practical model analysis tools. In
addition, we also provide standard mixup image classification benchmarks on
various datasets, which expedites practitioners to make fair comparisons among
state-of-the-art methods under the same settings. The source code and user
documents are available at https://github.com/Westlake-AI/openmixup.

本文提出了一种称之为 OpenMixup 的深度神经网络图像数据拼接工具，可以应用于监督、半监督和自监督学习，具有集成模型设计和训练平台、各种数据混合增强方法以及实用的模型分析工具等功能。同时还在多个数据集上提供图像分类基准测试，以加速研究人员在同一设置下进行公平比较。