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 \url{https://github.com/Westlake-AI/openmixup}.
翻译:随着计算机视觉方面深层神经网络的显著进展,对数据混合增强技术进行了广泛研究,以缓解在培训数据数量有限的情况下退化的概括化问题;然而,在目前的视觉工具箱中,尚未很好地组合组合战略;在本文件中,我们提议为监督、半和自我监督的视觉表现学习提供开放源全线工具箱,用于监督、半和自我监督的混合;它提供了一个综合模型设计和培训平台,包括一套丰富的现有网络结构和模块,收集数据组合增加方法以及实用模型分析工具;此外,我们还为各种数据集提供标准混合图像分类基准,加速从业人员在同一环境中对最新方法进行公平的比较;源代码和用户文件可在以下网址查阅:https://github.com/Westlake-AI/openmixup}。