DIVA-DAF：一个用于历史文档图像分析的深度学习框架
DIVA-DAF: A Deep Learning Framework for Historical Document Image Analysis
AI总结：
针对历史文档图像分析实验耗时问题，提出基于PyTorch Lightning的开源深度学习框架DIVA-DAF，提供预实现任务与强大数据加载模块，显著节省编程与模型训练时间。
摘要：
深度学习方法在解决历史文档图像分析任务中表现出色。然而，尽管有现有的库和框架，编写和执行一个或一组实验仍然可能非常耗时。为此，我们提出了一个开源深度学习框架 DIVA-DAF，它基于 PyTorch Lightning 构建，专为历史文档分析而设计。诸如分割和分类等预实现任务可以轻松使用或进行定制。借助强大的数据加载模块，即使面对大型数据集和不同形式的真实标注，也能轻松创建自定义任务。已开展的应用表明，该框架不仅节省了文档分析任务的编程时间，还在预训练或更改架构等不同场景中展现出优势。得益于其数据模块，该框架还能显著缩短模型训练时间。
英文摘要：
Deep learning methods have shown strong performance in solving tasks for historical document image analysis. However, despite current libraries and frameworks, programming an experiment or a set of experiments and executing them can be time-consuming. This is why we propose an open-source deep learning framework, DIVA-DAF, which is based on PyTorch Lightning and specifically designed for historical document analysis. Pre-implemented tasks such as segmentation and classification can be easily used or customized. It is also easy to create one's own tasks with the benefit of powerful modules for loading data, even large data sets, and different forms of ground truth. The applications conducted have demonstrated time savings for the programming of a document analysis task, as well as for different scenarios such as pre-training or changing the architecture. Thanks to its data module, the framework also allows to reduce the time of model training significantly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
