论文标题
DNNS生产性可重复的工作流程：工业缺陷检测的案例研究
Productive Reproducible Workflows for DNNs: A Case Study for Industrial Defect Detection
论文作者
论文摘要
由于深度神经网络（DNN）已成为越来越普遍的工作量，因此可用来帮助其开发和部署的图书馆和工具的范围已大大增长。可扩展的生产质量工具可在宽敞的许可下免费获得，并且足够访问，即使小型团队也能够非常有生产力。但是，在研究界，该工具的意识和使用不一定是普遍的，研究人员可能会因利用最新工具和工作流而缺少潜在的生产力提高。本文介绍了一个案例研究，我们讨论了我们最近生成端到端人工智能检测应用程序的经验。我们详细介绍了我们利用的高级深度学习库，容器化的工作流程，连续集成/部署管道以及开源代码模板，以产生竞争成果，与三个目标数据集的其他排名解决方案的性能相匹配。我们强调了利用此类系统甚至可以为研究带来的价值，并详细介绍我们的解决方案，并在服务器类GPU上的准确性和推理时间以及服务器类CPU上的推理时间以及Raspberry Pi 4上提出我们的最佳结果。
As Deep Neural Networks (DNNs) have become an increasingly ubiquitous workload, the range of libraries and tooling available to aid in their development and deployment has grown significantly. Scalable, production quality tools are freely available under permissive licenses, and are accessible enough to enable even small teams to be very productive. However within the research community, awareness and usage of said tools is not necessarily widespread, and researchers may be missing out on potential productivity gains from exploiting the latest tools and workflows. This paper presents a case study where we discuss our recent experience producing an end-to-end artificial intelligence application for industrial defect detection. We detail the high level deep learning libraries, containerized workflows, continuous integration/deployment pipelines, and open source code templates we leveraged to produce a competitive result, matching the performance of other ranked solutions to our three target datasets. We highlight the value that exploiting such systems can bring, even for research, and detail our solution and present our best results in terms of accuracy and inference time on a server class GPU, as well as inference times on a server class CPU, and a Raspberry Pi 4.
