技术
- 分析与建模 - 机器学习
- 平台即服务 (PaaS) - 应用开发平台
适用行业
- 教育
- 公用事业
用例
- 计算机视觉
- 时间敏感网络
服务
- 数据科学服务
- 培训
关于客户
Sharper Shape 是一家为公用事业公司创建安全、高效的输电和配电解决方案的技术公司。他们使用无人机执行公用事业检查,并在先进的航空传感器系统中使用计算机视觉模型来支持无人机检查数据的自动收集和分析。他们的技术通常用于识别危险的电线设置,例如植被生长得太近、绝缘体破损等,以便公用事业公司可以发现并解决潜在危险。作为一家以人工智能为推动力的公司,Sharper Shape 凭借强大、成熟的 ML 模型开发渠道而脱颖而出。
挑战
Sharper Shape 是一家为公用事业公司提供安全、高效输电和配电解决方案技术的公司,该公司在开发机器学习 (ML) 模型时面临着挑战。该公司在先进的航空传感器系统中使用计算机视觉模型来支持无人机检查数据的自动收集和分析。他们的技术的一个常见用例是识别危险的电线设置,例如植被生长得太近、绝缘体破损等。然而,训练多个计算机视觉模型需要大量精确标记的图像。在使用 Labelbox 之前,Sharper Shape 团队严重依赖手动工作流程,并尝试使用开源标签工具,但这些工具无法提供满足其需求所需的配置量。此外,每位数据科学家花费了多达三分之一的时间在基础设施和实验管理上。
解决方案
Sharper Shape 采用 Labelbox 来简化标记流程,使他们能够使用一系列数据类型(包括平铺图像)并组织现有数据。借助 Labelbox,团队可以通过简单的 API 将原始数据连接到 Labelbox。 Labelbox 的协作功能还支持内部和熟练的外部贴标人员快速入职、培训和提高吞吐量,以便在一个集中式环境中协同工作。在一项新计划中,Sharper Shape 团队正在通过模型辅助标记进一步加速其标记流程,该标记允许团队将其模型导入 Labelbox 并解决边缘情况。在 Labelbox 内对数据进行完全注释后,他们的数据将导出到 Valohai MLOps 平台,Sharper Shape 团队在该平台上运行机器学习实验和训练管道。 Valohai 使 Sharper Shape 能够在强大的云硬件上训练模型,无需 DevOps 支持,并将所有协作实验容纳在单个应用程序中。已建立的 ML 流程可以完全自动化到 Valohai 管道中,因此每次 Labelbox 提供新的注释数据时都可以训练模型。
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