YMIR: A Rapid Data-centric Development Platform for Vision Applications
This platform addresses the need for scalable and efficient data-centric workflows in machine learning teams, particularly for real-world vision applications, though it is incremental in integrating existing tools and methods.
The paper tackles the challenge of rapid development for computer vision applications by introducing YMIR, an open-source platform that centers on efficient data development, integrates active learning and version control, and enables fast parallel iterations on multiple datasets.
This paper introduces an open source platform to support the rapid development of computer vision applications at scale. The platform puts the efficient data development at the center of the machine learning development process, integrates active learning methods, data and model version control, and uses concepts such as projects to enable fast iterations of multiple task specific datasets in parallel. This platform abstracts the development process into core states and operations, and integrates third party tools via open APIs as implementations of the operations. This open design reduces the development cost and adoption cost for ML teams with existing tools. At the same time, the platform supports recording project development histories, through which successful projects can be shared to further boost model production efficiency on similar tasks. The platform is open source and is already used internally to meet the increasing demand for different real world computer vision applications.