YMIR: A Rapid Data-centric Development Platform for Vision Applications
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Abstract (arXiv)
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.
Key facts. Cited by 5 papers in the map (data through 7 Oct 2026). Builds on 2 of its 6 references that are in the map. First posted on arXiv 19 Nov 2021.
Builds on
2 of 6 references.
References · 2
- 2020YOLOv4: Optimal Speed and Accuracy of Object Detectioncited by 1,624
- 2019Active Learning for Deep Detection Neural Networkscited by 62
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Citing papers per year: 2022: 4; 2023: 0; 2024: 0; 2025: 1.
5 papers.
- 2022Deep Active Learning for Computer Vision: Past and Futurecited by 7
- 2022ALBench: A Framework for Evaluating Active Learning in Object Detectioncited by 6
- 2022Implementation of an Automated Learning System for Non-expertscited by 0
- 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AIcited by 0
- 2025Active Learning Methods for Efficient Data Utilization and Model Performance Enhancementcited by 0
- 2025Active Learning Methods for Efficient Data Utilization and Model Performance Enhancement21 Apr 2025
- 2022Deep Active Learning for Computer Vision: Past and Future27 Nov 2022
- 2022ALBench: A Framework for Evaluating Active Learning in Object Detection27 Jul 2022
- 2022Active-Learning-as-a-Service: An Automatic and Efficient MLOps System for Data-Centric AI19 Jul 2022
- 2022Implementation of an Automated Learning System for Non-experts26 Mar 2022