# OneLabeler: A Flexible System for Building Data Labeling Tools

arXiv: https://arxiv.org/abs/2203.14227 · HTML: https://mlsanity.com/paper/2203.14227/

## Key facts

- Cited by 9 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 96 references that are in the map.
- First posted on arXiv 27 Mar 2022.

## Builds on (most cited first, 9 of 9)

- [Confident Learning: Estimating Uncertainty in Dataset Labels](https://mlsanity.com/paper/1911.00068/) (2019)
- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [The VIA Annotation Software for Images, Audio and Video](https://mlsanity.com/paper/1904.10699/) (2019)
- [DeepIGeoS: A Deep Interactive Geodesic Framework for Medical Image Segmentation](https://mlsanity.com/paper/1707.00652/) (2017)
- [Text-to-Viz: Automatic Generation of Infographics from Proportion-Related Natural Language Statements](https://mlsanity.com/paper/1907.09091/) (2019)
- [A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping](https://mlsanity.com/paper/1409.5165/) (2014)
- [Fluid Annotation: A Human-Machine Collaboration Interface for Full Image Annotation](https://mlsanity.com/paper/1806.07527/) (2018)
- [II-20: Intelligent and pragmatic analytic categorization of image collections](https://mlsanity.com/paper/2005.02149/) (2020)
- [Collaborative Annotation of Semantic Objects in Images with Multi-granularity Supervisions](https://mlsanity.com/paper/1806.10269/) (2018)

## Built on by (most cited first, 9 of 9)

- [VideoPro: A Visual Analytics Approach for Interactive Video Programming](https://mlsanity.com/paper/2308.00401/) (2023)
- [VISTA: A Visual Analytics Framework to Enhance Foundation Model-Generated Data Labels](https://mlsanity.com/paper/2507.09008/) (2025)
- [All Data on the Table: Novel Dataset and Benchmark for Cross-Modality Scientific Information Extraction](https://mlsanity.com/paper/2311.08189/) (2023)
- [The Consensus Trap: Dissecting Subjectivity and the "Ground Truth" Illusion in Data Annotation](https://mlsanity.com/paper/2602.11318/) (2026)
- [FARPLS: A Feature-Augmented Robot Trajectory Preference Labeling System to Assist Human Labelers' Preference Elicitation](https://mlsanity.com/paper/2403.06267/) (2024)
- [Exploratory Visual Analysis for Increasing Data Readiness in Artificial Intelligence Projects](https://mlsanity.com/paper/2409.03805/) (2024)
- [More efficient manual review of automatically transcribed tabular data](https://mlsanity.com/paper/2306.16126/) (2023)
- [Scalability and Maintainability Challenges and Solutions in Machine Learning: Systematic Literature Review](https://mlsanity.com/paper/2504.11079/) (2025)
- [Viewpoint Recommendation for Point Cloud Labeling through Interaction Cost Modeling](https://mlsanity.com/paper/2602.10871/) (2026)

## Abstract (arXiv)

Labeled datasets are essential for supervised machine learning. Various data labeling tools have been built to collect labels in different usage scenarios. However, developing labeling tools is time-consuming, costly, and expertise-demanding on software development. In this paper, we propose a conceptual framework for data labeling and OneLabeler based on the conceptual framework to support easy building of labeling tools for diverse usage scenarios. The framework consists of common modules and states in labeling tools summarized through coding of existing tools. OneLabeler supports configuration and composition of common software modules through visual programming to build data labeling tools. A module can be a human, machine, or mixed computation procedure in data labeling. We demonstrate the expressiveness and utility of the system through ten example labeling tools built with OneLabeler. A user study with developers provides evidence that OneLabeler supports efficient building of diverse data labeling tools.