AutoDC: Automated data-centric processing

TL;DR

AutoDC is an automated tool designed to streamline data-centric tasks in machine learning, significantly reducing manual effort and improving model accuracy on image classification datasets.

Contribution

AutoDC introduces an automated approach to dataset improvement, addressing the artisanal nature of data fixing and augmentation in data-centric machine learning.

Findings

Reduces manual data improvement time by approximately 80%.

Improves model accuracy by 10-15%.

Effective on open source image classification datasets.

Abstract

AutoML (automated machine learning) has been extensively developed in the past few years for the model-centric approach. As for the data-centric approach, the processes to improve the dataset, such as fixing incorrect labels, adding examples that represent edge cases, and applying data augmentation, are still very artisanal and expensive. Here we develop an automated data-centric tool (AutoDC), similar to the purpose of AutoML, aims to speed up the dataset improvement processes. In our preliminary tests on 3 open source image classification datasets, AutoDC is estimated to reduce roughly 80% of the manual time for data improvement tasks, at the same time, improve the model accuracy by 10-15% with the fixed ML code.