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This article introduces the Dataset Quality Auditor open-source project, a unified multimodal data quality audit platform that can detect issues such as label noise, class imbalance, duplicate entries, and annotation inconsistencies before model training, applicable to tabular, text, and visual data.
Section 01
This article introduces the Dataset Quality Auditor open-source project, a unified multimodal data quality audit platform that can detect issues such as label noise, class imbalance, duplicate entries, and annotation inconsistencies before model training, applicable to tabular, text, and visual data.
Section 02
Section 03
In the field of machine learning and deep learning, there is a widely recognized principle: "Garbage in, garbage out". No matter how advanced the model architecture is, the quality of training data directly determines the upper limit of model performance. However, real-world datasets often have various issues: label errors, class imbalance, duplicate samples, annotation inconsistencies, etc.
According to statistics, in actual machine learning projects, data preparation and cleaning usually take up 60-80% of the entire project cycle. Traditional manual inspection methods are inefficient and prone to missing issues, so there is an urgent need for automated data quality audit tools.
Section 04
Dataset Quality Auditor is a unified multimodal data quality audit platform designed to automatically detect and report potential issues in datasets before model training. The project supports three mainstream data modalities: tabular, text, and visual data, providing comprehensive data quality insights for data scientists and ML engineers.
Section 05
The platform provides the following key detection functions:
Section 06
Common quality issues in tabular data (structured data) include:
Section 07
Challenges faced by text data (unstructured data):
Section 08
Unique issues with image and video data:
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