Cleanlab is a data-centric AI framework that shifts the focus of machine learning improvement from model architecture to data quality. The library automatically identifies label errors, outliers, near-duplicate entries, and other data quality issues in any dataset by analyzing the predictions of any trained classifier. This model-agnostic approach means Cleanlab works with scikit-learn, PyTorch, TensorFlow, XGBoost, and any other framework that produces class probability predictions.
The library supports text classification, image classification, multi-label tasks, token classification for NER, object detection, tabular data, and audio classification. Its confident learning algorithm provides mathematically principled methods for estimating the joint distribution of noisy and true labels, enabling reliable detection of systematic labeling errors even in datasets with millions of examples. Teams typically discover that 5-15% of real-world training labels contain errors that silently degrade model performance.
Cleanlab has accumulated over 11,000 GitHub stars and established itself as the leading open-source tool for data quality in machine learning. The project originated from research at MIT and has been adopted by major technology companies and research institutions. Beyond the free library, Cleanlab Studio offers a no-code web interface for non-technical users to audit and improve datasets. The open-source package integrates with popular ML experiment tracking tools and can be added to existing training pipelines with minimal code changes.
