Cleanlab's open-source library is the standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

Refine high-quality datasets and visual AI models

A Doctor for your data

The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.

Automatically find issues in image datasets and practice data-centric computer vision.

Official PyTorch implementation of the paper "Dataset Distillation with Neural Characteristic Function: A Minmax Perspective" (NCFM) in CVPR 2025 (Highlight).

[ICML 2025] Programming Every Example: Lifting Pre-training Data Quality Like Experts at Scale

[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

[NeurIPS 2023] This is the code for the paper `Large Language Model as Attributed Training Data Generator: A Tale of Diversity and Bias`.

pyDVL is a library of stable implementations of algorithms for data valuation and influence function computation

OpenDataVal: a Unified Benchmark for Data Valuation in Python (NeurIPS 2023)

Self-Evolved Diverse Data Sampling for Efficient Instruction Tuning

[ECCV 2022] Official Implementation for Unsupervised Selective Labeling for More Effective Semi-Supervised Learning

🧼🔎 A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors (NeurIPS'24).

This data-centric AI repository implements a robust deep learning method (LFBNet) for fully automated tumor segmentation in whole-body [18]F-FDG PET/CT images.

Client interface to Cleanlab Studio and the Trustworthy Language Model

Frontiers in Neuroinformatics 2022: Local Label Point Correction for Edge Detection of Overlapping Cervical Cells

Unsupervised classification to improve the quality of a bird song recording dataset. https://doi.org/10.1016/j.ecoinf.2022.101952

Estimate dataset difficulty and detect label mistakes using reconstruction error ratios!

Official Python SDK for Kern AI refinery.