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.

A Doctor for your data

fastdup is a powerful, free tool designed to rapidly generate valuable insights from image and video datasets. It helps enhance the quality of both images and labels, while significantly reducing data operation costs, all with unmatched scalability.

Scalable data pre processing and curation toolkit for LLMs

🏭 Mega Scale Multimodal DataPipeline for SOTA Foundation Models

Open-source PyTorch tool that traces live training signals back to the data samples causing them. Pause the training to find mislabels, outliers, and class imbalance in image, video, and LiDAR datasets. And train again.

Streamline on-policy/off-policy distillation workflows in a few lines of code

[ICLR 2025] Official implementation of paper "Improving Data Efficiency via Curating LLM-Driven Rating Systems"

Metamapper is a data discovery and documentation platform for improving how teams understand and interact with their data.

A library for detecting problematic data segments in structured and unstructured data with few lines of code.

Learn2Clean: Optimizing the Sequence of Tasks for Data Preparation and Cleaning

Data Curation Tool is a local-first dataset curation, tagging, model-management, downloader, 3D generation, media metadata, and tool-orchestration application. The current documentation set is stored in docs/wiki/

[NeurIPS 2024] 🧼🔎 A holistic self-supervised data cleaning strategy to detect irrelevant samples, near duplicates and label errors.

AqSolDB: A curated aqueous solubility dataset contains 9.982 unique compounds.

Client interface to Cleanlab Studio

Curation of BIDS (CuBIDS): A sanity-preserving software package for processing BIDS datasets.

[EMNLP 2023] Target-oriented Proactive Dialogue Systems with Personalization: Problem Formulation and Dataset Curation

Rebalancing chemical reaction

Turn documents into high-quality instruction datasets with grounding, quality filtering, deduplication, and provenance.

Multi-task framework for breast cancer segmentation and classification