The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.

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.

Resources for Data Centric AI

A curated, but incomplete, list of data-centric AI resources.

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

Curated list of open source tooling for data-centric AI on unstructured data.

Interactively explore unstructured datasets from your dataframe.

Lab assignments for Introduction to Data-Centric AI, MIT IAP 2023 👩🏽‍💻

[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark

MOOSE (Multi-organ objective segmentation) a data-centric AI solution that generates multilabel organ segmentations to facilitate systemic TB whole-person research.The pipeline is based on nn-UNet and has the capability to segment 120 unique tissue classes from a whole-body 18F-FDG PET/CT image.

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

Introduction to Data-Centric AI, MIT IAP 2023 🤖

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

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

nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasets

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

A Data Centric NER annotation tool for your Named Entity Recognition projects

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.

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