The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
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Updated
Jul 12, 2024 - Python
The standard data-centric AI package for data quality and machine learning with messy, real-world data and labels.
The open-source tool for building high-quality datasets and computer vision models
A Doctor for your data
Lab assignments for Introduction to Data-Centric AI, MIT IAP 2024 👩🏽💻
Resources for Data Centric AI
Interactively explore unstructured datasets from your dataframe.
Automatically find issues in image datasets and practice data-centric computer vision.
A curated, but incomplete, list of data-centric AI resources.
The data scientist's open-source choice to scale, assess and maintain natural language data. Treat training data like a software artifact.
Curated list of open source tooling for data-centric AI on unstructured data.
[NeurIPS 2021] WRENCH: Weak supeRvision bENCHmark
Introduction to Data-Centric AI, MIT IAP 2023 🤖
nbsynthetic is simple and robust tabular synthetic data generation library for small and medium size datasets
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.
[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)
Client interface for all things Cleanlab Studio
[ECCV 2022] Official Implementation for Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
Frontiers in Neuroinformatics 2022: Local Label Point Correction for Edge Detection of Overlapping Cervical Cells
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