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One of the biggest barriers to widespread machine learning adoption is the difficulty in collecting a 'good' dataset. There is an overall consensus that a 'good' dataset is a big dataset, but we believe that we can do better. As such the VennData project was created to develop tools to guide in the collection, curation, augmentation and validation of data.

License: Apache License 2.0

Jupyter Notebook 99.88% Python 0.12%
data-science data model machine-learning data-curation model-verification data-verification

venndata's Introduction

VennData

One of the biggest barriers to widespread machine learning adoption is the difficulty in collecting a 'good' dataset. There is an overall consensus that a 'good' dataset is a big dataset, but we believe that we can do better. As such the VennData project was created to develop tools to guide in the collection, curation, augmentation and validation of data.

Research

Our research focuses on tackling a wide range of questions into the impacts of datasets on model performance such as,

  • Providing scores for data quality, and identifying mislabels.
  • Identifying and correcting distribution shifts between test and train data.
  • Understanding the role of augmentations such as pixel, perspective, rotation and translation on datasets.

Environment and Software

Structure

Data_augmentation/ - Techniques and tools used to augment data for improved performance.

Data_Value/ - tools for scoring the value of individual data in a dataset.

Data_3D/ - classes and notes for Pytorch3D

Utils/ - Contains model definitions, training and evaluation scripts.

venndata's People

Contributors

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