awesome-open-data-centric-ai VS awesome-synthetic-data

Compare awesome-open-data-centric-ai vs awesome-synthetic-data and see what are their differences.

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awesome-open-data-centric-ai awesome-synthetic-data
1 1
678 100
- 10.0%
5.8 10.0
6 months ago almost 2 years ago
Creative Commons Attribution 4.0 Apache License 2.0
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.

awesome-open-data-centric-ai

Posts with mentions or reviews of awesome-open-data-centric-ai. We have used some of these posts to build our list of alternatives and similar projects.

awesome-synthetic-data

Posts with mentions or reviews of awesome-synthetic-data. We have used some of these posts to build our list of alternatives and similar projects.
  • Synthetic Data
    1 project | /r/dataengineering | 2 Mar 2023
    Btw, there's this Awesome list here with some nice pointers to interesting material for synthetic data.

What are some alternatives?

When comparing awesome-open-data-centric-ai and awesome-synthetic-data you can also consider the following projects:

internet-explorer - Internet Explorer explores the web in a self-supervised manner to progressively find relevant examples that improve performance on a desired target dataset.

spotlight - Interactively explore unstructured datasets from your dataframe.

Encord Active - Open source active learning toolkit to find failure modes in your computer vision models, prioritize data to label next, and drive data curation to improve model performance.

WhereIsAI - AI company, product, and tool collection.

Awesome-Learning-with-Label-Noise - A curated list of resources for Learning with Noisy Labels

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

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