joisino/seafaring
Code for "Active Learning from the Web" (WWW 2023)
This project helps machine learning engineers or researchers efficiently gather high-quality, labeled image data from vast online sources like Flickr or Open Images. By processing a small set of initial labeled images and a large pool of unlabeled web data, it identifies the most informative images to label next. The output is a more accurate machine learning model trained with less manual labeling effort.
116 stars. No commits in the last 6 months.
Use this if you are building an image classification model and want to reduce the cost and time spent manually labeling training data by intelligently selecting which images to annotate from the web.
Not ideal if your data is not publicly available on the web, if you are not working with image data, or if you need to label data from a private, curated dataset rather than broad web sources.
Stars
116
Forks
23
Language
Python
License
MIT
Category
Last pushed
Feb 14, 2023
Commits (30d)
0
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