TonyLianLong/UnsupervisedSelectiveLabeling
[ECCV 2022] Official Implementation for Unsupervised Selective Labeling for More Effective Semi-Supervised Learning
This project helps machine learning practitioners improve the accuracy of their image classification models when working with limited labeled data. It takes a large set of unlabeled images and intelligently selects a small subset of the most informative ones to be labeled, which then serves as improved training data for semi-supervised learning methods. This is ideal for data scientists, ML engineers, or researchers building image recognition systems.
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Use this if you need to build highly accurate image classification models but have a large amount of unlabeled image data and limited resources for manual labeling.
Not ideal if you already have fully labeled datasets or are working with non-image data types, as this tool is specifically designed for image-based semi-supervised learning.
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64
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3
Language
Python
License
MIT
Category
Last pushed
Jul 14, 2023
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