goamegah/Self-or-Semi-Supervised-Learning-Images
Exploration of Self or Semi-Supervised learning for handling unlabeled data
This project helps machine learning practitioners classify images more effectively, especially when they have very few labeled examples. It takes a small set of labeled images (like 100 examples) and a larger set of unlabeled images, then outputs a highly accurate image classification model. This is for data scientists or ML engineers building computer vision systems who struggle with scarce labeled data.
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Use this if you need to build an image classifier but only have a small, limited number of labeled examples.
Not ideal if you already have a large, well-labeled image dataset for your classification task.
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Apr 11, 2024
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