KaiyangZhou/ssdg-benchmark
Benchmarks for semi-supervised domain generalization.
This project helps machine learning researchers compare and evaluate different approaches for a challenging problem: training a model that performs well across various real-world scenarios, even when labeled data is scarce. It takes in image datasets with both limited labeled examples and abundant unlabeled data from multiple distinct environments. The output helps researchers understand which techniques create models that generalize best, providing a benchmark for semi-supervised domain generalization methods.
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Use this if you are a machine learning researcher developing or evaluating methods to train robust models from limited labeled data that perform well across different visual domains.
Not ideal if you are looking for a pre-trained model or a tool to directly apply to a specific business problem without deep experimentation and research.
Stars
72
Forks
10
Language
Python
License
MIT
Category
Last pushed
Aug 25, 2022
Commits (30d)
0
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