lorenzobrigato/gem
A Pytorch-based library to evaluate learning methods on small image classification datasets
This project helps machine learning engineers and researchers evaluate how well different image classification methods perform when only a small amount of training data is available. You input a small image dataset and select a learning algorithm, and the project outputs performance metrics showing how effectively that algorithm generalizes to new, unseen images. This is for professionals building or researching image classification systems, especially in specialized domains where large datasets are hard to come by.
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Use this if you need to compare and benchmark various image classification algorithms to find the most effective one for your specific task, particularly when you only have small image datasets.
Not ideal if you are looking for a pre-trained, production-ready image classifier or if you have very large datasets and are not focused on small-sample learning challenges.
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Jun 22, 2022
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