few-shot and easy-few-shot-learning
About few-shot
oscarknagg/few-shot
Repository for few-shot learning machine learning projects
This project provides pre-built machine learning models that can learn to classify new types of images with very few examples. You input standard image datasets like Omniglot or miniImageNet, and the models output classifications for new, previously unseen image categories, even if you only have a handful of images per category. This is ideal for machine learning researchers and practitioners who need to explore and compare few-shot learning techniques for image classification.
About easy-few-shot-learning
sicara/easy-few-shot-learning
Ready-to-use code and tutorial notebooks to boost your way into few-shot learning for image classification.
This helps machine learning engineers and researchers quickly build and experiment with few-shot image classification models. You provide a small set of labeled images, and the system outputs a model that can recognize new image categories with minimal additional training data. This is ideal for those working on computer vision tasks with limited data for new classes.
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