few-shot and LightningFSL
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 LightningFSL
Frankluox/LightningFSL
LightningFSL: Pytorch-Lightning implementations of Few-Shot Learning models.
This project offers a collection of few-shot learning models to help machine learning practitioners build classification systems that can learn effectively from very limited data. It takes image datasets with few examples per category as input and outputs highly accurate classification models. This is ideal for AI/ML engineers and researchers who need to develop robust image classification systems with minimal data.
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