few-shot and few-shot-meta-baseline
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 few-shot-meta-baseline
yinboc/few-shot-meta-baseline
Meta-Baseline: Exploring Simple Meta-Learning for Few-Shot Learning, in ICCV 2021
This project helps researchers and machine learning practitioners train image classification models with very limited data. It takes a small collection of example images for new categories and outputs a model capable of recognizing those categories. This is particularly useful for specialists working with rare data or in fields where extensive datasets are unavailable, such as medical imaging or specialized object detection.
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