victor-iyi/few-shot-learning
One-shot Learning: Learning from fewer dataset with a single or few training examples. Exploration of method and techniques for state-of-the-art results
This project helps machine learning practitioners build models that can identify new objects or categories even when very little training data is available. You feed it image examples, and it learns to compare them to determine if they belong to the same category. This is especially useful for researchers or developers working with rare datasets where gathering many examples is impossible.
No commits in the last 6 months.
Use this if you need to classify new types of images, like recognizing rare species or identifying defects, with only one or a few examples per category.
Not ideal if you have a large dataset for training, as traditional machine learning models might offer simpler or more robust solutions.
Stars
9
Forks
3
Language
Jupyter Notebook
License
MIT
Category
Last pushed
Oct 24, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/victor-iyi/few-shot-learning"
Open to everyone — 100 requests/day, no key needed. Get a free key for 1,000/day.
Higher-rated alternatives
oscarknagg/few-shot
Repository for few-shot learning machine learning projects
jakesnell/prototypical-networks
Code for the NeurIPS 2017 Paper "Prototypical Networks for Few-shot Learning"
harveyslash/Facial-Similarity-with-Siamese-Networks-in-Pytorch
Implementing Siamese networks with a contrastive loss for similarity learning
google-research/meta-dataset
A dataset of datasets for learning to learn from few examples
akshaysharma096/Siamese-Networks
Few Shot Learning by Siamese Networks, using Keras.