yassouali/SCL
:page_facing_up: Spatial Contrastive Learning for Few-Shot Classification (ECML/PKDD 2021).
This project helps machine learning engineers and researchers classify images, especially when they have very little training data for new categories. It takes a collection of labeled images and outputs an improved image classification model. The end-users are machine learning practitioners building image recognition systems.
No commits in the last 6 months.
Use this if you are building an image classification system and need to quickly adapt to new visual categories with only a handful of examples.
Not ideal if you are a business user or an individual looking for a ready-to-use application, as this project requires programming and machine learning expertise.
Stars
50
Forks
5
Language
Python
License
MIT
Category
Last pushed
Dec 19, 2022
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/yassouali/SCL"
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.