ya0002/Colab-Siamese_Neural_Nets_for_One-shot_Image_Recognition
A ready to go implementation of the "Siamese Neural Networks for One-shot Image Recognition" paper in PyTorch on Google Colab with training and testing on the Omniglot/custom datasets.
This tool helps researchers and AI practitioners quickly set up and experiment with 'one-shot' image recognition models. You provide a dataset of images, and it trains a Siamese Neural Network to identify new, unseen images with very few examples. This is ideal for those exploring advanced image classification techniques.
No commits in the last 6 months.
Use this if you need to build an image recognition system that can learn to identify new objects from just one or a handful of examples.
Not ideal if you're looking for a simple, off-the-shelf image classifier for common objects where large datasets are readily available.
Stars
9
Forks
5
Language
Jupyter Notebook
License
—
Category
Last pushed
Feb 22, 2021
Commits (30d)
0
Get this data via API
curl "https://pt-edge.onrender.com/api/v1/quality/ml-frameworks/ya0002/Colab-Siamese_Neural_Nets_for_One-shot_Image_Recognition"
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.