Renovamen/metallic

A clean, lightweight and modularized PyTorch meta-learning library.

Metallic

Metallic is a meta-learning library based on PyTorch.

Different from other meta-learning libraries, Metallic tries to:

• Provide a clean, lightweight and modularized toolbox for setting up meta-learning experiments quickly with the least amount of code.
• For gradient-based meta-learning methods (like MAML), support more optimizers instead of SGD only using higher.

The library is work in progress.

 

Features

Algorithms

The supported interface algorithms currently include:

Gradient-based

• Model-Agnostic Meta-Learning (MAML), including first-order version (FOMAML)
• Reptile
• Minibatch Proximal Update
• Almost No Inner Loop (ANIL)

Metric-based

• Matching Networks
• Prototypical Networks

Datasets

The supported datasets currently include:

• Omniglot
• Mini-ImageNet

 

Installation

git clone https://github.com/Renovamen/metallic.git
cd metallic
python setup.py install

or

pip install git+https://github.com/Renovamen/metallic.git --upgrade

 

Documentations

Check the API documentation here: metallic-docs.vercel.app

 

Acknowledgements

Metallic is highly inspired by the following awesome libraries:

• learn2learn
• Torchmeta
• higher

 

License

Metallic is MIT licensed, see the LICENSE file for more details.