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A base CL framework to speed-up prototyping and testing

Project description

Continual learning framework

This is a Continual Learning library based on Pytorch, mainly born for personal use, which can be used for fast prototyping, training and to compare different build-in methods over a various numbers of scenarios and benchmarks.



pip install continual-learning

Continual learning framework

The library is organized in four main modules:

  • Benchmarks: This module contains the most used dataset in CL, reimplemented to give more flexibility.
  • Logging: This module provides different supervised scenarios which you can use in combination with a dataset to create your own Cl scenario.
  • Extras: It contains many Cl methods, that can be easily used and evalauted.
  • Training: This module contains many popular networks used to extract the features from the input samples.
  • Evaluation: This modules provides a unified way to evaluate a method over a flexible numbers of metrics/
  • Models: In this module you will find different solvers, used to classify the features extracted by a backbone network.


This is a framework which is born to improve coding, and the reproducibility of the papers in which I have worked during the years. Being constantly under development, it may be unstable.

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