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A clean and simple library for Continual Learning in PyTorch.

Project description

Continuum: Simple Management of Complex Continual Learning Scenarios

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A library for PyTorch's loading of datasets in the field of Continual Learning

Aka Continual Learning, Lifelong-Learning, Incremental Learning, etc.

Read the documentation.
Test Continuum on Colab !


Install from and PyPi:

pip3 install continuum

And run!

from import DataLoader

from continuum import ClassIncremental
from continuum.datasets import MNIST
from continuum.tasks import split_train_val

dataset = MNIST("my/data/path", download=True, train=True)
scenario = ClassIncremental(

print(f"Number of classes: {scenario.nb_classes}.")
print(f"Number of tasks: {scenario.nb_tasks}.")

for task_id, train_taskset in enumerate(scenario):
    train_taskset, val_taskset = split_train_val(train_taskset, val_split=0.1)
    train_loader = DataLoader(train_taskset, batch_size=32, shuffle=True)
    val_loader = DataLoader(val_taskset, batch_size=32, shuffle=True)

    for x, y, t in train_loader:
        # Do your cool stuff here

Supported Types of Scenarios

Name Acronym  Supported Scenario
New Instances  NI :white_check_mark: Instances Incremental
New Classes  NC :white_check_mark: Classes Incremental
New Instances & Classes  NIC :white_check_mark: Data Incremental

Supported Datasets:

Most dataset from torchvision.dasasets are supported, for the complete list, look at the documentation page on datasets here.

Furthermore some "Meta"-datasets are can be create or used from numpy array or any torchvision.datasets or from a folder for datasets having a tree-like structure or by combining several dataset and creating dataset fellowships!


All our continual loader are iterable (i.e. you can for loop on them), and are also indexable.

Meaning that clloader[2] returns the third task (index starts at 0). Likewise, if you want to evaluate after each task, on all seen tasks do clloader_test[:n].

Example of Sample Images from a Continuum scenario


Task 0 Task 1 Task 2 Task 3 Task 4

MNIST Fellowship (MNIST + FashionMNIST + KMNIST):

Task 0 Task 1 Task 2


Task 0 Task 1 Task 2 Task 3 Task 4


Task 0 Task 1 Task 2 Task 3 Task 4

TransformIncremental + BackgroundSwap:

Task 0 Task 1 Task 2


If you find this library useful in your work, please consider citing it:

  author={Douillard, Arthur and Lesort, Timothée},
  title={Continuum: Simple Management of Complex Continual Learning Scenarios},
  publisher={arXiv: 2102.06253},


This project was started by a joint effort from Arthur Douillard & Timothée Lesort, and we are currently the two maintainers.

Feel free to contribute! If you want to propose new features, please create an issue.

Contributors: Lucas Caccia Lucas Cecchi Pau Rodriguez, Yury Antonov, psychicmario, fcld94, Ashok Arjun, Md Rifat Arefin, DanieleMugnai, Xiaohan Zou, Umberto Cappellazzo.

On PyPi

Our project is available on PyPi!

pip3 install continuum

Note that previously another project, a CI tool, was using that name. It is now there continuum_ci.

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