Classic Scenarios

We propose here a list of classic continual learning scenarios used in the literature. For each, scenarios we show how to create it. For using it, you may look at scenarios documentation

  • split MNIST: 5 tasks, number of classes per tasks: 2

from continuum import ClassIncremental
from continuum.datasets import MNIST

scenario = ClassIncremental(
    MNIST(data_path="my/data/path", download=True, train=True),
    increment=2
 )

  • split CIFAR100: 6 tasks, first 50 classes then 10 classes per tasks.

from continuum import ClassIncremental
from continuum.datasets import CIFAR100

scenario = ClassIncremental(
    CIFAR100(data_path="my/data/path", download=True, train=True),
    increment=10,
    initial_increment=50
  )

  • Concatenation of CIFAR10 & CIFAR100, made of 11 tasks of 10 classes each

from continuum import ClassIncremental
from continuum.datasets import CIFARFellowship

scenario = ClassIncremental(
    CIFARFellowship(data_path="my/data/path", download=True, train=True),
    increment=10,
)

  • Permut MNIST: 5 tasks with different label space for each task

from continuum import Permutations
from continuum.datasets import MNIST

scenario = Permutations(
    MNIST(data_path="my/data/path", download=True, train=True),
    nb_tasks=5,
    seed=0,
    shared_label_space=False
)

  • Rotations MNIST: 3 tasks, rotation 0-45-90 degrees with different label space for each task

from continuum import Rotations
from continuum.datasets import MNIST
scenario = Rotations(
    MNIST(data_path="my/data/path", download=True, train=True),
    nb_tasks=3,
    list_degrees=[0,45,90]
)

For more info scenarios documentation.