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.