DDU's Dirty-MNIST

You'll never want to use MNIST again for OOD or AL.

Install

pip install ddu_dirty_mnist

How to use

After installing, you get a Dirty-MNIST train or test set just like you would for MNIST in PyTorch.

# gpu

import ddu_dirty_mnist

dirty_mnist_train = ddu_dirty_mnist.DirtyMNIST(".", train=True, download=True, device="cuda")
dirty_mnist_test = ddu_dirty_mnist.DirtyMNIST(".", train=False, download=True, device="cuda")
len(dirty_mnist_train), len(dirty_mnist_test)

(120000, 30000)

Here is how to create torch.utils.data.DataLoader, see the documentation for details.

# gpu
import torch

dirty_mnist_train_dataloader = torch.utils.data.DataLoader(
    dirty_mnist_train,
    batch_size=128,
    shuffle=True,
    num_workers=0,
    pin_memory=False,
)
dirty_mnist_test_dataloader = torch.utils.data.DataLoader(
    dirty_mnist_test,
    batch_size=128,
    shuffle=False,
    num_workers=0,
    pin_memory=False,
)

If you only care about Ambiguous-MNIST, you can use:

# gpu

import ddu_dirty_mnist

ambiguous_mnist_train = ddu_dirty_mnist.AmbiguousMNIST(".", train=True, download=True, device="cuda")
ambiguous_mnist_test = ddu_dirty_mnist.AmbiguousMNIST(".", train=False, download=True, device="cuda")

ambiguous_mnist_train, ambiguous_mnist_test

(Dataset AmbiguousMNIST
     Number of datapoints: 60000
     Root location: .,
 Dataset AmbiguousMNIST
     Number of datapoints: 20000
     Root location: .)

Here is how to create torch.utils.data.DataLoader, see the documentation for details.

# gpu
import torch

ambiguous_mnist_train_dataloader = torch.utils.data.DataLoader(
    ambiguous_mnist_train,
    batch_size=128,
    shuffle=True,
    num_workers=0,
    pin_memory=False,
)
ambiguous_mnist_test_dataloader = torch.utils.data.DataLoader(
    ambiguous_mnist_test,
    batch_size=128,
    shuffle=False,
    num_workers=0,
    pin_memory=False,
)