not-MNIST

Overview

The not-MNIST dataset was created by Yaroslav Bulatov as a more challenging alternative to the classic MNIST dataset. While the original MNIST consists of handwritten digits, not-MNIST is composed of glyphs extracted from various publicly available fonts.

This dataset features the letters A through J (10 classes) and serves as a rigorous benchmark for machine learning models. It is significantly more difficult than MNIST because the fonts range from standard typefaces to highly artistic, experimental, or even barely legible designs.

Split sizes:

Train: 60,000 images
Test: 10,000 images

Data Structure

When accessing an example using ds[i], you will receive a dictionary with the following keys:

Key	Type	Description
`image`	`PIL.Image.Image`	28×28 Grayscale image of a letter (A-J)
`label`	int	Class label (0-9) corresponding to letters A through J

Usage Example

Basic Usage

from stable_datasets.images.not_mnist import NotMNIST

# First run will download + prepare cache, then return the split as a HF Dataset
ds = NotMNIST(split="train")

# If you omit the split (split=None), you get a DatasetDict with all available splits
ds_all = NotMNIST(split=None)

sample = ds[0]
print(sample.keys())  # {"image", "label"}

# Access the image and label
image = sample["image"]  # PIL.Image.Image
label = sample["label"]  # int (0-9)
print(f"Label: {label} -> Letter: {chr(ord('A') + label)}")

# Optional: make it PyTorch-friendly
ds_torch = ds.with_format("torch")

With Transforms

from torchvision import transforms

transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.5,), (0.5,))
])

ds = NotMNIST(split="train")
sample = ds[0]
tensor = transform(sample["image"])
print(f"Tensor shape: {tensor.shape}")  # torch.Size([1, 28, 28])

References

Creator: Yaroslav Bulatov
Blog: http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html
Data source: https://github.com/davidflanagan/notMNIST-to-MNIST

Citation

@misc{bulatov2011notmnist,
    author={Yaroslav Bulatov},
    title={notMNIST dataset},
    year={2011},
    url={http://yaroslavvb.blogspot.com/2011/09/notmnist-dataset.html}
}