QuickDraw-MNIST

QuickDraw-MNIST is a 20-class sketch-recognition dataset prepared for Texas A&M's CSCE 624 (Sketch Recognition) class.

The data is sourced from Google's Quick, Draw! dataset.

Dataset Structure

• Number of images: 100,000
• Number of classes: 20
• Images: 64 x 64 grayscale
• Labels: integer class ids with a human-readable label_name column

Classes: The Eiffel Tower, airplane, angel, bed, chair, clock, diamond, donut, fork, frog, hourglass, leaf, line, mushroom, octagon, palm tree, pants, pencil, square, squiggle

Loading The Dataset

from datasets import load_dataset

dataset = load_dataset("oriyonay/quickdraw-mnist", split="train")
print(dataset)
print(dataset[0])

For PyTorch:

from datasets import load_dataset
from torchvision import transforms

dataset = load_dataset("oriyonay/quickdraw-mnist", split="train")
to_tensor = transforms.ToTensor()

example = dataset[0]
image = to_tensor(example["image"])   # shape: [1, 64, 64], values in [0, 1]
label = example["label"]
label_name = example["label_name"]

Source

• Original source: Google's Quick, Draw! dataset
• This version uses a class-balanced subset of 20 categories selected for CSCE 624.

Notes For Students

• This repository intentionally contains only the training split.
• Create your own train/validation split for model development.