Datasets:

oriyonay
/

quickdraw-mnist

pretty_name: QuickDraw-MNIST
license: cc-by-4.0
task_categories:
  - image-classification
language:
  - en
tags:
  - computer-vision
  - image-classification
  - education
  - quickdraw
  - mnist-like
size_categories:
  - 100K<n<1M
configs:
  - config_name: default
    data_files:
      - split: train
        path: train-*.parquet
dataset_info:
  features:
    - name: image
      dtype: image
    - name: label
      dtype:
        class_label:
          names:
            - The Eiffel Tower
            - airplane
            - angel
            - bed
            - chair
            - clock
            - diamond
            - donut
            - fork
            - frog
            - hourglass
            - leaf
            - line
            - mushroom
            - octagon
            - palm tree
            - pants
            - pencil
            - square
            - squiggle
    - name: label_name
      dtype: string

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.