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

```python
from datasets import load_dataset

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

For PyTorch:

```python
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.