---
dataset_info:
  features:
  - name: image
    dtype: PIL.Image.Image
  - name: label
    dtype: int
  class_label:
    names:
      '0': bowtie
      '1': windmill
      '2': tree
      '3': river
      '4': ice cream
      '5': eye
      '6': book
      '7': sun
      '8': star
      '9': airplane
      '10': butterfly
      '11': clock
      '12': car
      '13': fish
      '14': face
      '15': umbrella
      '16': cat
      '17': bicycle
      '18': pizza
      '19': house
      '20': cake
      '21': bucket
      '22': crown
      '23': light bulb
      '24': cell phone
      '25': t-shirt
  splits:
  - name: train
    num_bytes: 174683075.2
    num_examples: 416000
  - name: val
    num_bytes: 21851140.4
    num_examples: 52000
  - name: test
    num_bytes: 21675900.4
    num_examples: 52000
  download_size: 218844448
  dataset_size: 218210116
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: val
    path: data/val-*
  - split: test
    path: data/test-*
task_categories:
- image-classification
tags:
- art
size_categories:
- 100K<n<1M
---


# Quick! Draw 26 Class Dataset

This dataset is derived from the Google Quick! Draw dataset and contains 26 classes of doodle images drawn by users. The classes include common objects and entities like animals, vehicles, food items, and everyday objects.

## Dataset Details

- **Number of Classes:** 26
- **Total Images:** 520,000 (416,000 train, 52,000 val, 52,000 test)
- **Image Format:** PNG images of size 28x28 pixels (grayscale)
- **Data Fields:**
  - `image`: PIL Image object
  - `label`: Integer label corresponding to class

## Class Labels

0: bowtie, 1: windmill, 2: tree, 3: river, 4: ice cream, 5: eye, 6: book, 7: sun, 8: star, 9: airplane, 10: butterfly, 11: clock, 12: car, 13: fish, 14: face, 15: umbrella, 16: cat, 17: bicycle, 18: pizza, 19: house, 20: cake, 21: bucket, 22: crown, 23: light bulb, 24: cell phone, 25: t-shirt

## Download and Loading

You can load this dataset using the `load_dataset` function from the `datasets` library:

```python
from datasets import load_dataset

dataset = load_dataset("OmAlve/quickdraw_26_classes")
```

This will download and cache the dataset locally.


## Maintainers

- [Om Alve](https://huggingface.co/OmAlve)