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---
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) |