- Download dataset.npy into a new directory
dataset/
. - Load dataset using
dataset = np.load('dataset/dataset.npy', allow_pickle=True)[()]
.
- CIFAR10
- Image shape: (32, 32, 3)
- No. classes: 9
- Classes: airplane, car, bird, cat, dog, frog, horse, ship, truck
- Count per class (train/test):
- airplane - 5000/1000
- car - 5000/1000
- bird - 5000/1000
- cat - 5000/1000
- dog - 5000/1000
- frog - 5000/1000
- horse - 5000/1000
- ship - 5000/1000
- truck - 5000/1000
- Dataset size (train/test): 45000/9000
- Download - Run
download-cifar.ipynb
, then rundataset.ipynb
. - Website
- QuickDraw
- Image shape: (28, 28)
- No. classes: 9
- Classes: airplane, bird, car, cat, ship, dog, frog, horse, truck
- Count per class:
- airplane - 151623
- bird - 133572
- car - 182764
- cat - 123202
- ship - 123410
- dog - 152159
- frog - 159047
- horse - 178286
- truck - 131354
- Dataset size: 1335417
- Code
- Dataset (Numpy 28x28 grayscale bitmap .npy)
- Sketchy
- Real
- Image shape: (256, 256, 3)
- No. classes: 8
- Classes: airplane, car, cat, dog, frog, horse, truck, bird
- Count per class:
- airplane - 100
- car - 100
- cat - 100
- dog - 100
- frog - 100
- horse - 100
- truck - 100
- bird - 100
- Dataset size: 800
- Doodle
- Image shape: (256, 256)
- No. classes: 8
- Classes: airplane, car, cat, dog, frog, horse, truck, bird
- Count per class:
- airplane - 528
- car - 534
- cat - 512
- dog - 512
- frog - 502
- horse - 525
- truck - 524
- bird - 504
- Dataset size: 4141
- Paper
- Website
- Code
- Dataset (Sketches and Photos)
- Supplementary Report
- TUBerlin
- Image shape: (1111, 1111)
- No. classes: 8
- Classes: airplane, car, cat, dog, bird, frog, horse, truck
- Count per class:
- airplane - 80
- car - 80
- cat - 80
- dog - 80
- bird - 80
- frog - 80
- horse - 80
- truck - 80
- Dataset size: 640
- Paper
- Website
- Dataset (Sketches in png)
- Google Doodle
- Google Real