text_plain_output_1.png
import numpy as np # linear algebra

path = Path('../input/dataset')

train = path / 'training_set'
test = path / 'test_set'

np.random.seed(42)
data = ImageDataBunch.from_folder(train, train='.', valid_pct=0.2, ds_tfms=get_transforms(do_flip=True), size=224, num_workers=4).normalize(imagenet_stats)

data.classes

(data.classes, data.c, len(data.train_ds), len(data.valid_ds))