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You can get the cinic-10 dataset follow this link
https://datashare.is.ed.ac.uk/bitstream/handle/10283/3192/CINIC-10.tar.gz
This loader does not include the download function, so you need to use other tools to download the original dataset and extract it..
Find the folder containing train, valid, test folder. (dataset root folder)
You can load dataset from dataset folder follow
ds = cinic10_dataset()
ds.load('datasets/cinic10')
then you can save the dataset in npz file to improve next loading speed.
ds.save_npz('datasets/cinic10.npz')
load from npz file is fast than load from dataset folder.
ds.load_from_npz('datasets/cinic10.npz')
when you load dataset complete, you can use data() get all image and label.
train_x, train_y, valid_x, valid_y, test_x, test_y = ds.data()
or you can use some simple api get a data batch.
print(ds.get_train_batch_count(100))
print(ds.get_valid_batch_count(100))
print(ds.get_test_batch_count(100))
for b in range(ds.get_train_batch_count(100)):
ds.get_train_batch(b, 100)
for b in range(ds.get_valid_batch_count(100)):
ds.get_valid_batch(b, 100)
for b in range(ds.get_test_batch_count(100)):
ds.get_test_batch(b, 100)