Context CIFAR-10 is the excellent Dataset for many Image processing experiments. Content Usage instructions # in Keras from os import listdir, makedirs from os.path import join, exists, expanduser cache_dir = expanduser(join('~', '.keras')) if not exists(cache_dir): makedirs(cache_dir) datasets_dir = join(cache_dir, 'datasets') # /cifar-10-batches-py if not exists(datasets_dir): makedirs(datasets_dir) # If you have multiple input datasets, change the below cp command accordingly, typically: # !cp ../input/cifar10-python/cifar-10-python.tar.gz ~/.keras/datasets/ !cp ../input/cifar-10-python.tar.gz ~/.keras/datasets/ !ln -s ~/.keras/datasets/cifar-10-python.tar.gz ~/.keras/datasets/cifar-10-batches-py.tar.gz !tar xzvf ~/.keras/datasets/cifar-10-python.tar.gz -C ~/.keras/datasets/ # general Python 3 def unpickle(file): import pickle with open(file, 'rb') as fo: dict = pickle.load(fo, encoding='bytes') return dict !tar xzvf ../input/cifar-10-python.tar.gz then see section "Dataset layout" in [https://www.cs.toronto.edu/~kriz/cifar.html][1] for details Acknowledgements Downloaded directly from here: [https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz][2] See description: [https://www.cs.toronto.edu/~kriz/cifar.html][3] Inspiration Your data will be in front of the world's largest data science community. What questions do you want to see answered? [1]: https://www.cs.toronto.edu/~kriz/cifar.html [2]: https://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz [3]: https://www.cs.toronto.edu/~kriz/cifar.html