gholomia / datasets

A collection of datasets ready to use with TensorFlow

TensorFlow Datasets

Note: tensorflow-datasets is not yet released. Follow the release tracking issue to be notified of release.

TensorFlow Datasets provides many public datasets as tf.data.Datasets.

Try it in a Colab notebook.

See all our datasets on our datasets documentation page or see our API docs

Installation

# A stable tensorflow-datasets has not yet been released.
# To use our nightly release:
pip install tfds-nightly

# Currently requires TF 1.13+, i.e. tf-nightly or tf-nightly-gpu, to be
# installed.
# Some datasets require additional libraries; see setup.py extras_require
pip install tf-nightly

Usage

import tensorflow_datasets as tfds
import tensorflow as tf

# tfds works in both Eager and Graph modes
tf.enable_eager_execution()

# See available datasets
print(tfds.list_builders())

# Construct a tf.data.Dataset
ds_train, ds_test = tfds.load(name="mnist", split=["train", "test"])

# Build your input pipeline
ds_train = ds_train.shuffle(1000).batch(128).prefetch(10)
for features in ds_train.take(1):
  image, label = features["image"], features["label"]

DatasetBuilder

All datasets are implemented as subclasses of DatasetBuilder and tfds.load is a thin convenience wrapper.

import tensorflow_datasets as tfds

# The following is the equivalent of the `load` call above.

# You can fetch the DatasetBuilder class by string
mnist_builder = tfds.builder("mnist")

# Download the dataset
mnist_builder.download_and_prepare()

# Construct a tf.data.Dataset
dataset = mnist_builder.as_dataset(split=tfds.Split.TRAIN)

# Get the `DatasetInfo` object, which contains useful information about the
# dataset and its features
info = mnist_builder.info
print(info)

    tfds.core.DatasetInfo(
        name='mnist',
        version=1.0.0,
        description='The MNIST database of handwritten digits.',
        urls=[u'http://yann.lecun.com/exdb/mnist/'],
        features=FeaturesDict({
            'image': Image(shape=(28, 28, 1), dtype=tf.uint8),
            'label': ClassLabel(shape=(), dtype=tf.int64, num_classes=10)
        },
        total_num_examples=70000,
        splits={
            u'test': <tfds.core.SplitInfo num_examples=10000>,
            u'train': <tfds.core.SplitInfo num_examples=60000>
        },
        supervised_keys=(u'image', u'label'),
        citation='"""
            @article{lecun2010mnist,
              title={MNIST handwritten digit database},
              author={LeCun, Yann and Cortes, Corinna and Burges, CJ},
              journal={ATT Labs [Online]. Available: http://yann. lecun. com/exdb/mnist},
              volume={2},
              year={2010}
            }
      """',
  )

NumPy Usage with tfds.dataset_as_numpy

As a convenience for users that want simple NumPy arrays in their programs, you can use tfds.dataset_as_numpy to return a generator that yields NumPy array records out of a tf.data.Dataset. This allows you to build high-performance input pipelines with tf.data but use whatever you'd like for your model components.

train_ds = tfds.load("mnist", split=tfds.Split.TRAIN)
train_ds = train_ds.shuffle(1024).batch(128).repeat(5).prefetch(10)
for example in tfds.dataset_as_numpy(train_ds):
  numpy_images, numpy_labels = example["image"], example["label"]

You can also use tfds.dataset_as_numpy in conjunction with batch_size=-1 to get the full dataset in NumPy arrays from the returned tf.Tensor object:

train_data = tfds.load("mnist", split=tfds.Split.TRAIN, batch_size=-1)
numpy_data = tfds.dataset_as_numpy(train_data)
numpy_images, numpy_labels = numpy_dataset["image"], numpy_dataset["label"]

Note that the library still requires tensorflow as an internal dependency.

Contributing a dataset

Thanks for considering a contribution! We're eager to grow the available set of datasets. See the doc on adding a new dataset.

Want a certain dataset?

Consider contributing (see above). But if you'd just like to request a dataset, open a Dataset request GitHub issue and the community can vote on which datasets they'd like most by adding +1/thumbs-up to the issue.

Vote on the current set of requests.

Disclaimers

This is a utility library that downloads and prepares public datasets. We do not host or distribute these datasets, vouch for their quality or fairness, or claim that you have license to use the dataset. It is your responsibility to determine whether you have permission to use the dataset under the dataset's license.

If you're a dataset owner and wish to update any part of it (description, citation, etc.), or do not want your dataset to be included in this library, please get in touch through a GitHub issue. Thanks for your contribution to the ML community!

If you're interested in learning more about responsible AI practices, including fairness, please see Google AI's Responsible AI Practices.

tensorflow/datasets is Apache 2.0 licensed. See the LICENSE file.