TensorFlow Datasets

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

Documentation

To install and use TFDS, we strongly encourage to start with our getting started guide. Try it interactively in a Colab notebook.

Our documentation contains:

# !pip install tensorflow-datasets
import tensorflow_datasets as tfds
import tensorflow as tf

# Construct a tf.data.Dataset
ds = tfds.load('mnist', split='train', as_supervised=True, shuffle_files=True)

# Build your input pipeline
ds = ds.shuffle(1000).batch(128).prefetch(10).take(5)
for image, label in ds:
  pass

TFDS core values

TFDS has been built with these principles in mind:

Simplicity: Standard use-cases should work out-of-the box
Performance: TFDS follows best practices and can achieve state-of-the-art speed
Determinism/reproducibility: All users get the same examples in the same order
Customisability: Advanced users can have fine-grained control

If those use cases are not satisfied, please send us feedback.

Want a certain dataset?

Adding a dataset is really straightforward by following our guide.

Request a dataset by opening a Dataset request GitHub issue.

And vote on the current set of requests by adding a thumbs-up reaction to the issue.

Citation

Please include the following citation when using tensorflow-datasets for a paper, in addition to any citation specific to the used datasets.

@misc{TFDS,
  title = {{TensorFlow Datasets}, A collection of ready-to-use datasets},
  howpublished = {\url{https://www.tensorflow.org/datasets}},
}

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.