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Datasets


Usage

# Build the `tf.data.Dataset` pipeline.
ds, info = tfds.load('cifar10', split='train', shuffle_files=True, with_info=True)
ds = ds.shuffle(info.splits['train'].num_examples)
ds = ds.batch(32)

# `tfds.as_numpy` converts `tf.Tensor` -> `np.array`
for ex in tfds.as_numpy(ds):
  # `int2str` returns the human readable label ('dog', 'car',...)
  print(info.features['label'].int2str(ex['label']))

All Datasets

Audio

Image

Image classification

Object detection

Structured

Summarization

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