v4.4.0
API:
- Add
PartialDecoding
support, to decode only a subset of the features (for performances) - Catalog now expose links to KnowYourData visualisations
tfds.as_numpy
supports datasets withNone
- Dataset generated with
disable_shuffling=True
are now read in generation order. - Loading datasets from files now supports custom
tfds.features.FeatureConnector
tfds.testing.mock_data
now supports- non-scalar tensors with dtype
tf.string
builder_from_files
and path-based community datasets
- non-scalar tensors with dtype
- File format automatically restored (for datasets generated with
tfds.builder(..., file_format=)
). - Many new reinforcement learning datasets
- Various bug fixes and internal improvements like:
- Dynamically set number of worker thread during extraction
- Update progression bar during download even if downloads are cached
Dataset creation:
- Add
tfds.features.LabeledImage
for semantic segmentation (like image but with additionalinfo.features['image_label'].name
label metadata) - Add float32 support for
tfds.features.Image
(e.g. for depth map) - All FeatureConnector can now have a
None
dimension anywhere (previously restricted to the first position). tfds.features.Tensor()
can have arbitrary number of dynamic dimension (Tensor(..., shape=(None, None, 3, None)
))tfds.features.Tensor
can now be serialised as bytes, instead of float/int values (to allow better compression):Tensor(..., encoding='zlib')
- Add script to add TFDS metadata files to existing TF-record (see doc).
- New guide on common implementation gotchas
Thank you all for your support and contribution!