Version 1.1

[agarsev]

Version 1.1 comes with some minor quality of life improvements, but a new an incompatible dataset schema that will need existing datasets to be migrated.

Files in this release

• quevedo-1.1.0-py3-none-any.whl: wheel for installing quevedo with pip.
• quevedo-1.1.0.tar.gz: sdist file for when wheel is not an option.
• toy_arithmetic.zip: toy dataset for demonstration purposes.

Changes

New dataset version 1

Before, Quevedo datasets were not versioned. Now, a field has been added to the config.toml file to track the Quevedo dataset schema, and a migrate command has been added to let users upgrade datasets to new versions.

Some of the following additions are incompatible changes to dataset functionality and annotation files which have made this version upgrading necessary.

Tags are now a dictionary

Grapheme tags (both free and bound) are now represented with a dictinary, with keys the names in the dataset tag_schema. This makes Annotation objects easier to use in custom code. The change affects both the library code and the files on disk, hence the migration.

Splitting now works differently

Instead of assigning annotations to either a train or test split, they are assigned to a "fold". Groups of folds can then be defined as being available for train or for test (or none). This is also an incompatible change to annotation code and file representation. Old partitions will be lost, so after migration you will need to run the split command again.

Net configuration improvements

• Detection networks can now have width and height parameters to tune network input size.
• All networks can now have a max_batches parameter to customize when to stop training the net. This can serve to prevent overfitting and shorten training times.

Annotation flags

A new option "flags" has been added to the config.toml file. These flags are matadata values just like those in meta_tags, so assigned to both Logograms and Free Graphemes. The difference is that they are presented as checkboxes in the web interface, and shown as icons in annotation listings. This can serve to quickly mark annotations for annotators, for example if some have dubious or problematic tags, need some other kind of attention, or simply you want to keep track of them.

Other

• When building the tag map for darknet, user tags are combined using the ASCII FS character instead of "", which can be problematic if tag values in the dataset contain "". This is an internal change and user code and data should not be affected.
• The dataset.get_network method now returns the same Network object if called many times with the same network name. This helps save memory, which in the case of neural networks can be crucial, without requiring the user to keep the network in their own variable.
• The web interface now can be used with touch on mobile devices.

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This discussion was created from the release Version 1.1.