Sleap

Latest version: v1.4.0

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1.0.10a5

Not secure
Pre-release of minor version update with performance tweaks and bug fixes.

Changes:
- Update to TensorFlow 2.1.2 (security patch)
- Switch to ID-based hashing for `LabeledFrame`. This dramatically increases the performance of frame manipulation operations.
- Several convenience methods for `sleap.Labels`:
- Add `describe` method to Labels for easy inspection of dataset stats
- Add `has_frame` method to Labels for quick checking of frame existence
- Add `remove_user_instances` and `remove_predictions` for quick dataset cleanup
- Remove predicted instances in existing frames before merging in active learning results (fixes 413)
- Conda `environment.yml` clean-up: de-duplicates dependencies managed by `pip`

1.0.10a4

Not secure
Pre-release of minor version update with performance tweaks and bug fixes.

Changes:
- Update to TensorFlow 2.1.2 (security patch)
- Switch to ID-based hashing for `LabeledFrame`. This dramatically increases the performance of frame manipulation operations.
- Several convenience methods for `sleap.Labels`:
- Add `describe` method to Labels for easy inspection of dataset stats
- Add `has_frame` method to Labels for quick checking of frame existence
- Add `remove_user_instances` and `remove_predictions` for quick dataset cleanup
- Remove predicted instances in existing frames before merging in active learning results (fixes 413)
- Conda `environment.yml` clean-up: de-duplicates dependencies managed by `pip`

1.0.10a2

Pre-release of minor version update with performance tweaks.

Changes:
- Switch to ID-based hashing for `LabeledFrame`. This dramatically increases the performance of frame manipulation operations.
- Version fence numpy for compatibility with TensorFlow.

1.0.10a1

Pre-release of minor version update with performance tweaks.

Changes:
- Switch to ID-based hashing for `LabeledFrame`. This dramatically increases the performance of frame manipulation operations.

1.0.9

Not secure

1.0.9a3

Not secure
- Hotfix for 389: Disable GPU memory preallocation during training by default.

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