SLEAP v1.0.10a7
Pre-release
Pre-release
Pre-release of minor version update with performance tweaks and bug fixes.
Changelog
- 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
andremove_predictions
for quick dataset cleanup
- Add
- Remove predicted instances in existing frames before merging in active learning results (fixes #413)
- Conda
environment.yml
clean-up: de-duplicates dependencies managed bypip
- Set h5py version requirement to 2.10.0 to prevent TensorFlow model loading issue
- Added experimental maDLC CSV labels importing support (#412)
- Keep previous zoom state when navigating across frames with fit to instances (#416)
- Add head type to the run name suffix when saving a training pipeline (#415)
- Update built-in baseline profiles
- Remove dataset specific fields (e.g., "anchor_part")
- Add medium/large RF variants
- Remove unused profiles
- Disable tensorboard logging by default
- Standardize optimization parameters
Installing
Using Conda (Windows):
Create new environment sleap_alpha
(recommended):
conda create -n sleap_alpha -c sleap/label/dev sleap=1.0.10a7
or to update inside an existing environment:
conda install -c sleap/label/dev sleap=1.0.10a7
Using PyPI (Linux/Mac):
pip install sleap==1.0.10a7