Highlights

Pre-release of SLEAP v1.1.0 update.

In this update, we have updated TensorFlow to 2.3, overhauled the inference module for performance and much more. See the highlights and full list of changes below.

Highlights

2-8x performance improvements in inference
32 new pretrained neural network backbones
Learnable offset regression for subpixel localization
High-level API for labels and model loading: sleap.load_file() and sleap.load_model()
Linux conda package and smaller package size for updates

Changelog

Update to TensorFlow 2.3.1.
Implement top-down, bottom-up and single-instance model inference as self-contained tf.keras.Models (sleap.nn.inference.InferenceModel base class).
Re-implement peak finding and refinement with batch-level functions for improved performance.
Re-implement PAF grouping methods for batch-level inference and drastically improved test coverage.
Add pipeline utility methods peek() and describe() to query the output a sleap.data.Pipeline.
Color by track when plotting with LabeledFrame.plot() method and tracks are available.
Add slice indexing to sleap.Labels.
Better string representations for core data structures Labels, LabeledFrame and `Skeleton.
Fix sleap.nn.evals.evaluate_model() saving even with save=False specified.
Add RandomCropper transformer for augmentation.
sleap.nn.data.pipelines and sleap.nn.inference submodules now available as top-level imports (sleap.pipelines and sleap.inference).
sleap.nn.viz.plot_instance() now accepts raw point arrays.
Switch to tf.data.Dataset.cache() for preloading transformer.
Add LambdaMap for user-function transformer creation.
Add high level model loading interface (sleap.load_model())
Switch to using external conda packages for TensorFlow and PySide2.
Switch to GitHub Actions for CI and builds.
Add pretrained encoder UNet-style model backbones based on qubvel/segmentation_models (#435)
Breaking sleap.nn.inference changes (#445)
- API change: TopDownModel -> TopDownInferenceModel
- API change: Predictor.predict() no longer has make_instances kwarg
Added some GPU-related aliases to top-level imports (#446)
- sleap.use_cpu_only(): Disable GPU use.
- sleap.disable_preallocation(): Disable preallocation of entire GPU memory which causes crashes on some systems.
- sleap.system_summary(): Print a summary of GPUs detected on the system and their state.
Import folders of (ma)DLC labeled data with multiple videos (#437)
Implement trainable offset regression (#447)
Fix GUI freezing in Mac OS Big Sur (#450)
Fix single instance inference and RGB video detection (#459)

Installing

We strongly recommend using Miniconda to install and manage your Python environments. This will also make GPU support work transparently without installing additional dependencies.

Using Conda (Windows/Linux)

Delete any existing environment and start fresh (recommended):

conda env remove -n sleap_alpha

Create new environment sleap_alpha (recommended):

conda create -n sleap_alpha -c sleap/label/dev sleap=1.1.0a9

Or to update inside an existing environment:

conda install -c sleap/label/dev sleap=1.1.0a9

Using PyPI (Windows/Linux/Mac)

Create a new conda environment (recommended):

conda create -n sleap_alpha python=3.6
conda activate sleap_alpha

Install from PyPI:

pip install sleap==1.1.0a9

Or to upgrade an existing installation:

pip install --upgrade --force-reinstall sleap==1.1.0a9

From source (development)

Clone the repository at this tag:

git clone https://github.com/murthylab/sleap --branch v1.1.0a9 sleap_v1.1.0a9
cd sleap_v1.1.0a9

Install conda environment and activate:

conda install -f environment.yml -n sleap_v1.1.0a9
conda activate sleap_v1.1.0a9

Changes made in the code will be immediately reflected when running SLEAP in this environment.

Pre-release of SLEAP v1.1.0 update.

In this update, we have updated TensorFlow to 2.3, overhauled the inference module for performance and much more. See the highlights and full list of changes below.

Highlights

2-8x performance improvements in inference
32 new pretrained neural network backbones
Learnable offset regression for subpixel localization
High-level API for labels and model loading: sleap.load_file() and sleap.load_model()
Linux conda package and smaller package size for updates

Changelog

Update to TensorFlow 2.3.1.
Implement top-down, bottom-up and single-instance model inference as self-contained tf.keras.Models (sleap.nn.inference.InferenceModel base class).
Re-implement peak finding and refinement with batch-level functions for improved performance.
Re-implement PAF grouping methods for batch-level inference and drastically improved test coverage.
Add pipeline utility methods peek() and describe() to query the output a sleap.data.Pipeline.
Color by track when plotting with LabeledFrame.plot() method and tracks are available.
Add slice indexing to sleap.Labels.
Better string representations for core data structures Labels, LabeledFrame and `Skeleton.
Fix sleap.nn.evals.evaluate_model() saving even with save=False specified.
Add RandomCropper transformer for augmentation.
sleap.nn.data.pipelines and sleap.nn.inference submodules now available as top-level imports (sleap.pipelines and sleap.inference).
sleap.nn.viz.plot_instance() now accepts raw point arrays.
Switch to tf.data.Dataset.cache() for preloading transformer.
Add LambdaMap for user-function transformer creation.
Add high level model loading interface (sleap.load_model())
Switch to using external conda packages for TensorFlow and PySide2.
Switch to GitHub Actions for CI and builds.
Add pretrained encoder UNet-style model backbones based on qubvel/segmentation_models (#435)
Breaking sleap.nn.inference changes (#445)
- API change: TopDownModel -> TopDownInferenceModel
- API change: Predictor.predict() no longer has make_instances kwarg
Added some GPU-related aliases to top-level imports (#446)
- sleap.use_cpu_only(): Disable GPU use.
- sleap.disable_preallocation(): Disable preallocation of entire GPU memory which causes crashes on some systems.
- sleap.system_summary(): Print a summary of GPUs detected on the system and their state.
Import folders of (ma)DLC labeled data with multiple videos (#437)
Implement trainable offset regression (#447)
Fix GUI freezing in Mac OS Big Sur (#450)

Installing

We strongly recommend using Miniconda to install and manage your Python environments. This will also make GPU support work transparently without installing additional dependencies.

Using Conda (Windows/Linux)

Delete any existing environment and start fresh (recommended):

conda env remove -n sleap_alpha

Create new environment sleap_alpha (recommended):

conda create -n sleap_alpha -c sleap/label/dev sleap=1.1.0a8

Or to update inside an existing environment:

conda install -c sleap/label/dev sleap=1.1.0a8

Using PyPI (Windows/Linux/Mac)

Create a new conda environment (recommended):

conda create -n sleap_alpha python=3.6
conda activate sleap_alpha

Install from PyPI:

pip install sleap==1.1.0a8

From source (development)

Clone the repository at this tag:

git clone https://github.com/murthylab/sleap --branch v1.1.0a8 sleap_v1.1.0a8
cd sleap_v1.1.0a8

Install conda environment and activate:

conda install -f environment.yml -n sleap_v1.1.0a8
conda activate sleap_v1.1.0a8

Changes made in the code will be immediately reflected when running SLEAP in this environment.

Pre-release of SLEAP v1.1 update.

In this update, we have updated TensorFlow to 2.3 and overhauled the inference module.

Highlights

2-8x performance improvements in inference!
High-level data and model methods
Linux conda package and smaller package size for updates

Changelog

Update to TensorFlow 2.3.1.
Implement top-down, bottom-up and single-instance model inference as self-contained tf.keras.Models (sleap.nn.inference.InferenceModel base class).
Re-implement peak finding and refinement with batch-level functions for improved performance.
Re-implement PAF grouping methods for batch-level inference and drastically improved test coverage.
Add pipeline utility methods peek() and describe() to query the output a sleap.data.Pipeline.
Color by track when plotting with LabeledFrame.plot() method and tracks are available.
Add slice indexing to sleap.Labels.
Better string representations for core data structures Labels, LabeledFrame and `Skeleton.
Fix sleap.nn.evals.evaluate_model() saving even with save=False specified.
Add RandomCropper transformer for augmentation.
sleap.nn.data.pipelines and sleap.nn.inference submodules now available as top-level imports (sleap.pipelines and sleap.inference).
sleap.nn.viz.plot_instance() now accepts raw point arrays.
Switch to tf.data.Dataset.cache() for preloading transformer.
Add LambdaMap for user-function transformer creation.
Add high level model loading interface (sleap.load_model())
Switch to using external conda packages for TensorFlow and PySide2.
Switch to GitHub Actions for CI and builds.

Installing

Using Conda (Windows/Linux)

Create new environment sleap_alpha (recommended):
conda create -n sleap_alpha -c sleap -c sleap/label/dev sleap=1.1.0a7

To update inside an existing environment:
conda install -c sleap -c sleap/label/dev sleap=1.1.0a7

Using PyPI (Windows/Linux/Mac)

pip install sleap==1.1.0a7

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 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
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 to prevent overwriting models (#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
Add convenience methods for generating and interacting with exported packages
- HDF5Videos now have has_embedded_images, source_video_available, and embedded_frame_inds properties to check for embedded images
- HDF5Video now auto-detects the exported package format, indicated by having sub-datasets named "/video" and "/frame_numbers"
- Labels.save_file and Labels.save now provide options for saving images for all LabeledFrames or suggestions
Release checking
- Help menu now displays the latest versions of SLEAP and links to the webpage
Menu re-organization and cleanup
Fix GUI skeleton not updating when skeleton updates after inference (#414)

Installing

Using Conda (Windows):
Create new environment sleap (recommended):
conda create -n sleap -c sleap sleap=1.0.10
or to update inside an existing environment:
conda install -c sleap sleap=1.0.10

Using PyPI (Linux/Mac):
pip install sleap==1.0.10

Pre-release of SLEAP v1.1.

Changelog

Update to TensorFlow 2.3.1

Installing