Overview

Here's an overview of the guides:

Documentation for New SLEAP Versions

This documentation is for the latest version of SLEAP.
If you are using SLEAP version 1.4.1 or earlier, please visit the legacy documentation.

Major Changes in SLEAP 1.5+

Want to learn about the major changes and updates in the latest release? See Migrating to SLEAP 1.5+ for a summary of what's new and how to update your workflows.

New in v1.6: Label Quality Control for automated detection of labeling errors.

Importing predictions for labeling when you have predictions that aren’t in the same project as your original training data and you want to correct some of the predictions and use these corrections to train a better model.

Tracking and proofreading provides tips and tools you can use to speed up proofreading when you're happy enough with the frame-by-frame predictions but you need to correct the identities tracked across frames.

Label Quality Control for detecting and fixing labeling errors using automated quality checks.

Negative Frames for marking empty frames as background training examples to reduce false positives.

Instance Size Distribution for determining the optimal crop size for top-down models.

Run training and inference on Colab when you have a project with labeled training data and you’d like to run training or inference in a Colab notebook.

Creating a custom training profile for creating custom training profiles (i.e., non-default model hyperparameters) from the GUI.

Running SLEAP remotely when you have a project with training data and you want to train on a different machine using a command-line interface.

Bonsai Integration

Bonsai is not natively supported with the new Torch backend in SLEAP.
If you want to use Bonsai with legacy SLEAP models, please refer to the legacy Bonsai guide.