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What's New in SLEAP 1.5

SLEAP 1.5 represents a major milestone with significant architectural improvements, performance enhancements, and new installation methods. Here are the key changes:

Major Changes

UV-Based Installation

SLEAP 1.5+ now uses uv for installation, making it much faster than previous methods. Get up and running in seconds with our streamlined installation process.

PyTorch Backend

Neural network backend switched from TensorFlow to PyTorch, providing:

  • Much faster training and inference speeds
  • Modern deep learning capabilities
  • Improved developer experience
  • Multi-GPU training

Standalone Libraries

SLEAP GUI is now supported by two new packages for modular workflows:

SLEAP-IO

I/O backend for handling labels, processing .slp files, and data manipulation. Essential for any SLEAP workflow and can be used independently for data processing tasks.

SLEAP-NN

PyTorch-based neural network backend for training and inference. Perfect for custom training pipelines, remote processing, and headless server deployments.

Torch Backend Changes

New Backbones

SLEAP 1.5 introduces three powerful new backbone architectures (check here for more details):

  • UNet - Classic encoder-decoder architecture for precise pose estimation
  • SwinT - Swin Transformer for state-of-the-art performance
  • ConvNeXt - Modern convolutional architecture with improved efficiency

Legacy Support

We've maintained full backward compatibility:

  • GUI Support: SLEAP now uses a new YAML-based config file structure, but you can still upload and work with old SLEAP JSON files in the GUI. For details on converting legacy SLEAP 1.4 config/JSON files to the new YAML format, see our conversion guide.
  • TensorFlow Model Inference: Continue to support running inference on old TensorFlow models (UNet backbone only). Check using legacy models for more details.

For a complete list of changes, see our Changelog.