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DeepLabCut Live GUI

A graphical application for real-time pose estimation with DeepLabCut using one or more cameras.

This GUI is designed for scientists and experimenters who want to preview, run inference, and record synchronized video with pose overlays—without writing code.

Table of Contents

:::{toc} :::

What this software does

  • Live camera preview from one or multiple cameras
  • Real-time pose inference using DeepLabCut Live models
  • Multi-camera support with tiled display
  • Video recording (raw or with pose and bounding-box overlays)
  • Session-based data organization with reproducible naming
  • Optional processor plugins to extend behavior (e.g. remote control, triggers)

The application is built with PySide6 (Qt) and is intended for interactive experimental use rather than offline batch processing.

Typical workflow

  1. Install the application and required camera backends
  2. Configure cameras (single or multi-camera)
  3. Select a DeepLabCut Live model
  4. Start preview and verify frame rate
  5. Run pose inference on a selected camera
  6. Record video (optionally with overlays)
  7. Organize results by session and run

Each of these steps is covered in the Quickstart and User Guide sections of this documentation.

Who this is for

  • Neuroscience and behavior labs
  • Experimentalists running real-time tracking
  • Users who want a GUI-first workflow for DeepLabCut Live

You do not need to be a software developer to use this tool.

What this documentation covers

  • Installation and first-run setup
  • Camera configuration and supported backends
  • Pose inference settings and visualization
  • Recording options and file organization
  • Known limitations of the current release

This documentation intentionally focuses on end-user operation. Developer-oriented material (APIs, internals, extension points) is out of scope for now.

Current limitations (high-level)

Before getting started, be aware of the following constraints:

  • Pose inference runs on one selected camera at a time (even in multi-camera mode)
  • Camera synchronization depends on backend capabilities and hardware
  • DeepLabCut Live models must be exported and compatible with the selected backend
  • Performance depends on camera resolution, frame rate, GPU availability, and codec choice

A detailed and up-to-date list is maintained in the Limitations section.

About DeepLabCut Live

DeepLabCut Live enables low-latency, real-time pose estimation using models trained with DeepLabCut. This GUI provides an accessible interface on top of that ecosystem for interactive experiments.

This project is under active development. Feedback from real experimental use is highly valued.