| FazBrowse GitHub Viewer | Trending | | Home |
| Tools: [Download Repo ZIP] [Original HTTPS Page] |
Comprehensive documentation audit — The user guide has been thoroughly reviewed, updated, and improved. Please check out:
Structured & validated configurations — The config system has been migrated to typed Pydantic models. Existing config.yaml files remain fully compatible, but configs are now schema-validated on load with clear error messages for invalid entries. Some user-benefits include:
Full Changelog: v3.0.0...v3.0.1
DeepLabCut 3.0 introduces a PyTorch-first training and inference stack while keeping the core DeepLabCut workflow familiar.
Projects still follow the same labeling, training, evaluation, and video-analysis pipeline used throughout the 2.x series, but the underlying engine has been substantially modernized.
For users who have already been following the release candidates, many of these changes will already feel familiar.
DeepLabCut 3.0 consolidates these incremental changes into a stable release.
![]() |
![]() |
| Pose estimation performance of the 3.0 PyTorch models compared against previous TensorFlow models on the DeepLabCut Openfield dataset (see PR #2613); RMSE: root mean squared error. *Values from Mathis et al. 2018. | |
![]() |
| Comparison of the new BUCTD model architectures with DLCRNet and DEKR on the Marmoset, Fish and Trimice dataset. From Zhou et al., ICCV 2023. |
A quick recap of some of the major milestones leading to this release:
And more, find the full PR reference on GitHub!
Credits to @n-poulsen, @jeylau, @maximpavliv, @MMathisLab, @yeshaokai, @LucZot, @AlexEMG, @arashsm79 and many other contributors.
DeepLabCut 3.0 adds a new PyTorch backend while retaining TensorFlow support for legacy workflows.
Project management remains the same and labeled datasets remain compatible.
PyTorch models can be trained alongside previous TensorFlow models on the same train/test splits for direct benchmarking and comparison.
DeepLabCut 3.0 significantly broadens the supported model ecosystem beyond the classic ResNet-based workflows. The PyTorch stack includes:
The documentation now includes dedicated architecture guides to help users choose models based on scene complexity and experimental needs.
The PyTorch engine introduces a modern training stack with expanded augmentation options, training schedules, device management, and model architectures. For each training run, the settings are stored in a pytorch_config.yaml, enabling easy reproducibility.
The new PyTorch data pipeline introduces loaders for both standard DeepLabCut projects and COCO-style datasets, making it easier to integrate DeepLabCut with broader computer-vision workflows and external annotation formats.
DeepLabCut 3.0 continues to expand the Model Zoo and SuperAnimal ecosystem, making pretrained models and transfer learning more accessible.
Colab notebooks and updated GUI tooling make it easier to experiment with modern architectures without extensive setup. (see the documentation)
The project has been moved to a newer packaging system, and is now based around pyproject.toml. This enables the use of modern package-managers & dependency resolvers, such as uv or pdm.
Users can still install only the components they require, be it GUI support, TensorFlow compatibility, ModelZoo features, and optional experimental integrations.
DeepLabCut 3.0 is shipped with a new release of the napari-deeplabcut plugin. Our napari-based labeling GUI has undergone a major internal re-write and modernization: while preserving familiar UI and the DeepLabCut workflow, the update substantially improves stability, data handling, usability, visualization, and annotation workflows, now with automated point tracking for faster labeling. See the release notes to find out about all improvements.
We have updated and streamlined the documentation, with a focus on clarity and up-to-date information in core areas (installation, getting started guides, and more).
Expect the documentation to continue evolving soon after the release!
The jump from the final DeepLabCut 2.x releases to the current codebase is best understood as a transition to more recent Python & deep learning ecosystems rather than a routine update.
Taken together, the PyTorch backend, broader architecture support, ModelZoo integration, packaging modernization, updated labeling GUI, and documentation improvements represent a major evolution of DeepLabCut, which we are happy to release as 3.0.
We hope you enjoy this new version, and we aim to keep sharing many exciting improvements in the future in all areas, be it performance and speed, codebase quality improvements, foundation models integration, user experience and documentation.
New installation procedure via pyproject.toml : easier, more modern installation. Enables uv, pdm and other modern package managers use.
Many fixes and improvements:
Note: these changes are based on all merges since rc10
Full Changelog: v3.0.0rc10...v3.0.0rc14
@maximpavliv joined the software team!
Funding includes ChanZuckerberg Initiative's Essential Open Source Software for Science. Thanks!
Full Changelog: v2.3.10...v2.3.11
Full Changelog: v2.3.9...v2.9.10
Full Changelog: v2.3.8...v2.3.9
Funding includes ChanZuckerberg Initiative's Essential Open Source Software for Science. Thanks!
Full Changelog: v2.3.7...v2.3.8
Full Changelog: v2.3.6...v2.3.7
Funding includes ChanZuckerberg Initiative's Essential Open Source Software for Science. Thanks!
Full Changelog: v2.3.5...v2.3.6
| Back | FazBrowse Home | New Git URL |