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Releases: DeepLabCut/DeepLabCut

DeepLabCut - 3.0

22 May 05:42
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Familiar workflows, modern foundations, better performance

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.

Increased model performance and speed

Benchmark table Openfield benchmark results
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.
Trimice dataset Speed comparison: PyTorch vs TensorFlow
Speed of the current PyTorch implementation (ResNet50) compared to the TensorFlow implementation. Results were obtained using a NVIDIA GeForce RTX 2080 Ti with CUDA 12.2 on the DeepLabCut Trimice dataset.
BUCTD benchmarks
Comparison of the new BUCTD model architectures with DLCRNet and DEKR on the Marmoset, Fish and Trimice dataset. From Zhou et al., ICCV 2023.

The journey to 3.0

A quick recap of some of the major milestones leading to this release:

  • #2613 - Full initial PyTorch backend implementation
  • #2952 - New bottom-up conditional top-down (BUCTD) model architecture
  • #2795 - New RTMPose top-down architecture
  • #2868 - Updated notebooks and Colab examples for PyTorch workflows
  • #2804 - PyTorch model export

And more, find the full PR reference on GitHub!

Credits to @n-poulsen, @jeylau, @maximpavliv, @MMathisLab, @yeshaokai, @LucZot, @AlexEMG, @arashsm79 and many other contributors.

Notable features in 3.0.0

PyTorch-first, TensorFlow-compatible

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.

Expanded architecture support

DeepLabCut 3.0 significantly broadens the supported model ecosystem beyond the classic ResNet-based workflows. The PyTorch stack includes:

  • ResNet and HRNet backbones
  • Bottom-up multi-animal approaches such as DEKR and PAF/DLCRNet variants
  • Top-down detector-plus-pose pipelines including RTMPose
  • Hybrid architectures such as BUCTD and CTD variants
  • SuperAnimal-related pretrained workflows

The documentation now includes dedicated architecture guides to help users choose models based on scene complexity and experimental needs.

Flexible PyTorch training configuration

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.

Improved interoperability

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.

Model Zoo and SuperAnimal workflows

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)

Modernized installation and packaging

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.

Labeling GUI

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.

Upcoming: refreshed documentation

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!

A major transition

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.

Closing thoughts

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.


Changelog since 3.0.0rc14

  • DLC-live-GUI docs : Install from PyPI pre-release (#3238) by @C-Achard
  • Apply linting to entire codebase & add CI workflow to check linting (#3216) by @deruyter92
  • Bump requests from 2.32.5 to 2.33.0 (#3259) by @app/dependabot
  • Refactor Analyze Videos tab (#3268) by @C-Achard
  • Consolidate test workflow infrastructure in CI (#3254) by @C-Achard
  • Move protobuf requirement to pyproject.toml (#3235) by @C-Achard
  • Use pinned ffmpeg version in CI (#3276) by @C-Achard
  • Docs versioning: Add glob support, better validation and reporting (#3278) by @C-Achard
  • Bump cryptography from 46.0.5 to 46.0.7 (#3277) by @app/dependabot
  • Fix failing local Windows tests due to ruamel parsing (#3275) by @C-Achard
  • Update & de-duplicate skeleton builder (#3258) by @C-Achard
  • Bump pygments from 2.19.2 to 2.20.0 (#3262) by @app/dependabot
  • update conda yaml: add pyside6 as conda dependency (#3253) by @deruyter92
  • Bump pyasn1 from 0.6.2 to 0.6.3 (#3249) by @app/dependabot
  • Fix SuperAnimal / pretrained load for RTMPose: implement convert_weights on RTMCCHead (#3270) by @deruyter92
  • Run update check in a separate thread (#3234) by @mikhailmikhalchuk
  • Update napari-DLC docs for refactor (#3280) by @C-Achard
  • Refactor/predict multianimal (#3220) by @juan-cobos
  • Fix incorrect MultiLevel construction in outlier_frames.compute_deviations (#3247) by @C-Achard
  • Bump pillow from 12.1.1 to 12.2.0 (#3283) by @app/dependabot
  • Bump pytest from 9.0.2 to 9.0.3 (#3284) by @app/dependabot
  • CircleCI: disable hugginface xet (#3316) by @deruyter92
  • Update and diversify TensorFlow optional installations. (#3292) by @deruyter92
  • Bump urllib3 from 2.6.3 to 2.7.0 (#3325) by @app/dependabot
  • Bump gitpython from 3.1.47 to 3.1.50 (#3322) by @app/dependabot
  • make CTD GenerativeSampler visibility-aware (#3305) by @deruyter92
  • Add isatty method to StreamWriter + eval GUI fix (#3314) by @yyexela
  • Add conditional replacement of @torch.inference_mode for inference on AMD DirectML GPUs (#3295) by @deruyter92
  • Robustness fix: Annotation file not dropping likelihood column if present from machine labels (#3323) by @C-Achard
  • Remove trailing comma in models_to_framework.json (#3330) by @C-Achard
  • update list_videos_in_folder (#3303) by @deruyter92
  • Improve TrainingDatasetMetadata and get_shuffle_engine for incomplete projects (#3313) by @deruyter92
  • update RTMPose SimCCPredictor: expose apply_softmax and fix visibility thresholding (#3306) by @deruyter92
  • GUI: Add "Generate debug log" action (#3328) by @C-Achard
  • [Docker 1] Simplify and modernize Dockerfile (#3290) by @deruyter92
  • [Docker 2] Update deeplabcut-docker package (#3291) by @deruyter92
  • Add additional drop_likelihood_columns guards (#3333) by @C-Achard
  • Resolve inconsistent parameter names via aliasing + depreca...
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v3.0.0rc14

23 Mar 21:21
9c74d49

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Highlights

  • New installation procedure via pyproject.toml : easier, more modern installation. Enables uv, pdm and other modern package managers use.

  • Many fixes and improvements:

    • PyTorch speed improvements
    • CI improvements

Note: these changes are based on all merges since rc10

What's Changed

New Contributors

Full Changelog: v3.0.0rc10...v3.0.0rc14

v3.0.0rc10

01 Jul 04:04
d11f093

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What's Changed

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v2.3.11 🐛 fixes, package maintenance (TF only)

18 Feb 09:06
fa2ebee

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What's Changed

New Contributors

@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

v2.3.10

07 Jun 16:32
fa89f7f

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What's Changed

New Contributors

Full Changelog: v2.3.9...v2.9.10

Updates and bug fixes

08 Feb 18:42
a2c9b03

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What's Changed

Full Changelog: v2.3.8...v2.3.9

Expanded GUI features for the DLC ModelZoo

07 Nov 20:27
3ba648f

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What's Changed

  • better augmentation for maDLC: Fix fliplr augmentation for multi animal 2 by @biol-jsh in #2043
  • Improved transfer learning for DLC ModelZoo by @yeshaokai in #2389
  • Improvements to the model zoo's GUI tab by @jeylau in #2431
  • "deeplabcut.filterpredictions" and "deeplabcut.analyze_skeleton" return mappings by @rahulporuri in #1908
  • Let user change the search window size during corner detection refinement by @jeylau in #2414
  • Install DLC from PyPI rather than its local version in M1 Macs by @jeylau in #2416
  • Correct link to platform specifications page by @molecules in #2423
  • Automatically check for napari-deeplabcut updates (in addition to DLC) by @jeylau in #2422
  • Smartly restore pretrained model weights by @jeylau in #2426
  • Fix video adaptation by @jeylau in #2436

New Contributors

Funding includes ChanZuckerberg Initiative's Essential Open Source Software for Science. Thanks!

Full Changelog: v2.3.7...v2.3.8

Minor fixes

10 Oct 14:27
7d7a57b

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What's Changed

Full Changelog: v2.3.6...v2.3.7

Funding includes ChanZuckerberg Initiative's Essential Open Source Software for Science. Thanks!

Bug fixes and minor updates

20 Sep 21:48
6e12bf9

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What's Changed

New Contributors

Full Changelog: v2.3.5...v2.3.6

v2.3.5

31 May 13:54
06afbe9

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What's Changed

New Contributors

Funding includes ChanZuckerberg Initiative's Essential Open Source Software for Science. Thanks!

Full Changelog: v2.3.4...v2.3.5