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name: deeplabcut description: "Route DeepLabCut markerless pose-estimation workflows, project setup, PyTorch training/inference, maDLC tracking, Model Zoo, post-processing, 3D, and export tasks." disable-model-invocation: true metadata: disco-role: operating license: LGPL 3.0

DeepLabCut repo skill

Use this skill when the user is working with the deeplabcut Python package for markerless animal or object pose estimation, including project setup, annotation data preparation, PyTorch training/evaluation/inference, multi-animal tracking, SuperAnimal/Model Zoo workflows, labeled-video outputs, 3D triangulation, and model export.

First checks

• Package import check:

  import deeplabcut
  print(deeplabcut.__version__)
  

• DeepLabCut supports Python 3.10-3.12. The default DeepLabCut 3.x engine is PyTorch; TensorFlow remains optional/legacy.

• Install the headless package for API/CLI-style workflows: pip install deeplabcut.

• Install GUI support only when the task needs it: pip install "deeplabcut[gui]".

• Install exactly one TensorFlow extra only when legacy TensorFlow projects are in scope: tf, tf-cu11, tf-cu12, tf-latest, or apple_mchips.

• Run scripts/check_deeplabcut_install.py when you need a safe local import, public API, launcher, and optional backend probe.

Route by task

1. install-and-project-setup
   • Install/import questions, GUI or lite launcher behavior, package extras, project creation, adding videos, 3D project skeletons, and safe config.yaml or project-tree inspection.
2. data-labeling-and-training-datasets
   • Frame extraction, label checking, annotation table formats, external data conversion, create_training_dataset, shuffles, train/test splits, and tiny safe fixture projects.
3. pytorch-training-evaluation-inference
   • DeepLabCut 3.x PyTorch training, evaluation, image/video inference, snapshot/device choices, pytorch_config.yaml, and model export.
4. multi-animal-tracking
   • maDLC identity decisions, raw detections, tracklet conversion, stitching, transformer re-identification, and tracking failure recovery.
5. model-zoo-superanimal
   • SuperAnimal and Model Zoo no-training inference, pretrained projects, adaptation, transfer learning handoffs, custom checkpoints, and download/cache planning.
6. postprocessing-3d-video-exports
   • Filtering, outlier refinement, dataset merge, labeled videos, trajectory plots, video utilities, 3D calibration/triangulation, and export outputs.

Shared references

• Repository provenance records the source version and evidence paths for refresh decisions.
• Installation and entry points explains install flavors, launcher behavior, and backend choices.
• API index maps important public APIs to the owning sub-skill.
• Compatibility notes summarizes PyTorch/TensorFlow, GUI, OpenVINO, FMPose3D, CPU/GPU, and optional dependency boundaries.
• Troubleshooting covers cross-cutting import, install, launcher, backend, and path failures before routing to workflow-specific troubleshooting.

Operating boundaries

• Do not run training, inference, model downloads, GUI launch, or video-writing steps until the user has confirmed the target project, data paths, outputs, and compute expectations.
• Treat DeepLabCut project config.yaml as the authority for project path, scorer, videos, bodyparts, individuals, training fractions, iteration, and engine.
• Use CPU-safe inspection and bundled planning scripts for diagnosis. GPU-backed training or inference is usually recommended for real workloads, but simple API/config checks can run on CPU.
• Do not mix TensorFlow extras in one environment. If a task is TensorFlow-specific, confirm the exact extra and Python/CUDA/Metal compatibility before installing or repairing packages.
• Keep source projects and generated outputs separate: DeepLabCut creates project artifacts such as labeled-data/, training-datasets/, dlc-models-pytorch/, prediction .h5/.csv, labeled videos, and exports under user-chosen project/output directories.