---

name: model-zoo-superanimal description: "Route DeepLabCut Model Zoo and SuperAnimal pretrained inference, adaptation, pretrained projects, and custom checkpoints." disable-model-invocation: true metadata: disco-role: operating license: LGPL 3.0

Model Zoo and SuperAnimal router

Use this sub-skill when a DeepLabCut task asks for Model Zoo or SuperAnimal pretrained models, no-training or zero-shot video inference, pretrained project creation, video adaptation, transfer learning from SuperAnimal weights, FMPose3D video inference, or custom pretrained checkpoints.

Handle here

• Plan or call deeplabcut.video_inference_superanimal(...) for videos, destination folders, detector choices, scale lists, crops, confidence thresholds, labeled-video outputs, video adaptation, custom checkpoints, and FMPose3D 3D return payloads.
• Create a new pretrained Model Zoo project with deeplabcut.create_pretrained_project(...) when the user wants a project initialized from pretrained weights rather than a blank project.
• Recognize deeplabcut.create_pretrained_human_project(...) as the legacy TensorFlow full-human helper and prefer the more general pretrained-project API for new work.
• Explain SuperAnimal project families: quadruped, top-view mouse, human body, and the evidenced bird configuration.
• Prepare the SuperAnimal part of a transfer-learning plan, then route actual custom labeled dataset training to the PyTorch training sub-skill.

Route elsewhere

• Installing extras, fixing import/backend/package compatibility, choosing PyTorch vs TensorFlow extras, or installing optional FMPose3D support: route to the root DeepLabCut install/compatibility guidance.
• Blank project creation, adding videos, and general config.yaml setup: route to ../install-and-project-setup/SKILL.md.
• Frame extraction, labeling, training dataset creation, conversion tables, and custom labeled data preparation: route to ../data-labeling-and-training-datasets/SKILL.md.
• Custom model training, evaluation, analyzing with trained project snapshots, and export after a training dataset exists: route to ../pytorch-training-evaluation-inference/SKILL.md.
• Filtering predictions, outlier refinement, labeled-video details, trajectory plots, stereo camera calibration, triangulation, and generic 3D workflows: route to ../postprocessing-3d-video-exports/SKILL.md.

Read in order

1. Model overview for model families, engine routing, API parameters, outputs, and cache/download behavior.
2. Workflows for no-training inference, video adaptation, pretrained projects, FMPose3D, and transfer-learning handoffs.
3. Troubleshooting for model names, downloads, detectors, scale shifts, custom checkpoints, adaptation failures, and optional dependencies.
4. Inference planner script when you need a no-download argument checklist before running real inference.

Operating reminders

• Treat real Model Zoo inference, adaptation, and pretrained project creation as download-capable operations unless the user supplies already-present custom checkpoint paths or confirms the cache is populated.
• For PyTorch top-down SuperAnimal animal models, plan both a pose model and detector. For TensorFlow bottom-up dlcrnet, plan scale choices instead of detector choices.
• Do not start long adaptation or training just to answer a planning question. Use the bundled planner script and hand off to the appropriate sub-skill when the task crosses this sub-skill boundary.
• Keep runtime instructions portable: use user-provided paths, relative project paths, or placeholders; never rely on a repository checkout, source examples, source tests, local logs, or private environment locations.