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Tao Train Depth Anything V2

nvidia/skillsOfficial

NVIDIA's TAO monocular depth estimation skill wraps both Metric and Relative Depth Anything v2 architectures to predict per-pixel depth from single RGB images. The workflow requires careful pairing of model type and dataset class based on your data encoding (disparity pixels versus metric meters), and you'll spend time getting annotation files right since the convert utility is picky about directory patterns and extension swaps. AutoML is on by default for training, which is helpful but adds routing complexity. The skill handles the full pipeline from training through TensorRT deployment, though deploy actions bounce to a separate container. If you're working with NYU depth data in raw uint16 millimeters, use the NYUDV2 dataset classes to avoid silent NaN losses from unit conversion failures.

skills.sh installs
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last updated
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Install

$ npx -y skills add nvidia/skills --skill tao-train-depth-anything-v2 --agent claude-code

Installs into .claude/skills of the current project.

Files

Depth Net Mono

Standalone install? If this session was not initialized by the TAO skill bank plugin, run the tao-setup skill first (host preflight, credentials, cross-skill discovery).

Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images.

Pretrained checkpoint loading varies by model variant and use case — see the Pretrained checkpoint loading — use case matrix in references/parameters.md.

The mono and stereo skills both invoke the unified TAO depth_net CLI inside the container; the mono/stereo family is selected via model.model_type (see references/parameters.md).

For TAO Deploy TensorRT actions (gen_trt_engine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-depth-anything-v2.md first. The deploy spec template lives in this skill's references/spec_template_deploy.yaml.

SKILL.md on GitHub

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Details

Repository
github.com/nvidia/skills
First seen
Jul 14, 2026

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