Individual skill
tao-train-depth-anything-v2
Monocular depth estimation using Metric Depth Anything v2 or Relative Depth Anything architectures. Predicts per-pixel depth from single RGB images. Use when training, evaluating, exporting, or running inference for a TAO monocular depth model. Trigger phrases include "train monocular depth", "DepthAnything v2", "metric depth from single image", "monocular depth estimation".
skills/tao-train-depth-anything-v2/SKILL.md
Source commit 25340619336e89bcc40365c28bfe9e790430468e
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- Network or broad shell
- Last checked
- Nov 2, 2026
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Repository context
- Repository stars (not skill ratings)
- 3,889
- Repository last updated
- Nov 6, 2026
- License
- Apache-2.0
Observed agent integration: No specific integration evidence. Standard SKILL.md installation does not imply verified compatibility.
- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
- claude-code Repository topic
- codex Repository topic
- coding-agents Repository topic
- cuda Repository topic
- developer-tools Repository topic
- llm Repository topic
- omniverse Repository topic
- monocular Declared skill tag
- depth Declared skill tag
- estimation Declared skill tag
- engineering Derived category
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BENCHMARK.mddoc · 7,212 bytesSKILL.mdcore · 15,669 bytesSKILL.mdcore · 15,669 bytesSKILL.mdcore · 15,669 bytesSKILL.mdcore · 15,669 bytesSKILL.mdcore · 15,669 bytesconfig/skillspector-baseline.yamlasset · 1,010 bytesevals/evals.jsonasset · 871 bytesreferences/finetuning-recipes.mddoc · 7,260 bytesreferences/parameters.mddoc · 5,490 bytesreferences/skill_info.yamlasset · 4,482 bytesreferences/spec-overrides.mddoc · 4,705 bytesreferences/spec-param-inference.mddoc · 3,523 bytesreferences/spec_template_deploy.yamlasset · 2,126 bytesreferences/spec_template_evaluate.yamlasset · 5,715 bytesreferences/spec_template_export.yamlasset · 5,784 bytesreferences/spec_template_gen_trt_engine.yamlasset · 5,764 bytesreferences/spec_template_inference.yamlasset · 5,721 bytesreferences/spec_template_quantize.yamlasset · 5,550 bytesreferences/spec_template_train.yamlasset · 5,550 bytesreferences/tao-deploy-depth-anything-v2.mddoc · 7,469 bytesreferences/tao-deploy-depth-anything-v2.skill_info.yamlasset · 2,507 bytesreferences/troubleshooting.mddoc · 2,147 bytesschemas/evaluate.schema.jsonasset · 106,992 bytesschemas/export.schema.jsonasset · 107,656 bytesschemas/gen_trt_engine.schema.jsonasset · 109,557 bytesschemas/inference.schema.jsonasset · 107,437 bytesschemas/manifest.jsonasset · 30,583 bytesschemas/quantize.schema.jsonasset · 103,802 bytesschemas/train.schema.jsonasset · 103,796 bytesskill-card.mddoc · 4,995 bytesskill.oms.sigasset · 10,305 bytes
Read excerpt
# 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`.
PyT actions packaged by this model skill: `train`, `evaluate`, `inference`, `export`, and `quantize`. The PyT `depth_net` entrypoint does not accept a PyT-side `gen_trt_engine` action in the current TAO image. The `gen_trt_engine` action metadata must run with the TAO Deploy container, and the deploy workflow remains the deploy-specific entrypoint.
