Agent skill · NVIDIA
tao-train-fast-foundation-stereo
Real-time stereo depth estimation using FastFoundationStereo (FFS), the distilled bp2 commercial variant of
What it needs
About 6k tokens when loaded.
What this skill does
Depth Net Fast Stereo 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). Real-time stereo depth estimation using FastFoundationStereo (FFS) — the bp2 commercial distilled variant of FoundationStereo. Predicts disparity maps from rectified stereo image pairs with per-layer pruned widths for real-time inference. The mono / stereo / fast-stereo skills share the unified TAO depthnet CLI; FFS is selected via model.modeltype: FastFoundationStereo. FFS differs from FoundationStereo only in pruned per-layer widths and a serialized forward path; everything else (entrypoint, action verbs, dataset classes, deploy chain) is identical to depth-net-stereo. For TAO Deploy TensorRT actions (gentrtengine, TensorRT evaluate, TensorRT inference), read references/tao-deploy-fast-foundation-stereo.md first. The deploy spec template lives at references/spectemplatedeploy.yaml. When to Use Use this skill to train, evaluate, export, or run inference for a TAO FastFoundationStereo model. Two supported use cases: FFS raw-deploy and bp2-finetune flows require a pre-trained bp2 commercial checkpoint (modelbestbp2serialize.pth). The default PyT image does not guarantee that this file is present on disk, so treat the checkpoint path as a required user/registry artifact. If no bp2 checkpoint is available, scratch training is still usable for workflow validation, but the resulting metrics are not representative of the bp2 model. 1. Raw deploy — use the bp2 ckpt as-is. Skip train; run inference / evaluate / export / gentrtengine directly with the bp2 file as the action's checkpoint. 2. Finetune on user data — set train.pretrainedmodelpath to the bp2 file, train on user data, then verify + deploy on the resulting ckpt. The full 7-action sequence (train → evaluate pyt → inference pyt → export → gentrtengine → inference deploy → evaluate deploy) is supported. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills NVIDIA/tao-train-fast-foundation-stereo