Agent skill · research science · nvidia
tao-train-dino
DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with
Why this skill is useful
Provides specific commands and workflows for training and deploying a DINO model that the AI wouldn't reliably generate on its own.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 5k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
DINO 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). DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with denoising training, multi-scale features, and optional distillation support. Uses pretrained backbone weights (e.g. ResNet-50 ImageNet). Set model.pretrainedbackbonepath for backbone-only or train.pretrainedmodelpath for full model. When To Use Train, evaluate, export, distill, quantize, or run inference for a TAO DINO 2D object detector. For TAO Deploy TensorRT actions (gentrtengine, TensorRT evaluate, and TensorRT inference), read references/tao-deploy-dino.md first. Deploy spec templates live in this skill's references/ folder with the spectemplatedeploy.yaml prefix. Reference Map references/dino-data-specs.md — dataset contracts, per-action dataset requirements, per-action spec-override examples (train, evaluate, export, deploy/gentrtengine, inference, quantize, distill), data-source arrays, checkpoint inference, and dataset layout. references/dino-actions-errors.md — important parameters, default values, evaluate/export defaults, hardware, and the full error-pattern catalog. references/dino-tuning-multigpu.md — full AutoML/HPO notes (metrics, hyperparameters, extractor) and multi-GPU spec consistency. references/tao-deploy-dino.md — TensorRT deploy workflow. references/detailed-guide.md — map to the detailed model guide. Dataclass Schemas Generated TAO Core schemas are packaged in schemas/<action>.schema.json, with schemas/manifest.json listing available actions. Each generated schema also emits references/spectemplate<action>.yaml from the schema top-level default field. AutoML enablement is declared at the model layer in references/skillinfo.yaml via automlenabled. Runnable AutoML still requires schemas/train.schema.json and references/spectemplatetrain.yaml to exist and parse. …
How to use it
Reference it in AdaL, Claude Code, Cursor or any coding agent — nothing to install:
@skills nvidia/tao-train-dino