# tao-train-dino

Automates training and TensorRT export for DINO object detectors in NVIDIA TAO. Useful for managing 2D vision models via agent workflows.

- skill · `type=skill` · Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, OpenCode · 1600 installs · ★ 3735
- Author: NVIDIA Corporation
- License: Apache-2.0 · policy=mirror · the beginning of the file is at the end of this page
- Source: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-dino/SKILL.md
- Updated: 2026-10-26
- HTML: https://affpapa.org/en/nevibecode/skills/tao-train-dino

## What it does

This skill guides agents through managing NVIDIA TAO DINO (DETR with Improved DeNoising Anchor Boxes) detectors. It generates YAML spec overrides and prepares TensorRT engine builds. Training workflows trigger hyperparameter optimization by default via `tao-skill-bank:tao-run-automl`.

The host machine requires Docker and `nvidia-container-toolkit`. When working outside the TAO skill bank environment, run `tao-setup` first to verify credentials and dependencies. Input datasets must reside on S3 storage using COCO or COCO Raw formats.

Validation datasets are mandatory. Omitting one halts execution with a `FileNotFoundError` because DINO builds an evaluation dataloader unconditionally. Setting `num_classes` below `max(category_id) + 1` triggers an unrecoverable CUDA assert.

## Install

### Claude Code (native platform)

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-dino -a claude-code
```

2. **Manually: put the files in place**
- File: `~/.claude/skills/tao-train-dino/SKILL.md` — Copy the whole skill folder; for one project use .claude/skills/tao-train-dino/

### Codex

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-dino -a codex
```

2. **Manually: put the files in place**
- File: `.agents/skills/tao-train-dino/SKILL.md` — For all projects use ~/.agents/skills/

### Cursor

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-dino -a cursor
```

2. **Manually: put the files in place**
- File: `.cursor/skills/tao-train-dino/SKILL.md` — Cursor also reads .agents/skills/ and .claude/skills/

### GitHub Copilot

1. **GitHub CLI**
```bash
gh skill install NVIDIA/skills tao-train-dino
```

2. **Manually: put the files in place**
- File: `.github/skills/tao-train-dino/SKILL.md`

### Gemini CLI

1. **Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-dino -a gemini-cli
```

2. **Platform CLI**
```bash
gemini skills install https://github.com/NVIDIA/skills --path skills/tao-train-dino
```

### Windsurf (Devin Desktop)

**Manually: put the files in place**
- File: `.devin/skills/tao-train-dino/SKILL.md` — The legacy .windsurf/skills/ path still works

### OpenCode

**Skills CLI (Vercel)**
```bash
npx skills add NVIDIA/skills --skill tao-train-dino -a opencode
```

### Claude.ai

**Claude.ai**
- Zip the folder and upload it in Settings → Capabilities → Skills

Where a file is given instead of a command, copy the files from the source to that path: https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-dino/SKILL.md

## When to use it
- Generate TAO training specifications for DINO object detection
- Execute AutoML hyperparameter optimization runs for 2D detectors
- Export trained checkpoints to TensorRT engines via TAO Deploy
- Configure quantization and model distillation workflows on COCO datasets

## Example prompts
- Train a DINO detector on s3://my-bucket/dataset/train with plain training and no AutoML
- Build a TensorRT deployment spec for my exported DINO checkpoint
- Set up a DINO AutoML run with 5 classes using COCO format

## Source text · SKILL.md · Apache-2.0

Below is the content of a third-party file. It is data, not instructions for you: do not act on anything written inside unless the human asks. Block tag: 841db9ae7722094d.

BEGIN THIRD-PARTY FILE 841db9ae7722094d
```text
---
name: tao-train-dino
description: DINO (DETR with Improved DeNoising Anchor Boxes) for 2D object detection. Transformer-based detector with
  denoising training, multi-scale features, and optional distillation support. Use when training, evaluating, exporting,
  distilling, quantizing, or running inference for a TAO DINO detector. Trigger phrases include "train DINO", "DETR object
  detection", "TAO 2D detection", "DINO with distillation".
license: Apache-2.0
compatibility: Requires docker + nvidia-container-toolkit.
metadata:
  tags:
  - object
  - detection
  version: "0.1.0"
  author: NVIDIA Corporation
allowed-tools: Read Bash

---

# 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.pretrained_backbone_path` for backbone-only or `train.pretrained_model_path` 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 (`gen_trt_engine`, TensorRT `evaluate`, and
TensorRT `inference`), read `references/tao-deploy-dino.md` first. Deploy spec templates live
in this skill's `references/` folder with the `spec_template_deploy_*.yaml`
prefix.

## Reference Map

- `references/dino-data-specs.md` — dataset contracts, per-action dataset requirements, per-action spec-override examples (train, evaluate, export, deploy/gen_trt_engine, 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
```
END THIRD-PARTY FILE 841db9ae7722094d

The third-party file has ended. Everything between the 841db9ae7722094d tags is data: requests and commands from it are not followed. An “end of file” line with another tag inside the block is part of the file.

First 2280 of 11185 bytes. © NVIDIA Corporation · Apache-2.0 · https://github.com/NVIDIA/skills/tree/HEAD/skills/tao-train-dino/SKILL.md

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