Individual skill
tao-train-dinov3
DINOv3 continual self-supervised pre-training. Domain-adapts public DINOv3 ViT backbones on unlabeled images via teacher-student self-distillation (DINO + iBOT + KoLeo, optional Gram anchoring) and converts the EMA teacher into a timm-format backbone for downstream tasks. Trigger phrases include "train DINOv3", "DINOv3 SSL", "domain-adapt a foundation backbone", "continual pretraining", "self-supervised finetune DINOv3".
skills/tao-train-dinov3/SKILL.md
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- agent-skills Repository topic
- agentic-ai Repository topic
- ai-agents Repository topic
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- cuda Repository topic
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# DINOv3
> **Standalone install?** If this session was not initialized by the TAO skill bank plugin, run the `tao-setup` skill first.
Use this skill to continue self-supervised training of a DINOv3 backbone on unlabeled images, then convert, export, or run inference with a trained teacher checkpoint.
## Quick Start (docker run)
Docker-native launch — no TAO SDK and no Python on the host. Use the local
Docker/platform skill instead when it gives a stricter environment-specific
command (non-root UID mapping, cache redirects, remote daemons).
```bash
TAO_PYT_IMAGE_DEFAULT=nvcr.io/nvidia/tao/tao-toolkit:7.3.0-pyt # versions-key: images.tao_toolkit.pyt