Agent skill · research science · nvidia
tao-train-visual-changenet
Visual ChangeNet for binary image classification and segmentation in AOI defect detection. Use when training,
Why this skill is useful
Provides specific commands and workflows for training and evaluating the Visual ChangeNet model for PCB defect detection that are not commonly known.
What it needs
Requires docker, nvidia-container-toolkit installed locally. About 8k tokens when loaded. Last updated 2026-08-07. 2,816 stars on the source repository.
What this skill does
Visual ChangeNet 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). Visual ChangeNet is a TAO Toolkit model for visual inspection and defect detection. It supports two tasks: Classify — Binary image classification using a siamese-style architecture with a shared backbone (C-RADIO ViT) and a learnable difference module. Compares image pairs to classify defects as PASS/NOPASS. Segment — Pixel-level change segmentation using a ViT-Large NVDINOv2 backbone. Compares before/after image pairs to produce a binary change mask. Classify supports the public C-RADIOv2-B backbone and six frozen DINOv3 variants. Read references/dinov3-backbones.md before selecting DINOv3; it contains the exact variant map, freeze requirement, Hugging Face access rules, and local-staging overlay. For C-RADIO, use the bundled scripts/stagebackbone.py and the mount in references/local-docker.md. Segment specs use model.backbone.type: vitlargenvdinov2 and the NVDINOv2 checkpoint family. Keep the checkpoint architecture aligned with the backbone type: NVDINOV251816256.ckpt is compatible with the packaged segment templates, but it must not be used with fansmall12p4hybrid. If you switch to a different segment backbone, use a matching checkpoint or leave model.backbone.pretrainedbackbonepath empty for default initialization. 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-visual-changenet