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/NO_PASS.
• 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/stage_backbone.py and the mount in references/local-docker.md.