Visual ChangeNet

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.

The backbone weight (c_radio_v2_vit_base_patch16_224) is the nvidia/C-RADIOv2-B model from HuggingFace, distributed as model.safetensors (~393 MB). The TAO 7.0.0-rc container does not auto-fetch from HF URLs — ptm_utils.load_pretrained_weights() hands the pretrained_backbone_path value to torch.load(path) / safetensors.torch.load_file(path) directly. Passing an https://huggingface.co/... URL or a repo id produces FileNotFoundError and the run fails with Execution status: FAIL within a few seconds. Stage the file locally before launch:

python3 -c "from huggingface_hub import hf_hub_download; import shutil; \
shutil.copy(hf_hub_download('nvidia/C-RADIOv2-B', 'model.safetensors'), '<workspace>/backbone/c_radio_v2_b.safetensors')"

Mount it into the container (-v <workspace>/backbone/c_radio_v2_b.safetensors:/data/pretrained_models/C-RADIOv2_B.safetensors) and set the spec model.backbone.pretrained_backbone_path to the container path. HF_TOKEN is only needed at staging time, not at training time.