tao-train-visual-changenet

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'), '/backbone/c_radio_v2_b.safetensors')" Mount it into the container ( -v /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. Show more Installs 586 Repository nvidia/skills GitHub Stars 1.9K First Seen Jun 8, 2026 Security Audits Gen Agent Trust Hub Pass Socket Pass Snyk Pass